Standardize parameters so that _->. and fix typo in parameter

.Rhistory

@@ -67,8 +67,8 @@ complexity.limits = c(NA,NA),
 mad.complexity.limits = c(5,NA),
 topNgenes.limits = c(NA,NA),
 mad.topNgenes.limits = c(5,5),
-n.topgnes=20,
-do.doublets.fitler=T
+n.topgenes=20,
+do.doublets.filter=T
 )
 ggsave(SO_filtered$plots$PostFilterCombined, filename = "./images/QC1.png", width = 10, height = 10)
 ggsave(SO_filtered$plots$ViolinPlotCombine, filename = "./images/QC2.png", width = 10, height = 10)
@@ -315,12 +315,12 @@ dev.off()
 modScore
 MS_object=modScore(object=Anno_SO$object,
 marker.table=Marker_Table,
-use_columns = c("Macrophages","M1","M2" )
-ms_threshold=c("Macrophages .40","M1 .25","M2 .14"),
+use.columns = c("Macrophages","M1","M2" )
+ms.threshold=c("Macrophages .40","M1 .25","M2 .14"),
 MS_object=modScore(object=Anno_SO$object,
 marker.table=Marker_Table,
-use_columns = c("Macrophages","M1","M2" ),
-ms_threshold=c("Macrophages .40","M1 .25","M2 .14"),
+use.columns = c("Macrophages","M1","M2" ),
+ms.threshold=c("Macrophages .40","M1 .25","M2 .14"),
 use_assay = "SCT",
 general.class=c("Macrophages"),
 lvl.vec = c('Macrophages-M1','Macrophages-M2'),
@@ -333,8 +333,8 @@ step.size = 0.1
 modScore
 MS_object=modScore(object=Anno_SO$object,
 marker.table=Marker_Table,
-use_columns = c("Macrophages","M1","M2" ),
-ms_threshold=c("Macrophages .40","M1 .25","M2 .14"),
+use.columns = c("Macrophages","M1","M2" ),
+ms.threshold=c("Macrophages .40","M1 .25","M2 .14"),
 use_assay = "SCT",
 general.class=c("Macrophages"),
 multi.lvl = FALSE,
@@ -347,8 +347,8 @@ step.size = 0.1
 modScore
 MS_object=modScore(object=Anno_SO$object,
 marker.table=Marker_Table,
-use_columns = c("Macrophages","M1","M2" ),
-ms_threshold=c("Macrophages .40","M1 .25","M2 .14"),
+use.columns = c("Macrophages","M1","M2" ),
+ms.threshold=c("Macrophages .40","M1 .25","M2 .14"),
 general.class=c("Macrophages"),
 multi.lvl = FALSE,
 reduction = "umap",
@@ -359,8 +359,8 @@ step.size = 0.1
 )
 MS_object=modScore(object=Anno_SO$object,
 marker.table=Marker_Table,
-use_columns = c("Macrophages","M1","M2" ),
-ms_threshold=c("Macrophages .40","M1 .25","M2 .14"),
+use.columns = c("Macrophages","M1","M2" ),
+ms.threshold=c("Macrophages .40","M1 .25","M2 .14"),
 multi.lvl = FALSE,
 reduction = "umap",
 nbins = 10,
@@ -370,8 +370,8 @@ step.size = 0.1
 )
 MS_object=modScore(object=Anno_SO$object,
 marker.table=Marker_Table,
-use_columns = c("Macrophages","M1","M2" ),
-ms_threshold=c("Macrophages .40","M1 .25","M2 .14"),
+use.columns = c("Macrophages","M1","M2" ),
+ms.threshold=c("Macrophages .40","M1 .25","M2 .14"),
 general.class=c("Macrophages","M1","M2"),
 multi.lvl = FALSE,
 reduction = "umap",
@@ -383,8 +383,8 @@ step.size = 0.1
 Marker_Table
 MS_object=modScore(object=Anno_SO$object,
 marker.table=Marker_Table,
-use_columns = c("Macrophages","Monocytes","CD8_T" ),
-ms_threshold=c("Macrophages .40","Monocytes .25","CD8_T .14"),
+use.columns = c("Macrophages","Monocytes","CD8_T" ),
+ms.threshold=c("Macrophages .40","Monocytes .25","CD8_T .14"),
 general.class=c("Macrophages","Monocytes","CD8_T"),
 multi.lvl = FALSE,
 reduction = "umap",
@@ -395,8 +395,8 @@ step.size = 0.1
 )
 MS_object=modScore(object=Anno_SO$object,
 marker.table=Marker_Table,
-use_columns = c("Macrophages","Neutrophils","CD8_T" ),
-ms_threshold=c("Macrophages .40","Neutrophils .25","CD8_T .14"),
+use.columns = c("Macrophages","Neutrophils","CD8_T" ),
+ms.threshold=c("Macrophages .40","Neutrophils .25","CD8_T .14"),
 general.class=c("Macrophages","Neutrophils","CD8_T"),
 multi.lvl = FALSE,
 reduction = "umap",
@@ -407,8 +407,8 @@ step.size = 0.1
 )
 MS_object=modScore(object=Anno_SO$object,
 marker.table=Marker_Table,
-use_columns = c("Neutrophils","Macrophages","CD8_T" ),
-ms_threshold=c("Neutrophils .25","Macrophages .40","CD8_T .14"),
+use.columns = c("Neutrophils","Macrophages","CD8_T" ),
+ms.threshold=c("Neutrophils .25","Macrophages .40","CD8_T .14"),
 general.class=c("Neutrophils","Macrophages","CD8_T"),
 multi.lvl = FALSE,
 reduction = "umap",

