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Fast, scalable and accurate differential expression analysis for single cells

Debarka Sengupta, Nirmala Arul Rayan, Michelle Lim, Bing Lim, Shyam Prabhakar
doi: https://doi.org/10.1101/049734
Debarka Sengupta
1Computational and Systems Biology, Genome Institute of Singapore, Singapore
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Nirmala Arul Rayan
1Computational and Systems Biology, Genome Institute of Singapore, Singapore
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Michelle Lim
1Computational and Systems Biology, Genome Institute of Singapore, Singapore
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Bing Lim
2Cancer Stem Cell Biology, Genome Institute of Singapore, Singapore
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Shyam Prabhakar
1Computational and Systems Biology, Genome Institute of Singapore, Singapore
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  • For correspondence: prabhakars@gis.a-star.edu.sg
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ABSTRACT

Analysis of single-cell RNA-seq data is challenging due to technical variability, high noise levels and massive sample sizes. Here, we describe a normalization technique that substantially reduces technical variability and improves the quality of downstream analyses. We also introduce a nonparametric method for detecting differentially expressed genes that scales to > 1,000 cells and is both more accurate and ~10 times faster than existing parametric approaches.

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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. All rights reserved. No reuse allowed without permission.
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Posted April 22, 2016.
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Fast, scalable and accurate differential expression analysis for single cells
Debarka Sengupta, Nirmala Arul Rayan, Michelle Lim, Bing Lim, Shyam Prabhakar
bioRxiv 049734; doi: https://doi.org/10.1101/049734
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Fast, scalable and accurate differential expression analysis for single cells
Debarka Sengupta, Nirmala Arul Rayan, Michelle Lim, Bing Lim, Shyam Prabhakar
bioRxiv 049734; doi: https://doi.org/10.1101/049734

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