Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.01.27.525964v1?rss=1
Authors: Yin, Y., Yajima, M., Campbell, J. D.
Abstract:
Assays such as CITE-seq can measure the abundance of cell surface proteins on individual cells using antibody derived tags (ADTs). However, many ADTs have high levels of background noise that can obfuscate down-stream analyses. We show a combination of two sources of contamination in the ADT data: 1) ambient ADTs from the cell suspension and 2) margin ADTs derived from the "spongelets" which are empty droplets with high levels of non-specific ADT expression. We propose a novel hierarchical Bayesian model, DecontPro, that can decontaminate ADT data by estimating and removing contamination from ambient and margin sources. DecontPro outperforms other decontamination tools in removing aberrantly expressed ADTs while retaining native ADTs and in improving clustering specificity after decontamination. Overall, DecontPro can be incorporated into CITE-seq workflows to improve the quality of downstream analyses.
Copy rights belong to original authors. Visit the link for more info
Podcast created by Paper Player, LLC
