ChooseMarker: Choose cell type-specific markers from pure cell type...

In ziyili20/TOAST: Tools for the analysis of heterogeneous tissues

Choose cell type-specific markers from pure cell type profiles or single cell data

Description

Choose cell type-specific markers from pure cell type profiles generated by microarray or RNA-seq, or from single cell RNA-seq data by differential analysis.

Usage

ChooseMarker(pure_all, CellType, nMarkCT = 10, chooseSig = FALSE, verbose = TRUE)

Arguments

pure_all	Input pure cell type profile matrix or single cell data matrix. Rows are for genes, columns are for cell types or cells.
CellType	A list object consisting of cell type information for columns in pure_all. Each element is a cell type, and contains the corresponding column number in pure_all matrix. For example, CellType = list(BCell = 1:3, CD4T = 4:5).
nMarkCT	Number of markers chosen per cell type. Default is 10.
chooseSig	A boolean variable representing whether to consider the significance of selected markers. When chooseSig = FALSE, all nMarkerCT number of markers will be chosen per cell type. Otherwise the non-significant (p value > 0.05) markers will be filtered out.
verbose	A boolean variable of whether to output messages.

Details

Here we provide more details for CellType variable. This variable should be a list, with each element being the corresponding column numbers in pure_all for each cell type. For example, suppose pure_all is a 1000 by 300 matrix with row being genes and column being cells (or cell types). The first 1 to 100 columns are cell A, 101 to 200 columns are cell B, and 201 to 300 columns are cell C. Then CellType should be assigned as CellType = list(A = 1:100, B = 101:200, C = 201:300). If pure_all only has three columns for three cell types A, B and C, then CellType = list(A = 1, B = 2, C = 3).

Value

A list variable, including the selected variables for all cell types.

Author(s)

Ziyi Li <[e-mail]>

References

Ziyi Li, Zhenxing Guo, Ying Cheng, Peng Jin, Hao Wu. "Robust partial reference-free cell compoisiton estimation from tissue expression profiles."

Examples

## randomly simulate pure cell type profiles
pure_all <- matrix(abs(rnorm(1000*9)), 1000, 9)
CellType <- list(CellA = 1:3,
                 CellB = 4:6,
                 CellC = 7:9)

## choose significant markers
SelMarker <- ChooseMarker(pure_all, CellType,
                         nMarkCT = 30,
                         chooseSig = TRUE,
                         verbose = FALSE)

ziyili20/TOAST documentation built on Aug. 28, 2022, 11:28 a.m.