Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data

Biostatistics

Volumn 18, Issue 3, 2017, Pages 451-464

  Lun, Aaron T L ^a   Marioni, John C ^a,b,c  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^c WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

Author keywords

Differential expression; Plate effects; Single cell RNA sequencing; Summation

Indexed keywords

CELL COUNT; GENE EXPRESSION; RNA SEQUENCE; SIMULATION; GENE EXPRESSION PROFILING; HIGH THROUGHPUT SEQUENCING; HUMAN; NUCLEOTIDE SEQUENCE; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS; SOFTWARE; STATISTICAL MODEL;

RNA; TRANSCRIPTOME;

BASE SEQUENCE; GENE EXPRESSION PROFILING; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MODELS, STATISTICAL; RNA; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; SOFTWARE; TRANSCRIPTOME;

EID: 85029226561     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxw055     Document Type: Article

References (27)

1. ANDERS, S. and HUBER, W. (2010). Differential expression analysis for sequence count data. Genome Biology 11, R106.
2. BATES, D., MÄCHLER, M., BOLKER, B. and WALKER, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 1-48.
3. FINAK, G., MCDAV ID, A., YAJIMA, M., DENG, J., GERSUK, V., SHALEK, A. K., SLICHTER, C. K., MILLER, H. W., MCELRATH, M. J., PRLIC, M., LINSLEY, P. S. and OTHERS. (2015). MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biology 16, 278.
4. HICKS, S. C., TENG, M. and IRIZARRY, R. A. (2015). On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data. bioRxiv. doi:10.1101/025528.
5. JAITIN, D. A., KENIGSBERG, E., KEREN-SHAUL, H., ELEFANT, N., PAUL, F., ZARETSKY, I., MILDNER, A., COHEN, N., JUNG, S., TANAY, A. and OTHERS. (2014). Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343(6172), 776-779.
6. KHARCHENKO, P. V., SILBERSTEIN, L. and SCADDEN, D. T. (2014). Bayesian approach to single-cell differential expression analysis. Nature Methods 11(7), 740-742.
7. KLEIN, A. M., MAZUTIS, L., AKARTUNA, I., TALLAPRAGADA, N., VERES, A., LI, V., PESHKIN, L., WEITZ, D. A. and KIRSCHNER, M. W. (2015). Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161(5), 1187-1201.
8. KOLODZIEJCZYK, A. A., KIM, J. K., TSANG, J. C., ILICIC, T., HENRIKSSON, J., NATARAJAN, K. N., TUCK, A. C., GAO, X., BUHLER, M., LIU, P., MARIONI, J. C. and OTHERS. (2015). Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation. Cell Stem Cell 17(4), 471-485.
9. KORTHAUER, K. D., CHU, L. F., NEWTON, M. A., LI, Y., THOMSON, J., STEWART, R. and KENDZIORSKI, C. (2016). A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology 17(1), 222.
10. KOWALCZYK, M. S., TIROSH, I., HECKL, D., RAO, T. N., DIXIT, A., HAAS, B. J., SCHNEIDER, R. K., WAGERS, A. J., EBERT, B. L. and REGEV, A. (2015). Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Research 25(12), 1860-1872.
11. KUMAR, R. M., CAHAN, P., SHALEK, A. K., SATIJA, R., DALEYKEYSER, A. J., LI, H., ZHANG, J., PARDEE, K., GENNERT, D., TROMBETTA, J. J., FERRANTE, T. C., REGEV, A., DALEY, G. Q. and OTHERS. (2014). Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature 516(7529), 56-61.
12. LAW, C. W., CHEN, Y., SHI, W. and SMYTH, G. K. (2014). Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology 15, R29.
13. LOVE, M. I., HUBER, W. and ANDERS, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15(12), 550.
14. LUN, A. T., BACH, K. and MARIONI, J. C. (2016). Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biology, 17, 75.
15. LUND, S. P., NETTLETON, D., MCCARTHY, D. J. and SMYTH, G. K. (2012). Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Statistical Applications in Genetics and Molecular Biology 11(5), Article 8, doi:10.1515/1544-6115.1826.
16. MCCARTHY, D. J., CHEN, Y. and SMYTH, G. K. (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research 40(10), 4288-4297.
17. PHIPSON, B., LEE, S., MAJEWSKI, I. J., ALEXANDER, W. S. and SMYTH, G. K. (2016). Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Annals of Applied Statistics 10(2), 946-963.
18. PICELLI, S., FARIDANI, O. R., BJORKLUND, A. K., WINBERG, G., SAGASSER, S. and SANDBERG, R. (2014). Full-length RNA-seq from single cells using smart-seq2. Nature Protocols 9(1), 171-181.
19. POLLEN, A. A., NOWAKOWSKI, T. J., SHUGA, J., WANG, X., LEYRAT, A. A., LUI, J. H., LI, N., SZPANKOWSKI, L., FOWLER, B., CHEN, P., RAMALINGAM, N., SUN, G., THU, M., NORRIS, M., LEBOFSKY, R., TOPPANI, D., KEMP, D. W., WONG, M., CLERKSON, B., JONES, B. N., WU, S., KNUTSSON, L., ALVARADO, B., WANG, J., WEAVER, L. S., MAY, A. P., JONES, R. C., UNGER, M. A., KRIEGSTEIN, A. R. and OTHERS. (2014). Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nature Biotechnology 32(10), 1053-1058.
20. ROBINSON, M. D. and OSHLACK, A. (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology 11(3), R25.
21. ROBINSON, M. D., MCCARTHY, D. J. and SMYTH, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1), 139-140.
22. SMYTH, G. K., MICHAUD, J. and SCOTT, H. S. (2005). Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 21(9), 2067-2075.
23. STEGLE, O., TEICHMANN, S. A. and MARIONI, J. C. (2015). Computational and analytical challenges in single-cell transcriptomics. Nature Reviews Genetics 16(3), 133-145.
24. TRAPNELL, C., CACCHIARELLI, D., GRIMSBY, J., POKHAREL, P., LI, S., MORSE, M., LENNON, N. J., LIVAK, K. J., MIKKELSEN, T. S. and RINN, J. L. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology 32(4), 381-386.
25. TUNG, P.-Y., BLISCHAK, J. D., HSIAO, C., KNOWLES, D. A., BURNETT, J. E., PRITCHARD, J. K. and GILAD, Y. (2016). Batch effects and the effective design of single-cell gene expression studies. bioRxiv. doi:10.1101/062919.
26. VALLEJOS, C. A., RICHARDSON, S. and MARIONI, J. C. (2016). Beyond comparisons of means: understanding changes in gene expression at the single-cell level. Genome Biology 17(1), 70.
27. ZEILEIS, A., KLEIBER, C. and JACKMAN, S. (2008). Regression models for count data in R. Journal of Statistical Software 27(1), 1-25.