This function computes the single-cell Stably Expressed Gene (scSEG) index from Lin. et al. (2019) for a given single-cell count data matrix. Each gene in the data is fitted with a gamma-normal mixture model and the final SEG index is computed as an average of key parameters that measure the expression stability of a gene.
We recommend using either the pre-computed genes (see "See Also" below) or the top SEG genes
from an user's own data as the control genes in the scMerge function
(see the ctl argument in the scMerge function).
scSEGIndex(
exprs_mat,
cell_type = NULL,
BPPARAM = SerialParam(progressbar = TRUE),
return_all = FALSE
)A log-transformed single-cell data, assumed to have no batch effect and covered a wide range of cell types. A n by m matrix, where n is the number of genes and m is the number of cells.
A vector indicating the cell type information for each cell in the gene expression matrix.
If it is NULL, the function calculates the scSEG index without using F-statistics.
A BiocParallelParam class object from the BiocParallel package is used. Default is SerialParam(progressbar = TRUE).
Default to FALSE. If set to TRUE, then all genes will be returned. Otherwise, only genes with less than 80 percent zeroes will be returned.
Returns a data frame.
Each row is a gene and each column is a statistic relating to the stability of expression of each gene.
The main statistic is the segIdx column, which is the SEG index.
Evaluating stably expressed genes in single cells (2019). doi:10.1093/gigascience/giz106.
## Loading example data
data('example_sce', package = 'scMerge')
## subsetting genes to illustrate usage.
exprs_mat = SummarizedExperiment::assay(example_sce, 'logcounts')[1:110, 1:20]
set.seed(1)
result1 = scSEGIndex(exprs_mat = exprs_mat)
#> Calculating scSEG index without F-statistics
#> Fitting the mixture model...
#>
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#>
## If parallelisation is needed:
param = BiocParallel::MulticoreParam(workers = 2, progressbar = TRUE)
result2 = scSEGIndex(exprs_mat = exprs_mat, BPPARAM = param)
#> Calculating scSEG index without F-statistics
#> Fitting the mixture model...
#>
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#>
## Closing the parallelisation
BiocParallel::register(BPPARAM = BiocParallel::SerialParam())