R/Filter_QC.R

@@ -77,12 +77,12 @@
 #' Usage c(lower limit, Upper Limit). E.g. setting to c(5,5) will remove all 
 #' cells with more than 5 absolute deviations greater than or 5 absolute 
 #' deviations less than the median percentage. (Default: c(5,5))
-#' @param n.topgnes Select the number of top highly expressed genes used to 
+#' @param n.topgenes Select the number of top highly expressed genes used to 
 #' calculate the percentage of reads found in these genes. 
 #' E.g. a value of 20 calculates the percentage of reads found in the top 20 
 #' most highly expressed Genes.
 #' (Default: 20)
-#' @param do.doublets.fitler Use scDblFinder to identify and remove doublet 
+#' @param do.doublets.filter Use scDblFinder to identify and remove doublet 
 #' cells. Doublets are defined as two cells that are sequenced under the same 
 #' cellular barcode, for example, if they were captured in the same droplet.
 #' (Default: TRUE)
@@ -125,8 +125,8 @@ filterQC <- function(object,
                      mad.complexity.limits = c(5,NA),
                      topNgenes.limits = c(NA,NA),
                      mad.topNgenes.limits = c(5,5),
-                     n.topgnes=20,
-                     do.doublets.fitler=T,
+                     n.topgenes=20,
+                     do.doublets.filter=TRUE,
 
                      ## dim Reduction settings
                      plot.outliers="None", #options(None,UMAP,tSNE) 
@@ -138,7 +138,7 @@ filterQC <- function(object,
                      high.cut.disp = 100000,
                      selection.method = "vst",
                      npcs = 30,
-                     vars_to_regress=NULL,
+                     vars.to.regress=NULL,
                      seed.for.PCA = 42,
                      seed.for.TSNE = 1,
                      seed.for.UMAP = 42
@@ -155,15 +155,15 @@ filterQC <- function(object,
 
   ### Helper Functions #####
 
-  .topNGenes <- function(so,n.topgnes) { 
+  .topNGenes <- function(so,n.topgenes) { 
     ##Extract counts table
     counts_matrix = GetAssayData(so, slot="counts")
 
