Representing high throughput expression profiles via perturbation barcodes reveals compound targets.

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 representative genes 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 r

SUBMITTER: Filzen TM 

PROVIDER: S-EPMC5300121 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature