Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes.
Atif KhanDejan KatanicJuilee ThakarPublished in: BMC bioinformatics (2017)
We validate our gene-sets and demonstrate that by identifying genes associated with multiple gene-sets, FCM clustering algorithm significantly improves interpretation of transcriptomic data facilitating investigation of novel biological processes by leveraging on transcriptomic data available in the public domain. We develop an interactive 'Fuzzy Inference of Gene-sets (FIGS)' package (GitHub: https://github.com/Thakar-Lab/FIGS ) to facilitate use of of pipeline. Future extension of FIGS across different immune cell-types will improve mechanistic investigation followed by high-throughput omics studies.
Keyphrases
- single cell
- rna seq
- high throughput
- genome wide
- genome wide identification
- electronic health record
- systematic review
- copy number
- big data
- machine learning
- sars cov
- dna methylation
- case control
- stem cells
- randomized controlled trial
- neural network
- deep learning
- meta analyses
- transcription factor
- drug delivery
- gene expression
- artificial intelligence
- bone marrow
- mesenchymal stem cells