     ## calculate Counts in Top n genes 
     tbl=  apply(counts_matrix,2,function(i){
       cnts=i[order(i,decreasing=T)]
 
-      t20=sum(cnts[1:n.topgnes])
+      t20=sum(cnts[1:n.topgenes])
       total=sum(cnts)
 
       pertop20=(t20/total)*100
@@ -214,7 +214,7 @@ filterQC <- function(object,
   .plotViolin2=function(count.df,value){
     axis.lab = unique(count.df$filt)
     ylabs=gsub(" \\(", "\n\\(",value)
-    ylabs=gsub(paste0(" Top",n.topgnes), paste0("\nTop",n.topgnes),ylabs)
+    ylabs=gsub(paste0(" Top",n.topgenes), paste0("\nTop",n.topgenes),ylabs)
 
     ### Set up table fore cut off lines
     ## clean up cutoff values
@@ -301,7 +301,7 @@ filterQC <- function(object,
     # count.df$filt=factor(count.df$filt,levels = c('filt','raw'))
     count.df$filt=factor(count.df$filt,levels = c('raw','filt'))
     ylabs=gsub(" \\(", "\n\\(",value)
-    ylabs=gsub(paste0(" Top",n.topgnes), paste0("\nTop",n.topgnes),ylabs)
+    ylabs=gsub(paste0(" Top",n.topgenes), paste0("\nTop",n.topgenes),ylabs)
 
     ### Set up table fore cut off lines
     ## clean up cutoff values
@@ -469,7 +469,7 @@ filterQC <- function(object,
     xlab = as.character(xaxis)	
     ylab = as.character(yaxis)	
     ylab=gsub(" \\(", "\n\\(",ylab)
-    ylab=gsub(paste0(" Top",n.topgnes), paste0("\nTop",n.topgnes),ylab)
+    ylab=gsub(paste0(" Top",n.topgenes), paste0("\nTop",n.topgenes),ylab)
 
     name = paste(ylab,"vs.",xlab)          
     g =ggplot(count.df, aes(x=.data[[xaxis]], y=.data[[yaxis]],color = Sample))+
@@ -510,7 +510,7 @@ filterQC <- function(object,
   .plotViolinPost2=function(count.df,yaxis){
     axis.lab = unique(count.df$Sample)
     ylabs=gsub(" \\(", "\n\\(",yaxis)
-    ylabs=gsub(paste0(" Top",n.topgnes), paste0("\nTop",n.topgnes),ylabs)
+    ylabs=gsub(paste0(" Top",n.topgenes), paste0("\nTop",n.topgenes),ylabs)
 
 
     g=ggplot(count.df, aes(x=Sample, y=(.data[[yaxis]]))) +
@@ -548,7 +548,7 @@ filterQC <- function(object,
 
     if(plot.outliers!="none"){
       so.nf.qcFiltr <- SCTransform(so.nf,do.correct.umi = TRUE,
-                                vars.to.regress=vars_to_regress, 
+                                vars.to.regress=vars.to.regress, 
                                 return.only.var.genes = FALSE)
       so.nf.qcFiltr = FindVariableFeatures(object = so.nf.qcFiltr, 
                              nfeatures = nfeatures, 
@@ -625,7 +625,7 @@ filterQC <- function(object,
     ## Caluclate filter Metrics
 
     ## calculate Counts in Top 20 Genes
-    so=.topNGenes(so,n.topgnes)
+    so=.topNGenes(so,n.topgenes)
 
     ## Counts(umi) Filter 
     mad.ncounts.limits=.madCalc(so,'nCount_RNA',mad.ncounts.limits)
@@ -700,7 +700,7 @@ filterQC <- function(object,
     )    
 
     ## doublets Filter
-    if(do.doublets.fitler==T){
+    if(do.doublets.filter==T){
       doublets.fitler <- so@meta.data$Doublet%in%"singlet"
     }else{
       doublets.fitler=rep(TRUE,nrow(so@meta.data))
@@ -749,7 +749,7 @@ filterQC <- function(object,
       filtSum[,"Cells before Filtering"]=nrow(filter_matrix)
       filtSum[,"Cells after all Filters"]=sum(filterIndex)
       filtSum[,"Percent Remaining"]=perc.remain
-    topN.filterRename=paste0('% Counts in Top',n.topgnes,' Genes filter')
+    topN.filterRename=paste0('% Counts in Top',n.topgenes,' Genes filter')
     filtTbl=colSums(filter_matrix==F)%>%t()%>%as.data.frame()
     filtTbl=rename(filtTbl,
                    'UMI Count (nCount_RNA)' = 'ncounts.filter',
@@ -767,7 +767,7 @@ filterQC <- function(object,
 
