Ying Chen1, Rebekah Wu2, James Felton2, David M Rocke3, Anu Chakicherla4. 1. Department of Statistics, University of California Davis, Davis, CA. 2. Chemistry, Materials and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA. 3. Division of Biostatistics, University of California Davis, Davis, CA. 4. Computation Directorate, Lawrence Livermore National Laboratory, Livermore, CA.
Abstract
MOTIVATION: Whole genome microarrays are increasingly becoming the method of choice to study responses in model organisms to disease, stressors or other stimuli. However, whole genome sequences are available for only some model organisms, and there are still many species whose genome sequences are not yet available. Cross-species studies, where arrays developed for one species are used to study gene expression in a closely related species, have been used to address this gap, with some promising results. Current analytical methods have included filtration of some probes or genes that showed low hybridization activities. But consensus filtration schemes are still not available. RESULTS: A novel masking procedure is proposed based on currently available target species sequences to filter out probes and study a cross-species data set using this masking procedure and gene-set analysis. Gene-set analysis evaluates the association of some priori defined gene groups with a phenotype of interest. Two methods, Gene Set Enrichment Analysis (GSEA) and Test of Test Statistics (ToTS) were investigated. The results showed that masking procedure together with ToTS method worked well in our data set. The results from an alternative way to study cross-species hybridization experiments without masking are also presented. We hypothesize that the multi-probes structure of Affymetrix microarrays makes it possible to aggregate the effects of both well-hybridized and poorly-hybridized probes to study a group of genes. The principles of gene-set analysis were applied to the probe-level data instead of gene-level data. The results showed that ToTS can give valuable information and thus can be used as a powerful technique for analyzing cross-species hybridization experiments. AVAILABILITY: Software in the form of R code is available at http://anson.ucdavis.edu/~ychen/cross-species.html. SUPPLEMENTARY DATA: Supplementary data are available at http://anson.ucdavis.edu/~ychen/cross-species.html.
MOTIVATION: Whole genome microarrays are increasingly becoming the method of choice to study responses in model organisms to disease, stressors or other stimuli. However, whole genome sequences are available for only some model organisms, and there are still many species whose genome sequences are not yet available. Cross-species studies, where arrays developed for one species are used to study gene expression in a closely related species, have been used to address this gap, with some promising results. Current analytical methods have included filtration of some probes or genes that showed low hybridization activities. But consensus filtration schemes are still not available. RESULTS: A novel masking procedure is proposed based on currently available target species sequences to filter out probes and study a cross-species data set using this masking procedure and gene-set analysis. Gene-set analysis evaluates the association of some priori defined gene groups with a phenotype of interest. Two methods, Gene Set Enrichment Analysis (GSEA) and Test of Test Statistics (ToTS) were investigated. The results showed that masking procedure together with ToTS method worked well in our data set. The results from an alternative way to study cross-species hybridization experiments without masking are also presented. We hypothesize that the multi-probes structure of Affymetrix microarrays makes it possible to aggregate the effects of both well-hybridized and poorly-hybridized probes to study a group of genes. The principles of gene-set analysis were applied to the probe-level data instead of gene-level data. The results showed that ToTS can give valuable information and thus can be used as a powerful technique for analyzing cross-species hybridization experiments. AVAILABILITY: Software in the form of R code is available at http://anson.ucdavis.edu/~ychen/cross-species.html. SUPPLEMENTARY DATA: Supplementary data are available at http://anson.ucdavis.edu/~ychen/cross-species.html.
Authors: Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov Journal: Proc Natl Acad Sci U S A Date: 2005-09-30 Impact factor: 11.205
Authors: Wolfgang Enard; Philipp Khaitovich; Joachim Klose; Sebastian Zöllner; Florian Heissig; Patrick Giavalisco; Kay Nieselt-Struwe; Elaine Muchmore; Ajit Varki; Rivka Ravid; Gaby M Doxiadis; Ronald E Bontrop; Svante Pääbo Journal: Science Date: 2002-04-12 Impact factor: 47.728