Statistical methods for gene set co-expression analysis Export

@article{citeulike:5488592, abstract = {Motivation: The power of a microarray experiment derives from the identification of genes differentially regulated across biological conditions. To date, differential regulation is most often taken to mean differential expression, and a number of useful methods for identifying differentially expressed (DE) genes or gene sets are available. However, such methods are not able to identify many relevant classes of differentially regulated genes. One important example concerns differentially co-expressed (DC) genes. Results: We propose an approach, gene set co-expression analysis (GSCA), to identify DC gene sets. The GSCA approach provides a false discovery rate controlled list of interesting gene sets, does not require that genes be highly correlated in at least one biological condition and is readily applied to data from individual or multiple experiments, as we demonstrate using data from studies of lung cancer and diabetes. Availability: The GSCA approach is implemented in R and available at www.biostat.wisc.edu/[\~{}]kendzior/GSCA/. Contact: kendzior@biostat.wisc.edu Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btp502}, author = {Choi, YounJeong and Kendziorski, Christina}, citeulike-article-id = {5488592}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btp502}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/content/25/21/2780.abstract}, citeulike-linkout-2 = {http://bioinformatics.oxfordjournals.org/content/25/21/2780.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/19689953}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=19689953}, day = {1}, doi = {10.1093/bioinformatics/btp502}, journal = {Bioinformatics}, keywords = {co-expression, diabetes, differential, gene, milka, set}, month = {November}, number = {21}, pages = {2780--2786}, posted-at = {2009-11-02 10:44:17}, priority = {2}, title = {Statistical methods for gene set co-expression analysis}, url = {http://dx.doi.org/10.1093/bioinformatics/btp502}, volume = {25}, year = {2009} }

TY - JOUR ID - citeulike:5488592 L3 - citeulike-article-id:5488592 N2 - Motivation: The power of a microarray experiment derives from the identification of genes differentially regulated across biological conditions. To date, differential regulation is most often taken to mean differential expression, and a number of useful methods for identifying differentially expressed (DE) genes or gene sets are available. However, such methods are not able to identify many relevant classes of differentially regulated genes. One important example concerns differentially co-expressed (DC) genes. Results: We propose an approach, gene set co-expression analysis (GSCA), to identify DC gene sets. The GSCA approach provides a false discovery rate controlled list of interesting gene sets, does not require that genes be highly correlated in at least one biological condition and is readily applied to data from individual or multiple experiments, as we demonstrate using data from studies of lung cancer and diabetes. Availability: The GSCA approach is implemented in R and available at www.biostat.wisc.edu/[~]kendzior/GSCA/. Contact: kendzior@biostat.wisc.edu Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btp502 IS - 21 JF - Bioinformatics EP - 2786 TI - Statistical methods for gene set co-expression analysis VL - 25 SP - 2780 KW - co-expression KW - diabetes KW - differential KW - gene KW - milka KW - set AU - Choi, YounJeong AU - Kendziorski, Christina PY - 2009/11/01/ UR - http://dx.doi.org/10.1093/bioinformatics/btp502 ER -