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Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling
Romain Lopez · Nataša Tagasovska · Stephen Ra · Kyunghyun Cho · Jonathan Pritchard · Aviv Regev
Event URL: https://openreview.net/forum?id=gdTXCy7fZf7 »

Latent variable models have become a go-to tool for analyzing biological data, especially in the field of single-cell genomics. One remaining challenge is the identification of individual latent variables related to biological pathways, more generally conceptualized as disentanglement. Although versions of variational autoencoders that explicitly promote disentanglement were introduced and applied to single-cell genomics data, the theoretical feasibility of disentanglement from independent and identically distributed measurements has been challenged.Recent methods propose instead to leverage non-stationary data, as well as the sparse mechanism assumption in order to learn disentangled representations, with a causal semantic. Here, we explore the application of these methodological advances in the analysis of single-cell genomics data with genetic or chemical perturbations. We benchmark these methods on simulated single cell expression data to evaluate their performance regarding disentanglement, causal target identification and out-of-domain generalisation. Finally, by applying the approaches to a large-scale gene perturbation dataset, we find that the model relying on the sparse mechanism shift hypothesis surpasses contemporary methods on a transfer learning task.

Author Information

Romain Lopez (Genentech & Stanford University)
Nataša Tagasovska (Prescient Design, Genentech)
Stephen Ra (Prescient Design / Genentech)
Kyunghyun Cho (Genentech / NYU)

Kyunghyun Cho is an associate professor of computer science and data science at New York University and a research scientist at Facebook AI Research. He was a postdoctoral fellow at the Université de Montréal until summer 2015 under the supervision of Prof. Yoshua Bengio, and received PhD and MSc degrees from Aalto University early 2014 under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.

Jonathan Pritchard (Stanford)
Aviv Regev (Genentech)

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