extract_data_for_shiny_clustering: Function to perform differential expression analysis for all...

In FloWuenne/scFunctions: Functions for single cell data analysis

Description

This function will take a precomputed Seurat object and perform differential expression analysis using one of the differential expression tests included in Seurat (default= wilcox). If you want to perform DE analysis using edgeR, please check the function DE_edgeR_Seurat()! All the results will be saved in a folder above the current folder location named DE_Seurat (../DE_Seurat). The output folder can easily be modified using the parameter 'output_dir'.

Usage

extract_data_for_shiny_clustering(
  seurat_object,
  time_points = c("E14.5", "E16.5", "E18.5", "P1", "P4", "P7"),
  imputed = TRUE,
  output_dir = "."
)

Arguments

seurat_object	The S4 Seurat object which contains filtered and normalized cells in the data slot.
time_points	The time points that should be processed
imputed	logical that indicates whether data has been imputed and there is a data frame in @imputed or not. default = TRUE
output_dir	Directory where the subclustering module and all gene Rds objects will be saved. default = "."

Examples

extract_data_for_shiny_clustering()

FloWuenne/scFunctions documentation built on June 3, 2021, 6:42 p.m.