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PAUSE: principled feature attribution for unsupervised gene expression analysis.

ABSTRACT: 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 transcriptomic variation when combined with biologically-constrained neural network models.

SUBMITTER: Janizek JD

PROVIDER: S-EPMC10114348 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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PAUSE: principled feature attribution for unsupervised gene expression analysis.

Janizek Joseph D JD Spiro Anna A Celik Safiye S Blue Ben W BW Russell John C JC Lee Ting-I TI Kaeberlin Matt M Lee Su-In SI

Genome biology 20230419 1

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 usefu ...[more]

PMID: 37076856

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