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Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis

Jean Fan, Neeraj Salathia, Rui Liu, Gwen Kaeser, Yun Yung, Joseph Herman, Fiona Kaper, Jian-Bing Fan, Kun Zhang, Jerold Chun, Peter V. Kharchenko
doi: https://doi.org/10.1101/026948
Jean Fan
1Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Neeraj Salathia
2Illumina Inc., San Diego, CA, USA
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Rui Liu
3Department of Bioengineering, University of California, San Diego, CA, USA
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Gwen Kaeser
4Department of Molecular and Cellular Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, CA, USA
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Yun Yung
4Department of Molecular and Cellular Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, CA, USA
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Joseph Herman
1Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Fiona Kaper
2Illumina Inc., San Diego, CA, USA
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Jian-Bing Fan
2Illumina Inc., San Diego, CA, USA
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Kun Zhang
3Department of Bioengineering, University of California, San Diego, CA, USA
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Jerold Chun
4Department of Molecular and Cellular Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, CA, USA
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Peter V. Kharchenko
1Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Abstract

Single-cell transcriptome measurements are being applied at rapidly increasing scales to study cellular repertoires underpinning functions of complex tissues and organs, including mammalian brains. The transcriptional state of each cell, however, reflects a variety of biological factors, including persistent cell-type specific regulatory configurations, transient processes such as cell cycle, local metabolic demands, and extracellular signals. Depending on the biological setting, all such aspects of transcriptional heterogeneity can be of potential interest, but detecting complex heterogeneity structure from inherently uncertain single-cell data presents analytical challenges. We developed PAGODA to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by identifying known pathways or novel gene sets that show significant excess of coordinated variability among the measured cells. We demonstrate that PAGODA effectively recovers the subpopulations and their corresponding functional characteristics in a variety of single-cell samples, and use it to characterize transcriptional diversity of neuronal progenitors in the developing mouse cortex.

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Posted September 16, 2015.
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Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis
Jean Fan, Neeraj Salathia, Rui Liu, Gwen Kaeser, Yun Yung, Joseph Herman, Fiona Kaper, Jian-Bing Fan, Kun Zhang, Jerold Chun, Peter V. Kharchenko
bioRxiv 026948; doi: https://doi.org/10.1101/026948
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Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis
Jean Fan, Neeraj Salathia, Rui Liu, Gwen Kaeser, Yun Yung, Joseph Herman, Fiona Kaper, Jian-Bing Fan, Kun Zhang, Jerold Chun, Peter V. Kharchenko
bioRxiv 026948; doi: https://doi.org/10.1101/026948

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