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MultiPLIER Fileset

Version 2 2019-01-13, 18:43

Version 1 2018-08-19, 16:19

dataset

posted on 2019-01-13, 18:43 authored by Jaclyn TaroniJaclyn Taroni, Qiwen HuQiwen Hu, Casey GreeneCasey Greene

This is the code and data from the MultiPLIER project v0.2.0 (https://github.com/greenelab/multi-plier/tree/v0.2.0). Please see the GitHub repository for more information, including code licensing information.

Please see https://github.com/greenelab/rheum-plier-data for more information about individual datasets and citations and cite the original publications as appropriate.

If you use any PLIER models, please be sure to cite:

Mao W, Harmann B, Sealfon SC, Zaslavsky E, Chikina M. Pathway-Level Information ExtractoR (PLIER) for gene expression data. bioRxiv. 2017. DOI: 10.1101/116061

If you use any recount2 data or models trained on these data, please be sure to cite:

Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA, Hansen KD, Jaffe AE, Langmead B and Leek JT (2017). "Reproducible RNA-seq analysis using recount2." Nature Biotechnology. doi: 10.1038/nbt.3838

All recount2 data is CC-BY, recount2 authors.

Funding

This work was supported in part by grants to Casey S. Greene from the Gordon and Betty Moore Foundation (GBMF 4552), the National Institutes of Health (R01 HG010067), the Chan Zuckerberg Initiative Donor Advised Fund of the Silicon Valley Community Foundation (2018-182718), and Alex’s Lemonade Stand Foundation (Childhood Cancer Data Lab). This work was supported in part by The Vasculitis Clinical Research Consortium (VCRC) (U54 AR057319 and R01 AR064153) which is part of the Rare Diseases Clinical Research Network (RDCRN), an initiative of the Office of Rare Diseases Research (ORDR), National Center for Advancing Translational Science (NCATS). The VCRC is funded through collaboration between NCATS, and the National Institute of Arthritis and Musculoskeletal and Skin Diseases, and has received funding from the National Center for Research Resources (U54 RR019497). Jaclyn N. Taroni received support from T32 AR007442.

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machine learning dataset gene expression approach Bioinformatics Computational Biology

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CC BY 4.0

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