Conditionally Invariant Representation Learning for Disentangling
  Cellular Heterogeneity
Hananeh Aliee Ferdinand Kapl Soroor Hediyeh-Zadeh Fabian J. Theis
This paper presents a novel approach that leverages domain variability to
learn representations that are conditionally invariant to unwanted variability
or distractors. Our approach identifies both spurious and invariant latent
features necessary for achieving accurate reconstruction by placing distinct
conditional priors on latent features. The invariant signals are disentangled
from noise by enforcing independence which facilitates the construction of an
interpretable model with a causal semantic. By exploiting the interplay between
data domains and labels, our method simultaneously identifies invariant
features and builds invariant predictors. We apply our method to grand
biological challenges, such as data integration in single-cell genomics with
the aim of capturing biological variations across datasets with many samples,
obtained from different conditions or multiple laboratories. Our approach
allows for the incorporation of specific biological mechanisms, including gene
programs, disease states, or treatment conditions into the data integration
process, bridging the gap between the theoretical assumptions and real
biological applications. Specifically, the proposed approach helps to
disentangle biological signals from data biases that are unrelated to the
target task or the causal explanation of interest. Through extensive
benchmarking using large-scale human hematopoiesis and human lung cancer data,
we validate the superiority of our approach over existing methods and
demonstrate that it can empower deeper insights into cellular heterogeneity and
the identification of disease cell states.