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 patte...

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Main Authors: Ruoxin Li, Gerald Quon
Format: Article
Language:English
Published: BMC 2019-09-01
Series:Genome Biology
Subjects:
Online Access:http://link.springer.com/article/10.1186/s13059-019-1806-0
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author Ruoxin Li
Gerald Quon
author_facet Ruoxin Li
Gerald Quon
author_sort Ruoxin Li
collection DOAJ
description 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.
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spelling doaj.art-65678efa49da45f59c9465792793cb7a2022-12-21T19:42:24ZengBMCGenome Biology1474-760X2019-09-0120112010.1186/s13059-019-1806-0scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics dataRuoxin Li0Gerald Quon1Graduate Group in Biostatistics, University of California, DavisGraduate Group in Biostatistics, University of California, DavisAbstract 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.http://link.springer.com/article/10.1186/s13059-019-1806-0scRNA-seqDimensionality reductionscATAC-seqTechnical noiseGene detectionGene quantification
spellingShingle Ruoxin Li
Gerald Quon
scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data
Genome Biology
scRNA-seq
Dimensionality reduction
scATAC-seq
Technical noise
Gene detection
Gene quantification
title scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data
title_full scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data
title_fullStr scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data
title_full_unstemmed scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data
title_short scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data
title_sort scbfa modeling detection patterns to mitigate technical noise in large scale single cell genomics data
topic scRNA-seq
Dimensionality reduction
scATAC-seq
Technical noise
Gene detection
Gene quantification
url http://link.springer.com/article/10.1186/s13059-019-1806-0
work_keys_str_mv AT ruoxinli scbfamodelingdetectionpatternstomitigatetechnicalnoiseinlargescalesinglecellgenomicsdata
AT geraldquon scbfamodelingdetectionpatternstomitigatetechnicalnoiseinlargescalesinglecellgenomicsdata