Fuzzy Inference of Gene-Sets (FIGS): A fuzzy c-means clustering based package for data-driven contex
By taking advantage of the availability of a large number of sequenced genomes in public depositories and the strength of modern computational methods, we have developed robust computational package named 'Fuzzy Inference of Gene-Sets (FIGS)' to understand context specific gene regulation. FIGS apply fuzzy c-means clustering to find gene-sets from cell-type and pathogen specific transcriptomic datasets. Specifically, we apply our pipeline to derive gene-sets from transcriptomic data measuring response of monocyte derived dendritic cells and A549 epithelial cells to influenza infections. In future, FIGS package will be updated to accommodate other immune cell-types. This pipeline can be easily adapted for other pathogens for better understanding the gene regulatory mechanism in normal and disease development. A user friendly GUI version of this package is now available for testing. The package can be downloaded from GitHub (here).
Khan, Atif, Dejan Katanic, and Juilee Thakar. "Meta-analysis of cell-specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes." BMC bioinformatics 18, no. 1 (2017): 295.