%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 }