diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 16d682c0..bf67014b 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,8 +1,6 @@ -# See https://pre-commit.com for more information -# See https://pre-commit.com/hooks.html for more hooks repos: -- repo: https://github.com/opensource-nepal/commitlint - rev: v1.2.0 + - repo: https://github.com/commitizen-tools/commitizen + rev: master hooks: - - id: commitlint - name: Commit Lint + - id: commitizen + stages: [ commit-msg ] \ No newline at end of file diff --git a/main/COMO.ipynb b/main/COMO.ipynb index 8178cd67..3cbebec1 100644 --- a/main/COMO.ipynb +++ b/main/COMO.ipynb @@ -2,7 +2,11 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, "source": [ "# COMO: Constraint-based Optomization of Metabolic Objectives\n", "\n", @@ -56,7 +60,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, "source": [ "# Step 1: Data Preprocessing and Analysis\n", "\n", @@ -223,6 +231,36 @@ "" ] }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "from pathlib import Path\n", + "\n", + "from como.rnaseq_preprocess import rnaseq_preprocess\n", + "from como.types import RNAPrepMethod\n", + "\n", + "taxon_id = 9606\n", + "context_names = [\"naiveB\"]\n", + "\n", + "gene_info_filepath = {context: Path(f\"data/results/{context}/gene_info.csv\") for context in context_names}\n", + "como_context_dir = {context: Path(f\"data/COMO_input/{context}\") for context in context_names}\n", + "trna_matrix_filepath = {context: Path(f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for context in context_names}\n", + "mrna_matrix_filepath = {context: Path(f\"data/results/{context}/polya-rna/polyarna_{context}.csv\") for context in context_names}\n", + "proteomics_matrix_filepath = {context: Path(f\"data/data_matrices/{context}/protein_abundance_{context}.csv\") for context in context_names}\n", + "\n", + "trna_metadata_filepath = Path(\"data/config_sheets/trna_config.xlsx\")\n", + "mrna_metadata_filepath = Path(\"data/config_sheets/mrna_config.xlsx\")\n", + "proteomics_metadata_filepath = Path(\"data/config_sheets/proteomics_config.xlsx\")\n", + "\n", + "# No single-cell data is provided by default; COMO accepts single-cell data in CSV or h5ad format\n", + "# If you are using single-cell data, adjust the following lines accordingly\n", + "scrna_matrix_filepath = {context: Path(f\"data/results/{context}/scrna/scrna_{context}.csv\") for context in context_names}\n", + "# scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.h5ad\") for context in context_names]\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -240,22 +278,22 @@ "outputs": [], "execution_count": null, "source": [ - "context_names = \"naiveB\"\n", - "taxon_id = \"human\" # accepts integer (bioDBnet taxon id) or \"human\" or \"mouse\"\n", - "preprocess_mode = \"create\" # \"create\" or \"provide\"\n", - "\n", - "# fmt: off\n", - "cmd = \" \".join(\n", - " [\n", - " \"python3\", \"como/rnaseq_preprocess.py\",\n", - " \"--context-names\", context_names,\n", - " \"--taxon-id\", taxon_id,\n", - " \"--mode\", preprocess_mode,\n", - " ]\n", - ")\n", - "# fmt: on\n", - "\n", - "!{cmd}" + "for context in context_names:\n", + " if context not in {*trna_matrix_filepath, *mrna_matrix_filepath}:\n", + " continue\n", + " await rnaseq_preprocess(\n", + " context_name=context,\n", + " taxon=taxon_id,\n", + " como_context_dir=como_context_dir[context],\n", + " input_matrix_filepath=None,\n", + " output_gene_info_filepath=gene_info_filepath[context],\n", + " output_trna_config_filepath=trna_metadata_filepath,\n", + " output_trna_count_matrix_filepath=trna_matrix_filepath[context],\n", + " output_mrna_config_filepath=mrna_metadata_filepath,\n", + " output_mrna_count_matrix_filepath=mrna_matrix_filepath[context],\n", + " cache=True,\n", + " log_level=\"INFO\",\n", + " )" ] }, { @@ -334,35 +372,33 @@ "outputs": [], "execution_count": null, "source": [ - "# step 2.2 RNA-seq Analysis for Total RNA-seq library preparation\n", - "\n", - "trnaseq_config_file = \"trnaseq_data_inputs_auto.xlsx\"\n", - "rep_ratio = 0.75\n", - "group_ratio = 0.75\n", - "rep_ratio_h = 1.0\n", - "group_ratio_h = 1.0\n", - "technique = \"zFPKM\"\n", - "minimum_cutoff = -3\n", - "taxon_id = \"human\"\n", - "\n", - "# fmt: off\n", - "cmd = \" \".join(\n", - " [\n", - " \"python3\", \"como/rnaseq_gen.py\",\n", - " \"--config-file\", trnaseq_config_file,\n", - " \"--replicate-ratio\", str(rep_ratio),\n", - " \"--batch-ratio\", str(group_ratio),\n", - " \"--high-replicate-ratio\", str(rep_ratio_h),\n", - " \"--high-batch-ratio\", str(group_ratio_h),\n", - " \"--minimum-cutoff\", str(minimum_cutoff),\n", - " \"--filt-technique\", f\"{technique}\",\n", - " \"--library-prep\", \"total\",\n", - " \"--taxon-id\", taxon_id\n", - " ]\n", - ")\n", - "# fmt: on\n", + "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n", "\n", - "!{cmd}" + "replicate_ratio = 0.75\n", + "high_confidence_replicate_ratio = 1.0\n", + "batch_ratio = 0.75\n", + "high_confidence_batch_ratio = 1.0\n", + "technique = FilteringTechnique.zfpkm\n", + "cutoff = -3\n", + "\n", + "for context in context_names:\n", + " if context not in trna_matrix_filepath:\n", + " continue\n", + " await rnaseq_gen(\n", + " context_name=context,\n", + " input_rnaseq_filepath=trna_matrix_filepath[context],\n", + " input_gene_info_filepath=gene_info_filepath[context],\n", + " output_rnaseq_filepath=trna_matrix_filepath[context],\n", + " prep=RNAPrepMethod.TOTAL,\n", + " taxon=taxon_id,\n", + " input_metadata_filepath=trna_metadata_filepath,\n", + " replicate_ratio=replicate_ratio,\n", + " high_replicate_ratio=high_confidence_replicate_ratio,\n", + " batch_ratio=batch_ratio,\n", + " high_batch_ratio=high_confidence_batch_ratio,\n", + " technique=technique,\n", + " cutoff=cutoff\n", + " )" ] }, { @@ -390,33 +426,33 @@ "outputs": [], "execution_count": null, "source": [ - "mrnaseq_config_file = \"mrnaseq_data_inputs_auto.xlsx\"\n", - "rep_ratio = 0.75\n", - "group_ratio = 0.75\n", - "rep_ratio_h = 1.0\n", - "group_ratio_h = 1.0\n", - "technique = \"zfpkm\"\n", - "minimum_cutoff = -3\n", - "taxon_id = \"human\"\n", - "\n", - "# fmt: off\n", - "cmd = \" \".join(\n", - " [\n", - " \"python3\", \"como/rnaseq_gen.py\",\n", - " \"--config-file\", mrnaseq_config_file,\n", - " \"--replicate-ratio\", str(rep_ratio),\n", - " \"--batch-ratio\", str(group_ratio),\n", - " \"--high-replicate-ratio\", str(rep_ratio_h),\n", - " \"--high-batch-ratio\", str(group_ratio_h),\n", - " \"--minimum-cutoff\", str(minimum_cutoff),\n", - " \"--filt-technique\", f\"{technique}\",\n", - " \"--library-prep\", \"mrna\",\n", - " \"--taxon-id\", taxon_id\n", - " ]\n", - ")\n", - "# fmt: on\n", + "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n", "\n", - "!{cmd}" + "replicate_ratio = 0.75\n", + "high_confidence_replicate_ratio = 1.0\n", + "batch_ratio = 0.75\n", + "high_confidence_batch_ratio = 1.0\n", + "technique = FilteringTechnique.zfpkm\n", + "cutoff = -3\n", + "\n", + "for context in context_names:\n", + " if context not in mrna_matrix_filepath:\n", + " continue\n", + " await rnaseq_gen(\n", + " context_name=context,\n", + " input_rnaseq_filepath=mrna_matrix_filepath[context],\n", + " input_gene_info_filepath=gene_info_filepath[context],\n", + " output_rnaseq_filepath=mrna_matrix_filepath[context],\n", + " prep=RNAPrepMethod.MRNA,\n", + " taxon=taxon_id,\n", + " input_metadata_filepath=mrna_metadata_filepath,\n", + " replicate_ratio=replicate_ratio,\n", + " high_replicate_ratio=high_confidence_replicate_ratio,\n", + " batch_ratio=batch_ratio,\n", + " high_batch_ratio=high_confidence_batch_ratio,\n", + " technique=technique,\n", + " cutoff=cutoff\n", + " )" ] }, { @@ -444,33 +480,33 @@ "outputs": [], "execution_count": null, "source": [ - "scrnaseq_config_file = \"scrnaseq_data_inputs_auto.xlsx\"\n", - "rep_ratio = 0.75\n", - "group_ratio = 0.75\n", - "rep_ratio_h = 1.0\n", - "group_ratio_h = 1.0\n", - "quantile = 50\n", - "minimum_cutoff = -3\n", - "taxon_id = \"human\"\n", + "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n", "\n", - "# fmt: off\n", - "cmd = \" \".join(\n", - " [\n", - " \"python3\", \"como/rnaseq_gen.py\",\n", - " \"--config-file\", scrnaseq_config_file,\n", - " \"--replicate-ratio\", str(rep_ratio),\n", - " \"--batch-ratio\", str(group_ratio),\n", - " \"--high-replicate-ratio\", str(rep_ratio_h),\n", - " \"--high-batch-ratio\", str(group_ratio_h),\n", - " \"--minimum-cutoff\", str(minimum_cutoff),\n", - " \"--filt-technique\", \"umi\",\n", - " \"--library-prep\", \"scrna\",\n", - " \"--taxon-id\", taxon_id\n", - " ]\n", - ")\n", - "# fmt: on\n", - "\n", - "!{cmd}" + "replicate_ratio = 0.75\n", + "high_confidence_replicate_ratio = 1.0\n", + "batch_ratio = 0.75\n", + "high_confidence_batch_ratio = 1.0\n", + "technique = FilteringTechnique.umi\n", + "cutoff = -3\n", + "\n", + "for context in context_names:\n", + " if context not in scrna_matrix_filepath:\n", + " continue\n", + " await rnaseq_gen(\n", + " context_name=context,\n", + " input_rnaseq_filepath=scrna_matrix_filepath[context],\n", + " input_gene_info_filepath=gene_info_filepath[context],\n", + " output_rnaseq_filepath=scrna_matrix_filepath[context],\n", + " prep=RNAPrepMethod.SCRNA,\n", + " taxon=taxon_id,\n", + " input_metadata_filepath=Path(\"./data/config_sheets/scrna_config.xlsx\"),\n", + " replicate_ratio=replicate_ratio,\n", + " high_replicate_ratio=high_confidence_replicate_ratio,\n", + " batch_ratio=batch_ratio,\n", + " high_batch_ratio=high_confidence_batch_ratio,\n", + " technique=technique,\n", + " cutoff=cutoff\n", + " )" ] }, { @@ -490,38 +526,34 @@ ] }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "cell_type": "code", "outputs": [], + "execution_count": null, "source": [ - "proteomics_config_file = \"proteomics_data_inputs_paper.xlsx\"\n", - "rep_ratio = 0.75\n", - "batch_ratio = 0.75\n", - "high_rep_ratio = 1.0\n", - "high_batch_ratio = 1.0\n", - "quantile = 25\n", - "\n", - "# fmt: off\n", - "cmd = \" \".join(\n", - " [\n", - " \"python3\", \"como/proteomics_gen.py\",\n", - " \"--config-file\", proteomics_config_file,\n", - " \"--replicate-ratio\", str(rep_ratio),\n", - " \"--high-replicate-ratio\", str(high_rep_ratio),\n", - " \"--batch-ratio\", str(batch_ratio),\n", - " \"--high-batch-ratio\", str(high_batch_ratio),\n", - " \"--quantile\", str(quantile),\n", - " ]\n", - ")\n", - "# fmt: on\n", - "\n", - "!{cmd}" + "from como.proteomics_gen import proteomics_gen\n", + "\n", + "for context in context_names:\n", + " await proteomics_gen(\n", + " context_name=context_names,\n", + " config_filepath=proteomics_metadata_filepath,\n", + " matrix_filepath=proteomics_matrix_filepath[context],\n", + " output_boolean_filepath=Path(f\"data/results/{context}/proteomics/{context}_proteomics_boolean_matrix.csv\"),\n", + " output_gaussian_png_filepath=Path(f\"data/results/{context}/proteomics/{context}_proteomics_gaussian.png\"),\n", + " output_gaussian_html_filepath=Path(f\"data/results/{context}/proteomics/{context}_proteomics_gaussian.html\"),\n", + " output_z_score_matrix_filepath=Path(f\"data/results/{context}/proteomics/{context}_zscore_matrix.csv\"),\n", + " input_entrez_map=Path(f\"data/results/{context}/proteomics/{context}_entrez_map.csv\"),\n", + " replicate_ratio=0.5,\n", + " batch_ratio=0.5,\n", + " high_confidence_replicate_ratio=0.7,\n", + " high_confidence_batch_ratio=0.7,\n", + " quantile=25,\n", + " )" ] }, { - "cell_type": "markdown", "metadata": {}, + "cell_type": "markdown", "source": [ "# Cluster Sample Data (Optional)\n", "This step is used to cluster the samples based on their expression values. This can be used to determine which samples are more similar to each other. In a perfect world, one cluster would be created for each context type used. This is done using the `como/cluster_rnaseq.py` script.\n", @@ -545,10 +577,10 @@ ] }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "cell_type": "code", "outputs": [], + "execution_count": null, "source": [ "filt_technique = \"zfpkm\"\n", "cluster_algorithm = \"umap\"\n", @@ -627,10 +659,10 @@ ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "expression_requirement = 3\n", "requirement_adjust = \"regressive\"\n", @@ -644,8 +676,8 @@ " [\n", " \"python3\", \"como/merge_xomics.py\",\n", " \"--merge-zfpkm-distribution\",\n", - " \"--total-rnaseq-config-file\", trnaseq_config_file,\n", - " \"--mrnaseq-config-file\", mrnaseq_config_file,\n", + " \"--total-rnaseq-config-file\", trnaseq_config_file, # noqa: F821\n", + " \"--mrnaseq-config-file\", mrnaseq_config_file, # noqa: F821\n", " # \"--scrnaseq-config-file\", scrnaseq_config_file, # If using single-cell data, uncomment the start of this line\n", " # \"--proteomics-config-file\", proteomics_config_file, # If using proteomics data, uncomment the start of this line\n", " \"--requirement-adjust\", requirement_adjust,\n", @@ -730,125 +762,16 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/joshl/Projects/COMO/.venv/lib/python3.10/site-packages/cobamp/wrappers/external_wrappers.py:9: UserWarning: \n", - "The wrappers.external_wrappers module will be deprecated in a future release in favour of the wrappers module. \n", - " Available ModelObjectReader classes can still be loaded using cobamp.wrappers.. An appropriate model \n", - " reader can also be created using the get_model_reader function on cobamp.wrappers\n", - " warnings.warn(\n", - "\u001B[32m2024-12-04 10:27:00.656\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m654\u001B[0m - \u001B[1mCreating 'naiveB' model using 'IMAT' reconstruction and 'GUROBI' solver\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:00.656\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mcomo.project\u001B[0m:\u001B[36m__init__\u001B[0m:\u001B[36m30\u001B[0m - \u001B[33m\u001B[1m'data_dir' not provided to Config, using /Users/joshl/Projects/COMO/main/data\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:00.656\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mcomo.project\u001B[0m:\u001B[36m__init__\u001B[0m:\u001B[36m36\u001B[0m - \u001B[33m\u001B[1m'config_dir' not provided to Config, using /Users/joshl/Projects/COMO/main/data/config_sheets\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:00.656\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mcomo.project\u001B[0m:\u001B[36m__init__\u001B[0m:\u001B[36m42\u001B[0m - \u001B[33m\u001B[1m'results_dir' not provided to Config, using /Users/joshl/Projects/COMO/main/data/results\u001B[0m\n", - "Set parameter WLSAccessID\n", - "Set parameter WLSSecret\n", - "Set parameter LicenseID to value 898845\n", - "Academic license 898845 - for non-commercial use only - registered to jl___@huskers.unl.edu\n", - "No defined compartments in model GeneralModelUpdatedV2. Compartments will be deduced heuristically using regular expressions.\n", - "Using regular expression found the following compartments:c, e, g, i, l, m, n, r, x\n", - "/Users/joshl/Projects/COMO/main/como/utils.py:154: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['1' '10' '100' ... '9992' '9993' '9997']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", - " expression_data.loc[:, \"entrez_gene_id\"] = expression_data[\"entrez_gene_id\"].astype(str)\n", - "\u001B[32m2024-12-04 10:27:04.957\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m_map_expression_to_reaction\u001B[0m:\u001B[36m391\u001B[0m - \u001B[1mMapped gene expression to reactions, found 2806 error(s).\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:04.959\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m_build_model\u001B[0m:\u001B[36m468\u001B[0m - \u001B[33m\u001B[1mThe force reaction 'ICDH_m' was not found in the general reference_model. Check BiGG, or the relevant database for your general reference_model, for synonyms.\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:04.959\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m_build_model\u001B[0m:\u001B[36m468\u001B[0m - \u001B[33m\u001B[1mThe force reaction 'CATC140_c' was not found in the general reference_model. Check BiGG, or the relevant database for your general reference_model, for synonyms.\u001B[0m\n", - "Read LP format model from file /var/folders/6s/9l9z74v90tn8lprrp7fchb9w0000gn/T/tmprdfbwfb2.lp\n", - "Reading time = 0.02 seconds\n", - ": 5837 rows, 21220 columns, 81018 nonzeros\n", - "/Users/joshl/Projects/COMO/.venv/lib/python3.10/site-packages/cobra/core/group.py:147: UserWarning: need to pass in a list\n", - " warn(\"need to pass in a list\")\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "Error encountered trying to >.\n", - "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n", - "\u001B[32m2024-12-04 10:27:45.905\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m690\u001B[0m - \u001B[32m\u001B[1mSaved output file to /Users/joshl/Projects/COMO/main/data/results/naiveB\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:45.905\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m691\u001B[0m - \u001B[1mNumber of Genes: 1,477\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:45.905\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m692\u001B[0m - \u001B[1mNumber of Metabolites: 3,204\u001B[0m\n", - "\u001B[32m2024-12-04 10:27:45.905\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m693\u001B[0m - \u001B[1mNumber of Reactions: 5,263\u001B[0m\n", - "Warning: environment still referenced so free is deferred (Continue to use WLS)\n" - ] - } - ], + "outputs": [], "source": [ + "import json\n", + "import os\n", + "from pathlib import Path\n", + "\n", + "from como.project import Config\n", + "\n", "# Set your objectives before running!\n", "objective_dict = {\"naiveB\": \"biomass_maintenance\", \"smB\": \"biomass_maintenance\"}\n", "# -----------------\n", @@ -860,34 +783,28 @@ "recon_algorithms = [\"IMAT\"]\n", "solver = \"GUROBI\"\n", "\n", - "import json\n", - "import os\n", - "from pathlib import Path\n", - "\n", - "from como.project import Config\n", - "\n", "config = Config()\n", "\n", "# Load the output of step 1, which is a dictionary that specifies the merged list of active Gene IDs for each tissue\n", - "step1_results_file = os.path.join(config.data_dir, \"results\", \"step1_results_files.json\")\n", - "with open(step1_results_file) as json_file:\n", + "step1_results_file = config.data_dir / \"results\" \"step1_results_files.json\"\n", + "with step1_results_file.open(\"r\") as json_file:\n", " context_gene_exp = json.load(json_file)\n", "\n", "for recon_algorithm in recon_algorithms:\n", - " for context in context_gene_exp.keys():\n", + " for context in context_gene_exp:\n", " objective = objective_dict[context]\n", "\n", " if recon_algorithm.upper() in [\"IMAT\", \"TINIT\"]:\n", - " active_genes_filepath = os.path.join(config.data_dir, \"results\", context, f\"model_scores_{context}.csv\")\n", + " active_genes_filepath = config.data_dir / \"results\" / context / f\"model_scores_{context}.csv\"\n", " else:\n", " gene_expression_file = context_gene_exp[context]\n", " active_genes_filename = Path(gene_expression_file).name\n", - " active_genes_filepath = os.path.join(config.data_dir, \"results\", context, active_genes_filename)\n", + " active_genes_filepath = config.data_dir / \"results\" / context / active_genes_filename\n", "\n", - " general_model_filepath = os.path.join(config.data_dir, \"GeneralModelUpdatedV2.mat\")\n", - " boundary_reactions_filepath = os.path.join(config.data_dir, \"boundary_rxns\", f\"{context}_boundary_rxns.csv\")\n", - " force_reactions_filepath = os.path.join(config.data_dir, \"force_rxns\", f\"{context}_force_rxns.csv\")\n", - " exclude_reactions_filepath = os.path.join(config.data_dir, \"exclude_rxns\", f\"{context}_exclude_rxns.csv\")\n", + " general_model_filepath = config.data_dir / \"GeneralModelUpdatedV2.mat\"\n", + " boundary_reactions_filepath = config.data_dir / \"boundary_rxns\" / f\"{context}_boundary_rxns.csv\"\n", + " force_reactions_filepath = config.data_dir / \"force_rxns\" / f\"{context}_force_rxns.csv\"\n", + " exclude_reactions_filepath = config.data_dir / \"exclude_rxns\" / f\"{context}_exclude_rxns.csv\"\n", "\n", " # fmt: off\n", " cmd = \" \".join(\n", @@ -941,16 +858,14 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", - "from pathlib import Path\n", - "\n", "import cobra\n", "from como.project import Config\n", "from escher import Builder\n", + "from loguru import logger\n", "\n", "config = Config()\n", "\n", - "user_map_dir = Path(f\"{config.data_dir}/local_files/maps/\")\n", + "user_map_dir = config.data_dir / \"local_files\" / \"maps\"\n", "map_dict = {\n", " \"trypto\": f\"{config.data_dir}/maps/RECON1/RECON1.tryptophan_metabolism.json\",\n", " # \"lipid\": f\"{config.data_dir}/maps/RECON1/RECON1.