DEVarSelect {TOAST} | R Documentation

## Feature selection for reference-free deconvolution using cross-cell type differential analysis

### Description

This function selects cross-cell type differential features for reference-free deconvolution.

### Usage

    DEVarSelect(Y_raw, Prop0, nMarker, bound_negative)

### Arguments

`Y_raw` | A data matrix containing P features and N samples; or a SummarizedExperiment object.
`Prop0` | A N by K proportion matrix with K as number of cell types.
`nMarker` | Number of markers selected.
`bound_negative` | Whether to bound all negative parameter estimators to zero.

### Value

Selected markers using cross-cell type differential analysis.

### Author(s)

Ziyi Li <ziyi.li@emory.edu>

### References

Ziyi Li, Zhijin Wu, Peng Jin, Hao Wu. "Dissecting differential signals in high-throughput data from complex tissues."

### Examples

    Y_raw <- matrix(runif(5000*20, 0, 1), 5000, 20)
    tmp <- matrix(runif(20*4), 20, 4)
    Prop0 <- sweep(tmp, 1, rowSums(tmp), "/")
    varlist <- DEVarSelect(Y_raw, Prop0,
                           nMarker=1000,
                           bound_negative=FALSE)

[Package TOAST version 1.6.0 Index]