scSEGIndex {scMerge} | R Documentation

## Single Cell Stably Express Gene Index

### Description

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).

### Usage

    scSEGIndex(
      exprs_mat,
      cell_type = NULL,
      BPPARAM = SerialParam(progressbar = TRUE),
      return_all = FALSE
    )

### Arguments

`exprs_mat` | 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.
`cell_type` | 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.
`BPPARAM` | A `BiocParallelParam` class object from the `BiocParallel` package is used. Default is SerialParam(progressbar = TRUE).
`return_all` | 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.

### Value

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.

### Author(s)

Shila Ghazanfar, Yingxin Lin, Pengyi Yang

### References

Evaluating stably expressed genes in single cells (2019). doi:10.1093/gigascience/giz106.

### See Also

Download human SEG directly from this link; Download mouse SEG directly from this link.

### Examples

    ## 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)
    ## If parallelisation is needed:
    param = BiocParallel::MulticoreParam(workers = 2, progressbar = TRUE)
    result2 = scSEGIndex(exprs_mat = exprs_mat, BPPARAM = param)
    ## Closing the parallelisation
    BiocParallel::register(BPPARAM = BiocParallel::SerialParam())

[Package scMerge version 1.10.0 Index]