Oct 26, 2018

scBFA: modeling detection patterns to mitigate technical noise in large-scale single cell genomics data

BioRxiv : the Preprint Server for Biology
Ruoxin Li, Gerald Quon


Technical variation in feature measurements such as gene expression and locus accessibility is a key challenge of large-scale single cell genomic datasets. We show that this technical variation in both scRNA-seq and scATAC-seq datasets can be mitigated by performing analysis on feature detection patterns alone and ignoring feature quantification measurements. This result holds when datasets have low detection noise relative to quantification noise. We demonstrate state-of-the-art performance of detection pattern models using our new framework, scBFA, for both cell type identification and trajectory inference. Performance gains can also be realized in one line of R code in existing pipelines.

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Mentioned in this Paper

Gene Expression
Sequence Analysis
RNA, Small Cytoplasmic
Genes, vif
Research Design
Research Study
Protein Expression

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