STREAK: Receptor Abundance Estimation using Feature Selection and Gene Set Scoring

Description

Performs receptor abundance estimation for single cell RNA-sequencing data using a supervised feature selection mechanism and a thresholded gene set scoring procedure. Seurat's normalization method is described in: Hao et al., (2021) doi:10.1016/j.cell.2021.04.048, Stuart et al., (2019) doi:10.1016/j.cell.2019.05.031, Butler et al., (2018) doi:10.1038/nbt.4096 and Satija et al., (2015) doi:10.1038/nbt.3192. Method for reduced rank reconstruction and rank-k selection is detailed in: Javaid et al., (2022) doi:10.1101/2022.10.08.511197. Gene set scoring procedure is described in: Frost et al., (2020) doi:10.1093/nar/gkaa582. Clustering method is outlined in: Song et al., (2020) doi:10.1093/bioinformatics/btaa613 and Wang et al., (2011) doi:10.32614/RJ-2011-015.

Author(s)

Maintainer: Azka Javaid azka.javaid.gr@dartmouth.edu

Authors:

• H. Robert Frost hildreth.r.frost@dartmouth.edu

Receptor abundance estimation for single cell RNA-sequencing (scRNA-seq) data using gene set scoring and thresholding.

Description

Performs receptor abundance estimation for m x n scRNA-seq target data using gene set scoring and thresholding. scRNA-seq target counts are normalized and reduced rank reconstructed (RRR) using the SPECK::randomizedRRR() function. Gene set scoring is next performed leveraging expression from the top most weighted genes based on the gene sets weights membership matrix with the VAM::vam() function. The resulting cell-specific gene set scores are then thresholded utilizing the Ckmeans.1d.dp::Ckmeans.1d.dp() function. Note that this function only performs normalization and does not perform any quality control (QC) checks on the inputted target scRNA-seq counts matrix. Any QC needed can be performed on the target matrix before passing it as an input to the function.

Usage

receptorAbundanceEstimation(
  target.rnaseq,
  receptor.geneset.matrix,
  num.genes = 10,
  rank.range.end = 100,
  min.consec.diff = 0.01,
  rep.consec.diff = 2,
  manual.rank = NULL,
  seed.rsvd = 1,
  max.num.clusters = 4,
  seed.ckmeans = 2
)

Arguments

Value

• receptor.abundance.estimates - A m x h matrix consisting of abundance estimates for m cells in h receptors.

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)
data("target.malt.rna.mat")
receptor.abundance.estimates.out <- receptorAbundanceEstimation(target.rnaseq =
                                    target.malt.rna.mat[1:200,1:80],
                                    receptor.geneset.matrix =
                                    receptor.geneset.matrix.out,
                                    num.genes = 10, rank.range.end = 70,
                                    min.consec.diff = 0.01,
                                    rep.consec.diff = 2,
                                    manual.rank = NULL, seed.rsvd = 1,
                                    max.num.clusters = 4, seed.ckmeans = 2)
dim(receptor.abundance.estimates.out)
head(receptor.abundance.estimates.out)

Gene sets weights membership matrix construction for receptor abundance estimation.

Description

Computes n x h gene sets weights membership matrix using associations learned between log-normalized and reduced rank reconstructed (RRR) m x n scRNA-seq training data and m 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

Value

• receptor.geneset.matrix - A n x h gene sets weights membership matrix where a column i from h corresponds to the weights for n genes from the scRNA-seq matrix trained against the corresponding CITE-seq ADT transcript h.

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)

Single cell RNA-sequencing (scRNA-seq) target subset of the 10X Genomics MALT counts.

Description

A random subset of joint scRNA-seq/CITE-seq 10X Genomics human extranodal marginal zone B-cell tumor/mucosa-associated lymphoid tissue (MALT) target data. See the dataProcessing.R file from data-raw folder for code to recreate data subset.

Usage

target.malt.rna.mat

Format

A scRNA-seq counts matrix of dgCMatrix-class from the Matrix package with 4000 cells and 33538 genes.

Source

<https://www.10xgenomics.com/resources/datasets/10-k-cells-from-a-malt-tumor-gene-expression-and-cell-surface-protein-3-standard-3-0-0>

Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) training subset of the 10X Genomics MALT counts.

Description

A random subset of joint scRNA-seq/CITE-seq 10X Genomics human extranodal marginal zone B-cell tumor/mucosa-associated lymphoid tissue (MALT) training data. See the dataProcessing.R file from data-raw folder for code to recreate data subset.

Usage

train.malt.adt.mat

Format

A CITE-seq counts matrix of dgeMatrix-class from the Matrix package with 1000 cells and 17 genes.

Source

<https://www.10xgenomics.com/resources/datasets/10-k-cells-from-a-malt-tumor-gene-expression-and-cell-surface-protein-3-standard-3-0-0>

Single cell RNA-sequencing (scRNA-seq) training subset of the 10X Genomics MALT counts.

Description

A random subset of joint scRNA-seq/CITE-seq 10X Genomics human extranodal marginal zone B-cell tumor/mucosa-associated lymphoid tissue (MALT) training data. See the dataProcessing.R file from data-raw folder for code to recreate data subset.

Usage

train.malt.rna.mat

Format

A scRNA-seq counts matrix of dgCMatrix-class from the Matrix package with 1000 cells and 33538 genes.

Source

<https://www.10xgenomics.com/resources/datasets/10-k-cells-from-a-malt-tumor-gene-expression-and-cell-surface-protein-3-standard-3-0-0>