load_scdata {SCdeconR} | R Documentation |
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,
...
)
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 |
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 |
Details
For more details, refer to construct_ref
Value
a Seurat-class
object.
Examples
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)