\", # Not present in COMO by default yet\n", @@ -968,20 +883,18 @@ " index += 1\n", "\n", "# Collect any additional maps under the `{config.data_dir}/maps/` directory\n", - "for file in Path(f\"{config.data_dir}/maps\").glob(\"**/*.json\"):\n", + "for file in (config.data_dir / \"maps\").glob(\"**/*.json\"):\n", " if file not in map_dict.values():\n", " map_dict[file.stem] = file\n", "\n", "for recon_algorithm in recon_algorithms:\n", - " for context in context_gene_exp.keys():\n", - " # for context in [\"naiveB\", \"smB\"]:\n", - " print(f\"Starting {context}\")\n", - " model_json = os.path.join(config.data_dir, \"results\", context, f\"{context}_SpecificModel_{recon_algorithm}.json\")\n", + " for context in context_gene_exp:\n", + " model_json = config.data_dir / \"results\" / context / f\"{context}_SpecificModel_{recon_algorithm}.json\"\n", "\n", - " print(f\"Loading '{context}', this may take some time...\")\n", + " logger.info(f\"Loading '{context}', this may take some time...\")\n", " model = cobra.io.load_json_model(model_json)\n", - " for key in map_dict.keys():\n", - " print(f\"Running with: {key}\")\n", + " for key in map_dict:\n", + " logger.info(f\"Running with: {key}\")\n", " builder = Builder(map_json=str(map_dict[key]))\n", " builder.model = model\n", " solution = cobra.flux_analysis.pfba(model)\n", @@ -995,19 +908,22 @@ " ]\n", " builder.reaction_no_data_color = \"#8e8e8e\"\n", "\n", - " builder.save_html(os.path.join(config.data_dir, \"results\", context, \"figures\", f\"{key}_map_{context}_{recon_algorithm}.html\"))\n", + " builder.save_html(\n", + " config.data_dir / \"results\" / context / \"figures\" / f\"{key}_map_{context}_{recon_algorithm}.html\"\n", + " )\n", "\n", - " out_dir = os.path.join(config.data_dir, \"results\", context)\n", - " # for algorithm in [\"GIMME\", \"IMAT\", \"FASTCORE\", \"tINIT\"]:\n", - " report_file = os.path.join(out_dir, f\"memote_report_{context}_{recon_algorithm}.html\")\n", - " model_file = os.path.join(out_dir, f\"{context}_SpecificModel_{recon_algorithm}.xml\")\n", - " log_dir = os.path.join(out_dir, \"memote\")\n", - " log_file = os.path.join(log_dir, f\"{context}_{recon_algorithm}_memote.log\")\n", + " out_dir = config.data_dir / \"results\" / context\n", + " report_file = out_dir / f\"memote_report_{context}_{recon_algorithm}.html\"\n", + " model_file = out_dir / f\"{context}_SpecificModel_{recon_algorithm}.xml\"\n", + " log_dir = out_dir / \"memote\"\n", + " log_file = log_dir / f\"{context}_{recon_algorithm}_memote.log\"\n", "\n", - " if not os.path.exists(log_dir):\n", - " os.mkdir(log_dir)\n", + " if not log_dir.exists():\n", + " log_dir.mkdir(parents=True, exist_ok=True)\n", "\n", - " cmd = \" \".join([\"memote\", \"report\", \"snapshot\", \"--filename\", f\"{report_file}\", f\"{model_file}\", \">\", f\"{log_file}\"])\n", + " cmd = \" \".join(\n", + " [\"memote\", \"report\", \"snapshot\", \"--filename\", f\"{report_file}\", f\"{model_file}\", \">\", f\"{log_file}\"]\n", + " )\n", "\n", " !{cmd}" ] @@ -1036,11 +952,12 @@ "metadata": {}, "outputs": [], "source": [ + "from como.utils import stringlist_to_list\n", + "\n", "disease_names = [\"arthritis\", \"lupus_a\", \"lupus_b\"]\n", "data_source = \"rnaseq\"\n", "taxon_id = \"human\"\n", "\n", - "from como.utils import stringlist_to_list\n", "\n", "for context_name in stringlist_to_list(context_names):\n", " disease_config_file = f\"disease_data_inputs_{context_name}.xlsx\"\n", @@ -1100,6 +1017,12 @@ "metadata": {}, "outputs": [], "source": [ + "import json\n", + "\n", + "from como.project import Config\n", + "from como.utils import stringlist_to_list\n", + "from loguru import logger\n", + "\n", "# Knock out simulation for the analyzed tissues and diseases\n", "model_files = {\n", " # \"context_name\": \"/path/to/model.mat\"\n", @@ -1107,11 +1030,6 @@ "}\n", "sovler = \"gurobi\"\n", "\n", - "import json\n", - "import os\n", - "\n", - "from como.utils import stringlist_to_list\n", - "from como.project import Config\n", "\n", "config = Config()\n", "\n", @@ -1119,27 +1037,29 @@ "for context in stringlist_to_list(context_names):\n", " for recon_algorithm in recon_algorithms:\n", " for disease in disease_names:\n", - " disease_path = os.path.join(config.data_dir, \"results\", context, disease)\n", - " out_dir = os.path.join(config.data_dir, \"results\", context, disease)\n", - " tissue_gene_folder = os.path.join(config.data_dir, context)\n", - " os.makedirs(tissue_gene_folder, exist_ok=True)\n", + " disease_path = config.data_dir / \"results\" / context / disease\n", + " out_dir = config.data_dir / \"results\" / context / disease\n", + " tissue_gene_folder = config.data_dir / context\n", + " tissue_gene_folder.mkdir(parents=True, exist_ok=True)\n", "\n", - " if not os.path.exists(disease_path):\n", - " print(f\"Disease path doesn't exist! Looking for {disease_path}\")\n", + " if not disease_path.exists():\n", + " logger.warning(f\"Disease path doesn't exist! Looking for {disease_path}\")\n", " continue\n", "\n", " # load the results of step 3 to dictionary \"disease_files\"\n", - " step3_results_file = os.path.join(config.data_dir, \"results\", context, disease, \"step2_results_files.json\")\n", + " step3_results_file = config.data_dir / \"results\" / context / disease / \"step2_results_files.json\"\n", "\n", - " with open(step3_results_file) as json_file:\n", + " with step3_results_file.open(\"r\") as json_file:\n", " disease_files = json.load(json_file)\n", " down_regulated_disease_genes = disease_files[\"down_regulated\"]\n", " up_regulated_disease_genes = disease_files[\"up_regulated\"]\n", "\n", - " if context in model_files.keys():\n", + " if context in model_files:\n", " tissue_specific_model_filepath = model_files[context]\n", " else:\n", - " tissue_specific_model_filepath = os.path.join(config.data_dir, \"results\", context, f\"{context}_SpecificModel_{recon_algorithm}.mat\")\n", + " tissue_specific_model_filepath = (\n", + " config.data_dir / \"results\" / context / f\"{context}_SpecificModel_{recon_algorithm}.mat\"\n", + " )\n", "\n", " # fmt: off\n", " cmd = [\n", @@ -1156,7 +1076,7 @@ " # fmt: on\n", "\n", " if recon_algorithm == \"IMAT\":\n", - " cmd.extend([\"--reference-flux-file\", os.path.join(config.data_dir, \"results\", context, \"IMAT_flux.csv\")])\n", + " cmd.extend([\"--reference-flux-file\", config.data_dir / \"results\" / context / \"IMAT_flux.csv\"])\n", "\n", " cmd = \" \".join(cmd)\n", " !{cmd}" diff --git a/main/como/custom_types.py b/main/como/custom_types.py deleted file mode 100644 index a4fcd19b..00000000 --- a/main/como/custom_types.py +++ /dev/null @@ -1,23 +0,0 @@ -from enum import Enum - - -class RNASeqPreparationMethod(Enum): - TOTAL = "total" - MRNA = "mrna" - SCRNA = "scrna" - - @staticmethod - def from_string(value: str) -> "RNASeqPreparationMethod": - """Build a preparation method object from a string.""" - match_value = "".join(c for c in value if c.isascii()).lower() - - match match_value: - case "total" | "trna": - return RNASeqPreparationMethod.TOTAL - case "mrna": - return RNASeqPreparationMethod.MRNA - case "scrna": - return RNASeqPreparationMethod.SCRNA - case _: - possible_values = [t.value for t in RNASeqPreparationMethod] - raise ValueError(f"Filtering technique must be one of {possible_values}; got: {value}") diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py index 7079d456..8dc2a6fd 100644 --- a/main/como/merge_xomics.py +++ b/main/como/merge_xomics.py @@ -16,7 +16,7 @@ from como import proteomics_gen, return_placeholder_data from como.combine_distributions import _combine_zscores -from como.custom_types import RNASeqPreparationMethod +from como.custom_types import RNAPrepMethod from como.project import Config from como.utils import split_gene_expression_data @@ -93,7 +93,7 @@ def __post_init__(self): raise ValueError("Adjust method must be either 'progressive', 'regressive', 'flat', or 'custom'") -def _load_rnaseq_tests(filename, context_name, prep_method: RNASeqPreparationMethod) -> tuple[str, pd.DataFrame]: +def _load_rnaseq_tests(filename, context_name, prep_method: RNAPrepMethod) -> tuple[str, pd.DataFrame]: """Load rnaseq results. Returns a dictionary of test (context, context, cell, etc ) names and rnaseq expression data @@ -112,11 +112,11 @@ def load_dummy_dict(): raise FileNotFoundError(f"Error: Config file not found at {inquiry_full_path}") match prep_method: - case RNASeqPreparationMethod.TOTAL: + case RNAPrepMethod.TOTAL: filename = f"rnaseq_total_{context_name}.csv" - case RNASeqPreparationMethod.MRNA: + case RNAPrepMethod.MRNA: filename = f"rnaseq_mrna_{context_name}.csv" - case RNASeqPreparationMethod.SCRNA: + case RNAPrepMethod.SCRNA: filename = f"rnaseq_scrna_{context_name}.csv" case _: raise ValueError( @@ -344,15 +344,9 @@ async def _merge_xomics( config = Config() logger.info(f"Merging data for {context_name}") # load data for each source if it exists. IF not load an empty dummy dataset - trnaseq = _load_rnaseq_tests( - filename=trnaseq_file, context_name=context_name, prep_method=RNASeqPreparationMethod.TOTAL - ) - mrnaseq = _load_rnaseq_tests( - filename=mrnaseq_file, context_name=context_name, prep_method=RNASeqPreparationMethod.MRNA - ) - scrnaseq = _load_rnaseq_tests( - filename=scrnaseq_file, context_name=context_name, prep_method=RNASeqPreparationMethod.SCRNA - ) + trnaseq = _load_rnaseq_tests(filename=trnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.TOTAL) + mrnaseq = _load_rnaseq_tests(filename=mrnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.MRNA) + scrnaseq = _load_rnaseq_tests(filename=scrnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.SCRNA) proteomics = proteomics_gen.load_proteomics_tests(filename=proteomics_file, context_name=context_name) expression_list = [] diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py index b1e17b34..d020d71d 100644 --- a/main/como/proteomics_gen.py +++ b/main/como/proteomics_gen.py @@ -1,5 +1,6 @@ -import argparse -import asyncio +from __future__ import annotations + +from pathlib import Path import numpy as np import pandas as pd @@ -12,85 +13,73 @@ # Load Proteomics -def load_proteomics_data(datafilename, context_name): +def process_proteomics_data(path: Path) -> pd.DataFrame: """Load proteomics data from a given context and filename.""" - config = Config() - data_path = config.data_dir / "data_matrices" / context_name / datafilename - logger.info(f"Data Matrix Path: {data_path}") - - if data_path.exists(): - proteomics_data = pd.read_csv(data_path, header=0) - else: - logger.error(f"Error: file not found: {data_path}") - - return None - # Preprocess data, drop na, duplicate ';' in symbol, - proteomics_data["gene_symbol"] = proteomics_data["gene_symbol"].astype(str) - proteomics_data.dropna(subset=["gene_symbol"], inplace=True) - pluralnames = proteomics_data[proteomics_data["gene_symbol"].str.contains(";") == True] # noqa: E712 - - for idx, row in pluralnames.iterrows(): - names = row["gene_symbol"].split(";") - rows = [] + matrix: pd.DataFrame = pd.read_csv(path) + if "gene_symbol" not in matrix.columns: + raise ValueError("No gene_symbol column found in proteomics data.") - for name in names: - rowcopy = row.copy() - rowcopy["gene_symbol"] = name - rows.append(rowcopy) - proteomics_data.drop(index=idx, inplace=True) - proteomics_data = pd.concat([proteomics_data, pd.DataFrame(rows)], ignore_index=True) - - return proteomics_data + matrix["gene_symbol"] = matrix["gene_symbol"].astype(str) + matrix.dropna(subset=["gene_symbol"], inplace=True) + matrix = matrix.assign(gene_symbol=matrix["gene_symbol"].str.split(";")).explode("gene_symbol") + return matrix # read map to convert to entrez -async def load_gene_symbol_map(gene_symbols: list[str]): +async def load_gene_symbol_map(gene_symbols: list[str], entrez_map: Path | None = None): """Add descirption....""" - config = Config() - filepath = config.data_dir / "proteomics_entrez_map.csv" - if filepath.exists(): - df = pd.read_csv(filepath, index_col="gene_symbol") + if entrez_map and entrez_map.exists(): + df = pd.read_csv(entrez_map, index_col="gene_symbol") else: biodbnet = BioDBNet() df = await biodbnet.async_db2db( - values=gene_symbols, input_db=Input.GENE_SYMBOL, output_db=[Output.GENE_ID, Output.ENSEMBL_GENE_ID] + values=gene_symbols, + input_db=Input.GENE_SYMBOL, + output_db=[Output.GENE_ID, Output.ENSEMBL_GENE_ID], ) df.loc[df["gene_id"] == "-", ["gene_id"]] = np.nan - df.to_csv(filepath, index_label="gene_symbol") + df.to_csv(entrez_map, index_label="gene_symbol") return df[~df.index.duplicated()] -def abundance_to_bool_group(context_name, group_name, abundance_matrix, rep_ratio, hi_rep_ratio, quantile): - """Descrioption....""" - config = Config() - output_dir = config.result_dir / context_name / "proteomics" - output_dir.mkdir(parents=True, exist_ok=True) - - # write group abundances to individual files - abundance_filepath = ( - config.result_dir / context_name / "proteomics" / "".join(["protein_abundance_", group_name, ".csv"]) - ) +def abundance_to_bool_group( + context_name, + abundance_filepath: Path, + output_gaussian_png_filepath: Path, + output_gaussian_html_filepath: Path, + output_z_score_matrix_filepath: Path, + abundance_matrix: pd.DataFrame, + replicate_ratio: float, + high_confidence_replicate_ratio: float, + quantile: float, + output_boolean_filepath: Path, +): + """Convert proteomic data to boolean expression.""" abundance_matrix.to_csv(abundance_filepath, index_label="entrez_gene_id") - protein_transform_main(abundance_matrix, output_dir, group_name) + protein_transform_main( + abundance_df=abundance_matrix, + output_gaussian_png_filepath=output_gaussian_png_filepath, + output_gaussian_html_filepath=output_gaussian_html_filepath, + output_z_score_matrix_filepath=output_z_score_matrix_filepath, + ) # Logical Calculation abundance_matrix_nozero = abundance_matrix.replace(0, np.nan) thresholds = abundance_matrix_nozero.quantile(quantile, axis=0) - testbool = pd.DataFrame(0, columns=list(abundance_matrix), index=abundance_matrix.index) + testbool = pd.DataFrame(0, columns=abundance_matrix.columns, index=abundance_matrix.index) - for col in list(abundance_matrix): + for col in abundance_matrix.columns: testbool.loc[abundance_matrix[col] > thresholds[col], [col]] = 1 - abundance_matrix["pos"] = (abundance_matrix > 0).sum(axis=1) / abundance_matrix.count(axis=1) abundance_matrix["expressed"] = 0 - abundance_matrix.loc[(abundance_matrix["pos"] >= rep_ratio), ["expressed"]] = 1 abundance_matrix["high"] = 0 - abundance_matrix.loc[(abundance_matrix["pos"] >= hi_rep_ratio), ["high"]] = 1 + abundance_matrix["pos"] = abundance_matrix[abundance_matrix > 0].sum(axis=1) / abundance_matrix.count(axis=1) + abundance_matrix.loc[(abundance_matrix["pos"] >= replicate_ratio), ["expressed"]] = 1 + abundance_matrix.loc[(abundance_matrix["pos"] >= high_confidence_replicate_ratio), ["high"]] = 1 - bool_filepath = output_dir / f"bool_prot_Matrix_{context_name}_{group_name}.csv" - abundance_matrix.to_csv(bool_filepath, index_label="entrez_gene_id") + abundance_matrix.to_csv(output_boolean_filepath, index_label="entrez_gene_id") def to_bool_context(context_name, group_ratio, hi_group_ratio, group_names): @@ -156,129 +145,74 @@ def load_empty_dict(): async def proteomics_gen( - config_file: str, - rep_ratio: float = 0.5, - group_ratio: float = 0.5, - hi_rep_ratio: float = 0.5, - hi_group_ratio: float = 0.5, + context_name: str, + config_filepath: Path, + matrix_filepath: Path, + output_boolean_filepath: Path, + output_z_score_matrix_filepath: Path, + output_gaussian_png_filepath: Path | None = None, + output_gaussian_html_filepath: Path | None = None, + input_entrez_map: Path | None = None, + replicate_ratio: float = 0.5, + batch_ratio: float = 0.5, + high_confidence_replicate_ratio: float = 0.7, + high_confidence_batch_ratio: float = 0.7, quantile: int = 25, ): """Generate proteomics data.""" - config = Config() - if not config_file: - raise ValueError("Config file must be provided") + if not config_filepath.exists(): + raise FileNotFoundError(f"Config file not found at {config_filepath}") + if config_filepath.suffix not in (".xlsx", ".xls"): + raise FileNotFoundError(f"Config file must be an xlsx or xls file at {config_filepath}") + + if not matrix_filepath.exists(): + raise FileNotFoundError(f"Matrix file not found at {matrix_filepath}") + if matrix_filepath.suffix not in {".csv"}: + raise FileNotFoundError(f"Matrix file must be a csv file at {matrix_filepath}") if quantile < 0 or quantile > 100: raise ValueError("Quantile must be an integer from 0 to 100") quantile /= 100 - prot_config_filepath = config.data_dir / "config_sheets" / config_file - logger.info(f"Config file is at '{prot_config_filepath}'") + config_df = pd.read_excel(config_filepath, sheet_name=context_name) + matrix: pd.DataFrame = process_proteomics_data(matrix_filepath) - xl = pd.ExcelFile(prot_config_filepath) - sheet_names = xl.sheet_names + groups = config_df["group"].unique().tolist() - for context_name in sheet_names: - datafilename = "".join(["protein_abundance_", context_name, ".csv"]) - config_sheet = pd.read_excel(prot_config_filepath, sheet_name=context_name) - groups = config_sheet["group"].unique().tolist() + for group in groups: + indices = np.where([g == group for g in config_df["group"]]) + sample_columns = [*np.take(config_df["sample_name"].to_numpy(), indices).ravel().tolist(), "gene_symbol"] + matrix = matrix.loc[:, sample_columns] - for group in groups: - group_idx = np.where([g == group for g in config_sheet["group"].tolist()]) - cols = [*np.take(config_sheet["sample_name"].to_numpy(), group_idx).ravel().tolist(), "gene_symbol"] - - proteomics_data = load_proteomics_data(datafilename, context_name) - proteomics_data = proteomics_data.loc[:, cols] - - symbols_to_ids = await load_gene_symbol_map(gene_symbols=proteomics_data["gene_symbol"].tolist()) - proteomics_data.dropna(subset=["gene_symbol"], inplace=True) - if "uniprot" in proteomics_data.columns: - proteomics_data.drop(columns=["uniprot"], inplace=True) - - proteomics_data = proteomics_data.groupby(["gene_symbol"]).agg("max") - proteomics_data["entrez_gene_id"] = symbols_to_ids["gene_id"] - proteomics_data.dropna(subset=["entrez_gene_id"], inplace=True) - proteomics_data.set_index("entrez_gene_id", inplace=True) - - # save proteomics data by test - abundance_to_bool_group(context_name, group, proteomics_data, rep_ratio, hi_rep_ratio, quantile) - to_bool_context(context_name, group_ratio, hi_group_ratio, groups) - - -def _main(): - parser = argparse.ArgumentParser( - prog="proteomics_gen.py", - description="Description goes here", - epilog="For additional help, please post questions/issues in the MADRID GitHub repo at " - "https://github.com/HelikarLab/MADRID or email babessell@gmail.com", - ) - parser.add_argument( - "-c", - "--config-file", - type=str, - required=True, - dest="config_file", - help="The configuration file for proteomics", - ) - parser.add_argument( - "-r", - "--replicate-ratio", - type=float, - required=False, - default=0.5, - dest="rep_ratio", - help="Ratio of replicates required for a gene to be considered active in that group", - ) - parser.add_argument( - "-b", - "--batch-ratio", - type=float, - required=False, - default=0.5, - dest="group_ratio", - help="Ratio of groups (batches or studies) required for a gene to be considered active in a context", - ) - parser.add_argument( - "-hr", - "--high-replicate-ratio", - type=float, - required=False, - default=0.5, - dest="hi_rep_ratio", - help="Ratio of replicates required for a gene to be considered high-confidence in that group", - ) - parser.add_argument( - "-hb", - "--high-batch-ratio", - type=float, - required=False, - default=0.5, - dest="hi_group_ratio", - help="Ratio of groups (batches or studies) required for a gene to be considered high-confidence in a context", - ) - - parser.add_argument( - "-q", - "--quantile", - type=int, - required=False, - default=25, - dest="quantile", - help="The quantile of genes to accept. This should be an integer from 0% (no proteins pass) " - "to 100% (all proteins pass).", - ) - args = parser.parse_args() - asyncio.run( - proteomics_gen( - args.config_file, - args.rep_ratio, - args.group_ratio, - args.hi_rep_ratio, - args.hi_group_ratio, - args.quantile, + symbols_to_gene_ids = await load_gene_symbol_map( + gene_symbols=matrix["gene_symbol"].tolist(), + entrez_map=input_entrez_map, + ) + matrix.dropna(subset=["gene_symbol"], inplace=True) + if "uniprot" in matrix.columns: + matrix.drop(columns=["uniprot"], inplace=True) + + matrix = matrix.groupby(["gene_symbol"]).agg("max") + matrix["entrez_gene_id"] = symbols_to_gene_ids["gene_id"] + matrix.dropna(subset=["entrez_gene_id"], inplace=True) + matrix.set_index("entrez_gene_id", inplace=True) + + # bool_filepath = output_dir / f"bool_prot_Matrix_{context_name}_{group_name}.csv" + abundance_to_bool_group( + context_name=context_name, + abundance_filepath=matrix_filepath, + abundance_matrix=matrix, + replicate_ratio=replicate_ratio, + high_confidence_replicate_ratio=high_confidence_replicate_ratio, + quantile=quantile, + output_boolean_filepath=output_boolean_filepath, + output_gaussian_png_filepath=output_gaussian_png_filepath, + output_gaussian_html_filepath=output_gaussian_html_filepath, + output_z_score_matrix_filepath=output_z_score_matrix_filepath, ) + to_bool_context( + context_name=context_name, + group_ratio=batch_ratio, + hi_group_ratio=high_confidence_batch_ratio, + group_names=groups, ) - - -if __name__ == "__main__": - _main() diff --git a/main/como/proteomics_preprocessing.py b/main/como/proteomics_preprocessing.py index 9b215439..1329406d 100644 --- a/main/como/proteomics_preprocessing.py +++ b/main/como/proteomics_preprocessing.py @@ -129,12 +129,13 @@ def plot_gaussian_fit(z_results: ZResult, facet_titles: bool = True, x_min: int # Main function for protein abundance transformation -def protein_transform_main(abundance_df: pd.DataFrame | str | Path, out_dir: str | Path, group_name: str) -> None: +def protein_transform_main( + abundance_df: pd.DataFrame | str | Path, + output_gaussian_png_filepath: Path, + output_gaussian_html_filepath: Path, + output_z_score_matrix_filepath: Path, +) -> None: """Transform protein abundance data.""" - out_dir: Path = Path(out_dir) - output_figure_directory = out_dir / "figures" - output_figure_directory.mkdir(parents=True, exist_ok=True) - abundance_df: pd.DataFrame = ( pd.read_csv(abundance_df) if isinstance(abundance_df, (str, Path)) else abundance_df.fillna(0) ) @@ -142,10 +143,14 @@ def protein_transform_main(abundance_df: pd.DataFrame | str | Path, out_dir: str z_transform: ZResult = z_score_calc(abundance_df, min_thresh=0) fig = plot_gaussian_fit(z_results=z_transform, facet_titles=True, x_min=-4) - fig.write_image(out_dir / "gaussian_fit.png") - fig.write_html(out_dir / "gaussian_fit.html") - logger.info(f"Wrote image to {out_dir / 'gaussian_fit.png'}") + + if output_gaussian_png_filepath: + fig.write_image(output_gaussian_png_filepath.with_suffix(".png")) + logger.info(f"PNG gaussian figure written to {output_gaussian_png_filepath}") + if output_gaussian_html_filepath: + fig.write_html(output_gaussian_html_filepath.with_suffix(".html")) + logger.info(f"Interactive HTML gaussian figure written to {output_gaussian_png_filepath}") z_transformed_abundances = z_transform.zfpkm z_transformed_abundances[abundance_df == 0] = -4 - z_transformed_abundances.to_csv(out_dir / f"protein_zscore_Matrix_{group_name}.csv", index=False) + z_transformed_abundances.to_csv(output_z_score_matrix_filepath, index=False) diff --git a/main/como/rnaseq.py b/main/como/rnaseq.py deleted file mode 100644 index 055078fc..00000000 --- a/main/como/rnaseq.py +++ /dev/null @@ -1,783 +0,0 @@ -from __future__ import annotations - -import gc -import math -import multiprocessing -import time -from collections import namedtuple -from dataclasses import dataclass, field -from enum import Enum -from functools import partial -from multiprocessing.pool import Pool -from pathlib import Path -from typing import Callable, NamedTuple - -import numpy as np -import numpy.typing as npt -import pandas as pd -import plotly.graph_objs as go -import scanpy as sc -import sklearn -import sklearn.neighbors -from fast_bioservices import Taxon -from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol -from loguru import logger -from pandas import DataFrame -from plotly.subplots import make_subplots -from scipy.signal import find_peaks -from sklearn.neighbors import KernelDensity - -from como.custom_types import RNASeqPreparationMethod -from como.migrations import gene_info_migrations -from como.project import Config -from como.utils import convert_gene_data - - -class _FilteringOptions(NamedTuple): - replicate_ratio: float - batch_ratio: float - cut_off: float - high_replicate_ratio: float - high_batch_ratio: float - - -class FilteringTechnique(Enum): - """RNA sequencing filtering capabilities.""" - - cpm = "cpm" - zfpkm = "zfpkm" - tpm = "quantile" - umi = "umi" - - @staticmethod - def from_string(value: str) -> FilteringTechnique: - """Create a filtering technique object from a string.""" - match value.lower(): - case "cpm": - return FilteringTechnique.cpm - case "zfpkm": - return FilteringTechnique.zfpkm - case "quantile": - return FilteringTechnique.tpm - case "umi": - return FilteringTechnique.umi - case _: - possible_values = [t.value for t in FilteringTechnique] - raise ValueError(f"Filtering technique must be one of {possible_values}; got: {value}") - - -class LayoutMethod(Enum): - """RNA sequencing layout method.""" - - paired_end = "paired-end" - single_end = "single-end" - - -@dataclass -class _StudyMetrics: - study: str - num_samples: int - count_matrix: pd.DataFrame - fragment_lengths: npt.NDArray[np.float32] - sample_names: list[str] - layout: list[LayoutMethod] - entrez_gene_ids: list[str] - gene_sizes: npt.NDArray[np.float32] - __normalization_matrix: pd.DataFrame = field(default_factory=pd.DataFrame) - __z_score_matrix: pd.DataFrame = field(default_factory=pd.DataFrame) - __high_confidence_entrez_gene_ids: list[str] = field(default=list) - - def __post_init__(self): - for layout in self.layout: - if layout not in LayoutMethod: - raise ValueError(f"Layout must be 'paired-end' or 'single-end'; got: {layout}") - - @property - def normalization_matrix(self) -> pd.DataFrame: - return self.__normalization_matrix - - @normalization_matrix.setter - def normalization_matrix(self, value: pd.DataFrame) -> None: - self.__normalization_matrix = value - - @property - def z_score_matrix(self) -> pd.DataFrame: - return self.__z_score_matrix - - @z_score_matrix.setter - def z_score_matrix(self, value: pd.DataFrame) -> None: - self.__z_score_matrix = value - - @property - def high_confidence_entrez_gene_ids(self) -> list[str]: - return self.__high_confidence_entrez_gene_ids - - @high_confidence_entrez_gene_ids.setter - def high_confidence_entrez_gene_ids(self, values: list[str]) -> None: - self.__high_confidence_entrez_gene_ids = values - - -Density = namedtuple("Density", ["x", "y"]) - - -class _ZFPKMResult(NamedTuple): - zfpkm: pd.Series - density: Density - mu: float - std_dev: float - max_fpkm: float - - -class _ReadMatrixResults(NamedTuple): - metrics: dict[str, _StudyMetrics] - entrez_gene_ids: list[str] - - -NamedMetrics = dict[str, _StudyMetrics] - - -def k_over_a(k: int, a: float) -> Callable[[npt.NDArray], bool]: - """Return a function that filters rows of an array based on the sum of elements being greater than or equal to A at least k times. - - This code is based on the `kOverA` function found in R's `genefilter` package: https://www.rdocumentation.org/packages/genefilter/versions/1.54.2/topics/kOverA - - :param k: The minimum number of times the sum of elements must be greater than or equal to A. - :param a: The value to compare the sum of elements to. - :return: A function that accepts a NumPy array to perform the actual filtering - """ # noqa: E501 - - def filter_func(row: npt.NDArray) -> bool: - return np.sum(row >= a) >= k - - return filter_func - - -def genefilter(data: pd.DataFrame | npt.NDArray, filter_func: Callable[[npt.NDArray], bool]) -> npt.NDArray: - """Apply a filter function to the rows of the data and return the filtered array. - - This code is based on the `genefilter` function found in R's `genefilter` package: https://www.rdocumentation.org/packages/genefilter/versions/1.54.2/topics/genefilter - - :param data: The data to filter - :param filter_func: THe function to filter the data by - :return: A NumPy array of the filtered data. - """ - match type(data): - case pd.DataFrame: - return data.apply(filter_func, axis=1).values - case npt.NDArray: - return np.apply_along_axis(filter_func, axis=1, arr=data) - case _: - raise ValueError("Unsupported data type. Must be a Pandas DataFrame or a NumPy array.") - - -async def _read_counts_matrix( - *, - context_name: str, - counts_matrix_filepath: Path, - config_filepath: Path, - gene_info_filepath: Path, - taxon_id: Taxon, -) -> _ReadMatrixResults: - """Read the counts matrix and returns the results. - - :param context_name: The context name being processed. Usually a cell type, but can be any string - :param counts_matrix_filepath: The file path to the gene count matrix - :param config_filepath: The file path to the Excel configuration file - :param gene_info_filepath: The file path to gene information generated by `rnaseq_preprocess.py` - :param taxon_id: The NCBI Taxon ID - :return: A dataclass `ReadMatrixResults` - """ - logger.trace(f"Reading config_filepath at '{config_filepath}'") - config_df: pd.DataFrame = pd.read_excel(config_filepath, sheet_name=context_name, header=0) - gene_info: pd.DataFrame = pd.read_csv(gene_info_filepath) - gene_info = gene_info[gene_info["ensembl_gene_id"] != "-"].reset_index(drop=True) - gene_info = gene_info_migrations(gene_info) - - match counts_matrix_filepath.suffix: - case ".csv": - logger.debug(f"Reading CSV file at '{counts_matrix_filepath}'") - counts_matrix: pd.DataFrame = pd.read_csv(counts_matrix_filepath, header=0) - if "ensembl_gene_id" not in counts_matrix.columns: - raise ValueError( - f"Counts matrix must contain a column named 'ensembl_gene_id'. " - f"Ensure the file '{counts_matrix_filepath}' contains this column." - ) - conversion = await ensembl_to_gene_id_and_symbol( - ids=counts_matrix["ensembl_gene_id"].tolist(), taxon=taxon_id - ) - counts_matrix = counts_matrix.merge(conversion, on="ensembl_gene_id", how="left") - - case ".h5ad": - logger.debug(f"Reading h5ad file at '{counts_matrix_filepath}'") - adata: sc.AnnData = sc.read_h5ad(counts_matrix_filepath) - counts_matrix: pd.DataFrame = adata.to_df().T # Make sample names the columns and gene data the index - - # Coherce the incoming gene data (i.e., Gene Symbols) into Entrez and Ensembl Gene IDs - conversion = await convert_gene_data(counts_matrix.index.tolist(), taxon_id) - counts_matrix.index.name = conversion.index.name - counts_matrix = counts_matrix.merge(conversion, left_index=True, right_index=True) - counts_matrix = counts_matrix[counts_matrix["entrez_gene_id"] != "-"] - counts_matrix.reset_index(inplace=True) - - # explicit garbage collection because this function runs for a little while - del adata - del conversion - gc.collect() - - case _: - raise ValueError( - f"Unknown file extension '{counts_matrix_filepath.suffix}'. Valid options are '.csv' or '.h5ad'." - ) - - if counts_matrix.empty: - raise ValueError( - f"Counts matrix is empty. Ensure the file contains data. " - f"Attempted to process file '{counts_matrix_filepath}'" - ) - - # Only include Entrez and Ensembl Gene IDs that are present in `gene_info` - counts_matrix["entrez_gene_id"] = counts_matrix["entrez_gene_id"].str.split("//") - counts_matrix = counts_matrix.explode("entrez_gene_id") - counts_matrix = counts_matrix.replace(to_replace="-", value=pd.NA).dropna() - counts_matrix["entrez_gene_id"] = counts_matrix["entrez_gene_id"].astype(int) - - gene_info = gene_info.replace(to_replace="-", value=pd.NA).dropna() - gene_info["entrez_gene_id"] = gene_info["entrez_gene_id"].astype(int) - - counts_matrix = counts_matrix.merge( - gene_info[["entrez_gene_id", "ensembl_gene_id"]], - on=["entrez_gene_id", "ensembl_gene_id"], - how="inner", - ) - gene_info = gene_info.merge( - counts_matrix[["entrez_gene_id", "ensembl_gene_id"]], - on=["entrez_gene_id", "ensembl_gene_id"], - how="inner", - ) - - entrez_gene_ids: list[str] = gene_info["entrez_gene_id"].tolist() - metrics: NamedMetrics = {} - for study in config_df["study"].unique().tolist(): - study_sample_names = config_df[config_df["study"] == study]["sample_name"].tolist() - layouts = config_df[config_df["study"] == study]["layout"].tolist() - metrics[study] = _StudyMetrics( - count_matrix=counts_matrix[counts_matrix.columns.intersection(study_sample_names)], - fragment_lengths=config_df[config_df["study"] == study]["fragment_length"].values, - sample_names=study_sample_names, - layout=[LayoutMethod(layout) for layout in layouts], - num_samples=len(study_sample_names), - entrez_gene_ids=entrez_gene_ids, - gene_sizes=np.array(gene_info["size"].values).astype(np.float32), - study=study, - ) - metrics[study].fragment_lengths[np.isnan(metrics[study].fragment_lengths)] = 0 - metrics[study].count_matrix.index = pd.Index(entrez_gene_ids, name="entrez_gene_id") - - return _ReadMatrixResults(metrics=metrics, entrez_gene_ids=gene_info["entrez_gene_id"].tolist()) - - -def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics: - """Calculate the Transcripts Per Million (TPM) for each sample in the metrics dictionary.""" - for sample in metrics: - count_matrix = metrics[sample].count_matrix - - gene_sizes = metrics[sample].gene_sizes - - tpm_matrix = pd.DataFrame(data=None, index=count_matrix.index, columns=count_matrix.columns) - for i in range(len(count_matrix.columns)): - values: pd.Series = count_matrix.iloc[:, i] + 1 # Add 1 to prevent division by 0 - rate = np.log(values.tolist()) - np.log(gene_sizes) - denominator = np.log(np.sum(np.exp(rate))) - tpm_value = np.exp(rate - denominator + np.log(1e6)) - tpm_matrix.iloc[:, i] = tpm_value - metrics[sample].normalization_matrix = tpm_matrix - - return metrics - - -def calculate_fpkm(metrics: NamedMetrics) -> NamedMetrics: - """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary.""" # noqa: E501 - matrix_values = [] - for study in metrics: - for sample in range(metrics[study].num_samples): - layout = metrics[study].layout[sample] - count_matrix: npt.NDArray = metrics[study].count_matrix.iloc[:, sample].values - gene_size = metrics[study].gene_sizes - - count_matrix = count_matrix.astype(np.float32) - gene_size = gene_size.astype(np.float32) - - match layout: - case LayoutMethod.paired_end: # FPKM - mean_fragment_lengths = metrics[study].fragment_lengths[sample] - # Ensure non-negative value - effective_length = [max(0, size - (mean_fragment_lengths + 1)) for size in gene_size] - n = count_matrix.sum() - fpkm = ((count_matrix + 1) * 1e9) / (np.array(effective_length) * n) - matrix_values.append(fpkm) - case LayoutMethod.single_end: # RPKM - # Add a pseudocount before log to ensure log(0) does not happen - rate = np.log(count_matrix + 1) - np.log(gene_size) - exp_rate = np.exp(rate - np.log(np.sum(count_matrix)) + np.log(1e9)) - matrix_values.append(exp_rate) - case _: - raise ValueError("Invalid normalization method specified") - - fpkm_matrix = pd.DataFrame(matrix_values).T # Transpose is needed because values were appended as rows - fpkm_matrix = fpkm_matrix[~pd.isna(fpkm_matrix)] - metrics[study].normalization_matrix = fpkm_matrix - - metrics[study].normalization_matrix.columns = metrics[study].count_matrix.columns - - return metrics - - -def _zfpkm_calculation(col: pd.Series, kernel: KernelDensity, peak_parameters: tuple[float, float]) -> _ZFPKMResult: - """Log2 Transformations. - - Stabilize the variance in the data to make the distribution more symmetric; this is helpful for Gaussian fitting - - Kernel Density Estimation (kde) - - Non-parametric method to estimate the probability density function (PDF) of a random variable - - Estimates the distribution of log2-transformed FPKM values - - Bandwidth parameter controls the smoothness of the density estimate - - KDE Explanation - - A way to smooth a histogram to get a better idea of the underlying distribution of the data - - Given a set of data points, we want to understand how they are distributed. - Histograms can be useful, but are sensitive to bin size and number - - The KDE places a "kernel" - a small symmetric function (i.e., Gaussian curve) - at each data point - - The "kernel" acts as a weight, giving more weight to points closer to the center of the kernel, - and less weight to points farther away - - Kernel functions are summed along each point on the x-axis - - A smooth curve is created that represents the estimated density of the data - - Peak Finding - - Identifies that are above a certain height and separated by a minimum distance - - Represent potential local maxima in the distribution - - Peak Selection - - The peak with the highest x-value (from log2-FPKM) is chosen as the mean (mu) - of the "inactive" gene distribution - - The peak representing unexpressed or inactive genes should be at a lower FPKM - value compared to the peak representing expressed genes - - Standard Deviation Estimation - - The mean of log2-FPKM values are greater than the calculated mu - - Standard deviation is estimated based on the assumption that the right tail of the distribution - This represents expressed genes) can be approximated by a half-normal distribution - - zFPKM Transformation - - Centers disbribution around 0 and scales it by the standard deviation. - This makes it easier to compare gene expression across different samples - - Represents the number of standard deviations away from the mean of the "inactive" gene distribution - - Higher zFPKM values indicate higher expression levels relative to the "inactive" peak - - A zFPKM value of 0 represents the mean of the "inactive" distribution - - Research shows that a zFPKM value of -3 or greater can be used as - a threshold for calling a gene as "expressed" - : https://doi.org/10.1186/1471-2164-14-778 - """ - col_log2: npt.NDArray = np.log2(col + 1) - col_log2 = np.nan_to_num(col_log2, nan=0) - refit: KernelDensity = kernel.fit(col_log2.reshape(-1, 1)) # type: ignore - - # kde: KernelDensity = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(col_log2.reshape(-1, 1)) - x_range = np.linspace(col_log2.min(), col_log2.max(), 1000) - density = np.exp(refit.score_samples(x_range.reshape(-1, 1))) - peaks, _ = find_peaks(density, height=peak_parameters[0], distance=peak_parameters[1]) - peak_positions = x_range[peaks] - - mu = 0 - max_fpkm = 0 - stddev = 1 - - if len(peaks) != 0: - mu = peak_positions.max() - max_fpkm = density[peaks[np.argmax(peak_positions)]] - u = col_log2[col_log2 > mu].mean() - stddev = (u - mu) * np.sqrt(np.pi / 2) - zfpkm = pd.Series((col_log2 - mu) / stddev, dtype=np.float32, name=col.name) - - return _ZFPKMResult(zfpkm=zfpkm, density=Density(x_range, density), mu=mu, std_dev=stddev, max_fpkm=max_fpkm) - - -def zfpkm_transform( - fpkm_df: pd.DataFrame, - bandwidth: int = 0.5, - peak_parameters: tuple[float, float] = (0.02, 1.0), - update_every_percent: float = 0.1, -) -> tuple[dict[str, _ZFPKMResult], DataFrame]: - """Perform zFPKM calculation/transformation.""" - if update_every_percent > 1: - logger.warning( - f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - " - f"will convert to percentage" - ) - update_every_percent /= 100 - - total = len(fpkm_df.columns) - update_per_step: int = int(np.ceil(total * update_every_percent)) - cores = multiprocessing.cpu_count() - 2 - logger.debug(f"Processing {total:,} samples through zFPKM transform using {cores} cores") - logger.debug( - f"Will update every {update_per_step:,} steps as this is approximately " - f"{update_every_percent:.1%} of {total:,}" - ) - - with Pool(processes=cores) as pool: - kernel = KernelDensity(kernel="gaussian", bandwidth=bandwidth) - chunksize = int(math.ceil(len(fpkm_df.columns) / (4 * cores))) - partial_func = partial(_zfpkm_calculation, kernel=kernel, peak_parameters=peak_parameters) - chunk_time = time.time() - start_time = time.time() - - log_padding = len(str(f"{total:,}")) - zfpkm_df = pd.DataFrame(data=0, index=fpkm_df.index, columns=fpkm_df.columns) - results: dict[str, _ZFPKMResult] = {} - result: _ZFPKMResult - for i, result in enumerate( - pool.imap( - partial_func, - (fpkm_df[col] for col in fpkm_df.columns), - chunksize=chunksize, - ) - ): - key = str(result.zfpkm.name) - results[key] = result - zfpkm_df[key] = result.zfpkm - - # show updates every X% and at the end, but skip on first iteration - if i != 0 and (i % update_per_step == 0 or i == total): - current_time = time.time() - chunk = current_time - chunk_time - total_time = current_time - start_time - formatted = f"{i:,}" - logger.debug( - f"Processed {formatted:>{log_padding}} of {total:,} - " - f"chunk took {chunk:.1f} seconds - " - f"running for {total_time:.1f} seconds" - ) - chunk_time = current_time - return results, zfpkm_df - - -def zfpkm_plot(results, *, write_png_filepath: Path, plot_xfloor: int = -4, subplot_titles: bool = True): - """Plot the log2(FPKM) density and fitted Gaussian for each sample. - - :param results: A dictionary of