We present immgenT-GP, a gene-program framework for resolving mouse T cell heterogeneity across the immgenT atlas. On ~ 800,000 T cells spanning lineages, organs, and immune challenges, we defined 200 reproducible gene programs, discovered by empirical Bayes matrix factorization approach and validated through a new deep learning approach, that capture major axes of T cell variation, including lineage identity, activation states and tissue location. Gene-program analysis complemented cluster-based annotation by decomposing T cell states into molecular modules, revealing quantitative, shared, modules not represented with discrete labels alone. Across tissues, gene programs reflected both tissue-imposed programs and changes in cluster composition. Integrating GP activity with cell-surface marker expression from the CITE-seq data, revealed that markers can report different programs depending on lineage and context. Together, immgenT-GP extends the atlas from a map of T cell states to a molecular reference of the programs that underlie them.
Zhang, Z., Wang, T., Panigrahi, S. S., Carbonetto, P., Stephens, M., Benoist, C., Mostafavi, S., Brbic, M., Zemmour, D., the immgenT Project
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