Single Cell Research

Recent advances in single-cell sequencing hold great potential for exploring biological systems with unprecedented resolution."- Grun & van Oudenaarden 2015"

PINS+ provides a robust approach for data integration and disease subtyping. It allows for unsupervised clustering using multi-omics data. The method automatically determines the optimal number of clusters and then partitions the samples in a way such that the results are robust to noise and data perturbation. PINS+ has been validated on thousands of cancer samples obtained from the Gene Expression Omnibus, the Broad Institute, The Cancer Genome Atlas (TCGA), and the European Genome-Phenome Archive. The approach can accurately identify known subtypes and discover novel groups of patients with significantly different survival profiles. The software is extremely fast and able to cluster hundreds of matched samples in minutes.

Tin Nguyen
Assistant Professor