bioRxiv preprint doi: https://doi.org/10.1101/2021.03.09.433671; this version posted March 10, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Trajectory inference across multiple conditions with condiments: 2 differential topology, progression, differentiation, and expression 1;2 3;4 3 Hector Roux de B´ezieux , Koen Van den Berge , Kelly Street5;6;7;8, Sandrine Dudoit1;2;3;7;8 1 Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA 2 Center for Computational Biology, University of California, Berkeley, CA, USA 3 Department of Statistics, University of California, Berkeley, CA, USA 4 Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium 5 Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA 6 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA 7 These authors contributed equally. 8 To whom correspondence should be addressed:
[email protected],
[email protected] 4 March 9, 2021 5 Abstract 6 In single-cell RNA-sequencing (scRNA-seq), gene expression is assessed individually for each cell, 7 allowing the investigation of developmental processes, such as embryogenesis and cellular differenti- 8 ation and regeneration, at unprecedented resolutions. In such dynamic biological systems, grouping 9 cells into discrete groups is not reflective of the biology. Cellular states rather form a continuum, 10 e.g., for the differentiation of stem cells into mature cell types.