DECENT: Differential Expression with Capture Efficiency AdjustmeNT for Single-cell RNA-seq Data

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2019 Jun 15

PMID 31197307

Citations 23

Authors

Chengzhong Ye

Terence P Speed

Agus Salim

Affiliations

Soon will be listed here.

Abstract

Motivation: Dropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed it affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the process that gives rise to the dropout events. We develop DECENT, a method for DE analysis of scRNA-seq data that explicitly and accurately models the molecule capture process in scRNA-seq experiments.

Results: We show that DECENT demonstrates improved DE performance over existing DE methods that do not explicitly model dropout. This improvement is consistently observed across several public scRNA-seq datasets generated using different technological platforms. The gain in improvement is especially large when the capture process is overdispersed. DECENT maintains type I error well while achieving better sensitivity. Its performance without spike-ins is almost as good as when spike-ins are used to calibrate the capture model.

Availability And Implementation: The method is implemented as a publicly available R package available from https://github.com/cz-ye/DECENT.

Supplementary Information: Supplementary data are available at Bioinformatics online.

Citing Articles

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Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE.

Missarova A, Dann E, Rosen L, Satija R, Marioni J Genome Biol. 2024; 25(1):189.

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scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.

Magana-Lopez G, Calzone L, Zinovyev A, Pauleve L PLoS Comput Biol. 2024; 20(7):e1011620.

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Single-cell RNA sequencing data imputation using bi-level feature propagation.

Lee J, Yun S, Kim Y, Chen T, Kellis M, Park C Brief Bioinform. 2024; 25(3).

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Xue J, Zhou X, Yang J, Niu A PLoS One. 2024; 19(3):e0299358.

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References

Hashimshony T, Wagner F, Sher N, Yanai I . CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2012; 2(3):666-73. DOI: 10.1016/j.celrep.2012.08.003. View

McCarthy D, Chen Y, Smyth G . Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012; 40(10):4288-97. PMC: 3378882. DOI: 10.1093/nar/gks042. View

Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A, Smets M . Comparative Analysis of Single-Cell RNA Sequencing Methods. Mol Cell. 2017; 65(4):631-643.e4. DOI: 10.1016/j.molcel.2017.01.023. View

Bacher R, Chu L, Leng N, Gasch A, Thomson J, Stewart R . SCnorm: robust normalization of single-cell RNA-seq data. Nat Methods. 2017; 14(6):584-586. PMC: 5473255. DOI: 10.1038/nmeth.4263. View

Lun A, Calero-Nieto F, Haim-Vilmovsky L, Gottgens B, Marioni J . Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data. Genome Res. 2017; 27(11):1795-1806. PMC: 5668938. DOI: 10.1101/gr.222877.117. View

Huang M, Wang J, Torre E, Dueck H, Shaffer S, Bonasio R . SAVER: gene expression recovery for single-cell RNA sequencing. Nat Methods. 2018; 15(7):539-542. PMC: 6030502. DOI: 10.1038/s41592-018-0033-z. View

Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M . Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods. 2013; 11(2):163-6. DOI: 10.1038/nmeth.2772. View

Macosko E, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M . Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015; 161(5):1202-1214. PMC: 4481139. DOI: 10.1016/j.cell.2015.05.002. View

Van den Berge K, Perraudeau F, Soneson C, Love M, Risso D, Vert J . Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications. Genome Biol. 2018; 19(1):24. PMC: 6251479. DOI: 10.1186/s13059-018-1406-4. View

10.

Tung P, Blischak J, Hsiao C, Knowles D, Burnett J, Pritchard J . Batch effects and the effective design of single-cell gene expression studies. Sci Rep. 2017; 7:39921. PMC: 5206706. DOI: 10.1038/srep39921. View

11.

Jia C, Hu Y, Kelly D, Kim J, Li M, Zhang N . Accounting for technical noise in differential expression analysis of single-cell RNA sequencing data. Nucleic Acids Res. 2017; 45(19):10978-10988. PMC: 5737676. DOI: 10.1093/nar/gkx754. View

12.

Zeisel A, Munoz-Manchado A, Codeluppi S, Lonnerberg P, La Manno G, Jureus A . Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science. 2015; 347(6226):1138-42. DOI: 10.1126/science.aaa1934. View

13.

Savas P, Virassamy B, Ye C, Salim A, Mintoff C, Caramia F . Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat Med. 2018; 24(7):986-993. DOI: 10.1038/s41591-018-0078-7. View

14.

Love M, Huber W, Anders S . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15(12):550. PMC: 4302049. DOI: 10.1186/s13059-014-0550-8. View

15.

Kiselev V, Kirschner K, Schaub M, Andrews T, Yiu A, Chandra T . SC3: consensus clustering of single-cell RNA-seq data. Nat Methods. 2017; 14(5):483-486. PMC: 5410170. DOI: 10.1038/nmeth.4236. View

16.

Sun Z, Wang C, Lawson D, Kwek S, Gonzalez Velozo H, Owyong M . Single-cell RNA sequencing reveals gene expression signatures of breast cancer-associated endothelial cells. Oncotarget. 2018; 9(13):10945-10961. PMC: 5834272. DOI: 10.18632/oncotarget.23760. View

17.

Chen W, Li Y, Easton J, Finkelstein D, Wu G, Chen X . UMI-count modeling and differential expression analysis for single-cell RNA sequencing. Genome Biol. 2018; 19(1):70. PMC: 5984373. DOI: 10.1186/s13059-018-1438-9. View

18.

Raj A, Bogaard P, Rifkin S, van Oudenaarden A, Tyagi S . Imaging individual mRNA molecules using multiple singly labeled probes. Nat Methods. 2008; 5(10):877-9. PMC: 3126653. DOI: 10.1038/nmeth.1253. View

19.

Kolodziejczyk A, Kim J, Svensson V, Marioni J, Teichmann S . The technology and biology of single-cell RNA sequencing. Mol Cell. 2015; 58(4):610-20. DOI: 10.1016/j.molcel.2015.04.005. View

20.

Wagner A, Regev A, Yosef N . Revealing the vectors of cellular identity with single-cell genomics. Nat Biotechnol. 2016; 34(11):1145-1160. PMC: 5465644. DOI: 10.1038/nbt.3711. View