cells_citeseq_mtx

A matrix of raw UMI counts for surface proteins for surface proteins measured with CITE-seq antibodies. This data is used for example scripts in the dsb package. Raw data was processed with CITE-seq-Count.

datasets

This lightweight R package provides a method for normalizing and denoising protein expression data from droplet based single cell experiments. Raw protein Unique Molecular Index (UMI) counts from sequencing DNA-conjugated antibody derived tags (ADT) in droplets (e.g. 'CITE-seq') have substantial measurement noise. Our experiments and computational modeling revealed two major components of this noise: 1) protein-specific noise originating from ambient, unbound antibody encapsulated in droplets that can be accurately inferred via the expected protein counts detected in empty droplets, and 2) droplet/cell-specific noise revealed via the shared variance component associated with isotype antibody controls and background protein counts in each cell. This package normalizes and removes both of these sources of noise from raw protein data derived from methods such as 'CITE-seq', 'REAP-seq', 'ASAP-seq', 'TEA-seq', 'proteogenomic' data from the Mission Bio platform, etc. See the vignette for tutorials on how to integrate dsb with 'Seurat' and 'Bioconductor' and how to use dsb in 'Python'. Please see our paper Mulè M.P., Martins A.J., and Tsang J.S. Nature Communications 2022 <https://www.nature.com/articles/s41467-022-29356-8> for more details on the method.

Matthew Mul<c3><a8>

Normalize & Denoise Droplet Single Cell Protein Data (CITE-Seq)

Matthew Mulè

Andrew Martins

John Tsang

cells_citeseq_mtx function

An R matrix, rows: 87 proteins, columns: 2872 cells<dl>
<dt>cells_citeseq_mtx</dt>
<dd>R matrix of cells by proteins; a random distribution of a maximum of 100 cells per cluster from the 30 clusters reported in Kotliarov et. al. 2020</dd>
</dl>

Format

small example CITE-seq protein dataset for 87 surface protein in 2872 cells — cells_citeseq_mtx

An R matrix, rows: 87 proteins, columns: 2872 cells<dl>
<dt>cells_citeseq_mtx</dt>
<dd>R matrix of cells by proteins; a random distribution of a maximum of 100 cells per cluster from the 30 clusters reported in Kotliarov et. al. 2020</dd>


</dl>

cells_citeseq_mtx: small example CITE-seq protein dataset for 87 surface protein in 2872 cells

Description

Usage

Arguments

Format

References