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A robust statistical framework to detect multiple sources of hidden variation in single-cell transcriptomes

Donghyung Lee, Anthony Cheng, Duygu Ucar
doi: https://doi.org/10.1101/151217
Donghyung Lee
1 The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, Unites States of America,
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  • For correspondence: donghyung.lee@jax.org duygu.ucar@jax.org
Anthony Cheng
1 The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, Unites States of America,
2 University of Connecticut Health Center, Farmington, Connecticut, Unites States of America
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Duygu Ucar
1 The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, Unites States of America,
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  • For correspondence: donghyung.lee@jax.org duygu.ucar@jax.org
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Abstract

Single-cell RNA-Sequencing data often harbor variation from multiple correlated sources, which cannot be accurately detected by existing methods. Here we present a novel and robust statistical framework that can capture correlated sources of variation in an iterative fashion: iteratively adjusted surrogate variable analysis (IA-SVA). We demonstrate that IA-SVA accurately captures hidden variation in single cell RNA-Sequencing data arising from cell contamination, cell-cycle stage, and differences in cell types along with the marker genes associated with the source.

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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-NC-ND 4.0 International license.
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Posted June 18, 2017.
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A robust statistical framework to detect multiple sources of hidden variation in single-cell transcriptomes
Donghyung Lee, Anthony Cheng, Duygu Ucar
bioRxiv 151217; doi: https://doi.org/10.1101/151217
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A robust statistical framework to detect multiple sources of hidden variation in single-cell transcriptomes
Donghyung Lee, Anthony Cheng, Duygu Ucar
bioRxiv 151217; doi: https://doi.org/10.1101/151217

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