receptorGeneSetConstruction {STREAK}R Documentation

Gene sets weights membership matrix construction for receptor abundance estimation.

Description

Computes nxhn x h gene sets weights membership matrix using associations learned between log-normalized and reduced rank reconstructed (RRR) mxnm x n scRNA-seq training data and mxhm x h CITE-seq ADT training counts normalized using the centered log ratio (CLR) transformation. scRNA-seq counts are normalized and RRR using the SPECK::randomizedRRR() function while CITE-seq counts are normalized using the Seurat::NormalizeData() function with the normalization.method parameter set to CLR. Spearman rank correlations are computed between the normalized CITE-seq data and the normalized and RRR scRNA-seq data.

Usage

receptorGeneSetConstruction(
  train.rnaseq,
  train.citeseq,
  rank.range.end = 100,
  min.consec.diff = 0.01,
  rep.consec.diff = 2,
  manual.rank = NULL,
  seed.rsvd = 1
)

Arguments

train.rnaseq

mxnm x n scRNA-seq counts matrix for mm cells and nn genes.

train.citeseq

mxhm x h CITE-seq ADT counts matrix for mm cells (same cells as the train.rnaseq matrix) and hh cell-surface proteins.

rank.range.end

See documentation for the randomizedRRR function from the SPECK package.

min.consec.diff

See documentation for the randomizedRRR function from the SPECK package.

rep.consec.diff

See documentation for the randomizedRRR function from the SPECK package.

manual.rank

See documentation for the randomizedRRR function from the SPECK package.

seed.rsvd

See documentation for the randomizedRRR function from the SPECK package.

Value

Examples

data("train.malt.rna.mat")
data("train.malt.adt.mat")
receptor.geneset.matrix.out <- receptorGeneSetConstruction(train.rnaseq =
                                         train.malt.rna.mat[1:100,1:80],
                                         train.citeseq =
                                         train.malt.adt.mat[1:100,1:2],
                                         rank.range.end = 70,
                                         min.consec.diff = 0.01,
                                         rep.consec.diff = 2,
                                         manual.rank = NULL, seed.rsvd = 1)
dim(receptor.geneset.matrix.out)
head(receptor.geneset.matrix.out)

[Package STREAK version 1.0.0 Index]