PCM


FGMD	FGMD: A Novel Approach for Functional Gene Module Detection in Cancer

News	2017/10/22: Source codes and simulated data sets are updated.
Introduction	In this study, we propose a new functional gene module detection algorithm (FGMD), which is based on a hierarchical clustering algorithm that was modified to reflect actual biological observations, including the fact that a single gene may be involved in multiple biological pathways. Application of existing algorithms and the new FGMD algorithm to breast cancer and ovarian cancer datasets from The Cancer Genome Atlas showed that the FGMD algorithm had the best performance for most of the functional pathway enrichment tests and in the transcription factor enrichment test. We expect that the FGMD algorithm will contribute to improving the identification of functional gene modules related to cancer.
Paper	Daeyong Jin and Hyunju Lee. "FGMD: A Novel Approach for Functional Gene Module Detection in Cancer."
Source codes	Source codes implemented in r and simulation input data are available here. Instructions for running source codes are here. Note that the attached input and output files are not real data but have the same format with them. Required input files for each step are described in instructions.
Data sets	Ovarian cancer (OVC) and breast cancer (BRCA) data sets including the expression data of mRNA used in this paper were collected from TCGA.
Contacts	daeyongjin at naver.com, hyunjulee at gist.ac.kr

FGMD

FGMD: A Novel Approach for Functional Gene Module Detection in Cancer

News

2017/10/22: Source codes and simulated data sets are updated.

Introduction

In this study, we propose a new functional gene module detection algorithm (FGMD), which is based on a hierarchical clustering algorithm that was modified to reflect actual biological observations, including the fact that a single gene may be involved in multiple biological pathways. Application of existing algorithms and the new FGMD algorithm to breast cancer and ovarian cancer datasets from The Cancer Genome Atlas showed that the FGMD algorithm had the best performance for most of the functional pathway enrichment tests and in the transcription factor enrichment test. We expect that the FGMD algorithm will contribute to improving the identification of functional gene modules related to cancer.

Paper

Daeyong Jin and Hyunju Lee. "FGMD: A Novel Approach for Functional Gene Module Detection in Cancer."

Source codes

Source codes implemented in r and simulation input data are available here. Instructions for running source codes are here. Note that the attached input and output files are not real data but have the same format with them. Required input files for each step are described in instructions.

Data sets

Ovarian cancer (OVC) and breast cancer (BRCA) data sets including the expression data of mRNA used in this paper were collected from TCGA.

Contacts

daeyongjin at naver.com, hyunjulee at gist.ac.kr