intermediate results from zfpkm_transform. - :param write_png_filepath: The path to write the plot to - :param: subplot_titles: Whether to display facet titles (sample names). - :param plot_xfloor: Lower limit for the x-axis. - :param subplot_titles: Whether to display facet titles (sample names). - """ - mega_df = pd.DataFrame(columns=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"]) - for name, result in results.items(): - stddev = result.std_dev - x = np.array(result.density.x) - y = np.array(result.density.y) - - fitted = np.exp(-0.5 * ((x - result.mu) / stddev) ** 2) / (stddev * np.sqrt(2 * np.pi)) - max_fpkm = y.max() - max_fitted = fitted.max() - scale_fitted = fitted * (max_fpkm / max_fitted) - - df = pd.DataFrame( - { - "sample_name": [name] * len(x), - "log2fpkm": x, - "fpkm_density": y, - "fitted_density_scaled": scale_fitted, - } - ) - mega_df = pd.concat([mega_df, df], ignore_index=True) - - mega_df = mega_df.melt(id_vars=["log2fpkm", "sample_name"], var_name="source", value_name="density") - subplot_titles = list(results.keys()) if subplot_titles else None - fig = make_subplots( - rows=len(results), - cols=1, - subplot_titles=subplot_titles, - vertical_spacing=min(0.05, (1 / (len(results) - 1))), - ) - - for i, (name, group) in enumerate(mega_df.groupby("sample_name"), start=1): - fig.add_trace( - trace=go.Scatter(x=group["log2fpkm"], y=group["density"], mode="lines", name=name, legendgroup=name), - row=i, - col=1, - ) - fig.update_xaxes(title_text="log2(FPKM)", range=[plot_xfloor, max(group["log2fpkm"].tolist())], row=i, col=1) - fig.update_yaxes(title_text="density [scaled]", row=i, col=1) - fig.update_layout(legend_tracegroupgap=0) - - fig.update_layout(height=600 * len(results), width=1000, title_text="zFPKM Plots", showlegend=True) - fig.write_image(write_png_filepath) - - -def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics: - """Calculate the z-score for each sample in the metrics dictionary.""" - for sample in metrics: - log_matrix = np.log(metrics[sample].normalization_matrix) - z_matrix = pd.DataFrame( - data=sklearn.preprocessing.scale(log_matrix, axis=1), columns=metrics[sample].sample_names - ) - metrics[sample].z_score_matrix = z_matrix - return metrics - - -def cpm_filter( - *, - context_name: str, - metrics: NamedMetrics, - filtering_options: _FilteringOptions, - prep: RNASeqPreparationMethod, -) -> NamedMetrics: - """Apply Counts Per Million (CPM) filtering to the count matrix for a given sample.""" - config = Config() - n_exp = filtering_options.replicate_ratio - n_top = filtering_options.high_replicate_ratio - cut_off = filtering_options.cut_off - - sample: str - metric: _StudyMetrics - for sample, metric in metrics.items(): - counts: pd.DataFrame = metric.count_matrix - entrez_ids: list[str] = metric.entrez_gene_ids - library_size: pd.DataFrame = counts.sum(axis=1) - - # For library_sizes equal to 0, add 1 to prevent divide by 0 - # This will not impact the final counts per million calculation because the original counts are still 0 - # thus, (0 / 1) * 1_000_000 = 0 - library_size[library_size == 0] = 1 - - output_filepath = config.result_dir / context_name / prep.value / f"CPM_Matrix_{prep.value}_{sample}.csv" - output_filepath.parent.mkdir(parents=True, exist_ok=True) - counts_per_million: pd.DataFrame = (counts / library_size) * 1_000_000 - counts_per_million.insert(0, "entrez_gene_ids", pd.Series(entrez_ids)) - logger.debug(f"Writing CPM matrix to {output_filepath}") - counts_per_million.to_csv(output_filepath, index=False) - - # TODO: Counts per million is adding ~61,500 columns (equal to the number of genes) for some reason. - # Most likely due to multiplying by 1_000_000, not exactly sure why - - min_samples = round(n_exp * len(counts.columns)) # noqa: F841 - top_samples = round(n_top * len(counts.columns)) # noqa: F841 - test_bools = pd.DataFrame({"entrez_gene_ids": entrez_ids}) - for i in range(len(counts_per_million.columns)): - cutoff = ( - 10e6 / (np.median(np.sum(counts[:, i]))) - if cut_off == "default" - else 1e6 * cut_off / np.median(np.sum(counts[:, i])) - ) - test_bools = test_bools.merge(counts_per_million[counts_per_million.iloc[:, i] > cutoff]) - - return metrics - - -def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions) -> NamedMetrics: - """Apply quantile-based filtering to the TPM matrix for a given sample.""" - # TODO: Write the TPM matrix to disk - - n_exp = filtering_options.replicate_ratio - n_top = filtering_options.high_replicate_ratio - cut_off = filtering_options.cut_off - metrics = calculate_tpm(metrics) - - sample: str - metric: _StudyMetrics - for sample, metric in metrics.items(): - entrez_ids = metric.entrez_gene_ids - gene_size = metric.gene_sizes - tpm_matrix: pd.DataFrame = metric.normalization_matrix - - min_samples = round(n_exp * len(tpm_matrix.columns)) - top_samples = round(n_top * len(tpm_matrix.columns)) - - tpm_quantile = tpm_matrix[tpm_matrix > 0] - quantile_cutoff = np.quantile( - a=tpm_quantile.values, q=1 - (cut_off / 100), axis=0 - ) # Compute quantile across columns - boolean_expression = pd.DataFrame( - data=tpm_matrix > quantile_cutoff, index=tpm_matrix.index, columns=tpm_matrix.columns - ).astype(int) - - min_func = k_over_a(min_samples, 0.9) - top_func = k_over_a(top_samples, 0.9) - - min_genes: npt.NDArray[bool] = genefilter(boolean_expression, min_func) - top_genes: npt.NDArray[bool] = genefilter(boolean_expression, top_func) - - # Only keep `entrez_gene_ids` that pass `min_genes` - metric.entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, min_genes) if keep] - metric.gene_sizes = [gene for gene, keep in zip(gene_size, min_genes) if keep] - metric.count_matrix = metric.count_matrix.iloc[min_genes, :] - metric.normalization_matrix = metrics[sample].normalization_matrix.iloc[min_genes, :] - - keep_top_genes = [gene for gene, keep in zip(entrez_ids, top_genes) if keep] - metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, keep_top_genes) if keep] - - metrics = calculate_z_score(metrics) - - return metrics - - -def zfpkm_filter( - *, - metrics: NamedMetrics, - filtering_options: _FilteringOptions, - write_png_filepath: Path, - calcualte_fpkm: bool, -) -> NamedMetrics: - """Apply zFPKM filtering to the FPKM matrix for a given sample.""" - min_sample_expression = filtering_options.replicate_ratio - high_confidence_sample_expression = filtering_options.high_replicate_ratio - cut_off = filtering_options.cut_off - - if calcualte_fpkm: - metrics = calculate_fpkm(metrics) - - metric: _StudyMetrics - for metric in metrics.values(): - # if fpkm was not calculated, the normalization matrix will be empty; collect the count matrix instead - matrix = metric.count_matrix if metric.normalization_matrix.empty else metric.normalization_matrix - matrix = matrix[matrix.sum(axis=1) > 0] - - minimums = matrix == 0 - results, zfpkm_df = zfpkm_transform(matrix) - zfpkm_df[minimums] = -4 - zfpkm_plot(results, write_png_filepath=write_png_filepath) - - # determine which genes are expressed - min_samples = round(min_sample_expression * len(zfpkm_df.columns)) - min_func = k_over_a(min_samples, cut_off) - min_genes: npt.NDArray[bool] = genefilter(zfpkm_df, min_func) - metric.entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, min_genes) if keep] - - # determine which genes are confidently expressed - top_samples = round(high_confidence_sample_expression * len(zfpkm_df.columns)) - top_func = k_over_a(top_samples, cut_off) - top_genes: npt.NDArray[bool] = genefilter(zfpkm_df, top_func) - metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, top_genes) if keep] - - return metrics - - -def filter_counts( - *, - context_name: str, - metrics: NamedMetrics, - technique: FilteringTechnique, - filtering_options: _FilteringOptions, - prep: RNASeqPreparationMethod, - write_zfpkm_png_filepath: Path, -) -> NamedMetrics: - """Filter the count matrix based on the specified technique.""" - match technique: - case FilteringTechnique.cpm: - return cpm_filter( - context_name=context_name, metrics=metrics, filtering_options=filtering_options, prep=prep - ) - case FilteringTechnique.tpm: - return tpm_quantile_filter(metrics=metrics, filtering_options=filtering_options) - case FilteringTechnique.zfpkm: - return zfpkm_filter( - metrics=metrics, - filtering_options=filtering_options, - write_png_filepath=write_zfpkm_png_filepath, - calcualte_fpkm=True, - ) - case FilteringTechnique.umi: - return zfpkm_filter( - metrics=metrics, - filtering_options=filtering_options, - write_png_filepath=write_zfpkm_png_filepath, - calcualte_fpkm=False, - ) - case _: - raise ValueError(f"Technique must be one of {FilteringTechnique}") - - -async def save_rnaseq_tests( - context_name: str, - counts_matrix_filepath: Path, - config_filepath: Path, - gene_info_filepath: Path, - output_filepath: Path, - prep: RNASeqPreparationMethod, - taxon_id: Taxon, - replicate_ratio: float, - batch_ratio: float, - high_replicate_ratio: float, - high_batch_ratio: float, - technique: FilteringTechnique, - cut_off: int | float, - write_zfpkm_png_filepath: Path, -): - """Save the results of the RNA-Seq tests to a CSV file.""" - filtering_options = _FilteringOptions( - replicate_ratio=replicate_ratio, - batch_ratio=batch_ratio, - cut_off=cut_off, - high_replicate_ratio=high_replicate_ratio, - high_batch_ratio=high_batch_ratio, - ) - - if prep == RNASeqPreparationMethod.SCRNA: - technique = FilteringTechnique.umi - logger.warning( - "Single cell filtration does not normalize and assumes " - "gene counts are counted with Unique Molecular Identifiers (UMIs). " - "Setting filtering technique to UMI now." - ) - - read_counts_results: _ReadMatrixResults = await _read_counts_matrix( - context_name=context_name, - counts_matrix_filepath=counts_matrix_filepath, - config_filepath=config_filepath, - gene_info_filepath=gene_info_filepath, - taxon_id=taxon_id, - ) - metrics = read_counts_results.metrics - entrez_gene_ids = read_counts_results.entrez_gene_ids - - metrics = filter_counts( - context_name=context_name, - metrics=metrics, - technique=technique, - filtering_options=filtering_options, - prep=prep, - write_zfpkm_png_filepath=write_zfpkm_png_filepath, - ) - - expressed_genes: list[str] = [] - top_genes: list[str] = [] - for metric in metrics.values(): - expressed_genes.extend(metric.entrez_gene_ids) - top_genes.extend(metric.high_confidence_entrez_gene_ids) - - expression_frequency = pd.Series(expressed_genes).value_counts() - expression_df = pd.DataFrame( - {"entrez_gene_id": expression_frequency.index, "frequency": expression_frequency.values} - ) - expression_df["prop"] = expression_df["frequency"] / len(metrics) - expression_df = expression_df[expression_df["prop"] >= filtering_options.batch_ratio] - - top_frequency = pd.Series(top_genes).value_counts() - top_df = pd.DataFrame({"entrez_gene_id": top_frequency.index, "frequency": top_frequency.values}) - top_df["prop"] = top_df["frequency"] / len(metrics) - top_df = top_df[top_df["prop"] >= filtering_options.high_batch_ratio] - - boolean_matrix = pd.DataFrame(data={"entrez_gene_id": entrez_gene_ids, "expressed": 0, "high": 0}) - for gene in entrez_gene_ids: - if gene in expression_df["entrez_gene_id"]: - boolean_matrix.loc[gene, "expressed"] = 1 - if gene in top_df["entrez_gene_id"]: - boolean_matrix.loc[gene, "high"] = 1 - - expressed_count = len(boolean_matrix[boolean_matrix["expressed"] == 1]) - high_confidence_count = len(boolean_matrix[boolean_matrix["high"] == 1]) - - boolean_matrix.to_csv(output_filepath, index=False) - logger.info( - f"{context_name} - Found {expressed_count} expressed and {high_confidence_count} confidently expressed genes" - ) - logger.success(f"Wrote boolean matrix to {output_filepath}") diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py index 55234919..364bd4c3 100644 --- a/main/como/rnaseq_gen.py +++ b/main/como/rnaseq_gen.py @@ -1,135 +1,723 @@ from __future__ import annotations -import argparse -import asyncio -from dataclasses import dataclass +import math +import multiprocessing +import time +from collections import namedtuple +from dataclasses import dataclass, field +from enum import Enum +from functools import partial +from multiprocessing.pool import Pool from pathlib import Path +from typing import Callable, NamedTuple +import numpy as np +import numpy.typing as npt import pandas as pd -from fast_bioservices import Taxon +import plotly.graph_objs as go +import scanpy as sc +import sklearn +import sklearn.neighbors +from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol from loguru import logger +from pandas import DataFrame +from plotly.subplots import make_subplots +from scipy.signal import find_peaks +from sklearn.neighbors import KernelDensity -from como import Config -from como.custom_types import RNASeqPreparationMethod -from como.rnaseq import FilteringTechnique, save_rnaseq_tests +from como.migrations import gene_info_migrations +from como.project import Config +from como.types import FilteringTechnique, RNAPrepMethod -@dataclass -class _Arguments: - config_file: str +class _FilteringOptions(NamedTuple): replicate_ratio: float batch_ratio: float + cut_off: float high_replicate_ratio: float high_batch_ratio: float - filtering_technique: FilteringTechnique - minimum_cutoff: int | str - library_prep: RNASeqPreparationMethod - taxon: Taxon - write_zfpkm_png_filepath: Path + + +class LayoutMethod(Enum): + """RNA sequencing layout method.""" + + paired_end = "paired-end" + single_end = "single-end" + + +@dataclass +class _StudyMetrics: + study: str + num_samples: int + count_matrix: pd.DataFrame + fragment_lengths: npt.NDArray[np.float32] + sample_names: list[str] + layout: list[LayoutMethod] + entrez_gene_ids: list[str] + gene_sizes: npt.NDArray[np.float32] + __normalization_matrix: pd.DataFrame = field(default_factory=pd.DataFrame) + __z_score_matrix: pd.DataFrame = field(default_factory=pd.DataFrame) + __high_confidence_entrez_gene_ids: list[str] = field(default=list) def __post_init__(self): - self.library_prep = RNASeqPreparationMethod.from_string(str(self.library_prep)) - self.filtering_technique = FilteringTechnique.from_string(str(self.filtering_technique)) + for layout in self.layout: + if layout not in LayoutMethod: + raise ValueError(f"Layout must be 'paired-end' or 'single-end'; got: {layout}") - if self.minimum_cutoff is None: - if self.filtering_technique == FilteringTechnique.tpm: - self.minimum_cutoff = 25 - elif self.filtering_technique == FilteringTechnique.cpm: - self.minimum_cutoff = "default" - elif self.filtering_technique == FilteringTechnique.zfpkm: - self.minimum_cutoff = -3 + @property + def normalization_matrix(self) -> pd.DataFrame: + return self.__normalization_matrix + @normalization_matrix.setter + def normalization_matrix(self, value: pd.DataFrame) -> None: + self.__normalization_matrix = value -async def _handle_context_batch( - config_filename: str, - replicate_ratio: float, - batch_ratio: float, - replicate_ratio_high: float, - batch_ratio_high: float, - technique: FilteringTechnique, - cut_off: int | float | str, - prep: RNASeqPreparationMethod, - taxon: Taxon, - write_zfpkm_png_filepath: Path, -) -> None: - """Iterate through each context type and create rnaseq expression file. + @property + def z_score_matrix(self) -> pd.DataFrame: + return self.__z_score_matrix - :param config_filename: The configuration filename to read - :param replicate_ratio: The percentage of replicates that a gene must - appear in for a gene to be marked as "active" in a batch/study - :param batch_ratio: The percentage of batches that a gene must appear in for a gene to be marked as 'active" - :param replicate_ratio_high: The percentage of replicates that a gene must - appear in for a gene to be marked "highly confident" in its expression in a batch/study - :param batch_ratio_high: The percentage of batches that a gene must - appear in for a gene to be marked "highly confident" in its expression - :param technique: The filtering technique to use - :param cut_off: The cutoff value to use for the provided filtering technique - :param prep: The library preparation method + @z_score_matrix.setter + def z_score_matrix(self, value: pd.DataFrame) -> None: + self.__z_score_matrix = value + + @property + def high_confidence_entrez_gene_ids(self) -> list[str]: + return self.__high_confidence_entrez_gene_ids + + @high_confidence_entrez_gene_ids.setter + def high_confidence_entrez_gene_ids(self, values: list[str]) -> None: + self.__high_confidence_entrez_gene_ids = values + + +class _ZFPKMResult(NamedTuple): + zfpkm: pd.Series + density: Density + mu: float + std_dev: float + max_fpkm: float + + +class _ReadMatrixResults(NamedTuple): + metrics: dict[str, _StudyMetrics] + entrez_gene_ids: list[str] + + +Density = namedtuple("Density", ["x", "y"]) +NamedMetrics = dict[str, _StudyMetrics] + + +def k_over_a(k: int, a: float) -> Callable[[npt.NDArray], bool]: + """Return a function that filters rows of an array based on the sum of elements being greater than or equal to A at least k times. + + This code is based on the `kOverA` function found in R's `genefilter` package: https://www.rdocumentation.org/packages/genefilter/versions/1.54.2/topics/kOverA + + :param k: The minimum number of times the sum of elements must be greater than or equal to A. + :param a: The value to compare the sum of elements to. + :return: A function that accepts a NumPy array to perform the actual filtering + """ # noqa: E501 + + def filter_func(row: npt.NDArray) -> bool: + return np.sum(row >= a) >= k + + return filter_func + + +def genefilter(data: pd.DataFrame | npt.NDArray, filter_func: Callable[[npt.NDArray], bool]) -> npt.NDArray: + """Apply a filter function to the rows of the data and return the filtered array. + + This code is based on the `genefilter` function found in R's `genefilter` package: https://www.rdocumentation.org/packages/genefilter/versions/1.54.2/topics/genefilter + + :param data: The data to filter + :param filter_func: THe function to filter the data by + :return: A NumPy array of the filtered data. + """ + if not isinstance(data, (pd.DataFrame, npt.NDArray)): + raise TypeError("Unsupported data type. Must be a Pandas DataFrame or a NumPy array.") + + return ( + data.apply(filter_func, axis=1).values + if isinstance(data, pd.DataFrame) + else np.apply_along_axis(filter_func, axis=1, arr=data) + ) + + +async def _read_counts(path: Path) -> pd.DataFrame: + if path.suffix not in {".csv", ".h5ad"}: + raise ValueError(f"Unknown file extension '{path.suffix}'. Valid options are '.csv' or '.h5ad'.") + + matrix: pd.DataFrame + if path.suffix == ".csv": + logger.debug(f"Reading CSV file at '{path}'") + matrix = pd.read_csv(path, header=0) + elif path.suffix == ".h5ad": + logger.debug(f"Reading h5ad file at '{path}'") + # Make sample names the columns and gene data the index + matrix = sc.read_h5ad(path).to_df().T + + return matrix + + +async def _build_matrix_results( + *, + matrix: pd.DataFrame, + gene_info: pd.DataFrame, + metadata_df: pd.DataFrame, + taxon: int, +) -> _ReadMatrixResults: + """Read the counts matrix and returns the results. + + :param matrix: The gene counts matrix to process + :param metadata_df: The configuration dataframe related to the current context :param taxon: The NCBI Taxon ID - :return: None + :return: A dataclass `ReadMatrixResults` """ - config = Config() + gene_info = gene_info_migrations(gene_info) + conversion = await ensembl_to_gene_id_and_symbol(ids=matrix["ensembl_gene_id"].tolist(), taxon=taxon) + matrix = matrix.merge(conversion, on="ensembl_gene_id", how="left") + + # Only include Entrez and Ensembl Gene IDs that are present in `gene_info` + matrix["entrez_gene_id"] = matrix["entrez_gene_id"].str.split("//") + matrix = matrix.explode("entrez_gene_id") + matrix = matrix.replace(to_replace="-", value=pd.NA).dropna() + matrix["entrez_gene_id"] = matrix["entrez_gene_id"].astype(int) + + gene_info = gene_info.replace(to_replace="-", value=pd.NA).dropna() + gene_info["entrez_gene_id"] = gene_info["entrez_gene_id"].astype(int) + + counts_matrix = matrix.merge( + gene_info[["entrez_gene_id", "ensembl_gene_id"]], + on=["entrez_gene_id", "ensembl_gene_id"], + how="inner", + ) + gene_info = gene_info.merge( + counts_matrix[["entrez_gene_id", "ensembl_gene_id"]], + on=["entrez_gene_id", "ensembl_gene_id"], + how="inner", + ) + + entrez_gene_ids: list[str] = gene_info["entrez_gene_id"].tolist() + metrics: NamedMetrics = {} + for study in metadata_df["study"].unique().tolist(): + study_sample_names = metadata_df[metadata_df["study"] == study]["sample_name"].tolist() + layouts = metadata_df[metadata_df["study"] == study]["layout"].tolist() + metrics[study] = _StudyMetrics( + count_matrix=counts_matrix[counts_matrix.columns.intersection(study_sample_names)], + fragment_lengths=metadata_df[metadata_df["study"] == study]["fragment_length"].values, + sample_names=study_sample_names, + layout=[LayoutMethod(layout) for layout in layouts], + num_samples=len(study_sample_names), + entrez_gene_ids=entrez_gene_ids, + gene_sizes=np.array(gene_info["size"].values).astype(np.float32), + study=study, + ) + metrics[study].fragment_lengths[np.isnan(metrics[study].fragment_lengths)] = 0 + metrics[study].count_matrix.index = pd.Index(entrez_gene_ids, name="entrez_gene_id") + + return _ReadMatrixResults(metrics=metrics, entrez_gene_ids=gene_info["entrez_gene_id"].tolist()) + + +def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics: + """Calculate the Transcripts Per Million (TPM) for each sample in the metrics dictionary.""" + for sample in metrics: + count_matrix = metrics[sample].count_matrix + + gene_sizes = metrics[sample].gene_sizes + + tpm_matrix = pd.DataFrame(data=None, index=count_matrix.index, columns=count_matrix.columns) + for i in range(len(count_matrix.columns)): + values: pd.Series = count_matrix.iloc[:, i] + 1 # Add 1 to prevent division by 0 + rate = np.log(values.tolist()) - np.log(gene_sizes) + denominator = np.log(np.sum(np.exp(rate))) + tpm_value = np.exp(rate - denominator + np.log(1e6)) + tpm_matrix.iloc[:, i] = tpm_value + metrics[sample].normalization_matrix = tpm_matrix + + return metrics + + +def calculate_fpkm(metrics: NamedMetrics) -> NamedMetrics: + """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary.""" # noqa: E501 + matrix_values = [] + for study in metrics: + for sample in range(metrics[study].num_samples): + layout = metrics[study].layout[sample] + count_matrix: npt.NDArray = metrics[study].count_matrix.iloc[:, sample].values + gene_size = metrics[study].gene_sizes + + count_matrix = count_matrix.astype(np.float32) + gene_size = gene_size.astype(np.float32) + + match layout: + case LayoutMethod.paired_end: # FPKM + mean_fragment_lengths = metrics[study].fragment_lengths[sample] + # Ensure non-negative value + effective_length = [max(0, size - (mean_fragment_lengths + 1)) for size in gene_size] + n = count_matrix.sum() + fpkm = ((count_matrix + 1) * 1e9) / (np.array(effective_length) * n) + matrix_values.append(fpkm) + case LayoutMethod.single_end: # RPKM + # Add a pseudocount before log to ensure log(0) does not happen + rate = np.log(count_matrix + 1) - np.log(gene_size) + exp_rate = np.exp(rate - np.log(np.sum(count_matrix)) + np.log(1e9)) + matrix_values.append(exp_rate) + case _: + raise ValueError("Invalid normalization method specified") + + fpkm_matrix = pd.DataFrame(matrix_values).T # Transpose is needed because values were appended as rows + fpkm_matrix = fpkm_matrix[~pd.isna(fpkm_matrix)] + metrics[study].normalization_matrix = fpkm_matrix + + metrics[study].normalization_matrix.columns = metrics[study].count_matrix.columns + + return metrics + + +def _zfpkm_calculation(col: pd.Series, kernel: KernelDensity, peak_parameters: tuple[float, float]) -> _ZFPKMResult: + """Log2 Transformations. + + Stabilize the variance in the data to make the distribution more symmetric; this is helpful for Gaussian fitting + + Kernel Density Estimation (kde) + - Non-parametric method to estimate the probability density function (PDF) of a random variable + - Estimates the distribution of log2-transformed FPKM values + - Bandwidth parameter controls the smoothness of the density estimate + - KDE Explanation + - A way to smooth a histogram to get a better idea of the underlying distribution of the data + - Given a set of data points, we want to understand how they are distributed. + Histograms can be useful, but are sensitive to bin size and number + - The KDE places a "kernel" - a small symmetric function (i.e., Gaussian curve) - at each data point + - The "kernel" acts as a weight, giving more weight to points closer to the center of the kernel, + and less weight to points farther away + - Kernel functions are summed along each point on the x-axis + - A smooth curve is created that represents the estimated density of the data + + Peak Finding + - Identifies that are above a certain height and separated by a minimum distance + - Represent potential local maxima in the distribution + + Peak Selection + - The peak with the highest x-value (from log2-FPKM) is chosen as the mean (mu) + of the "inactive" gene distribution + - The peak representing unexpressed or inactive genes should be at a lower FPKM + value compared to the peak representing expressed genes + + Standard Deviation Estimation + - The mean of log2-FPKM values are greater than the calculated mu + - Standard deviation is estimated based on the assumption that the right tail of the distribution + This represents expressed genes) can be approximated by a half-normal distribution + + zFPKM Transformation + - Centers disbribution around 0 and scales it by the standard deviation. + This makes it easier to compare gene expression across different samples + - Represents the number of standard deviations away from the mean of the "inactive" gene distribution + - Higher zFPKM values indicate higher expression levels relative to the "inactive" peak + - A zFPKM value of 0 represents the mean of the "inactive" distribution + - Research shows that a zFPKM value of -3 or greater can be used as + a threshold for calling a gene as "expressed" + : https://doi.org/10.1186/1471-2164-14-778 + """ + col_log2: npt.NDArray = np.log2(col + 1) + col_log2 = np.nan_to_num(col_log2, nan=0) + refit: KernelDensity = kernel.fit(col_log2.reshape(-1, 1)) # type: ignore + + # kde: KernelDensity = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(col_log2.reshape(-1, 1)) + x_range = np.linspace(col_log2.min(), col_log2.max(), 1000) + density = np.exp(refit.score_samples(x_range.reshape(-1, 1))) + peaks, _ = find_peaks(density, height=peak_parameters[0], distance=peak_parameters[1]) + peak_positions = x_range[peaks] + + mu = 0 + max_fpkm = 0 + stddev = 1 + + if len(peaks) != 0: + mu = peak_positions.max() + max_fpkm = density[peaks[np.argmax(peak_positions)]] + u = col_log2[col_log2 > mu].mean() + stddev = (u - mu) * np.sqrt(np.pi / 2) + zfpkm = pd.Series((col_log2 - mu) / stddev, dtype=np.float32, name=col.name) + + return _ZFPKMResult(zfpkm=zfpkm, density=Density(x_range, density), mu=mu, std_dev=stddev, max_fpkm=max_fpkm) + + +def zfpkm_transform( + fpkm_df: pd.DataFrame, + bandwidth: int = 0.5, + peak_parameters: tuple[float, float] = (0.02, 1.0), + update_every_percent: float = 0.1, +) -> tuple[dict[str, _ZFPKMResult], DataFrame]: + """Perform zFPKM calculation/transformation.""" + if update_every_percent > 1: + logger.warning( + f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - " + f"will convert to percentage" + ) + update_every_percent /= 100 + + total = len(fpkm_df.columns) + update_per_step: int = int(np.ceil(total * update_every_percent)) + cores = min(multiprocessing.cpu_count() - 2, total) + logger.debug(f"Processing {total:,} samples through zFPKM transform using {cores} cores") + logger.debug( + f"Will update every {update_per_step:,} steps as this is approximately " + f"{update_every_percent:.1%} of {total:,}" + ) + + with Pool(processes=cores) as pool: + kernel = KernelDensity(kernel="gaussian", bandwidth=bandwidth) + chunksize = int(math.ceil(len(fpkm_df.columns) / (4 * cores))) + partial_func = partial(_zfpkm_calculation, kernel=kernel, peak_parameters=peak_parameters) + chunk_time = time.time() + start_time = time.time() + + log_padding = len(str(f"{total:,}")) + zfpkm_df = pd.DataFrame(data=0, index=fpkm_df.index, columns=fpkm_df.columns) + results: dict[str, _ZFPKMResult] = {} + result: _ZFPKMResult + for i, result in enumerate( + pool.imap( + partial_func, + (fpkm_df[col] for col in fpkm_df.columns), + chunksize=chunksize, + ) + ): + key = str(result.zfpkm.name) + results[key] = result + zfpkm_df[key] = result.zfpkm + + # show updates every X% and at the end, but skip on first iteration + if i != 0 and (i % update_per_step == 0 or i == total): + current_time = time.time() + chunk = current_time - chunk_time + total_time = current_time - start_time + formatted = f"{i:,}" + logger.debug( + f"Processed {formatted:>{log_padding}} of {total:,} - " + f"chunk took {chunk:.1f} seconds - " + f"running for {total_time:.1f} seconds" + ) + chunk_time = current_time + return results, zfpkm_df + + +def zfpkm_plot(results, *, plot_xfloor: int = -4, subplot_titles: bool = True): + """Plot the log2(FPKM) density and fitted Gaussian for each sample. + + :param results: A dictionary of intermediate results from zfpkm_transform. + :param: subplot_titles: Whether to display facet titles (sample names). + :param plot_xfloor: Lower limit for the x-axis. + :param subplot_titles: Whether to display facet titles (sample names). + """ + mega_df = pd.DataFrame(columns=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"]) + for name, result in results.items(): + stddev = result.std_dev + x = np.array(result.density.x) + y = np.array(result.density.y) - config_filepath = config.config_dir / config_filename - if not config_filepath.exists(): - raise FileNotFoundError(f"Unable to find '{config_filename}' at the path: '{config_filepath}'") - xl = pd.ExcelFile(config_filepath) - sheet_names = xl.sheet_names + fitted = np.exp(-0.5 * ((x - result.mu) / stddev) ** 2) / (stddev * np.sqrt(2 * np.pi)) + max_fpkm = y.max() + max_fitted = fitted.max() + scale_fitted = fitted * (max_fpkm / max_fitted) - logger.info(f"Reading config file: {config_filepath}") + df = pd.DataFrame( + { + "sample_name": [name] * len(x), + "log2fpkm": x, + "fpkm_density": y, + "fitted_density_scaled": scale_fitted, + } + ) + mega_df = pd.concat([mega_df, df], ignore_index=True) - for context_name in sheet_names: - logger.debug(f"Starting '{context_name}'") + mega_df = mega_df.melt(id_vars=["log2fpkm", "sample_name"], var_name="source", value_name="density") + subplot_titles = list(results.keys()) if subplot_titles else None + fig = make_subplots( + rows=len(results), + cols=1, + subplot_titles=subplot_titles, + vertical_spacing=min(0.05, (1 / (len(results) - 1))), + ) - rnaseq_input_filepath = ( - config.data_dir / "data_matrices" / context_name / f"gene_counts_matrix_{prep.value}_{context_name}" + for i, (name, group) in enumerate(mega_df.groupby("sample_name"), start=1): + fig.add_trace( + trace=go.Scatter(x=group["log2fpkm"], y=group["density"], mode="lines", name=name, legendgroup=name), + row=i, + col=1, ) - if prep == RNASeqPreparationMethod.SCRNA: - rnaseq_input_filepath = rnaseq_input_filepath.with_suffix(".h5ad") - elif prep in {RNASeqPreparationMethod.TOTAL, RNASeqPreparationMethod.MRNA}: - rnaseq_input_filepath = rnaseq_input_filepath.with_suffix(".csv") - - if not rnaseq_input_filepath.exists(): - logger.warning(f"Gene counts matrix not found at {rnaseq_input_filepath}, skipping...") - continue - - gene_info_filepath = config.data_dir / "gene_info.csv" - rnaseq_output_filepath = ( - config.result_dir / context_name / prep.value / f"rnaseq_{prep.value}_{context_name}.csv" + fig.update_xaxes(title_text="log2(FPKM)", range=[plot_xfloor, max(group["log2fpkm"].tolist())], row=i, col=1) + fig.update_yaxes(title_text="density [scaled]", row=i, col=1) + fig.update_layout(legend_tracegroupgap=0) + + fig.update_layout(height=600 * len(results), width=1000, title_text="zFPKM Plots", showlegend=True) + fig.write_image("zfpkm_plot.png") + + +def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics: + """Calculate the z-score for each sample in the metrics dictionary.""" + for sample in metrics: + log_matrix = np.log(metrics[sample].normalization_matrix) + z_matrix = pd.DataFrame( + data=sklearn.preprocessing.scale(log_matrix, axis=1), columns=metrics[sample].sample_names ) - rnaseq_output_filepath.parent.mkdir(parents=True, exist_ok=True) - - await save_rnaseq_tests( - context_name=context_name, - counts_matrix_filepath=rnaseq_input_filepath, - config_filepath=config_filepath, - output_filepath=rnaseq_output_filepath.as_posix(), - gene_info_filepath=gene_info_filepath, - prep=prep, - replicate_ratio=replicate_ratio, - batch_ratio=batch_ratio, - high_replicate_ratio=replicate_ratio_high, - high_batch_ratio=batch_ratio_high, - technique=technique, - cut_off=cut_off, - taxon_id=taxon, - write_zfpkm_png_filepath=write_zfpkm_png_filepath, + metrics[sample].z_score_matrix = z_matrix + return metrics + + +def cpm_filter( + *, + context_name: str, + metrics: NamedMetrics, + filtering_options: _FilteringOptions, + prep: RNAPrepMethod, +) -> NamedMetrics: + """Apply Counts Per Million (CPM) filtering to the count matrix for a given sample.""" + config = Config() + n_exp = filtering_options.replicate_ratio + n_top = filtering_options.high_replicate_ratio + cut_off = filtering_options.cut_off + + sample: str + metric: _StudyMetrics + for sample, metric in metrics.items(): + counts: pd.DataFrame = metric.count_matrix + entrez_ids: list[str] = metric.entrez_gene_ids + library_size: pd.DataFrame = counts.sum(axis=1) + + # For library_sizes equal to 0, add 1 to prevent divide by 0 + # This will not impact the final counts per million calculation because the original counts are still 0 + # thus, (0 / 1) * 1_000_000 = 0 + library_size[library_size == 0] = 1 + + output_filepath = config.result_dir / context_name / prep.value / f"CPM_Matrix_{prep.value}_{sample}.csv" + output_filepath.parent.mkdir(parents=True, exist_ok=True) + counts_per_million: pd.DataFrame = (counts / library_size) * 1_000_000 + counts_per_million.insert(0, "entrez_gene_ids", pd.Series(entrez_ids)) + logger.debug(f"Writing CPM matrix to {output_filepath}") + counts_per_million.to_csv(output_filepath, index=False) + + # TODO: Counts per million is adding ~61,500 columns (equal to the number of genes) for some reason. + # Most likely due to multiplying by 1_000_000, not exactly sure why + + min_samples = round(n_exp * len(counts.columns)) # noqa: F841 + top_samples = round(n_top * len(counts.columns)) # noqa: F841 + test_bools = pd.DataFrame({"entrez_gene_ids": entrez_ids}) + for i in range(len(counts_per_million.columns)): + cutoff = ( + 10e6 / (np.median(np.sum(counts[:, i]))) + if cut_off == "default" + else (1e6 * cut_off) / np.median(np.sum(counts[:, i])) + ) + test_bools = test_bools.merge(counts_per_million[counts_per_million.iloc[:, i] > cutoff]) + + return metrics + + +def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions) -> NamedMetrics: + """Apply quantile-based filtering to the TPM matrix for a given sample.""" + # TODO: Write the TPM matrix to disk + + n_exp = filtering_options.replicate_ratio + n_top = filtering_options.high_replicate_ratio + cut_off = filtering_options.cut_off + metrics = calculate_tpm(metrics) + + sample: str + metric: _StudyMetrics + for sample, metric in metrics.items(): + entrez_ids = metric.entrez_gene_ids + gene_size = metric.gene_sizes + tpm_matrix: pd.DataFrame = metric.normalization_matrix + + min_samples = round(n_exp * len(tpm_matrix.columns)) + top_samples = round(n_top * len(tpm_matrix.columns)) + + tpm_quantile = tpm_matrix[tpm_matrix > 0] + quantile_cutoff = np.quantile( + a=tpm_quantile.values, q=1 - (cut_off / 100), axis=0 + ) # Compute quantile across columns + boolean_expression = pd.DataFrame( + data=tpm_matrix > quantile_cutoff, index=tpm_matrix.index, columns=tpm_matrix.columns + ).astype(int) + + min_func = k_over_a(min_samples, 0.9) + top_func = k_over_a(top_samples, 0.9) + + min_genes: npt.NDArray[bool] = genefilter(boolean_expression, min_func) + top_genes: npt.NDArray[bool] = genefilter(boolean_expression, top_func) + + # Only keep `entrez_gene_ids` that pass `min_genes` + metric.entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, min_genes) if keep] + metric.gene_sizes = [gene for gene, keep in zip(gene_size, min_genes) if keep] + metric.count_matrix = metric.count_matrix.iloc[min_genes, :] + metric.normalization_matrix = metrics[sample].normalization_matrix.iloc[min_genes, :] + + keep_top_genes = [gene for gene, keep in zip(entrez_ids, top_genes) if keep] + metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, keep_top_genes) if keep] + + metrics = calculate_z_score(metrics) + + return metrics + + +def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions, calcualte_fpkm: bool) -> NamedMetrics: + """Apply zFPKM filtering to the FPKM matrix for a given sample.""" + min_sample_expression = filtering_options.replicate_ratio + high_confidence_sample_expression = filtering_options.high_replicate_ratio + cut_off = filtering_options.cut_off + + if calcualte_fpkm: + metrics = calculate_fpkm(metrics) + + metric: _StudyMetrics + for metric in metrics.values(): + # if fpkm was not calculated, the normalization matrix will be empty; collect the count matrix instead + matrix = metric.count_matrix if metric.normalization_matrix.empty else metric.normalization_matrix + matrix = matrix[matrix.sum(axis=1) > 0] + + minimums = matrix == 0 + results, zfpkm_df = zfpkm_transform(matrix) + zfpkm_df[minimums] = -4 + zfpkm_plot(results) + + # determine which genes are expressed + min_samples = round(min_sample_expression * len(zfpkm_df.columns)) + min_func = k_over_a(min_samples, cut_off) + min_genes: npt.NDArray[bool] = genefilter(zfpkm_df, min_func) + metric.entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, min_genes) if keep] + + # determine which genes are confidently expressed + top_samples = round(high_confidence_sample_expression * len(zfpkm_df.columns)) + top_func = k_over_a(top_samples, cut_off) + top_genes: npt.NDArray[bool] = genefilter(zfpkm_df, top_func) + metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, top_genes) if keep] + + return metrics + + +def filter_counts( + *, + context_name: str, + metrics: NamedMetrics, + technique: FilteringTechnique, + filtering_options: _FilteringOptions, + prep: RNAPrepMethod, +) -> NamedMetrics: + """Filter the count matrix based on the specified technique.""" + match technique: + case FilteringTechnique.cpm: + return cpm_filter( + context_name=context_name, metrics=metrics, filtering_options=filtering_options, prep=prep + ) + case FilteringTechnique.tpm: + return tpm_quantile_filter(metrics=metrics, filtering_options=filtering_options) + case FilteringTechnique.zfpkm: + return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calcualte_fpkm=True) + case FilteringTechnique.umi: + return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calcualte_fpkm=False) + case _: + raise ValueError(f"Technique must be one of {FilteringTechnique}") + + +async def _save_rnaseq_tests( + context_name: str, + rnaseq_matrix: pd.DataFrame, + metadata_df: pd.DataFrame, + gene_info_df: pd.DataFrame, + output_filepath: Path, + prep: RNAPrepMethod, + taxon: int, + replicate_ratio: float, + batch_ratio: float, + high_replicate_ratio: float, + high_batch_ratio: float, + technique: FilteringTechnique, + cut_off: int | float, +): + """Save the results of the RNA-Seq tests to a CSV file.""" + filtering_options = _FilteringOptions( + replicate_ratio=replicate_ratio, + batch_ratio=batch_ratio, + cut_off=float(cut_off), + high_replicate_ratio=high_replicate_ratio, + high_batch_ratio=high_batch_ratio, + ) + + read_counts_results: _ReadMatrixResults = await _build_matrix_results( + matrix=rnaseq_matrix, + gene_info=gene_info_df, + metadata_df=metadata_df, + taxon=taxon, + ) + metrics = read_counts_results.metrics + entrez_gene_ids = read_counts_results.entrez_gene_ids + + metrics = filter_counts( + context_name=context_name, + metrics=metrics, + technique=technique, + filtering_options=filtering_options, + prep=prep, + ) + + expressed_genes: list[str] = [] + top_genes: list[str] = [] + for metric in metrics.values(): + expressed_genes.extend(metric.entrez_gene_ids) + top_genes.extend(metric.high_confidence_entrez_gene_ids) + + expression_frequency = pd.Series(expressed_genes).value_counts() + expression_df = pd.DataFrame( + {"entrez_gene_id": expression_frequency.index, "frequency": expression_frequency.values} + ) + expression_df["prop"] = expression_df["frequency"] / len(metrics) + expression_df = expression_df[expression_df["prop"] >= filtering_options.batch_ratio] + + top_frequency = pd.Series(top_genes).value_counts() + top_df = pd.DataFrame({"entrez_gene_id": top_frequency.index, "frequency": top_frequency.values}) + top_df["prop"] = top_df["frequency"] / len(metrics) + top_df = top_df[top_df["prop"] >= filtering_options.high_batch_ratio] + + boolean_matrix = pd.DataFrame(data={"entrez_gene_id": entrez_gene_ids, "expressed": 0, "high": 0}) + for gene in entrez_gene_ids: + if gene in expression_df["entrez_gene_id"]: + boolean_matrix.loc[gene, "expressed"] = 1 + if gene in top_df["entrez_gene_id"]: + boolean_matrix.loc[gene, "high"] = 1 + + expressed_count = len(boolean_matrix[boolean_matrix["expressed"] == 1]) + high_confidence_count = len(boolean_matrix[boolean_matrix["high"] == 1]) + + boolean_matrix.to_csv(output_filepath, index=False) + logger.info( + f"{context_name} - Found {expressed_count} expressed and {high_confidence_count} confidently expressed genes" + ) + logger.success(f"Wrote boolean matrix to {output_filepath}") + + +async def _create_metadata_df(path: Path) -> pd.DataFrame: + if path.suffix not in {".xls", ".xlsx"}: + raise ValueError( + f"Expected an excel file with extension of '.xlsx' or '.xls', got '{path.suffix}'. " + f"Attempted to process: {path}" ) - logger.success(f"Results saved at '{rnaseq_output_filepath}'") + return pd.read_excel(path) -async def rnaseq_gen( - # config_filepath: Path, - config_filename: str, - prep: RNASeqPreparationMethod, - taxon_id: int | str | Taxon, - write_zfpkm_png_filepath: Path, +async def rnaseq_gen( # noqa: C901, allow complex function + context_name: str, + input_rnaseq_filepath: Path, + input_gene_info_filepath: Path, + output_rnaseq_filepath: Path, + prep: RNAPrepMethod, + taxon: int, + input_metadata_filepath: Path | None = None, + input_metadata_df: pd.DataFrame | None = None, replicate_ratio: float = 0.5, high_replicate_ratio: float = 1.0, batch_ratio: float = 0.5, high_batch_ratio: float = 1.0, technique: FilteringTechnique | str = FilteringTechnique.tpm, - cut_off: int | float | None = None, + cutoff: int | float | None = None, ) -> None: """Generate a list of active and high-confidence genes from a gene count matrix. @@ -137,9 +725,14 @@ async def rnaseq_gen( then study/batch numbers are checked for consensus according to batch ratios. The zFPKM method is outlined here: https://pubmed.ncbi.nlm.nih.gov/24215113/ - :param config_filename: The configuration filename to read + :param context_name: The name of the context being processed + :param input_rnaseq_filepath: The filepath to the gene count matrix + :param input_gene_info_filepath: The filepath to the gene info file + :param output_rnaseq_filepath: The filepath to write the output gene count matrix :param prep: The preparation method - :param taxon_id: The NCBI Taxon ID + :param taxon: The NCBI Taxon ID + :param input_metadata_filepath: The filepath to the metadata file + :param input_metadata_df: The metadata dataframe :param replicate_ratio: The percentage of replicates that a gene must appear in for a gene to be marked as "active" in a batch/study :param batch_ratio: The percentage of batches that a gene must appear in for a gene to be marked as 'active" @@ -148,176 +741,61 @@ async def rnaseq_gen( :param high_batch_ratio: The percentage of batches that a gene must appear in for a gene to be marked "highly confident" in its expression :param technique: The filtering technique to use - :param cut_off: The cutoff value to use for the provided filtering technique + :param cutoff: The cutoff value to use for the provided filtering technique :return: None """ - if isinstance(technique, str): - technique = FilteringTechnique(technique.lower()) - if isinstance(taxon_id, (str, int)): - taxon_id = Taxon.from_string(str(taxon_id)) + if not input_metadata_df and not input_metadata_filepath: + raise ValueError("At least one of input_metadata_filepath or input_metadata_df must be provided") + + technique = ( + FilteringTechnique.from_string(str(technique.lower())) if isinstance(technique, (str, int)) else technique + ) match technique: case FilteringTechnique.tpm: - cut_off = 25 if cut_off is None else cut_off - if cut_off < 1 or cut_off > 100: + cutoff = cutoff or 25 + if cutoff < 1 or cutoff > 100: raise ValueError("Quantile must be between 1 - 100") case FilteringTechnique.cpm: - if cut_off is not None and cut_off < 0: + if cutoff and cutoff < 0: raise ValueError("Cutoff must be greater than 0") - elif cut_off is None: - cut_off = "default" + elif cutoff: + cutoff = "default" case FilteringTechnique.zfpkm: - cut_off = "default" if cut_off is None else cut_off + cutoff = cutoff or -3 case FilteringTechnique.umi: pass case _: raise ValueError(f"Technique must be one of {FilteringTechnique}") - await _handle_context_batch( - config_filename=config_filename, + if not input_rnaseq_filepath.exists(): + raise FileNotFoundError(f"Input RNA-seq file not found! Searching for: '{input_rnaseq_filepath}'") + + if prep == RNAPrepMethod.SCRNA: + technique = FilteringTechnique.umi + logger.warning( + "Single cell filtration does not normalize and assumes " + "gene counts are counted with Unique Molecular Identifiers (UMIs). " + "Setting filtering technique to UMI now." + ) + + logger.debug(f"Starting '{context_name}'") + output_rnaseq_filepath.parent.mkdir(parents=True, exist_ok=True) + + await _save_rnaseq_tests( + context_name=context_name, + rnaseq_matrix=await _read_counts(input_rnaseq_filepath), + metadata_df=input_metadata_df or await _create_metadata_df(input_metadata_filepath), + gene_info_df=pd.read_csv(input_gene_info_filepath), + output_filepath=output_rnaseq_filepath, + prep=prep, + taxon=taxon, replicate_ratio=replicate_ratio, - replicate_ratio_high=high_replicate_ratio, batch_ratio=batch_ratio, - batch_ratio_high=high_batch_ratio, + high_replicate_ratio=high_replicate_ratio, + high_batch_ratio=high_batch_ratio, technique=technique, - cut_off=cut_off, - prep=prep, - taxon=taxon_id, - write_zfpkm_png_filepath=write_zfpkm_png_filepath, - ) - - -def _parse_args() -> _Arguments: - parser = argparse.ArgumentParser( - prog="rnaseq_gen.py", - description="Generate a list of active and high-confidence genes from a counts matrix using a user defined " - "at normalization-technique at /work/data/results//rnaseq_.csv: " - "https://github.com/HelikarLab/FastqToGeneCounts", - epilog="For additional help, please post questions/issues in the MADRID GitHub repo at " - "https://github.com/HelikarLab/MADRID or email babessell@gmail.com", - ) - parser.add_argument( - "-c", - "--config-file", - type=str, - required=True, - dest="config_file", - help="Name of config .xlsx file in the /work/data/config_files/. Can be generated using " - "rnaseq_preprocess.py or manually created and imported into the Juypterlab", - ) - parser.add_argument( - "-r", - "--replicate-ratio", - type=float, - required=False, - default=0.5, - dest="replicate_ratio", - help="Ratio of replicates required for a gene to be active within that study/batch group " - "Example: 0.7 means that for a gene to be active, at least 70% of replicates in a group " - "must pass the cutoff after normalization", - ) - parser.add_argument( - "-g", - "--batch-ratio", - type=float, - required=False, - default=0.5, - dest="batch_ratio", - help="Ratio of groups (studies or batches) required for a gene to be active " - "Example: 0.7 means that for a gene to be active, at least 70% of groups in a study must " - "have passed the replicate ratio test", - ) - parser.add_argument( - "-rh", - "--high-replicate-ratio", - type=float, - required=False, - default=1.0, - dest="high_replicate_ratio", - help="Ratio of replicates required for a gene to be considered high-confidence. " - "High-confidence genes ignore consensus with other data-sources, such as proteomics. " - "Example: 0.9 means that for a gene to be high-confidence, " - "at least 90% of replicates in a group must pass the cutoff after normalization", - ) - parser.add_argument( - "-gh", - "--high-batch-ratio", - type=float, - required=False, - default=1.0, - dest="high_batch_ratio", - help="Ratio of studies/batches required for a gene to be considered high-confidence within that group. " - "High-confidence genes ignore consensus with other data-sources, like proteomics. " - "Example: 0.9 means that for a gene to be high-confidence, " - "at least 90% of groups in a study must have passed the replicate ratio test", - ) - parser.add_argument( - "--taxon", - "--taxon-id", - type=str, - required=True, - dest="taxon", - help="The NCBI Taxonomy ID that is being proessed. '9606' for humans, '10090' for mice.", - ) - parser.add_argument( - "-t", - "--filt-technique", - type=str, - required=False, - default="quantile", - dest="filtering_technique", - help="Technique to normalize and filter counts with. " - "Either 'zfpkm', 'quantile', or 'cpm'. More info about each method is discussed in pipeline.ipynb.", - ) - parser.add_argument( - "--minimum-cutoff", - type=int, - required=False, - default=None, - dest="minimum_cutoff", - help="The minimum cutoff used for the filtration technique. " - "If the filtering technique is zFPKM, the default is -3. " - "If the filtering technique is quantile-tpm, the default is 25. " - "If the filtering technique is flat-cpm, the default is determined dynamically. " - "If the filtering technique is quantile, the default is 25.", - ) - parser.add_argument( - "-p", - "--library-prep", - required=True, - choices=["total", "mrna", "scrna"], - dest="library_prep", - help="Library preparation method. " - "Will separate samples into groups to only compare similarly prepared libraries. " - "For example, mRNA, total-rna, scRNA, etc", - ) - parser.add_argument( - "--write-zfpkm-png-filepath", - required=False, - type=Path, - help="If using zFPKM, the location to write graphs", - ) - args = parser.parse_args() - args.filtering_technique = args.filtering_technique.lower() - args.taxon = Taxon.from_int(int(args.taxon)) if str(args.taxon).isdigit() else Taxon.from_string(str(args.taxon)) # type: ignore - return _Arguments(**vars(args)) - - -if __name__ == "__main__": - args = _parse_args() - asyncio.run( - rnaseq_gen( - config_filename=args.config_file, - replicate_ratio=args.replicate_ratio, - batch_ratio=args.batch_ratio, - high_replicate_ratio=args.high_replicate_ratio, - high_batch_ratio=args.high_batch_ratio, - technique=args.filtering_technique, - cut_off=args.minimum_cutoff, - prep=args.library_prep, - taxon_id=args.taxon, - write_zfpkm_png_filepath=args.write_zfpkm_png_filepath, - ) + cut_off=cutoff, ) diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py index c522f0f1..83eb504f 100644 --- a/main/como/rnaseq_preprocess.py +++ b/main/como/rnaseq_preprocess.py @@ -1,30 +1,25 @@ from __future__ import annotations -import argparse import asyncio +import contextlib import re +import sys from dataclasses import dataclass, field +from io import StringIO, TextIOWrapper +from itertools import chain from pathlib import Path from typing import Literal +import aiofiles import numpy as np import pandas as pd import scanpy as sc -from fast_bioservices import Input, Taxon from fast_bioservices.biothings.mygene import MyGene +from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol, gene_symbol_to_ensembl_and_gene_id from loguru import logger -from como import Config, stringlist_to_list -from como.utils import convert_gene_data - - -@dataclass -class _Arguments: - context_names: list[str] - taxon_id: Taxon | int | str - mode: Literal["create", "provide"] - input_format: str - provided_matrix_fname: str = None +from como.types import RNAPrepMethod, type_path, type_rna +from como.utils import _listify @dataclass @@ -37,31 +32,34 @@ class _STARinformation: count_matrix: pd.DataFrame @property - def num_genes(self): + def num_genes(self) -> int: return len(self.count_matrix) @classmethod - def build_from_tab(cls, filepath: Path) -> _STARinformation: + async def build_from_tab(cls, filepath: Path) -> _STARinformation: if filepath.suffix != ".tab": raise ValueError(f"Building STAR information requires a '.tab' file; received: '{filepath}'") - with filepath.open("r") as i_stream: - num_unmapped = [int(i) for i in next(i_stream).rstrip("\n").split("\t")[1:]] - num_multimapping = [int(i) for i in next(i_stream).rstrip("\n").split("\t")[1:]] - num_no_feature = [int(i) for i in next(i_stream).rstrip("\n").split("\t")[1:]] - num_ambiguous = [int(i) for i in next(i_stream).rstrip("\n").split("\t")[1:]] - - df = pd.read_csv( - filepath, - sep="\t", - skiprows=4, - names=[ - "ensembl_gene_id", - "unstranded_rna_counts", - "first_read_transcription_strand", - "second_read_transcription_strand", - ], - ) - # Remove NA values + + async with aiofiles.open(filepath) as i_stream: + unmapped = await i_stream.readline() + multimapping = await i_stream.readline() + no_feature = await i_stream.readline() + ambiguous = await i_stream.readline() + + num_unmapped = [int(i) for i in unmapped.rstrip("\n").split("\t")[1:]] + num_multimapping = [int(i) for i in multimapping.rstrip("\n").split("\t")[1:]] + num_no_feature = [int(i) for i in no_feature.rstrip("\n").split("\t")[1:]] + num_ambiguous = [int(i) for i in ambiguous.rstrip("\n").split("\t")[1:]] + remainder = await i_stream.read() + + string_io = StringIO(remainder) + df = pd.read_csv(string_io, sep="\t", header=None) + df.columns = [ + "ensembl_gene_id", + "unstranded_rna_counts", + "first_read_transcription_strand", + "second_read_transcription_strand", + ] df = df[~df["ensembl_gene_id"].isna()] return _STARinformation( num_unmapped=num_unmapped, @@ -77,7 +75,7 @@ def build_from_tab(cls, filepath: Path) -> _STARinformation: class _StudyMetrics: study_name: str count_files: list[Path] - strandedness_files: list[Path] + strand_files: list[Path] __sample_names: list[str] = field(default_factory=list) __num_samples: int = 0 @@ -93,10 +91,10 @@ def __post_init__(self): self.__num_samples = len(self.count_files) self.__sample_names = [f.stem for f in self.count_files] - if len(self.count_files) != len(self.strandedness_files): + if len(self.count_files) != len(self.strand_files): raise ValueError( f"Unequal number of count files and strand files for study '{self.study_name}'. " - f"Found {len(self.count_files)} count files and {len(self.strandedness_files)} strand files." + f"Found {len(self.count_files)} count files and {len(self.strand_files)} strand files." ) if self.num_samples != len(self.count_files): @@ -105,68 +103,62 @@ def __post_init__(self): f"Found {self.num_samples} samples and {len(self.count_files)} count files." ) - if self.num_samples != len(self.strandedness_files): + if self.num_samples != len(self.strand_files): raise ValueError( f"Unequal number of samples and strand files for study '{self.study_name}'. " - f"Found {self.num_samples} samples and {len(self.strandedness_files)} strand files." + f"Found {self.num_samples} samples and {len(self.strand_files)} strand files." ) if self.__num_samples == 1: raise ValueError(f"Only one sample exists for study {self.study_name}. Provide at least two samples") self.count_files.sort() - self.strandedness_files.sort() + self.strand_files.sort() self.__sample_names.sort() -def _context_from_filepath(file: Path) -> str: - return file.stem.split("_S")[0] - - def _sample_name_from_filepath(file: Path) -> str: return re.search(r".+_S\d+R\d+", file.stem).group() def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]: - root_gene_count_dir = Path(data_dir, "geneCounts") - root_strandedness_dir = Path(data_dir, "strandedness") + gene_count_dir = Path(data_dir, "geneCounts") + strand_dir = Path(data_dir, "strandedness") - gene_counts_directories: list[Path] = sorted( - [p for p in Path(root_gene_count_dir).glob("*") if not p.name.startswith(".")] - ) - strandedness_directories: list[Path] = sorted( - [p for p in Path(root_strandedness_dir).glob("*") if not p.name.startswith(".")] - ) + gene_counts_directories: list[Path] = sorted([p for p in gene_count_dir.glob("*") if not p.name.startswith(".")]) + strandedness_directories: list[Path] = sorted([p for p in strand_dir.glob("*") if not p.name.startswith(".")]) if len(gene_counts_directories) != len(strandedness_directories): raise ValueError( f"Unequal number of gene count directories and strandedness directories. " f"Found {len(gene_counts_directories)} gene count directories and {len(strandedness_directories)} strandedness directories." # noqa: E501 - f"\nGene count directory: {root_gene_count_dir}\nStrandedness directory: {root_strandedness_dir}" + f"\nGene count directory: {gene_count_dir}\nStrandedness directory: {strand_dir}" ) # For each study, collect gene count files, fragment files, insert size files, layouts, and strandedness information study_metrics: list[_StudyMetrics] = [] for gene_dir, strand_dir in zip(gene_counts_directories, strandedness_directories): - if gene_dir.stem != strand_dir.stem: - raise ValueError( - f"Gene directory name of '{gene_dir.stem}' does not match stranded directory name of '{strand_dir.stem}'" # noqa: E501 - ) + count_files = list(gene_dir.glob("*.tab")) + strand_files = list(strand_dir.glob("*.txt")) + if len(count_files) == 0: + raise ValueError(f"No count files found for study '{gene_dir.stem}'.") + if len(strand_files) == 0: + raise ValueError(f"No strandedness files found for study '{gene_dir.stem}'.") study_metrics.append( _StudyMetrics( study_name=gene_dir.stem, - count_files=list(gene_dir.glob("*.tab")), - strandedness_files=list(strand_dir.glob("*.txt")), + count_files=count_files, + strand_files=strand_files, ) ) return study_metrics -def _process_first_multirun_sample(strand_file: Path, all_counts_files: list[Path]): +async def _process_first_multirun_sample(strand_file: Path, all_counts_files: list[Path]): sample_count = pd.DataFrame() for file in all_counts_files: - star_information = _STARinformation.build_from_tab(file) + star_information = await _STARinformation.build_from_tab(file) strand_information = strand_file.read_text().rstrip("\n").lower() if strand_information not in ("none", "first_read_transcription_strand", "second_read_transcription_strand"): @@ -178,13 +170,13 @@ def _process_first_multirun_sample(strand_file: Path, all_counts_files: list[Pat if strand_information == "none": strand_information = "unstranded_rna_counts" - run_counts = star_information.count_matrix[["gene_id", strand_information]] + run_counts = star_information.count_matrix[["ensembl_gene_id", strand_information]] run_counts.columns = pd.Index(["ensembl_gene_id", "counts"]) - if sample_count.empty: - sample_count = run_counts - else: - # Merge to take all items from both data frames - sample_count = sample_count.merge(run_counts, on="ensembl_gene_id", how="outer") + sample_count = ( + run_counts + if sample_count.empty + else sample_count.merge(run_counts, on=["ensembl_gene_id", "counts"], how="outer") + ) # Set na values to 0 sample_count = sample_count.fillna(value="0") @@ -196,8 +188,8 @@ def _process_first_multirun_sample(strand_file: Path, all_counts_files: list[Pat return count_sums -def _process_standard_replicate(counts_file: Path, strand_file: Path, sample_name: str): - star_information = _STARinformation.build_from_tab(counts_file) +async def _process_standard_replicate(counts_file: Path, strand_file: Path, sample_name: str): + star_information = await _STARinformation.build_from_tab(counts_file) strand_information = strand_file.read_text().rstrip("\n").lower() if strand_information not in ("none", "first_read_transcription_strand", "second_read_transcription_strand"): @@ -214,7 +206,7 @@ def _process_standard_replicate(counts_file: Path, strand_file: Path, sample_nam return sample_count -def _prepare_sample_counts( +async def _prepare_sample_counts( sample_name: str, counts_file: Path, strand_file: Path, @@ -222,27 +214,27 @@ def _prepare_sample_counts( ) -> pd.DataFrame | Literal["SKIP"]: # Test if the counts_file is the first run in a multi-run smaple if re.search(r"R\d+r1", counts_file.as_posix()): - return _process_first_multirun_sample(strand_file=strand_file, all_counts_files=all_counts_files) + return await _process_first_multirun_sample(strand_file=strand_file, all_counts_files=all_counts_files) elif re.search(r"R\d+r\d+", counts_file.as_posix()): return "SKIP" else: - return _process_standard_replicate(counts_file, strand_file, sample_name) + return await _process_standard_replicate(counts_file, strand_file, sample_name) async def _create_sample_counts_matrix(metrics: _StudyMetrics) -> pd.DataFrame: adjusted_index = 0 - counts: pd.DataFrame | Literal["SKIP"] = _prepare_sample_counts( + counts: pd.DataFrame | Literal["SKIP"] = await _prepare_sample_counts( sample_name=metrics.sample_names[0], counts_file=metrics.count_files[0], - strand_file=metrics.strandedness_files[0], + strand_file=metrics.strand_files[0], all_counts_files=metrics.count_files, ) for i in range(1, metrics.num_samples): - new_counts = _prepare_sample_counts( + new_counts = await _prepare_sample_counts( sample_name=metrics.sample_names[i], counts_file=metrics.count_files[i], - strand_file=metrics.strandedness_files[i], + strand_file=metrics.strand_files[i], all_counts_files=metrics.count_files, ) if