CIT: identification of differentially expressed clusters of genes from microarray data

被引:22
|
作者
Rhodes, DR
Miller, JC
Haab, BB
Furge, KA [1 ]
机构
[1] Van Andel Res Inst, Mol Oncol Lab, Grand Rapids, MI 49053 USA
[2] Van Andel Res Inst, Lab DNA & Prot Microarray Technol, Grand Rapids, MI 49053 USA
关键词
D O I
10.1093/bioinformatics/18.1.205
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Sum: Cluster Identification Tool (CIT) is a microarray analysis program that identifies differentially expressed genes. Following division of experimental samples based on a parameter of interest, CIT uses a statistical discrimination metric and permutation analysis to identify clusters of genes or individual genes that best differentiate between the experimental groups. CIT integrates with the freely available CLUSTER and TREEVIEW programs to form a more complete microarray analysis package. Availability: A Windows(R) binary executable is freely available at http://www.vai.org/vari/bioinformatics.htm Contact: Kyle.Furge@vai.org Supplementary information: http://www.vai.org/vari/ bioinfomatics.htm.
引用
收藏
页码:205 / 206
页数:2
相关论文
共 50 条
  • [1] Density based pruning for identification of differentially expressed genes from microarray data
    Hu, Jianjun
    Xu, Jia
    BMC GENOMICS, 2010, 11
  • [2] Density based pruning for identification of differentially expressed genes from microarray data
    Jianjun Hu
    Jia Xu
    BMC Genomics, 11
  • [3] Identification of differentially expressed genes in microarray data in a principal component space
    Ospina, Luis
    Lopez-Kleine, Liliana
    SPRINGERPLUS, 2013, 2 : 1 - 11
  • [4] Identification of differentially expressed spatial clusters using humoral response microarray data
    Wu, Jincao
    Patwa, Tasneem H.
    Lubman, David M.
    Ghosh, Debashis
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2009, 53 (08) : 3094 - 3102
  • [5] A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data
    Kayvan Najarian
    Maryam Zaheri
    Ali A Rad
    Siamak Najarian
    Javad Dargahi
    BMC Bioinformatics, 5
  • [6] A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data
    Najarian, K
    Zaheri, M
    Rad, AA
    Najarian, S
    Dargahi, J
    BMC BIOINFORMATICS, 2004, 5 (1)
  • [7] Identification of Differentially Expressed Genes in Pituitary Adenomas by Integrating Analysis of Microarray Data
    Zhao, Peng
    Hu, Wei
    Wang, Hongyun
    Yu, Shengyuan
    Li, Chuzhong
    Bai, Jiwei
    Gui, Songbai
    Zhang, Yazhuo
    INTERNATIONAL JOURNAL OF ENDOCRINOLOGY, 2015, 2015
  • [8] Selection of differentially expressed genes in microarray data analysis
    J J Chen
    S-J Wang
    C-A Tsai
    C-J Lin
    The Pharmacogenomics Journal, 2007, 7 : 212 - 220
  • [9] Selection of differentially expressed genes in microarray data analysis
    Chen, J. J.
    Wang, S-J
    Tsai, C-A
    Lin, C-J
    PHARMACOGENOMICS JOURNAL, 2007, 7 (03): : 212 - 220
  • [10] TTCA: an R package for the identification of differentially expressed genes in time course microarray data
    Marco Albrecht
    Damian Stichel
    Benedikt Müller
    Ruth Merkle
    Carsten Sticht
    Norbert Gretz
    Ursula Klingmüller
    Kai Breuhahn
    Franziska Matthäus
    BMC Bioinformatics, 18