R/processExper.R

In Morriseylab/scExtras: Single cell data analysis helper functions

#'Preprocess single cell RNA-seq data with seurat and return seurat object. This function only does normalization, finding variable genes and scaling
#' @param object Seurat object
#' @param ccscale Specify whether to scale for cell cycle genes or not
#' @param return_var_genes Define if scale.data matrix should contain only the variable genes. T/F
#' @param organism Organism (mouse/human)
#' @return Seurat object
#' @import dplyr tidyr Seurat
#' @export
#' @examples
#' scrna = processExper(object=scrna,ccscale=F,sc.transform =F)

processExper <- function(object,ccscale=F,return_var_genes = F,org){
  data('mouseortholog')
  m2h=mouse_human
  #normalize data
    object <- NormalizeData(object = object)

    #detection of variable genes
    #calculates the average expression and dispersion for each gene, places these genes into bins, and then calculates a z-score for dispersion within each bin
    object <-FindVariableFeatures(object = object,
                                  selection.method = "vst", nfeatures = 2000, verbose = FALSE)

    if(org=='human'){
      #Assign scores in the CellCycleScoring function.Stores S and G2/M scores in object@meta.data, along with the predicted classification of each cell in either G2M, S or G1 phase
      object <- CellCycleScoring(object = object, s.features = cc.genes$s.genes, g2m.features = cc.genes$g2m.genes)
    }else{
      #m2h <- readr::read_csv(mouseorthologfile)
      cc.genes$s.genes <- m2h %>% filter(human_name %in% cc.genes$s.genes) %>% pull(mouse_name)
      cc.genes$g2m.genes <- m2h %>% filter(human_name %in% cc.genes$g2m.genes) %>% pull(mouse_name)
      object <- CellCycleScoring(object = object, s.features  = cc.genes$s.genes, g2m.features = cc.genes$g2m.genes)
    }

    if(ccscale==T){
      #Scaling the data and removing unwanted sources of variation
      object <- ScaleData(object = object, vars.to.regress = c("nCount_RNA", "percent.mito","S.Score", "G2M.Score"))
    }else{
      object <- ScaleData(object = object, vars.to.regress = c("nCount_RNA", "percent.mito"))
    }
  object <- LogSeuratCommand(object = object)
}