isinstance(new_counts, str) and new_counts == "SKIP": @@ -260,35 +252,54 @@ async def _create_sample_counts_matrix(metrics: _StudyMetrics) -> pd.DataFrame: return counts -async def _create_counts_matrix(context_name: str, config: Config): - """Create a counts matrix by reading gene counts table(s).""" - data_dir = config.data_dir / "COMO_input" / context_name - matrix_output_dir = config.data_dir / "data_matrices" / context_name - - study_metrics = _organize_gene_counts_files(data_dir=data_dir) - final_matrix: pd.DataFrame = pd.DataFrame() - - for metric in study_metrics: - counts: pd.DataFrame = await _create_sample_counts_matrix(metric) - final_matrix = ( - counts if final_matrix.empty else pd.merge(final_matrix, counts, on="ensembl_gene_id", how="outer") - ) - - output_filename = matrix_output_dir / f"gene_counts_matrix_full_{data_dir.stem}.csv" - output_filename.parent.mkdir(parents=True, exist_ok=True) - final_matrix.to_csv(output_filename, index=False) - logger.success(f"Wrote gene count matrix for '{data_dir.stem}' at '{output_filename}'") - +async def _write_counts_matrix( + *, + config_df: pd.DataFrame, + como_context_dir: Path, + output_counts_matrix_filepath: Path, + rna: type_rna, +) -> pd.DataFrame: + """Create a counts matrix file by reading gene counts table(s).""" + study_metrics = _organize_gene_counts_files(data_dir=como_context_dir) + counts: list[pd.DataFrame] = await asyncio.gather( + *[_create_sample_counts_matrix(metric) for metric in study_metrics] + ) -async def _create_config_df(context_name: str) -> pd.DataFrame: # noqa: C901 + final_matrix = pd.DataFrame() + for count in counts: + final_matrix = count if final_matrix.empty else pd.merge(final_matrix, count, on="ensembl_gene_id", how="outer") + + rna_specific_sample_names = config_df.loc[config_df["library_prep"] == rna, "sample_name"].tolist() + final_matrix = final_matrix[["ensembl_gene_id", *rna_specific_sample_names]] + + output_counts_matrix_filepath.parent.mkdir(parents=True, exist_ok=True) + final_matrix.to_csv(output_counts_matrix_filepath, index=False) + logger.success(f"Wrote gene count matrix for '{rna}' RNA at '{output_counts_matrix_filepath}'") + return final_matrix + + +async def _create_config_df( # noqa: C901 + context_name: str, + /, + como_context_dir: Path, + gene_count_dirname: str = "geneCounts", + layout_dirname: str = "layouts", + strandedness_dirname: str = "strandedness", + fragment_sizes_dirname: str = "fragmentSizes", + prep_method_dirname: str = "prepMethods", +) -> pd.DataFrame: """Create configuration sheet. The configuration file created is based on the gene counts matrix. If using zFPKM normalization technique, mean fragment lengths will be fetched """ - config = Config() - gene_counts_files = list(Path(config.data_dir, "COMO_input", context_name, "geneCounts").rglob("*.tab")) + gene_counts_dir = como_context_dir / gene_count_dirname + layout_dir = como_context_dir / layout_dirname + strandedness_dir = como_context_dir / strandedness_dirname + fragment_sizes_dir = como_context_dir / fragment_sizes_dirname + prep_method_dir = como_context_dir / prep_method_dirname + gene_counts_files = list(gene_counts_dir.rglob("*.tab")) sample_names: list[str] = [] fragment_lengths: list[int | float] = [] layouts: list[str] = [] @@ -296,62 +307,54 @@ async def _create_config_df(context_name: str) -> pd.DataFrame: # noqa: C901 groups: list[str] = [] preparation_method: list[str] = [] + if len(gene_counts_files) == 0: + raise FileNotFoundError(f"No gene count files found in '{gene_counts_dir}'.") + for gene_count_filename in sorted(gene_counts_files): - try: - # Match S___R___r___ - # \d{1,3} matches 1-3 digits - # (?:r\d{1,3})? matches an option "r" followed by three digits - label = re.findall(r"S\d{1,3}R\d{1,3}(?:r\d{1,3})?", gene_count_filename.as_posix())[0] - - except IndexError as e: - raise IndexError( + # Match S___R___r___ + # \d{1,3} matches 1-3 digits + # (?:r\d{1,3})? optionally matches a "r" followed by three digits + label = re.findall(r"S\d{1,3}R\d{1,3}(?:r\d{1,3})?", gene_count_filename.as_posix())[0] + if not label: + raise ValueError( f"\n\nFilename of '{gene_count_filename}' is not valid. " f"Should be 'contextName_SXRYrZ.tab', where X is the study/batch number, Y is the replicate number, " f"and Z is the run number." "\n\nIf not a multi-run sample, exclude 'rZ' from the filename." - ) from e + ) study_number = re.findall(r"S\d{1,3}", label)[0] rep_number = re.findall(r"R\d{1,3}", label)[0] - run = re.findall(r"r\d{1,3}", label) + run_number = re.findall(r"r\d{1,3}", label) multi_flag = 0 - if len(run) > 0: - if run[0] != "r1": + if len(run_number) > 0: + if run_number[0] != "r1": continue - else: - label_glob = study_number + rep_number + "r*" - runs = [run for run in gene_counts_files if re.search(label_glob, run.as_posix())] - multi_flag = 1 - frag_files = [] - - for r in runs: - r_label = re.findall(r"r\d{1,3}", r.as_posix())[0] - R_label = re.findall(r"R\d{1,3}", r.as_posix())[0] # noqa: N806 - frag_filename = "".join([context_name, "_", study_number, R_label, r_label, "_fragment_size.txt"]) - frag_files.append( - config.data_dir / "COMO_input" / context_name / "fragmentSizes" / study_number / frag_filename - ) - - context_path = config.data_dir / "COMO_input" / context_name - - layout_files: list[Path] = list((context_path / "layouts").rglob(f"{context_name}_{label}_layout.txt")) - strand_files: list[Path] = list( - (context_path / "strandedness").rglob(f"{context_name}_{label}_strandedness.txt") - ) - frag_files: list[Path] = list( - (context_path / "fragmentSizes").rglob(f"{context_name}_{label}_fragment_size.txt") - ) - prep_files: list[Path] = list((context_path / "prepMethods").rglob(f"{context_name}_{label}_prep_method.txt")) + label_glob = f"{study_number}{rep_number}r*" # S__R__r* + runs = [run for run in gene_counts_files if re.search(label_glob, run.as_posix())] + multi_flag = 1 + frag_files = [] + + for run in runs: + run_number = re.findall(r"R\d{1,3}", run.as_posix())[0] + replicate = re.findall(r"r\d{1,3}", run.as_posix())[0] + frag_filename = "".join([context_name, "_", study_number, run_number, replicate, "_fragment_size.txt"]) + frag_files.append(como_context_dir / fragment_sizes_dirname / study_number / frag_filename) + + layout_files: list[Path] = list(layout_dir.rglob(f"{context_name}_{label}_layout.txt")) + strand_files: list[Path] = list(strandedness_dir.rglob(f"{context_name}_{label}_strandedness.txt")) + frag_files: list[Path] = list(fragment_sizes_dir.rglob(f"{context_name}_{label}_fragment_size.txt")) + prep_files: list[Path] = list(prep_method_dir.rglob(f"{context_name}_{label}_prep_method.txt")) layout = "UNKNOWN" if len(layout_files) == 0: logger.warning( f"No layout file found for {label}, writing as 'UNKNOWN', " - f"this should be defined by user if using zFPKM or rnaseq_gen.py will not run" + f"this should be defined if you are using zFPKM or downstream 'rnaseq_gen.py' will not run" ) elif len(layout_files) == 1: - with layout_files[0].open("w") as file: + with layout_files[0].open("r") as file: layout = file.read().strip() elif len(layout_files) > 1: raise ValueError( @@ -367,7 +370,7 @@ async def _create_config_df(context_name: str) -> pd.DataFrame: # noqa: C901 f"infer the strandedness when writing the counts matrix" ) elif len(strand_files) == 1: - with strand_files[0].open("w") as file: + with strand_files[0].open("r") as file: strand = file.read().strip() elif len(strand_files) > 1: raise ValueError( @@ -377,9 +380,9 @@ async def _create_config_df(context_name: str) -> pd.DataFrame: # noqa: C901 prep = "total" if len(prep_files) == 0: - logger.warning(f"No prep file found for {label}, assuming 'total' as in Total RNA library preparation") + logger.warning(f"No prep file found for {label}, assuming 'total', as in 'Total RNA' library preparation") elif len(prep_files) == 1: - with prep_files[0].open("w") as file: + with prep_files[0].open("r") as file: prep = file.read().strip().lower() if prep not in ["total", "mrna"]: raise ValueError(f"Prep method must be either 'total' or 'mrna' for {label}") @@ -390,10 +393,10 @@ async def _create_config_df(context_name: str) -> pd.DataFrame: # noqa: C901 ) mean_fragment_size = 100 - if len(frag_files) == 0: + if len(frag_files) == 0 and prep != RNAPrepMethod.TOTAL.value: logger.warning( f"No fragment file found for {label}, using '100'. " - f"This must be defined by the user in order to use zFPKM normalization" + "You should define this if you are going to use downstream zFPKM normalization" ) elif len(frag_files) == 1: if layout == "single-end": @@ -441,58 +444,32 @@ async def _create_config_df(context_name: str) -> pd.DataFrame: # noqa: C901 return out_df -def _split_config_df(df): - """Split a config dataframe to two. - - One for Total RNA library prep, one for mRNA - """ - df_t = df[df["library_prep"] == "total"] - df_m = df[df["library_prep"] == "mrna"] - - return df_t, df_m - - -def _split_counts_matrices( - count_matrix_all: Path, df_total: pd.DataFrame, df_mrna: pd.DataFrame -) -> tuple[pd.DataFrame, pd.DataFrame]: - """Split a counts-matrix dataframe to two. - - One for Total RNA library prep, one for mRNA - """ - logger.info(f"Reading gene count matrix file at '{count_matrix_all}'") - matrix_all = pd.read_csv(count_matrix_all) - matrix_total = matrix_all[ - ["ensembl_gene_id"] + [n for n in matrix_all.columns if n in df_total["sample_name"].tolist()] - ] - matrix_mrna = matrix_all[ - ["ensembl_gene_id"] + [n for n in matrix_all.columns if n in df_mrna["sample_name"].tolist()] - ] - - return matrix_total, matrix_mrna - - async def _create_gene_info_file( *, - matrix_files: list[Path], - taxon_id, - config: Config, + counts_matrix_filepaths: list[Path], + output_filepath: Path, + taxon: int, cache: bool, ): - """Create gene info file for specified context by reading first column in its count matrix file.""" - logger.info("Fetching gene info") - genes = set() - for file in matrix_files: - data: pd.DataFrame | sc.AnnData = pd.read_csv(file) if file.suffix == ".csv" else sc.read_h5ad(file) - input_values = data.iloc[:, 0].tolist() if isinstance(data, pd.DataFrame) else data.var_names.tolist() - conversion = await convert_gene_data(input_values, taxon_id) - genes.update(conversion["entrez_gene_id"].astype(str).tolist()) - - mygene = MyGene(cache=cache) - gene_data = await mygene.query( - items=list(genes), - taxon=taxon_id, - scopes="entrezgene", + """Create a gene information file context. + + The gene information file will be created by reading each matrix filepath in the provided list + """ + + async def read_counts(file: Path) -> list[str]: + data = await asyncio.to_thread(pd.read_csv if file.suffix == ".csv" else sc.read_h5ad, file) + conversion = ( + await ensembl_to_gene_id_and_symbol(ids=data["ensembl_gene_id"].tolist(), taxon=taxon) + if isinstance(data, pd.DataFrame) + else await gene_symbol_to_ensembl_and_gene_id(symbols=data.var_names.tolist(), taxon=taxon) + ) + return conversion["entrez_gene_id"].tolist() + + logger.info( + "Fetching gene info (this may take 1-5 minutes depending on the number of genes and your internet connection)" ) + genes = set(chain.from_iterable(await asyncio.gather(*[read_counts(f) for f in counts_matrix_filepaths]))) + gene_data = await MyGene(cache=cache).query(items=list(genes), taxon=taxon, scopes="entrezgene") gene_info: pd.DataFrame = pd.DataFrame( data=None, columns=pd.Index(data=["ensembl_gene_id", "gene_symbol", "entrez_gene_id", "start_position", "end_position"]), @@ -504,11 +481,9 @@ async def _create_gene_info_file( ensembl_ids = ensembl_ids[0] start_pos = data.get("genomic_pos.start", 0) + start_pos = sum(start_pos) / len(start_pos) if isinstance(start_pos, list) else start_pos end_pos = data.get("genomic_pos.end", 0) - if isinstance(start_pos, list): - start_pos = sum(start_pos) / len(start_pos) - if isinstance(end_pos, list): - end_pos = sum(end_pos) / len(end_pos) + end_pos = sum(end_pos) / len(end_pos) if isinstance(end_pos, list) else end_pos gene_info.at[i, "gene_symbol"] = data.get("symbol", "-") gene_info.at[i, "entrez_gene_id"] = data.get("entrezgene", "-") @@ -517,225 +492,157 @@ async def _create_gene_info_file( gene_info.at[i, "end_position"] = end_pos gene_info = gene_info[ - (gene_info["entrez_gene_id"] != "-") & (gene_info["ensembl_gene_id"] != "-") & (gene_info["gene_symbol"] != "-") + ( + (gene_info["entrez_gene_id"] != "-") + & (gene_info["ensembl_gene_id"] != "-") + & (gene_info["gene_symbol"] != "-") + ) ] gene_info["size"] = gene_info["end_position"].astype(int) - gene_info["start_position"].astype(int) gene_info.drop(columns=["start_position", "end_position"], inplace=True) - output_filepath = config.data_dir / "gene_info.csv" + gene_info.sort_values(by="ensembl_gene_id", inplace=True) gene_info.to_csv(output_filepath, index=False) logger.success(f"Gene Info file written at '{output_filepath}'") -async def _handle_context_batch( # noqa: C901 - context_names: list[str], - mode: Literal["create", "provide"], - taxon_id, - provided_matrix_file, - config: Config, +async def _create_matrix_file( + context_name: str, + output_config_filepath: Path, + como_context_dir: type_path, + output_counts_matrix_filepath: Path, + rna: type_rna, +) -> None: + config_df = await _create_config_df(context_name, como_context_dir=como_context_dir) + await _write_counts_matrix( + config_df=config_df, + como_context_dir=como_context_dir, + output_counts_matrix_filepath=output_counts_matrix_filepath, + rna=rna, + ) + with pd.ExcelWriter(output_config_filepath) as writer: + subset_config = config_df[config_df["library_prep"] == rna] + subset_config.to_excel(writer, sheet_name=context_name, header=True, index=False) + + +async def _process( + context_name: str, + taxon: int, + output_gene_info_filepath: Path, + como_context_dir: Path | None, + input_matrix_filepath: list[Path] | None, + output_trna_config_filepath: Path | None, + output_mrna_config_filepath: Path | None, + output_trna_matrix_filepath: Path | None, + output_mrna_matrix_filepath: Path | None, cache: bool, ): - """Handle iteration through each context type and create appropriate files.""" - trnaseq_config_filename = config.config_dir / "trnaseq_data_inputs_auto.xlsx" - mrnaseq_config_filename = config.config_dir / "mrnaseq_data_inputs_auto.xlsx" - - using_trna = False # turn on when any total set is found to prevent writer from being init multiple times or empty - using_mrna = False # turn on when any mrna set is found to prevent writer from being init multiple times or empty - - logger.success(f"Found {len(context_names)} contexts to process: {', '.join(context_names)}") - - tmatrix_files: list[Path] = [] - mmatrix_files: list[Path] = [] - match mode: - case "create": - for context_name in context_names: - context_name = context_name.strip(" ") - logger.info(f"Processing {context_name}") - gene_output_dir = config.result_dir / context_name - matrix_output_dir = config.data_dir / "data_matrices" / context_name - - gene_output_dir.parent.mkdir(parents=True, exist_ok=True) - matrix_output_dir.parent.mkdir(parents=True, exist_ok=True) - - logger.info(f"Gene info output directory is '{gene_output_dir}'") - - await _create_counts_matrix(context_name, config=config) - # TODO: warn user or remove samples that are all 0 to prevent density plot error in zFPKM - config_df = await _create_config_df(context_name) - trna_df, mrna_df = _split_config_df(config_df) - - matrix_path_total = matrix_output_dir / f"gene_counts_matrix_total_{context_name}.csv" - if not trna_df.empty: - if not using_trna: - using_trna = True - twriter = pd.ExcelWriter(trnaseq_config_filename) - - tmatrix_files.append(matrix_path_total) - trna_df.to_excel(twriter, sheet_name=context_name, header=True, index=False) - - matrix_path_mrna = matrix_output_dir / f"gene_counts_matrix_mrna_{context_name}.csv" - if not mrna_df.empty: - if not using_mrna: - using_mrna = True - mwriter = pd.ExcelWriter(mrnaseq_config_filename) - - mmatrix_files.append(matrix_path_mrna) - mrna_df.to_excel(mwriter, sheet_name=context_name, header=True, index=False) - - matrix_path_all = matrix_output_dir / f"gene_counts_matrix_full_{context_name}.csv" - trna_matrix, mrna_matrix = _split_counts_matrices(matrix_path_all, trna_df, mrna_df) - if len(trna_matrix.columns) >= 1: - trna_matrix.to_csv(matrix_path_total, header=True, index=False) - if len(mrna_matrix.columns) >= 1: - mrna_matrix.to_csv(matrix_path_mrna, header=True, index=False) - - if using_trna: - twriter.close() - if using_mrna: - mwriter.close() - - await _create_gene_info_file( - matrix_files=tmatrix_files + mmatrix_files, - taxon_id=taxon_id, - config=config, - cache=cache, + rna_types: list[tuple[type_rna, Path, Path]] = [] + if output_trna_config_filepath: + rna_types.append(("total", output_trna_config_filepath, output_trna_matrix_filepath)) + if output_mrna_config_filepath: + rna_types.append(("mrna", output_mrna_config_filepath, output_mrna_matrix_filepath)) + + # if provided, iterate through como-input specific directories + tasks = [] + for rna, output_config_filepath, output_matrix_filepath in rna_types: + tasks.append( + asyncio.create_task( + _create_matrix_file( + context_name=context_name, + output_config_filepath=output_config_filepath, + como_context_dir=como_context_dir, + output_counts_matrix_filepath=output_matrix_filepath, + rna=rna, + ) ) - case "provide": - matrix_files: list[Path] = [Path(p) for p in stringlist_to_list(provided_matrix_file)] - await _create_gene_info_file( - matrix_files=matrix_files, - taxon_id=taxon_id, - config=config, - cache=cache, - ) - case _: - raise ValueError("'--mode' must be either 'create' or 'provide'") + ) + + await asyncio.gather(*tasks) + + # create the gene info filepath based on provided data + await _create_gene_info_file( + counts_matrix_filepaths=[ + f + for f in [*input_matrix_filepath, output_trna_matrix_filepath, output_mrna_matrix_filepath] + if f is not None + ], + output_filepath=output_gene_info_filepath, + taxon=taxon, + cache=cache, + ) async def rnaseq_preprocess( - context_names: list[str], - mode: Literal["create", "provide"], - taxon_id: int | str, - input_format: Input | str, - matrix_file: str | Path | None = None, - config: Config = None, + context_name: str, + taxon: int, + output_gene_info_filepath: Path, + como_context_dir: Path | None = None, + input_matrix_filepath: Path | list[Path] | None = None, + output_trna_config_filepath: Path | None = None, + output_mrna_config_filepath: Path | None = None, + output_trna_count_matrix_filepath: Path | None = None, + output_mrna_count_matrix_filepath: Path | None = None, cache: bool = True, + log_level: Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] = "INFO", + log_location: str | TextIOWrapper = sys.stderr, ) -> None: """Preprocesses RNA-seq data for downstream analysis. Fetches additional gene information from a provided matrix or gene counts, or optionally creates this matrix using gene count files obtained using STAR aligner - """ - config = Config() if config is None else config - if mode not in {"create", "provide"}: - raise ValueError("mode must be either 'create' or 'provide'") - - if not isinstance(taxon_id, int) and taxon_id not in ["human", "mouse"]: - raise ValueError("taxon_id must be either an integer, or accepted string ('mouse', 'human')") - - await _handle_context_batch( - context_names=context_names, - mode=mode, - taxon_id=taxon_id, - provided_matrix_file=Path(matrix_file if matrix_file is not None else "").as_posix(), - config=config, - cache=cache, - ) + :param context_name: The context/cell type being processed + :param taxon: The NCBI taxonomy ID + :param output_gene_info_filepath: Path to the output gene information CSV file + :param output_trna_config_filepath: Path to the output tRNA config file (if in "create" mode) + :param output_mrna_config_filepath: Path to the output mRNA config file (if in "create" mode) + :param output_trna_count_matrix_filepath: The path to write total RNA count matrices + :param output_mrna_count_matrix_filepath: The path to write messenger RNA count matrices + :param como_context_dir: If in "create" mode, the input path(s) to the COMO_input directory of the current context + i.e., the directory containing "fragmentSizes", "geneCounts", "insertSizeMetrics", etc. directories + :param input_matrix_filepath: If in "provide" mode, the path(s) to the count matrices to be processed~ + :param cache: Should HTTP requests be cached + :param log_level: The logging level + :param log_location: The logging location + """ + with contextlib.suppress(ValueError): + logger.remove(0) + logger.add( + sink=log_location, + level=log_level, + format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{line} - {message}", # noqa: