Alsu Missarova

miloDE is a cluster-free framework for differential expression (DE) testing that leverages a graph representation of scRNA-seq data (Fig. 1). To construct a graph recapitulating distances between cells, we require count matrices and a pre-calculated joint latent embedding across all the replicates from tested conditions (Fig. 1, step 1).

New Results doi: https://doi.org/10.1101/2023.03.08.531744 Abstract Comparing molecular features, including the identification of genes with differential expression (DE) between conditions, is a powerful approach for characterising disease-specific phenotypes. When testing for DE in single-cell RNA sequencing data, current pipelines first assign cells into discrete clusters (or cell types), followed by testing for differences within each cluster.

Alsu Missarova1,2, Jaison Jain3, Andrew Butler3,4, Shila Ghazanfar2, Tim Stuart3,4, Maigan Brusko5, Clive Wasserfall5, Harry Nick6, Todd Brusko5, Mark Atkinson5, Rahul Satija3,4 & John C.