We present a novel method for automated identification of putative cell types from single-cell RNA-seq (scRNA-seq) data.
no code implementations • 28 Feb 2020 • Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, MAQC Society Board, Levi Waldron, Bo wang, Chris McIntosh, Anshul Kundaje, Casey S. Greene, Michael M. Hoffman, Jeffrey T. Leek, Wolfgang Huber, Alvis Brazma, Joelle Pineau, Robert Tibshirani, Trevor Hastie, John P. A. Ioannidis, John Quackenbush, Hugo J. W. L. Aerts
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening.
no code implementations • 31 Oct 2019 • Anja Füllgrabe, Nancy George, Matthew Green, Parisa Nejad, Bruce Aronow, Laura Clarke, Silvie Korena Fexova, Clay Fischer, Mallory Ann Freeberg, Laura Huerta, Norman Morrison, Richard H. Scheuermann, Deanne Taylor, Nicole Vasilevsky, Nils Gehlenborg, John Marioni, Sarah Teichmann, Alvis Brazma, Irene Papatheodorou
Single-cell RNA-Sequencing (scRNA-Seq) has undergone major technological advances in recent years, enabling the conception of various organism-level cell atlassing projects.
For retrieval of gene expression experiments, we use a probabilistic model called product partition model, which induces a clustering of genes that show similar expression patterns across a number of samples.