Overcoming Obstacles to Analyzing Complex Biological Data
Biomedical research data generated from genomics analyses, imaging, biochemistry and other assays are abundant yet difficult to integrate using conventional approaches and databases. To address this need, Dr. Peter Sorger and colleagues at Harvard Medical School, Massachusetts Institute of Technology, and the University of Applied Sciences in Germany, researchers supported through the Common Fund’s Library of Integrated Network Based Cellular Signatures (LINCS) program, have developed an innovative new adaptable method that allows different types of complex data sets to be stored, analyzed and extended. In a recent paper in Nature Methods, they demonstrate the utility of the approach, which exploits useful aspects of two data file formats (HDF5 and XML), for analyzing a complex imaging data set reflecting 160 experimental conditions in over a million different single cells. The approach led to the discovery of new pharmacological relationships between compounds that bind and inhibit epidermal growth factor receptors (EGFR), providing insights into cell-to-cell variability in response to drugs.
Millard BL, Niepel M, Menden MP, Muhlich JL, and Sorger PK. Adaptive informatics for multifactorial and high-content biological data. Nat Methods. 2011. Vol 8(6):487-493.