A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines: Library preparation and normalisation methods have the biggest impact on the performance of scRNA-seq studies
The recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not been established yet. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ~ 3,000 pipelines, allowing us to also assess interactions among pipeline steps.We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.
Beschreibung
A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines: Library preparation and normalisation methods have the biggest impact on the performance of scRNA-seq studies | bioRxiv
%0 Journal Article
%1 Vieth583013
%A Vieth, Beate
%A Parekh, Swati
%A Ziegenhain, Christoph
%A Enard, Wolfgang
%A Hellmann, Ines
%D 2019
%I Cold Spring Harbor Laboratory
%J bioRxiv
%K benchmark comparison fulltext open-access open-data open-science open-source rna-seq single-cell-sequencing software
%R 10.1101/583013
%T A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines: Library preparation and normalisation methods have the biggest impact on the performance of scRNA-seq studies
%U https://www.biorxiv.org/content/early/2019/03/19/583013
%X The recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not been established yet. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ~ 3,000 pipelines, allowing us to also assess interactions among pipeline steps.We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.
@article{Vieth583013,
abstract = {The recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not been established yet. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ~ 3,000 pipelines, allowing us to also assess interactions among pipeline steps.We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.},
added-at = {2019-09-22T19:36:56.000+0200},
author = {Vieth, Beate and Parekh, Swati and Ziegenhain, Christoph and Enard, Wolfgang and Hellmann, Ines},
biburl = {https://www.bibsonomy.org/bibtex/2064f253071339b68fd9e0230dc91b8a2/marcsaric},
description = {A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines: Library preparation and normalisation methods have the biggest impact on the performance of scRNA-seq studies | bioRxiv},
doi = {10.1101/583013},
elocation-id = {583013},
eprint = {https://www.biorxiv.org/content/early/2019/03/19/583013.full.pdf},
interhash = {54bd535997d6f61f3c6b855d33a8eb21},
intrahash = {064f253071339b68fd9e0230dc91b8a2},
journal = {bioRxiv},
keywords = {benchmark comparison fulltext open-access open-data open-science open-source rna-seq single-cell-sequencing software},
publisher = {Cold Spring Harbor Laboratory},
timestamp = {2019-09-22T19:36:56.000+0200},
title = {A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines: Library preparation and normalisation methods have the biggest impact on the performance of scRNA-seq studies},
url = {https://www.biorxiv.org/content/early/2019/03/19/583013},
year = 2019
}