Systematic Quality Control Analysis of LINCS Data

The Library of Integrated Cellular Signatures (LINCS) project provides comprehensive transcriptome profiling of human cell lines before and after chemical and genetic perturbations. Its L1000 platform utilizes 978 landmark genes to infer the transcript levels of 14,292 genes computationally. Here we conducted the L1000 data quality control analysis by using MCF7, PC3, and A375 cell lines as representative examples. Before perturbations, a promising 80% correlation in transcriptome was observed between L1000‐ and Affymetrix HU133A‐platforms. After library‐based shRNA perturbations, a moderate 30% of differentially expressed genes overlapped between any two selected controls viral vectors using the L1000 platform. The mitogen‐activated protein kinase, vascular endothelial growth factor, and T‐cell receptor pathways were identified as the most significantly shared pathways between chemical and genetic perturbations in cancer cells. In conclusion, L1000 platform is reliable in assessing transcriptome before perturbation. Its response to perturbagens needs to be interpreted with caution. A quality control analysis pipeline of L1000 is recommended before addressing biological questions.