Representing high throughput expression profiles via perturbation barcodes reveals compound targets

TL;DR

This paper introduces a deep learning-based perturbation barcode method for high throughput gene expression data that improves biological signal detection, reveals compound targets, and enhances functional annotation of unknown compounds.

Contribution

The study presents a novel deep learning approach to convert gene expression profiles into perturbation barcodes, outperforming raw data in biological insight extraction and compound target prediction.

Findings

Barcode captures compound structure and target information.

Better than raw data at predicting compound promiscuity.

Enables more sensitive functional assignment of compounds.

Abstract

High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in scope, it is important to homogenize, summarize, and analyze the resulting data in a manner that captures significant biological signals in spite of various noise sources such as batch effects and stochastic variation. We used the L1000 platform for large-scale profiling of 978 genes, chosen to be representative of the genome as whole, across thousands of compound treatments. Here, a method is described that uses deep learning techniques to convert the expression changes of the landmark genes into a perturbation barcode that reveals important features of the underlying data, performing better than the raw data in revealing important biological insights. The barcode captures compound structure and target information, in addition to predicting a compound's high throughput screening promiscuity, to a higher degree than the original data measurements, indicating that the approach uncovers underlying factors of the expression data that are otherwise entangled or masked by noise. Furthermore, we demonstrate that visualizations derived from the perturbation barcode can be used to more sensitively assign functions to unknown compounds through a guilt-by-association approach, which we use to predict and experimentally validate the activity of compounds on the MAPK pathway. The demonstrated application of deep metric learning to large-scale chemical genetics projects highlights the utility of this and related approaches to the extraction of insights and testable hypotheses from big, sometimes noisy data.