SMRI Seminar: Moran -- From causal representation learning to multi-study genomics

SMRI Seminar 

From causal representation learning to multi-study genomics 

Speaker: Gemma Moran, Rutgers University 

Date and time: Thursday 5th November, 1.00 - 2.00pm 

Location: SMRI Seminar Room (Macleay A12 Room 301) 

Abstract: In this talk, we begin with an introduction to causal representation learning,
an emerging area which combines latent variable modeling, causal graphical models, and
nonparametric statistics and deep learning.  We then introduce nonlinear multi-study
factor analysis, where the goal is to identify the graphical structure relating latent
factors to high-dimensional observed data, while separating components that are shared
across studies from those that are study-specific.  As a particular example, we consider
platelet gene expression data from patients in different disease groups.  In this data,
factors correspond to clusters of genes which are co-expressed; we may expect some
clusters (or biological pathways) to be active for all diseases, while some clusters are
only active for a specific disease.  To learn such structure, we propose a nonlinear
multi-study sparse factor model, in which each observed feature depends on only a small
subset of latent factors.  In the genomics example, this corresponds to the assumption
that each gene is active in only a few biological processes.  Under an anchor
assumption, we prove that the latent factors are identified up to permutations and
element-wise transformations.  Further, we demonstrate our method recovers meaningful
factors in the platelet gene expression data.