Literature DB >> 18314583

Statistical methods for identifying differentially expressed gene combinations.

Yen-Yi Ho1, Leslie Cope, Marcel Dettling, Giovanni Parmigiani.   

Abstract

Identification of coordinate gene expression changes across phenotypes or biological conditions is the basis of the ability to decode the role of gene expression regulatory networks. Statistically, the identification of these changes can be viewed as a search for groups (most typically pairs) of genes whose expression provides better phenotype discrimination when considered jointly than when considered individually. Such groups are defined as being jointly differentially expressed. In this chapter several approaches for identifying jointly differentially expressed groups of genes are reviewed of compared on a set of simulations.

Mesh:

Year:  2007        PMID: 18314583     DOI: 10.1007/978-1-59745-547-3_10

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  10 in total

Review 1.  Quality assurance of RNA expression profiling in clinical laboratories.

Authors:  Weihua Tang; Zhiyuan Hu; Hind Muallem; Margaret L Gulley
Journal:  J Mol Diagn       Date:  2011-10-20       Impact factor: 5.568

2.  Hypothesis testing for differentially correlated features.

Authors:  Elisa Sheng; Daniela Witten; Xiao-Hua Zhou
Journal:  Biostatistics       Date:  2016-04-04       Impact factor: 5.899

3.  BFDCA: A Comprehensive Tool of Using Bayes Factor for Differential Co-Expression Analysis.

Authors:  Duolin Wang; Juexin Wang; Yuexu Jiang; Yanchun Liang; Dong Xu
Journal:  J Mol Biol       Date:  2016-10-27       Impact factor: 5.469

Review 4.  Systems analysis of high-throughput data.

Authors:  Rosemary Braun
Journal:  Adv Exp Med Biol       Date:  2014       Impact factor: 2.622

5.  ROS-DET: robust detector of switching mechanisms in gene expression.

Authors:  Mitsunori Kayano; Ichigaku Takigawa; Motoki Shiga; Koji Tsuda; Hiroshi Mamitsuka
Journal:  Nucleic Acids Res       Date:  2011-04-01       Impact factor: 16.971

6.  Differential co-expression-based detection of conditional relationships in transcriptional data: comparative analysis and application to breast cancer.

Authors:  Dharmesh D Bhuva; Joseph Cursons; Gordon K Smyth; Melissa J Davis
Journal:  Genome Biol       Date:  2019-11-14       Impact factor: 13.583

Review 7.  Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression.

Authors:  Aurora Savino; Paolo Provero; Valeria Poli
Journal:  Int J Mol Sci       Date:  2020-12-12       Impact factor: 5.923

8.  Modeling dynamic correlation in zero-inflated bivariate count data with applications to single-cell RNA sequencing data.

Authors:  Zhen Yang; Yen-Yi Ho
Journal:  Biometrics       Date:  2021-03-30       Impact factor: 1.701

9.  Statistical methods for gene set co-expression analysis.

Authors:  YounJeong Choi; Christina Kendziorski
Journal:  Bioinformatics       Date:  2009-08-18       Impact factor: 6.937

10.  Identifying differential correlation in gene/pathway combinations.

Authors:  Rosemary Braun; Leslie Cope; Giovanni Parmigiani
Journal:  BMC Bioinformatics       Date:  2008-11-18       Impact factor: 3.169

  10 in total

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