Computational approaches towards reducing contamination in single-cell RNA-seq data

Single cell RNA sequencing has enabled quantification of single cells and identification of different cell types and subtypes as well as cell functions in different tissues. Single cell RNA sequence analyses assume acquired RNAs correspond to cells, however, RNAs from contamination within the input data are also captured by these assays. The sequencing of background contamination as well as unwanted cells making their way to the final assay potentially confound the correct biological interpretation of single cell transcriptomic data.

Researchers from the University of Tennessee Health Science Center demonstrate two approaches to deal with background contamination as well as profiling of unwanted cells in the assays. They use three real-life datasets of whole-cell capture and nucleotide single-cell captures generated by Fluidigm and 10x technologies and show that these methods reduce the effect of contamination, strengthen clustering of cells and improves biological interpretation.

rna-seq

Experimental workflow for generating single retinal ganglion cell (RGC) RNA sequencing from retinas of glaucoma mice using 10x chromium technology.

Yousefi S, Chen H, Ingles J, Centeno A, Chintalapudi S, Mulligan M, Jones B, Williams RW. (2020) Computational approaches towards reducing contamination in single-cell RNA-seq data. bioRXiv [online preprint]. [abstract]

Leave a Reply

Your email address will not be published. Required fields are marked *

*

Time limit is exhausted. Please reload CAPTCHA.