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

• Published in: PloS one Vol. 13; no. 8; p. e0201937
• Main Authors: Musa, Aliyu, Tripathi, Shailesh, Kandhavelu, Meenakshisundaram, Dehmer, Matthias, Emmert-Streib, Frank
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 29.08.2018; Public Library of Science (PLoS)
• Subjects: Big Data; Bioinformatics; Biological complexity; Biology; Biology and Life Sciences; Biotechnology; Cancer; Cell cycle; Cell Line; Cell lines; Complexity; Data analysis; Data management; Databases, Genetic; Design of experiments; Experimental design; Experiments; Gene expression; Genomes; Humans; Laboratories; Medical research; Medicine and Health Sciences; Pharmacogenetics - methods; Pharmacogenomic Variants; Pharmacogenomics; Pharmacology; Physical Sciences; R&D; Research & development; Research and analysis methods; Signal processing; Software; Stress, Physiological - genetics; Transcription; Transcriptome; Austria; Finland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0201937

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 result, our analysis helps the experimental design of follow-up studies, e.g., by selecting appropriate cell lines.