Characterization and decontamination of background noise in droplet-based single-cell protein expression data with DecontPro.
Characterization and decontamination of background noise in droplet-based single-cell protein expression data with DecontPro.
复制标题
使用 DecontPro 对基于液滴的单细胞蛋白质表达数据中的背景噪声进行表征和净化。
DOI:
10.1101/2023.01.27.525964
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发表时间:
2023
期刊:
影响因子:
--
通讯作者:
Campbell,JoshuaD
中科院分区:
文献类型:
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作者:
Yin,Yuan;Yajima,Masanao;Campbell,JoshuaD
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. In an exploratory analysis of PBMC datasets, we find that some droplets that were originally called ‘empty’ due to low levels of RNA contained high levels of ADTs and likely corresponded to neutrophils. We identified a novel type of artifact in the empty droplets called a ‘spongelet’ which has medium levels of ADT expression and is distinct from ambient noise. ADT expression levels in the spongelets correlate to ADT expression levels in the background peak of true cells in several datasets suggesting that they can contribute to background noise along with ambient ADTs. We then developed DecontPro, a novel Bayesian hierarchical model that can decontaminate ADT data by estimating and removing contamination from these sources. DecontPro outperforms other decontamination tools in removing aberrantly expressed ADTs while retaining native ADTs and in improving clustering specificity. Overall, these results suggest that identification of empty drops should be performed separately for RNA and ADT data and that DecontPro can be incorporated into CITE-seq workflows to improve the quality of downstream analyses.