Cellular State Transformations Using Deep Learning for Precision Medicine Applications

Patterns
Colin TargonskiF. Alex Feltus

Abstract

We introduce the Transcriptome State Perturbation Generator (TSPG) as a novel deep-learning method to identify changes in genomic expression that occur between tissue states using generative adversarial networks. TSPG learns the transcriptome perturbations from RNA-sequencing data required to shift from a source to a target class. We apply TSPG as an effective method of detecting biologically relevant alternate expression patterns between normal and tumor human tissue samples. We demonstrate that the application of TSPG to expression data obtained from a biopsy sample of a patient's kidney cancer can identify patient-specific differentially expressed genes between their individual tumor sample and a target class of healthy kidney gene expression. By utilizing TSPG in a precision medicine application in which the patient sample is not replicated (i.e., n = 1 ), we present a novel technique of determining significant transcriptional aberrations that can be used to help identify potential targeted therapies.

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Methods Mentioned

BETA
RNA-seq
biopsy

Software Mentioned

Genomic Data Commons
scikit
TSPG
Bioconductor
DESeq2
learn
STAR aligner
RSEM
Adam
AdvGAN

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