%0 Journal Article %1 20170493 %A Gill, Ryan %A Datta, Somnath %A Datta, Susmita %D 2010 %J BMC Bioinformatics %K imported %N 1 %P 95 %R 10.1186/1471-2105-11-95 %T A statistical framework for differential network analysis from microarray data %U http://www.biomedcentral.com/1471-2105/11/95 %V 11 %X BACKGROUND:It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of genes are dependent on each other. Experimental techniques to detect such interacting pairs of genes have been in place for quite some time. With the advent of microarray technology, newer computational techniques to detect such interaction or association between gene expressions are being proposed which lead to an association network. While most microarray analyses look for genes that are differentially expressed, it is of potentially greater significance to identify how entire association network structures change between two or more biological settings, say normal versus diseased cell types.RESULTS:We provide a recipe for conducting a differential analysis of networks constructed from microarray data under two experimental settings. At the core of our approach lies a connectivity score that represents the strength of genetic association or interaction between two genes. We use this score to propose formal statistical tests for each of following queries: (i) whether the overall modular structures of the two networks are different, (ii) whether the connectivity of a particular set of "interesting genes" has changed between the two networks, and (iii) whether the connectivity of a given single gene has changed between the two networks. A number of examples of this score is provided. We carried out our method on two types of simulated data: Gaussian networks and networks based on differential equations. We show that, for appropriate choices of the connectivity scores and tuning parameters, our method works well on simulated data. We also analyze a real data set involving normal versus heavy mice and identify an interesting set of genes that may play key roles in obesity.CONCLUSIONS:Examining changes in network structure can provide valuable information about the underlying biochemical pathways. Differential network analysis with appropriate connectivity scores is a useful tool in exploring changes in network structures under different biological conditions. An R package of our tests can be downloaded from the supplementary website http://www.somnathdatta.org/Supp/DNA.

@article{20170493, abstract = {BACKGROUND:It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of genes are dependent on each other. Experimental techniques to detect such interacting pairs of genes have been in place for quite some time. With the advent of microarray technology, newer computational techniques to detect such interaction or association between gene expressions are being proposed which lead to an association network. While most microarray analyses look for genes that are differentially expressed, it is of potentially greater significance to identify how entire association network structures change between two or more biological settings, say normal versus diseased cell types.RESULTS:We provide a recipe for conducting a differential analysis of networks constructed from microarray data under two experimental settings. At the core of our approach lies a connectivity score that represents the strength of genetic association or interaction between two genes. We use this score to propose formal statistical tests for each of following queries: (i) whether the overall modular structures of the two networks are different, (ii) whether the connectivity of a particular set of "interesting genes" has changed between the two networks, and (iii) whether the connectivity of a given single gene has changed between the two networks. A number of examples of this score is provided. We carried out our method on two types of simulated data: Gaussian networks and networks based on differential equations. We show that, for appropriate choices of the connectivity scores and tuning parameters, our method works well on simulated data. We also analyze a real data set involving normal versus heavy mice and identify an interesting set of genes that may play key roles in obesity.CONCLUSIONS:Examining changes in network structure can provide valuable information about the underlying biochemical pathways. Differential network analysis with appropriate connectivity scores is a useful tool in exploring changes in network structures under different biological conditions. An R package of our tests can be downloaded from the supplementary website http://www.somnathdatta.org/Supp/DNA.}, added-at = {2010-03-29T14:44:40.000+0200}, author = {Gill, Ryan and Datta, Somnath and Datta, Susmita}, biburl = {https://www.bibsonomy.org/bibtex/239bad80ba68dcc8371d6284d23ead8bf/wnpxrz}, description = {BioMed Central | Full text | A statistical framework for differential network analysis from microarray data}, doi = {10.1186/1471-2105-11-95}, interhash = {76fbd9adfd636d7fdf8a778a62b13fb4}, intrahash = {39bad80ba68dcc8371d6284d23ead8bf}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {imported}, number = 1, pages = 95, pubmedid = {20170493}, timestamp = {2010-03-29T14:44:40.000+0200}, title = {A statistical framework for differential network analysis from microarray data}, url = {http://www.biomedcentral.com/1471-2105/11/95}, volume = 11, year = 2010 }