DEVarSelect: Feature selection for reference-free deconvolution using...

In TOAST: Tools for the analysis of heterogeneous tissues

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 <[e-mail]>

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)

TOAST documentation built on Nov. 8, 2020, 5:55 p.m.