SCdeconR (version 1.0.0)

load_scdata: Load, filter and normalize scRNA-seq/snRNA-seq data

Description

Load and preprocess scRNA-seq/snRNA-seq data using seurat SCTransform workflow.

Usage

load_scdata(
  ref,
  data_type = c("cellranger", "h5", "matrix"),
  meta_info,
  nfeature_rna = 200,
  percent_mt = 40,
  cc.genes = NULL,
  vars_to_regress = c("percent_mt", "phase"),
  id,
  verbose,
  ...
)

Value

a Seurat-class object.

Arguments

ref

path to scRNA-seq/snRNA-seq data.

data_type

a character value specifying data type of the input scRNA-seq/snRNA-seq data, should be one of "cellranger", "h5", "matrix".

meta_info

a data.frame with rows representing cells, columns representing cell attributes.

nfeature_rna

minimum # of features with non-zero UMIs. Cells with # of features lower than nfeature_rna will be removed. Default to 200.

percent_mt

maximum percentage of mitochondria (MT) mapped UMIs. Cells with MT percentage higher than percent_mt will be removed. Default to 40.

cc.genes

cell-cycle genes curated by Seurat. Can be loaded via data(cc.genes)

vars_to_regress

a list of character values indicating the variables to regress for SCTransform normalization step. Default is to regress out MT percentage ("percent_mt") & cell cycle effects ("phase")

id

a character value specifying project or sample id. Only used for printing purposes.

verbose

logical value indicating whether to print messages.

...

additional parameters passed to SCTransform.

Details

For more details, refer to construct_ref

Examples

# \donttest{
samplepath1 <- paste0(system.file("extdata", package = "SCdeconR"), "/refdata/sample1")
samplepath2 <- paste0(system.file("extdata", package = "SCdeconR"), "/refdata/sample2")
ref_list <- c(samplepath1, samplepath2)
phenopath1 <- paste0(system.file("extdata", package = "SCdeconR"),
"/refdata/phenodata_sample1.txt")
phenopath2 <- paste0(system.file("extdata", package = "SCdeconR"),
"/refdata/phenodata_sample2.txt")
phenodata_list <- c(phenopath1,phenopath2)
tmp <- load_scdata(
  ref = ref_list[[1]],
  data_type = c("cellranger"),
  meta_info = data.table::fread(file = phenodata_list[[1]], check.names = FALSE, header = TRUE),
  nfeature_rna = 50,
  vars_to_regress = c("percent_mt"),
  id = 1,
  verbose = TRUE)
# }