scRecover: scRecover: Imputation for single-cell RNA-seq data

In miaozhun/ImputeSingle: scRecover for imputation of single-cell RNA-seq data

scRecover: Imputation for single-cell RNA-seq data

Description

This function is used to impute missing values in single-cell RNA-seq (scRNA-seq) data. It takes a non-negative matrix of scRNA-seq raw read counts or a SingleCellExperiment object as input. So users should map the reads (obtained from sequencing libraries of the samples) to the corresponding genome and count the reads mapped to each gene according to the gene annotation to get the raw read counts matrix in advance.

Usage

scRecover(counts, Kcluster = NULL, labels = NULL, outputDir = NULL,
  depth = 20, SAVER = FALSE, MAGIC = FALSE, UMI = FALSE,
  hist_raw_counts = NULL, hist_RUG_counts = NULL, parallel = FALSE,
  BPPARAM = bpparam(), verbose = TRUE)

Arguments

counts	A non-negative integer matrix of scRNA-seq raw read counts or a SingleCellExperiment object which contains the read counts matrix. The rows of the matrix are genes and columns are samples/cells.
Kcluster	An integer specifying the number of cell subpopulations. This parameter can be determined based on prior knowledge or clustering of raw data. Kcluster is used to determine the candidate neighbors of each cell.
labels	Optional. Only needed when Kcluster is blank or Kcluster = NULL. A character/integer vector specifying the cell type of each column in the raw count matrix. Each cell type should have at least two cells.
outputDir	The path of the output directory. If not specified, a folder named with prefix 'outDir_scRecover_' under the temporary directory will be used.
depth	Relative sequencing depth to be predicted compared with initial sample depth, should between 2-100, default is 20.
SAVER	Whether use and improve SAVER in imputation, default is FALSE.
MAGIC	Whether use and improve MAGIC in imputation, default is FALSE.
UMI	Whether use full UMI data, default is FALSE. If TRUE, hist_raw_counts and hist_RUG_counts should be specified.
hist_raw_counts	A list contains the histogram table of raw read counts for each cell in counts.
hist_RUG_counts	A list contains the histogram table of raw UMI-gene counts for each cell in counts.
parallel	If FALSE (default), no parallel computation is used; if TRUE, parallel computation using BiocParallel, with argument BPPARAM.
BPPARAM	An optional parameter object passed internally to bplapply when parallel=TRUE. If not specified, bpparam() (default) will be used.
verbose	Whether to show specific calculation progress, default is TRUE.

Value

Imputed counts matrices will be saved in the output directory specified by outputDir.

Author(s)

Zhun Miao.

See Also

estDropoutNum, for estimating dropout gene number in a cell.

countsSampling, for downsampling the read counts in a cell.

normalization, for normalization of single-cell RNA-seq data.

scRecoverTest, a test dataset for scRecover.

Examples

# Load test data for scRecover
data(scRecoverTest)

# Run scRecover with Kcluster specified
scRecover(counts = counts, Kcluster = 2)

# Or run scRecover with labels specified
# scRecover(counts = counts, labels = labels)

miaozhun/ImputeSingle documentation built on Jan. 12, 2023, 8:06 p.m.