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...
Main Authors: | , |
---|---|
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 |
_version_ | 1818958389331361792 |
---|---|
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. |
first_indexed | 2024-12-20T11:24:58Z |
format | Article |
id | doaj.art-65678efa49da45f59c9465792793cb7a |
institution | Directory Open Access Journal |
issn | 1474-760X |
language | English |
last_indexed | 2024-12-20T11:24:58Z |
publishDate | 2019-09-01 |
publisher | BMC |
record_format | Article |
series | Genome Biology |
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 |