Perturbation Clustering for Data Integration and Disease
Subtyping
Hung Nguyen <hungnp@nevada.unr.edu>, Sangam Shrestha <sangam@nevada.unr.edu> and Tin Nguyen <tinn@unr.edu>
Clustering algorithm for determining the number of clusters and location of each sample in the clusters. Perturbation clustering for data INtegration and disease Subtyping (PINS - Nguyen T, Tagett R, Diaz D, Draghici S (2017) <doi:10.1101/gr.215129.116>) is a novel approach for integration of data and classification of diseases into various subtypes. PINS supports both single and multiple data types. 'PINSPlus' is designed to be user-friendly and primarily makes use of two main functions. 'PINSPlus' fasten PINS algorithm by supporting parallel processing and using efficient stopping criteria.
R (>= 2.10), cluster, entropy, parallel, pbmcapply, doParallel

Clustering Algorithm for Data Integration and Disease Subtyping
Hung Nguyen, Sangam Shrestha and Tin Nguyen
Provides a robust approach for omics data integration and disease subtyping. It is an improved implementation of PINS - Perturbation clustering for data INtegration and disease Subtyping (Nguyen T, Tagett R, Diaz D, Draghici S, Genome Research, 2017, <doi:10.1101/gr.215129.116>). 'PINSPlus' supports both single and multiple data types. The software 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. 'PINSPlus' is fast and it supports parallel computing on Windows, Linux, and Mac OS.

Provides a robust approach for omics data integration and disease subtyping. 'PINSPlus' supports both single and multiple data types. The software 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. 'PINSPlus' is fast and it supports parallel computing on Windows, Linux, and Mac OS.

Bang Tran, Duc Tran, Hung Nguyen and Tin Nguyen
Provides a robust approach for omics data integration and disease subtyping. 'PINSPlus' supports both single and multiple data types. The software 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 (Hung Nguyen (2018) <doi:10.1093/bioinformatics/bty1049>, Tin Nguyen (2017) <doi:10.1101/gr.215129.116>). 'PINSPlus' is fast and it supports parallel computing on Windows, Linux, and Mac OS.
foreach, entropy , doParallel, matrixStats, RcppParallel, FNN,
cluster