Benchmarking single-cell RNA-sequencing protocols for cell atlas projects

Mereu, Elisabetta; Lafzi, Atefeh; Moutinho, Cátia; Ziegenhain, Christoph; McCarthy, Davis J.; Álvarez-Varela, Adrián; Batlle, Eduard; Sagar,; Grün, Dominic; Lau, Julia K; Boutet, Stéphane C.; Sanada, Chad; Ooi, Aik T.; Jones, Robert C.; Kaihara, Kelly A.; Brampton, Chris; Talaga, Yasha; Sasagawa, Yohei; Tanaka, Kaori; Hayashi, Tetsutaro; Braeuning, Caroline; Fischer, Cornelius; Sauer, Sascha; Trefzer, Timo B.; Conrad, Christian; Adiconis, Xian; Nguyễn, Lan; Regev, Aviv; Levin, Joshua Z.; Parekh, Swati; Janjic, Aleksandar; Wange, Lucas E.; Bagnoli, Johannes W.; Enard, Wolfgang; Gut, Marta; Sandberg, Rickard; Nikaido, Itoshi; Gut, Ivo; Stegle, Oliver; Heyn, Holger

doi:10.1038/s41587-020-0469-4

Cited by 393 publications

(379 citation statements)

References 54 publications

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“…The complexity of single-cell omics datasets is increasing. Current datasets often include many samples 1 , generated across multiple conditions 2 , with the involvement of multiple labs 3 . Such complexity, which is common in maps of specific tissues and organs or whole reference atlas initiatives such as the Human Cell Atlas 4 , creates inevitable batch effects.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking atlas-level data integration in single-cell genomics

Luecken

Büttner

Chaichoompu

et al. 2020

Preprint

117

142

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Cell atlases often include samples that span locations, labs, and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration .Choosing a data integration method is a challenge due to the difficulty of defining integration success. Here, we benchmark 38 method and preprocessing combinations on 77 batches of gene expression, chromatin accessibility, and simulation data from 23 publications, altogether representing >1.2 million cells distributed in nine atlas-level integration tasks. Our integration tasks span several common sources of variation such as individuals, species, and experimental labs. We evaluate methods according to scalability, usability, and their ability to remove batch effects while retaining biological variation.Using 14 evaluation metrics, we find that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, BBKNN, Scanorama, and scVI perform well, particularly on complex integration tasks; Seurat v3 performs well on simpler tasks with distinct biological signals; and methods that prioritize batch removal perform best for ATAC-seq data integration. Our freely available reproducible python module can be used to identify optimal data integration methods for new data, benchmark new methods, and improve method development.2

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Section: Introductionmentioning

confidence: 99%

Benchmarking atlas-level data integration in single-cell genomics

Luecken

Büttner

Chaichoompu

et al. 2020

Preprint

117

142

View full text Add to dashboard Cite

show abstract

“…Two recent benchmarking studies by Ding et al and Mereu et al . [11, 12] have systematically and comprehensively compared existing single-cell sequencing techniques, providing invaluable resource for users to make informed choices. To compare the technical performance between C4 and these single-cell platforms, we firstly analyzed the data from HEK293T/NIH3T3 mixture cells in replicates and the data generated from the same cell types in the study by Ding et al .…”

Section: Resultsmentioning

confidence: 99%

A portable and cost-effective microfluidic system for massively parallel single-cell transcriptome profiling

Fan

et al. 2019

Preprint

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29Single-cell technologies are becoming increasingly widespread and have been 30 revolutionizing our understanding of cell identity, state, diversity and function. However, 31 current platforms can be slow to apply to large-scale studies and resource-limited 32 clinical arenas due to a variety of reasons including cost, infrastructure, sample quality and requirements. Here we report DNBelab C4 (C4), a negative pressure orchestrated, 1 portable and cost-effective device that enables high-throughput single-cell 2 transcriptional profiling. C4 system can efficiently allow discrimination of species-3 specific cells at high resolution and dissect tissue heterogeneity in different organs, 4 such as murine lung and cerebral cortex. Finally, we show that the C4 system is 5 comparable to existing platforms but has huge benefits in cost and portability and, as 6 such, it will be of great interest for the wider scientific community. 7 8

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“…Unlike sc-based methods, sn approaches are compatible with snapfrozen tissue samples and minimize ex vivo expression changes 14 . The transcriptome complexity identified by snRNAseq is comparable to that of scRNAseq methods 15 . Besides its reliability for profiling the transcriptome at sc resolution 10, 12,16,17,18 , sn isolation also allows mapping of the chromatin-regulatory landscape 19, 20, 21 and genome-wide measurement of DNA methylation 22 .…”

Section: Introductionmentioning

confidence: 85%

Single nucleus multi-omics regulatory atlas of the murine pituitary

Ruf-Zamojski

Zhang

Zamojski

et al. 2020

Preprint

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The pituitary regulates growth, reproduction and other endocrine systems. To investigate transcriptional network epigenetic mechanisms, we generated paired single nucleus (sn) transcriptome and chromatin accessibility profiles in single mouse pituitaries and genome-wide sn methylation datasets. Our analysis provided insight into cell type epigenetics, regulatory circuit and gene control mechanisms. Latent variable pathway analysis detected corresponding transcriptome and chromatin accessibility programs showing both inter-sexual and inter-individual variation. Multi-omics analysis of gene regulatory networks identified cell type-specific regulons whose composition and function were shaped by the promoter accessibility state of target genes.

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Benchmarking single-cell RNA-sequencing protocols for cell atlas projects

Cited by 393 publications

References 54 publications

Benchmarking atlas-level data integration in single-cell genomics

Benchmarking atlas-level data integration in single-cell genomics

A portable and cost-effective microfluidic system for massively parallel single-cell transcriptome profiling

Single nucleus multi-omics regulatory atlas of the murine pituitary

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