Infer SE Abundances Using Pretrained NMF Model

This function predicts SE abundances in each mixture.

Usage

DeconvoluteSE(dat, scale = TRUE, W = NULL)

Arguments

dat

A numeric matrix of bulk gene expression data.

scale

Logical indicating whether to scale the input data for predictions.

W

A W matrix for inferring SE abundances from bulk expression data.

Value

A matrix of SE abundances in bulk tumors.

Examples

library(googledrive)
drive_deauth() # no Google sign-in is required
drive_download(as_id("14QvmgISxaArTzWt_UHvf55aAYN2zm84Q"), "SKCM_RNASeqV2.geneExp.rds",
                    overwrite = TRUE)
#> File downloaded:
#> • SKCM_RNASeqV2.geneExp.rds <id: 14QvmgISxaArTzWt_UHvf55aAYN2zm84Q>
#> Saved locally as:
#> • SKCM_RNASeqV2.geneExp.rds
bulkdata <- readRDS("SKCM_RNASeqV2.geneExp.rds")

# Predict SE abundances in bulk tumors
se_abundances <- DeconvoluteSE(dat = bulkdata)
head(se_abundances[, 1:5])
#>                      nonSE        SE1        SE2        SE3        SE4
#> TCGA-3N-A9WB-06 0.19380234 0.05372667 0.16735600 0.12581663 0.07216666
#> TCGA-3N-A9WC-06 0.05293689 0.13901340 0.08427617 0.08372047 0.04243859
#> TCGA-3N-A9WD-06 0.27200075 0.17114795 0.06288761 0.05641549 0.10166721
#> TCGA-BF-A1PU-01 0.16899510 0.13618971 0.06501306 0.09269615 0.17313296
#> TCGA-BF-A1PV-01 0.12078283 0.06921834 0.05970319 0.16813008 0.11059648
#> TCGA-BF-A1PX-01 0.21398415 0.04019509 0.08906215 0.04499893 0.08561680