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On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data

Stephanie C. Hicks, Mingxiang Teng, Rafael A. Irizarry
doi: https://doi.org/10.1101/025528
Stephanie C. Hicks
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute
2Department of Biostatistics, Harvard School of Public Health
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Mingxiang Teng
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute
2Department of Biostatistics, Harvard School of Public Health
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Rafael A. Irizarry
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute
2Department of Biostatistics, Harvard School of Public Health
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Abstract

Single-cell RNA-Sequencing (scRNA-Seq) has become the most widely used high-throughput method for transcription profiling of individual cells. Systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies. Surprisingly, these issues have received minimal attention in published studies based on scRNA-Seq technology. We examined data from five published studies and found that systematic errors can explain a substantial percentage of observed cell-to-cell expression variability. Specifically, we found that the proportion of genes reported as expressed explains a substantial part of observed variability and that this quantity varies systematically across experimental batches. Furthermore, we found that the implemented experimental designs confounded outcomes of interest with batch effects, a design that can bring into question some of the conclusions of these studies. Finally, we propose a simple experimental design that can ameliorate the effect of theses systematic errors have on downstream results.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 25, 2015.
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On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data
Stephanie C. Hicks, Mingxiang Teng, Rafael A. Irizarry
bioRxiv 025528; doi: https://doi.org/10.1101/025528
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On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data
Stephanie C. Hicks, Mingxiang Teng, Rafael A. Irizarry
bioRxiv 025528; doi: https://doi.org/10.1101/025528

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