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

Genome Biology

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[1] 0000 0004 1936 9684, grid.27860.3b, Graduate Group in Biostatistics, University of California, Davis, Davis, CA, USA;0000 0004 1936 9684, grid.27860.3b, Genome Center, University of California, Davis, Davis, CA, USA;0000 0004 1936 9684, grid.27860.3b, Graduate Group in Biostatistics, University of California, Davis, Davis, CA, USA;0000 0004 1936 9684, grid.27860.3b, Genome Center, University of California, Davis, Davis, CA, USA;0000 0004 1936 9684, grid.27860.3b, Department of Molecular and Cellular Biology, University of California, Davis, Davis, CA, USA;

关键词: scRNA-seq;    Dimensionality reduction;    scATAC-seq;    Technical noise;    Gene detection;    Gene quantification;    Cell type identification;    Trajectory inference;    Variable gene selection;   

DOI  :  10.1186/s13059-019-1806-0

来源: publisher

【 摘 要 】

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.

【 授权许可】

CC BY