Deep generative model deciphers derailed trajectories in acute myeloid leukemia

Nazaret, A.; Fan, J. L.; Lavallee, V.-P.; Cornish, A. E.; Kiseliovas, V.; Masilionis, I.; Chun, J.; Bowman, R. L.; Eisman, S. E.; Wang, J.; Shi, L.; Levine, R. L.; Mazutis, L.; Blei, D.; Pe'er, D.; Azizi, E.

doi:10.1101/2023.11.11.566719

bioRxiv · 10.1101/2023.11.11.566719

Deep generative model deciphers derailed trajectories in acute myeloid leukemia

Nazaret, A.·Fan, J. L.·Lavallee, V.-P.·Cornish, A. E.·Kiseliovas, V.·Masilionis, I.·Chun, J.·Bowman, R. L.·Eisman, S. E.·Wang, J.·Shi, L.·Levine, R. L.·Mazutis, L.·Blei, D.·Pe'er, D.·Azizi, E.

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Abstract

Biological insights often depend on comparing conditions such as disease and health, yet we lack effective computational tools for integrating single-cell genomics data across conditions or characterizing transitions from normal to deviant cell states. Here, we present Decipher, a deep generative model that characterizes derailed cell-state trajectories. Decipher jointly models and visualizes gene expression and cell state from normal and perturbed single-cell RNA-seq data, revealing shared and disrupted dynamics. We demonstrate its superior performance across diverse contexts, including in pancreatitis with oncogene mutation, acute myeloid leukemia, and gastric cancer.

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Nazaret, A., Fan, J. L., Lavallee, V.-P., Cornish, A. E., Kiseliovas, V., Masilionis, I., Chun, J., Bowman, R. L., Eisman, S. E., Wang, J., Shi, L., Levine, R. L., Mazutis, L., Blei, D., Pe'er, D., Azizi, E.. 2023-11-15. Deep generative model deciphers derailed trajectories in acute myeloid leukemia. https://doi.org/10.1101/2023.11.11.566719

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