Harnessing the biological complexity of Big Data from LINCS gene expression signatures

PloS One
Aliyu MusaFrank Emmert-Streib

Abstract

Gene expression profiling using transcriptional drug perturbations are useful for many biomedical discovery studies including drug repurposing and elucidation of drug mechanisms (MoA) and many other pharmacogenomic applications. However, limited data availability across cell types has severely hindered our capacity to progress in these areas. To fill this gap, recently, the LINCS program generated almost 1.3 million profiles for over 40,000 drug and genetic perturbations for over 70 different human cell types, including meta information about the experimental conditions and cell lines. Unfortunately, Big Data like the ones generated from the ongoing LINCS program do not enable easy insights from the data but possess considerable challenges toward their analysis. In this paper, we address some of these challenges. Specifically, first, we study the gene expression signature profiles from all cell lines and their perturbagents in order to obtain insights in the distributional characteristics of available conditions. Second, we investigate the differential expression of genes for all cell lines obtaining an understanding of condition dependent differential expression manifesting the biological complexity of perturbagents. As a resu...Continue Reading

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Datasets Mentioned

BETA
GSE92742

Methods Mentioned

BETA
gene knockdown

Software Mentioned

Reactome
Pert Service API
R L1000
LINCS
Cell
Library of Integrated Network - based Cellular Signatures ( LI...

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