CAsubtype is a flexible and well-integrated tool with simple R commands to identify gene sets for cancer subtype identification and clinical outcome prediction. By integrating more than 13,000 annotated gene sets, CAsubtype provides a comprehensive repertoire of candidates for new cancer subtype identification. For easy data access, CAsubtype further includes the gene expression and clinical data of more than 2,000 cancer patients from TCGA. CAsubtype first employs principal component analysis to identify gene sets (from user-provided or package-integrated ones) with robust principal components representing significantly large variation between cancer samples. Based on these principal components, CAsubtype visualizes the sample distribution in low-dimensional space for better understanding of the distinction between samples and classifies samples into subgroups with prevalent clustering algorithms. Finally, CAsubtype performs survival analysis to compare the clinical outcomes between the identified subgroups, assessing their clinical value as potentially novel cancer subtypes.
Author: Hualei Kong, Hua Li
Maintainer: Hualei Kong (firstname.lastname@example.org)
PANDA (Preferential Attachment based common Neighbor Distribution derived Associations) is designed to perform the following tasks in PPI networks: (1) identify significantly functionally associated protein pairs, (2) predict GO terms and KEGG pathways for proteins, (3) make a cluster of proteins based on the significant protein pairs, (4) identify subclusters whose members are enriched in KEGG pathways. For other types of biological networks, (1) and (3) can still be performed. For more details of the package, please refer to the paper "PAND: a distribution to identify functional linkage from networks with preferential attachment property", or contact Dr. Hua Li (email@example.com).
Author: Hua Li and Pan Tong
Maintainer: Hua Li (firstname.lastname@example.org)
dsPIG (deep sequencing-based Prediction of Imprinted Genes) is a Bayesian model developed to predict imprinted genes from the RNA-Seq data of multiple independent tissues (the type of tissues may be the same, or different). dsPIG is applicable to all mammals with genomic imprinting. For more details of this model, please refer to the paper "dsPIG: a tool to predict imprinted genes from the deep sequencing of whole transcriptomes", or contact Dr. Hua Li (email@example.com).
Author: Hua Li, Xiao Su and Shoudan Liang
Maintainer: Xiao Su (firstname.lastname@example.org)
Depends: R (>= 2.10.0)
License: GPL (>= 2)