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

ABSTRACT: 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 analyzing 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.

SUBMITTER: Li R

PROVIDER: S-EPMC6734238 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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

Li Ruoxin R Quon Gerald G

Genome biology 20190909 1

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 analyzing 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 p ...[more]

PMID: 31500668

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