Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease

<p>Collection of auxiliary data to reproduce the results from &quot;Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease&quot;.</p> <p>Source code is available at:&nbsp;https://github.com/saezlab/MOFAcell</p> <p>Exceptions: Spatial data is excluded</p> <p>This folder contains processed data of the following publications, when using the data cite accordingly:<br> 1) Kuppe C, Ramirez Flores RO, Li Z, Hayat S, Levinson RT, Liao X, Hannani MT, Tanevski J, W&uuml;nnemann F, Nagai JS, et al (2022) Spatial multi-omic map of human myocardial infarction. Nature 608: 766&ndash;777<br> 2)&nbsp;Ramirez Flores RO, Lanzer JD, Holland CH, Leuschner F, Most P, Schultz J-H, Levinson RT &amp; Saez-Rodriguez J (2021) Consensus Transcriptional Landscape of Human End-Stage Heart Failure. J Am Heart Assoc 10: e019667<br> 3) Reichart D, Lindberg EL, Maatz H, Miranda AMA, Viveiros A, Shvetsov N, G&auml;rtner A, Nadelmann ER, Lee M, Kanemaru K, et al (2022) Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies. Science 377: eabo1984<br> 4)&nbsp;Chaffin M, Papangeli I, Simonson B, Akkad A-D, Hill MC, Arduini A, Fleming SJ, Melanson M, Hayat S, Kost-Alimova M, et al (2022) Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy. Nature 608: 174&ndash;180&nbsp;</p>