E501 + ) -def _parse_args(): - parser = argparse.ArgumentParser( - prog="rnaseq_preprocess.py", - description=""" - Fetches additional gene information from a provided matrix or gene counts, or optionally creates this - matrix using gene count files obtained using STAR aligner. Creation of counts matrix from STAR aligner - output requires that the 'COMO_input' folder exists and is correctly structured according to the - normalization technique being used. A correctly structured folder can be made using our Snakemake-based - alignment pipeline at: - https://github.com/HelikarLab/FastqToGeneCounts""", - epilog=""" - For additional help, please post questions/issues in the MADRID GitHub repo at - https://github.com/HelikarLab/MADRID or email babessell@gmail.com""", - ) - parser.add_argument( - "-n", - "--context-names", - type=str, - nargs="+", - required=True, - dest="context_names", - help="""Tissue/cell name of models to generate. These names should correspond to the folders - in 'COMO_input/' if creating count matrix files, or to - 'work/data/data_matrices//gene_counts_matrix_.csv' if supplying - the count matrix as an imported .csv file. If making multiple models in a batch, then - use the format: "context1 context2 context3". """, + output_gene_info_filepath = output_gene_info_filepath.resolve() + como_context_dir = como_context_dir.resolve() + input_matrix_filepath = [i.resolve() for i in _listify(input_matrix_filepath)] if input_matrix_filepath else None + output_trna_config_filepath = ( + output_trna_config_filepath.resolve() if output_trna_config_filepath else output_trna_config_filepath ) - parser.add_argument( - "-i", - "--taxon-id", - required=False, - default=9606, - dest="taxon_id", - help="BioDbNet taxon ID number, also accepts 'human', or 'mouse'", + output_mrna_config_filepath = ( + output_mrna_config_filepath.resolve() if output_mrna_config_filepath else output_mrna_config_filepath ) - parser.add_argument( - "--input-format", - required=True, - dest="input_format", - help="The data input format, such as Ensembl, Entrez, etc.", + output_trna_count_matrix_filepath = ( + output_trna_count_matrix_filepath.resolve() + if output_trna_count_matrix_filepath + else output_trna_count_matrix_filepath ) - parser.add_argument( - "--mode", - type=str, - required=True, - dest="mode", - choices={"create", "provide"}, - help="Mode of rnaseq_preprocess.py, either 'make' or 'provide'", + output_mrna_count_matrix_filepath = ( + output_mrna_count_matrix_filepath.resolve() + if output_mrna_count_matrix_filepath + else output_mrna_count_matrix_filepath ) - parser.add_argument( - "--matrix", - required=False, - dest="provided_matrix_fname", - default=None, - help="Name of provided counts matrix in /work/data/data_matrices//.csv", - ) - - parsed = parser.parse_args() - parsed.context_names = stringlist_to_list(parsed.context_names) - if parsed.mode == "provide" and parsed.provided_matrix_fname is None: - raise ValueError("If provide_matrix is True, then provided_matrix_fname must be provided") - - # handle species alternative ids - taxon_id = str(parsed.taxon_id) - if taxon_id.isdigit(): - try: - parsed.taxon_id = Taxon.from_int(int(taxon_id)) - except ValueError: - parsed.taxon_id = int(taxon_id) - else: - if taxon_id.upper() in {"HUMAN", "HOMO SAPIENS"}: - parsed.taxon_id = Taxon.HOMO_SAPIENS - elif taxon_id.upper() in {"MOUSE", "MUS MUSCULUS"}: - parsed.taxon_id = Taxon.MUS_MUSCULUS - else: - raise ValueError( - f"Taxon id (--taxon-id) is invalid; accepts 'human', 'mouse', or an integer value; provided: {taxon_id}" - ) - - args = _Arguments(**vars(parsed)) - return args - - -if __name__ == "__main__": - args: _Arguments = _parse_args() - taxon_id_value = args.taxon_id.value if isinstance(args.taxon_id, Taxon) else args.taxon_id - - asyncio.run( - rnaseq_preprocess( - context_names=args.context_names, - mode=args.mode, - taxon_id=taxon_id_value, - matrix_file=args.provided_matrix_fname, - input_format=args.input_format, - ) + input_matrix_filepath = _listify(input_matrix_filepath) + await _process( + context_name=context_name, + taxon=taxon, + como_context_dir=como_context_dir, + input_matrix_filepath=input_matrix_filepath, + output_gene_info_filepath=output_gene_info_filepath, + output_trna_config_filepath=output_trna_config_filepath, + output_mrna_config_filepath=output_mrna_config_filepath, + output_trna_matrix_filepath=output_trna_count_matrix_filepath, + output_mrna_matrix_filepath=output_mrna_count_matrix_filepath, + cache=cache, ) diff --git a/main/como/types.py b/main/como/types.py new file mode 100644 index 00000000..916d3a25 --- /dev/null +++ b/main/como/types.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +from abc import ABC +from enum import Enum +from pathlib import Path +from typing import Literal + +from pydantic import BaseModel, ConfigDict +from pydantic_settings import BaseSettings, SettingsConfigDict + + +class _BaseModel(BaseModel, ABC): + model_config = ConfigDict() + + +class _BaseArguments(BaseSettings, ABC): + model_config = SettingsConfigDict( + cli_parse_args=True, + cli_kebab_case=True, + nested_model_default_partial_update=True, + case_sensitive=True, + cli_avoid_json=True, + cli_enforce_required=True, + ) + + +class RNAPrepMethod(Enum): + TOTAL = "total" + MRNA = "mrna" + SCRNA = "scrna" + + @staticmethod + def from_string(value: str) -> RNAPrepMethod: + """Build a preparation method object from a string.""" + match_value = "".join(c for c in value if c.isascii()).lower() + + match match_value: + case "total" | "trna": + return RNAPrepMethod.TOTAL + case "mrna": + return RNAPrepMethod.MRNA + case "scrna": + return RNAPrepMethod.SCRNA + case _: + possible_values = [t.value for t in RNAPrepMethod] + raise ValueError(f"Filtering technique must be one of {possible_values}; got: {value}") + + +class FilteringTechnique(Enum): + """RNA sequencing filtering capabilities.""" + + cpm = "cpm" + zfpkm = "zfpkm" + tpm = "quantile" + umi = "umi" + + @staticmethod + def from_string(value: str) -> FilteringTechnique: + """Create a filtering technique object from a string.""" + match value.lower(): + case "cpm": + return FilteringTechnique.cpm + case "zfpkm": + return FilteringTechnique.zfpkm + case "quantile": + return FilteringTechnique.tpm + case "umi": + return FilteringTechnique.umi + case _: + possible_values = [t.value for t in FilteringTechnique] + raise ValueError(f"Got a filtering technique of '{value}'; should be one of: {possible_values}") + + +type_path = str | Path +type_rna = Literal["total", "mrna"] diff --git a/main/como/utils.py b/main/como/utils.py index 9668a34d..a359de0f 100644 --- a/main/como/utils.py +++ b/main/como/utils.py @@ -115,32 +115,32 @@ def stringlist_to_list(stringlist: str | list[str]) -> list[str]: :param stringlist: The "string list" gathered from the command line. Example input: "['mat', 'xml', 'json']" """ - if isinstance(stringlist, str): - if stringlist.startswith("[") and stringlist.endswith("]"): - # Remove any brackets from the first and last items; replace quotation marks and commas with nothing - new_list: list[str] = stringlist.strip("[]").replace("'", "").replace(" ", "").split(",") - - # Show a warning if more than one item is present in the list (this means we are using the old method) - logger.critical( - "DeprecationWarning: Please use the new method of providing context names, " - "i.e. --output-filetypes 'type1 type2 type3'." - ) - logger.critical( - "If you are using COMO, this can be done by setting the 'context_names' variable to a " - "simple string separated by spaces. Here are a few examples!" - ) - logger.critical("context_names = 'cellType1 cellType2 cellType3'") - logger.critical("output_filetypes = 'output1 output2 output3'") - logger.critical( - "\nYour current method of passing context names will be removed in the future. " - "Update your variables above accordingly!\n\n" - ) - - else: - new_list: list[str] = stringlist.split(" ") - - return new_list - return stringlist + if isinstance(stringlist, list): + return stringlist + + if not (stringlist.startswith("[") and stringlist.endswith("]")): + return stringlist.split(" ") + + # Remove any brackets from the first and last items; replace quotation marks and commas with nothing + new_list: list[str] = stringlist.strip("[]").replace("'", "").replace(" ", "").split(",") + + # Show a warning if more than one item is present in the list (this means we are using the old method) + logger.critical( + "DeprecationWarning: Please use the new method of providing context names, " + "i.e. --output-filetypes 'type1 type2 type3'." + ) + logger.critical( + "If you are using COMO, this can be done by setting the 'context_names' variable to a " + "simple string separated by spaces. Here are a few examples!" + ) + logger.critical("context_names = 'cellType1 cellType2 cellType3'") + logger.critical("output_filetypes = 'output1 output2 output3'") + logger.critical( + "\nYour current method of passing context names will be removed in the future. " + "Update your variables above accordingly!\n\n" + ) + + return new_list def split_gene_expression_data(expression_data: pd.DataFrame, recon_algorithm: Algorithm | None = None): @@ -258,3 +258,8 @@ async def convert_gene_data(values: list[str], taxon_id: int | str | Taxon) -> p return await gene_id_to_ensembl_and_gene_symbol(ids=values, taxon=taxon_id) else: raise ValueError("Gene data must be of the same type (i.e., all Ensembl, Entrez, or Gene Symbols)") + + +def _listify(value): + """Convert items into a list.""" + return [value] if not isinstance(value, list) else value diff --git a/pyproject.toml b/pyproject.toml index 2f4fdb2f..fabd6c69 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,22 +3,30 @@ name = "COMO" dynamic = ["version"] requires-python = ">=3.10,<3.13" dependencies = [ + "aiofiles>=24.1.0", + "aioftp>=0.23.1", + "cobamp@git+https://github.com/JoshLoecker/cobamp@master", "cobra>=0.28.0", "fast-bioservices>=0.3.9", "gurobipy>=11.0", "kaleido==0.2.1", "loguru>=0.7.2", + "openpyxl>=3.1.5", "pandas>=1.3.5", "plotly>=5.24.1", + "pydantic-settings", + "pydantic>=2.10.3", "scanpy>=1.9.8", - "scipy>=1.7.3", "scikit-learn>=1.5.2", + "scipy>=1.7.3", "setuptools<60.0", - "openpyxl>=3.1.5", - "aiofiles>=24.1.0", - "aioftp>=0.23.1", "troppo@git+https://github.com/JoshLoecker/troppo@master", - "cobamp@git+https://github.com/JoshLoecker/cobamp@master", +] + +[project.optional-dependencies] +interactive = [ + "ipython>=8.0.0", + "jupyterlab>=4.3.2", ] [build-system] @@ -45,4 +53,13 @@ dev-dependencies = [ "ruff>=0.8.0", "hypothesis>=6.122.1", "pytest-cov>=6.0.0", + "commitizen>=4.1.0", + "cz-conventional-gitmoji>=0.6.1", ] + +[tool.commitizen] +name = "cz_conventional_commits" +tag_format = "$version" +version_scheme = "semver2" +version_provider = "pep621" +update_changelog_on_bump = true \ No newline at end of file diff --git a/ruff.toml b/ruff.toml index 9591bda3..3691008f 100644 --- a/ruff.toml +++ b/ruff.toml @@ -1,6 +1,6 @@ line-length = 120 -extend-include = ["docs/**/*.py", "tests/**/*.py"] -exclude = ["__init__.py", "main/COMO.ipynb"] +extend-include = ["docs/**/*.py", "tests/**/*.py", "**/*.ipynb"] +exclude = ["__init__.py"] [format] quote-style = "double" @@ -46,5 +46,9 @@ ignore = [ "D101", # allow undocumented public class "D102", # allow undocumented class method "D103", # allow undocumented public method definitions + "F811", # allow redefinition of variables, required for pytest fixtures "S101", # allow use of `assert` in test files ] +"main/COMO.ipynb" = [ + "E501", # allow long lines +] diff --git a/tests/fixtures/collect_files.py b/tests/fixtures/collect_files.py new file mode 100644 index 00000000..93d59e70 --- /dev/null +++ b/tests/fixtures/collect_files.py @@ -0,0 +1,93 @@ +from pathlib import Path +from typing import NamedTuple + +import pytest +from _pytest.fixtures import SubRequest + +_fragment_size_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*fragment_size*.txt")) +_gene_count_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*.tab")) +_insert_size_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*_insert_size.txt")) +_layout_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*_layout.txt")) +_preparation_method_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*_prep_method.txt")) +_strandedness_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*_strandedness.txt")) + + +class PackedFilepaths(NamedTuple): + sample_name: str + fragment_size: Path + gene_count: Path + insert_size: Path + layout: Path + preparation_method: Path + strandedness: Path + + +@pytest.fixture(params=_fragment_size_filepaths) +def fragment_size_filepath(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture(params=_gene_count_filepaths) +def gene_count_filepath(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture +def all_gene_count_filepaths() -> list[Path]: + return _gene_count_filepaths + + +@pytest.fixture(params=_insert_size_filepaths) +def insert_size_filepath(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture(params=_layout_filepaths) +def layout_filepath(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture(params=_preparation_method_filepaths) +def prep_method_filepath(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture(params=_strandedness_filepaths) +def strand_filepath(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture( + params=[ + file + for filepaths in [ + _fragment_size_filepaths, + _gene_count_filepaths, + _insert_size_filepaths, + _layout_filepaths, + _preparation_method_filepaths, + _strandedness_filepaths, + ] + for file in filepaths + ] +) +def any_como_input_filepath(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture(params=[Path("main/data/COMO_input/naiveB").absolute(), Path("main/data/COMO_input/smB").absolute()]) +def como_input_data_directory(request: SubRequest) -> Path: + return request.param + + +@pytest.fixture(params=["naiveB", "smB"]) +def packed_filepaths(request: SubRequest) -> PackedFilepaths: + return PackedFilepaths( + sample_name=request.param, + fragment_size=Path(f"main/data/COMO_input/{request.param}/fragmentSizes/{request.param}_fragment_size.txt"), + gene_count=Path(f"main/data/COMO_input/{request.param}/geneCounts/{request.param}.tab"), + insert_size=Path(f"main/data/COMO_input/{request.param}/insertSizes/{request.param}_insert_size.txt"), + layout=Path(f"main/data/COMO_input/{request.param}/layouts/{request.param}_layout.txt"), + preparation_method=Path(f"main/data/COMO_input/{request.param}/prepMethods/{request.param}_prep_method.txt"), + strandedness=Path(f"main/data/COMO_input/{request.param}/strandedness/{request.param}_strandedness.txt"), + ) diff --git a/tests/test_proteomics.py b/tests/test_proteomics.py index d3c6ad66..0f38be27 100644 --- a/tests/test_proteomics.py +++ b/tests/test_proteomics.py @@ -77,7 +77,7 @@ async def test_ftp_client(self): assert await client.quit() is None @pytest.mark.skip(reason="pyftpdlib is broken, no way to test this") - def test_reader(self, ftpserver, fixture_ftp_server, ftp_file_names): # noqa: F811 + def test_reader(self, ftpserver, fixture_ftp_server, ftp_file_names): # Use pytest_localftpserver and fixtures.fixture_ftp_server.fix # Now we can get login information for our local FTP server file_extensions: list[str] = ["raw"] diff --git a/tests/test_rnaseq_preprocess.py b/tests/test_rnaseq_preprocess.py deleted file mode 100644 index 752ec333..00000000 --- a/tests/test_rnaseq_preprocess.py +++ /dev/null @@ -1,47 +0,0 @@ -from __future__ import annotations - -import sys - -import pytest -from como import rnaseq_preprocess - - -# Define a list of arguments to test -@pytest.mark.skip("Not testing args because the input 'argv' has been removed") -@pytest.mark.parametrize( - "args", - [ - # Test using data in COMO_input data - ["--context-names", "naiveB immNK", "--gene-format", "Ensembl", "--taxon-id", "9606", "--create-matrix"], - [ - "--context-names", - "dimNK brightNK", - "--gene-format", - "SYMBOL", - "--taxon-id", - "human", - "--provide-matrix", - "--matrix", - "COMO_input/counts_matrix.tsv", - ], - ], -) -def test_arg_input(args: list[str]): - """Asserts that the arguments passed into the function are correct.""" - context_names = args[1] - gene_format = args[3] - taxon_id = args[5] - matrix_mode = args[6] - - sys.argv = args - parsed = rnaseq_preprocess._parse_args() - - assert [context_name in parsed.context_names for context_name in context_names.split()] - assert parsed.gene_format == gene_format - assert parsed.taxon_id == taxon_id - - if matrix_mode == "--create-matrix": - assert parsed.make_matrix is True - elif matrix_mode == "--provide-matrix": - assert parsed.make_matrix is False - assert parsed.provided_matrix_fname == args[8] diff --git a/tests/unit/test_rnaseq_preprocess.py b/tests/unit/test_rnaseq_preprocess.py new file mode 100644 index 00000000..c0d49c2e --- /dev/null +++ b/tests/unit/test_rnaseq_preprocess.py @@ -0,0 +1,82 @@ +from __future__ import annotations + +from pathlib import Path + +import pandas as pd +import pytest +from como.rnaseq_preprocess import ( + _organize_gene_counts_files, + _process_first_multirun_sample, + _process_standard_replicate, + _sample_name_from_filepath, + _STARinformation, + _StudyMetrics, +) + +from tests.fixtures.collect_files import ( + PackedFilepaths, + all_gene_count_filepaths, + any_como_input_filepath, + como_input_data_directory, + packed_filepaths, + strand_filepath, +) + + +class TestSTARInformation: + valid_data = Path("main/data/COMO_input/naiveB/geneCounts/S1/naiveB_S1R1.tab").resolve() + invalid_data = Path("main/data/COMO_input/naiveB/fragmentSizes/S1/naiveB_S1R1_fragment_size.txt").resolve() + + @pytest.mark.asyncio + async def test_build_from_tab_valid_file(self): + """Validate building STAR information object.""" + star: _STARinformation = await _STARinformation.build_from_tab(TestSTARInformation.valid_data) + assert len(star.gene_names) == len(star.count_matrix) == 61541 + assert len(star.num_unmapped) == 3 + assert len(star.num_multimapping) == 3 + assert len(star.num_no_feature) == 3 + assert len(star.num_ambiguous) == 3 + + @pytest.mark.asyncio + async def test_build_from_tab_invalid_file(self): + """Validate error on invalid file.""" + with pytest.raises(ValueError, match="Building STAR information requires a '.tab' file"): + await _STARinformation.build_from_tab(TestSTARInformation.invalid_data) + + +def test_sample_name_from_filepath(any_como_input_filepath: Path): + expected = "_".join(any_como_input_filepath.stem.split("_")[:2]) + assert _sample_name_from_filepath(any_como_input_filepath) == expected + + +def test_organize_gene_counts_files(como_input_data_directory: Path): + metrics: list[_StudyMetrics] = _organize_gene_counts_files(como_input_data_directory) + for metric in metrics: + assert len(metric.sample_names) == metric.num_samples == len(metric.count_files) == len(metric.strand_files) + + for file in metric.count_files: + assert f"/{metric.study_name}/" in file.as_posix() + assert "geneCounts" in file.as_posix() + assert file.suffix == ".tab" + + for file in metric.strand_files: + assert f"/{metric.study_name}/" in file.as_posix() + assert "strandedness" in file.as_posix() + assert file.suffix == ".txt" + + +@pytest.mark.asyncio +async def test_process_first_multirun_sample(strand_filepath: Path, all_gene_count_filepaths: list[Path]): + result: pd.DataFrame = await _process_first_multirun_sample(strand_filepath, all_gene_count_filepaths) + assert result.columns[0] == "ensembl_gene_id" + assert len(result.columns) == 2 + assert result.columns[1] in strand_filepath.as_posix() + + +def test_pack_filepaths(packed_filepaths: PackedFilepaths): + assert packed_filepaths.sample_name in packed_filepaths.fragment_size.as_posix() + assert packed_filepaths.sample_name in packed_filepaths.gene_count.as_posix() + assert packed_filepaths.sample_name in packed_filepaths.insert_size.as_posix() + assert packed_filepaths.sample_name in packed_filepaths.layout.as_posix() + assert packed_filepaths.sample_name in packed_filepaths.preparation_method.as_posix() + assert packed_filepaths.sample_name in packed_filepaths.strandedness.as_posix() diff --git a/uv.lock b/uv.lock index f090d8cc..3a417c7a 100644 --- a/uv.lock +++ b/uv.lock @@ -78,6 +78,57 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/3b/00/2344469e2084fb287c2e0b57b72910309874c3245463acd6cf5e3db69324/appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128", size = 9566 }, ] +[[package]] +name = "appnope" +version = "0.1.4" +source = { registry = 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