Published January 31, 2020 | Version v1
Journal article Open

A Bayesian approach to accurate and robust signature detection on LINCS L1000 data

Authors/Creators

  • 1. Ph.D. Program in Biology, The Graduate Center, The City University of New York, New York, NY 10016, USA
  • 2. Department of Astronomy, Columbia University, New York, NY 10027, USA
  • 3. Ph.D. Program in Biochemistry and 4 Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY 10016, USA
  • 4. 1 Ph.D. Program in Biology, The Graduate Center, The City University of New York, New York, NY 10016, USA, 2 Department of Astronomy, Columbia University, New York, NY 10027, USA, 3 Ph.D. Program in Biochemistry and 4 Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY 10016, USA, 5 Department of Computer Science, Hunter College, The City University of New York, New York, NY 10016, USA and 6 Helen and Robert Appel Alzheimer's Disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA

Description

LINCS L1000 Phase I (GSE92742) & Phase II (GSE70138) datasets generated by our pipeline are currently available.  Unless you are interested in managing z-score inference and combination, we encourage you to use combined z-scores by bio-replicates (Level 5 data).

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