Package 'STREAK'

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

target.rnaseq	$m x n$ scRNA-seq counts matrix for $m$ cells and $n$ genes.
receptor.geneset.matrix	$n x h$ Gene sets weights membership matrix.
num.genes	Number of top most weighted genes for subsequent gene set scoring and thresholding.
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.
max.num.clusters	See documentation for the ckmeansThreshold function from the SPECK package.
seed.ckmeans	See documentation for the ckmeansThreshold function from the SPECK package.

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

train.rnaseq	$m x n$ scRNA-seq counts matrix for $m$ cells and $n$ genes.
train.citeseq	$m x h$ CITE-seq ADT counts matrix for $m$ cells (same cells as the train.rnaseq matrix) and $h$ 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

• 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>

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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>

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