  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.
