Gene length and detection bias in single cell RNA sequencing protocols

F1000Research
Belinda PhipsonAlicia Oshlack

Abstract

Background: Single cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include unique molecular identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene, with PCR duplicates resulting in copies of the UMI not included in expression estimates. Methods: Here we investigate the effect of gene length bias in scRNA-Seq across a variety of datasets that differ in terms of capture technology, library preparation, cell types and species. Results: We find that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protoco...Continue Reading

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Citations

Jul 18, 2017·Molecular Aspects of Medicine·Tallulah S Andrews, Martin Hemberg
Jan 9, 2018·Nucleic Acids Research·Amit PandeCarsten A Raabe
Mar 3, 2018·Nature Communications·Edroaldo Lummertz da RochaGeorge Q Daley
Jan 18, 2018·Nature·Samuel F BakhoumLewis C Cantley
Apr 12, 2019·Briefings in Bioinformatics·Saman ZeeshanZeeshan Ahmed
Apr 24, 2020·Circulation Research·Jesse W WilliamsKlaus Ley
Apr 28, 2020·Journal of Bioinformatics and Computational Biology·Julie M Deeke, Johann A Gagnon-Bartsch
Jul 6, 2020·Genome Biology·F William Townes, Rafael A Irizarry
Jul 28, 2018·Nature Communications·Johannes W BagnoliWolfgang Enard
Nov 9, 2018·Genome Biology·Jennifer WestobyMartin Hemberg
Sep 14, 2017·Genome Biology·Luke ZappiaAlicia Oshlack
Jun 15, 2019·Nature Communications·Xiuwei ZhangNir Yosef
Nov 25, 2020·Journal of Neurochemistry·Kaitlin E SullivanMark S Cembrowski
May 8, 2021·Briefings in Bioinformatics·Philip DaviesDaniel Hebenstreit
May 11, 2021·Frontiers in Neuroscience·Asif AdilMohammed Asger
Jun 6, 2021·Neuroscience Bulletin·Yang Ying, Jian-Zhi Wang

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Datasets Mentioned

BETA
E-MTAB-2600
GSE63818
GSE54695
GSE77288
GSM1599500
GSE75790
SRP066834
PRJEB6989

Methods Mentioned

BETA
RNA-Seq
scRNA-Seq
InDrop
Drop-Seq
pulldown
PCR
SCRB-Seq
CEL-Seq

Software Mentioned

ArrayExpress
R
limma
FastQC
Bioconductor R package limma
Subjunc aligner
SMARTer
Fluidigm
STAR
Bowtie

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