     ########################################################## #    
     ## create Filter Limits table
-        topN.filterRename=paste0('% Counts in Top',n.topgnes,' Genes')
+        topN.filterRename=paste0('% Counts in Top',n.topgenes,' Genes')
   cat('VDJ Genes Removed: ',length(VDJgenesOut), '\n')      
   cat('Minimum Cells per Gene: ',min.cells,'\n')
   cat('UMI Count (nCount_RNA) Limits: ',ncounts.limits,'\n')
@@ -780,7 +780,7 @@ filterQC <- function(object,
   cat('MAD Complexity (log10GenesPerUMI) Limits: ',mad.complexity.limits,'\n')
   cat(topN.filterRename,' Limits: ',topNgenes.limits,'\n')
   cat('MAD ',topN.filterRename,' Limits: ',mad.topNgenes.limits,'\n')
-  cat('Doublets Filter: ',do.doublets.fitler,'\n')
+  cat('Doublets Filter: ',do.doublets.filter,'\n')
 
 
 
@@ -821,7 +821,7 @@ filterQC <- function(object,
       paste0(c("Low:","High:"),topNgenes.limits)%>%paste(collapse = "\n")
     FiltLmts[,paste0('MAD ',topN.filterRename,'')]=
       paste0(c("Low:","High:"),mad.topNgenes.limits)%>%paste(collapse = "\n")
-    FiltLmts[,'DoubletFinder (scDblFinder)']=do.doublets.fitler
+    FiltLmts[,'DoubletFinder (scDblFinder)']=do.doublets.filter
     rownames(FiltLmts)=i
 
     ### Apply Filters ####
@@ -878,13 +878,13 @@ filterQC <- function(object,
 
     ## calculate Counts in Top 20 Genes
     ##calculated after min.cell filter as well
-    so=.topNGenes(so,n.topgnes) 
+    so=.topNGenes(so,n.topgenes) 
 
     ## Annotate Doublets: ####
     ## Gene filter does not effect doublet ident and so not recalculated
 
 
-    if( do.doublets.fitler==T){
+    if( do.doublets.filter==T){
     sce <- as.SingleCellExperiment(so)
 
       set.seed(123)
@@ -995,7 +995,7 @@ filterQC <- function(object,
   table.meta$nFeature_RNA=as.numeric(table.meta$nFeature_RNA)
   table.meta$filt=factor(table.meta$filt,levels = c('raw','filt'))
 
-  topN.filterRename=paste0('% Counts in Top',n.topgnes,' Genes')
+  topN.filterRename=paste0('% Counts in Top',n.topgenes,' Genes')
 
   table.meta=rename(table.meta,
                     'UMI Count (nCount_RNA)' = 'nCount_RNA',

R/Harmony.R

@@ -42,7 +42,7 @@ harmonyBatchCorrect <- function(object,
                                 nvar = 2000, 
                                 genes.to.add = c(),
                                 group.by.var,
-                                return_lognorm = T,
+                                return.lognorm = T,
                                 npc = 30) {
 
 library(patchwork)  
@@ -145,7 +145,7 @@ library(RColorBrewer)
   object@reductions$pca@stdev <- pppca$d
 
    # Store original log-normalized data and scaling parameters for back-calculation
-    if (return_lognorm) {
+    if (return.lognorm) {
       library(Matrix)
       # Get log-normalized data for the variable features
       lognorm_data <- object@assays$SCT@data[mvf, , drop = FALSE]
@@ -225,7 +225,7 @@ library(RColorBrewer)
 
   # Store batch-corrected scaled data in Harmony assay
 
-  if (return_lognorm) {
+  if (return.lognorm) {
       # Fast conversion back to log-normalized space
       # Direct vectorized operations on the transposed matrix
       harm.lvl.backcalc.lognorm <- t(harm.lvl.backcalc.scaled) * scaling_params$scale[mvf] + scaling_params$center[mvf]