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PAUSE: principled feature attribution for unsupervised gene expression analysis
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DOI：10.1186/s13059-023-02901-4.png)
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摘要
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As interest in using unsupervised deep learning models to analyze gene expression data has grown, an increasing number of methods have been developed to make these models more interpretable. These methods can be separated into two groups: post hoc analyses of black box models through feature attribution methods and approaches to build inherently interpretable models through biologically-constrained architectures. We argue that these approaches are not mutually exclusive, but can in fact be usefully combined. We propose PAUSE (https://github.com/suinleelab/PAUSE), an unsupervised pathway attribution method that identifies major sources of transcrip-tomic variation when combined with biologically-constrained neural network models.
Keyword:
Transcriptomics
Gene expression
Deep learning
Explainable AI
Unsupervised learning
Feature attribution
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