ChooseMarker {TOAST} | R Documentation

## 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 <ziyi.li@emory.edu>

### 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)

[Package TOAST version 1.4.0 Index]