Trajectory clustering: a non-parametric method for grouping gene expression time courses, with applications to mammary development. Export

@article{citeulike:2258897, abstract = {Trajectory clustering is a novel and statistically well-founded method for clustering time series data from gene expression arrays. Trajectory clustering uses non-parametric statistics and is hence not sensitive to the particular distributions underlying gene expression data. Each cluster is clearly defined in terms of direction of change of expression for successive time points (its 'trajectory'), and therefore has easily appreciated biological meaning. Applying the method to a dataset from mouse mammary gland development, we demonstrate that it produces different clusters than Hierarchical, K-means, and Jackknife clustering methods, even when those methods are applied to differences between successive time points. Compared to all of the other methods, trajectory clustering was better able to match a manual clustering by a domain expert, and was better able to cluster groups of genes with known related functions.}, address = {University of Colorado School of Medicine, Denver, Colorado 80262, USA. tzu.phang@uchsc.edu}, author = {Phang, T. L. and Neville, M. C. and Rudolph, M. and Hunter, L.}, citeulike-article-id = {2258897}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/12603041}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=12603041}, issn = {1793-5091}, journal = {Pac Symp Biocomput}, keywords = {dmbi-gem-time-series}, pages = {351--362}, posted-at = {2008-01-19 23:21:10}, priority = {2}, title = {Trajectory clustering: a non-parametric method for grouping gene expression time courses, with applications to mammary development.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/12603041}, year = {2003} }

TY - JOUR ID - citeulike:2258897 L3 - citeulike-article-id:2258897 N2 - Trajectory clustering is a novel and statistically well-founded method for clustering time series data from gene expression arrays. Trajectory clustering uses non-parametric statistics and is hence not sensitive to the particular distributions underlying gene expression data. Each cluster is clearly defined in terms of direction of change of expression for successive time points (its 'trajectory'), and therefore has easily appreciated biological meaning. Applying the method to a dataset from mouse mammary gland development, we demonstrate that it produces different clusters than Hierarchical, K-means, and Jackknife clustering methods, even when those methods are applied to differences between successive time points. Compared to all of the other methods, trajectory clustering was better able to match a manual clustering by a domain expert, and was better able to cluster groups of genes with known related functions. JF - Pac Symp Biocomput SN - 1793-5091 AD - University of Colorado School of Medicine, Denver, Colorado 80262, USA. tzu.phang@uchsc.edu EP - 362 TI - Trajectory clustering: a non-parametric method for grouping gene expression time courses, with applications to mammary development. SP - 351 KW - dmbi-gem-time-series AU - Phang, TL AU - Neville, MC AU - Rudolph, M AU - Hunter, L PY - 2003/// UR - http://view.ncbi.nlm.nih.gov/pubmed/12603041 ER -