This function uses pretrained NMF models to recover cell states / spatial ecotypes. It takes a factorization matrix W representing a pretrained NMF model and a numeric gene expression matrix.
Arguments
- W
Matrix representing the factorization matrix W of a pretrained NMF model.
- testdat
Numeric matrix containing the new data for which NMF scores are to be predicted.
- scale
Logical indicating whether to scale the input data.
- ncell.per.run
Integer specifying the maximum number of cells per NMF prediction run to avoid memory issues.
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")
W <- readRDS(file.path(system.file("extdata", package = "SpatialEcoTyper"), "Bulk_SE_Recovery_W.rds"))
# Predict SE abundances in bulk tumors
preds <- NMFpredict(W = W, bulkdata, scale = TRUE)
head(preds[, 1:5])
#> TCGA-3N-A9WB-06 TCGA-3N-A9WC-06 TCGA-3N-A9WD-06 TCGA-BF-A1PU-01
#> nonSE 0.10378493 0.031103139 0.469932888 0.12913097
#> SE1 0.08326065 0.063022079 0.151414386 0.18470256
#> SE2 0.25368726 0.051110136 0.068248720 0.09710288
#> SE3 0.09417615 0.173416088 0.004969678 0.06966202
#> SE4 0.07931696 0.028829349 0.056418588 0.16008800
#> SE5 0.05088193 0.003203951 0.056310297 0.05303787
#> TCGA-BF-A1PV-01
#> nonSE 0.10221472
#> SE1 0.05277487
#> SE2 0.05740510
#> SE3 0.15960054
#> SE4 0.12876903
#> SE5 0.12002292