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キーワード:
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要旨:
Sparse linear regression is often used to identify key transcriptional regulators by
predicting gene expression abundance from regulatory features such as transcription
factor (TF) binding or epigenomics data. However, a single linear model explaining
the gene expression of thousands of genes is limited in capturing the complexity of
cis-regulatory modules and gene co-expression patterns. Indeed, certain TFs are
known to act as both activators or repressors depending on associated cofactors and
neighbouring DNA-bound proteins. It is therefore desirable to identify clusters or
modules of co-regulated genes and model their regulatory profiles separately.
Finite mixtures of regression models are a popular tool for modeling hetero-
geneous data, while maintaining a linearity assumption. Unfortunately, they do
not take advantage of available data sets containing the molecular profiles of many
biological samples. We propose to combine the power of mixture modeling and
multi-task learning by using a penalized maximum likelihood framework for infer-
ring gene modules and regulators in multiple samples simultaneously. More specif-
ically, we regularize the likelihood function with a tree-structured L1/L2 penalty
to enable knowledge transfer between models of related cells. We optimize the
parameters of our models with a generalized EM algorithm. Experimental evalu-
ation of our method on synthetic data suggests that multi-task mixture modelling
is more suitable for identifying the true underlying cluster structure compared to a
single-task regression mixture model. Finally, we apply the model to a dataset from
the BLUEPRINT project consisting of various types of haematopoietic cells and
uncover interesting regulatory patterns.
