pbmc.rna.mat

Single cell RNA-sequencing (scRNA-seq) subset of the Hao et al. 2021 human peripheral blood mononuclear cell (PBMC) data (GEO: GSE164378, DOI: 10.1016/j.cell.2021.04.048).

datasets

Surface Protein abundance Estimation using CKmeans-based clustered thresholding ('SPECK') is an unsupervised learning-based method that performs receptor abundance estimation for single cell RNA-sequencing data based on reduced rank reconstruction (RRR) and a clustered thresholding mechanism. 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 the RRR is further detailed in: Erichson et al., (2019) <doi:10.18637/jss.v089.i11> and Halko et al., (2009) <doi:10.48550/arXiv.0909.4061>. 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

SPECK

Receptor Abundance Estimation using Reduced Rank Reconstruction
and Clustered Thresholding

H. Robert Frost

pbmc.rna.mat function

A scRNA-seq data of dgCMatrix class with 1000 rows and 33538 columns<dl>
 <dt>pbmc.rna.mat</dt>
<dd>RNA expression data for 1000 cells and 33538 genes</dd>
</dl>

Format

Single cell RNA-sequencing (scRNA-seq) peripheral blood (PBMC) data sample. — pbmc.rna.mat

A scRNA-seq data of dgCMatrix class with 1000 rows and 33538 columns<dl>
 <dt>pbmc.rna.mat</dt>
<dd>RNA expression data for 1000 cells and 33538 genes</dd>


</dl>

pbmc.rna.mat: Single cell RNA-sequencing (scRNA-seq) peripheral blood (PBMC) data sample.

Description

Usage

Arguments

Format