train.malt.adt.mat

<p>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 <code>dataProcessing.R</code> file from <code>data-raw</code> folder for code to recreate data subset.</p>

datasets

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>.

Azka Javaid

STREAK

Receptor Abundance Estimation using Feature Selection and Gene
Set Scoring

H. Robert Frost

train.malt.adt.mat function

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

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

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