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Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data

Joseph N. Paulson, Cho-Yi Chen, Camila M. Lopes-Ramos, Marieke L Kuijjer, John Platig, Abhijeet R. Sonawane, Maud Fagny, Kimberly Glass, John Quackenbush
doi: https://doi.org/10.1101/081802
Joseph N. Paulson
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA
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Cho-Yi Chen
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA
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Camila M. Lopes-Ramos
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA
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Marieke L Kuijjer
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA
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John Platig
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA
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Abhijeet R. Sonawane
3Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02215, USA
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Maud Fagny
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA
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Kimberly Glass
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA
3Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02215, USA
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John Quackenbush
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA
3Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02215, USA
4Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
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  • For correspondence: johnq@jimmy.harvard.edu
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Abstract

Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data – critical first steps for any subsequent analysis. We find analysis of large RNA-Seq data sets requires both careful quality control and that one account for sparsity due to the heterogeneity intrinsic in multi-group studies. An R package instantiating our method for large-scale RNA-Seq normalization and preprocessing, YARN, is available at bioconductor.org/packages/yarn.

Highlights

  • Overview of assumptions used in preprocessing and normalization

  • Pipeline for preprocessing, quality control, and normalization of large heterogeneous data

  • A Bioconductor package for the YARN pipeline and easy manipulation of count data

  • Preprocessed GTEx data set using the YARN pipeline available as a resource

Copyright 
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-ND 4.0 International license.
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Posted October 20, 2016.
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Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data
Joseph N. Paulson, Cho-Yi Chen, Camila M. Lopes-Ramos, Marieke L Kuijjer, John Platig, Abhijeet R. Sonawane, Maud Fagny, Kimberly Glass, John Quackenbush
bioRxiv 081802; doi: https://doi.org/10.1101/081802
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Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data
Joseph N. Paulson, Cho-Yi Chen, Camila M. Lopes-Ramos, Marieke L Kuijjer, John Platig, Abhijeet R. Sonawane, Maud Fagny, Kimberly Glass, John Quackenbush
bioRxiv 081802; doi: https://doi.org/10.1101/081802

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