Metagene projection for cross-platform, cross-species characterization of global transcriptional states.
The high dimensionality of global transcription profiles, the expression level of 20,000 genes in a much small number of samples, presents challenges that affect the sensitivity and general applicability of analysis results. In principle, it would be better to describe the data in terms of a small number of metagenes, positive linear combinations of genes, which could reduce noise while still capturing the invariant biological features of the data. Here, we describe how to accomplish such a reduction in dimension by a metagene projection methodology, which can greatly reduce the number of features used to characterize microarray data. We show, in applications to the analysis of leukemia and lung cancer data sets, how this approach can help assess and interpret similarities and differences between independent data sets, enable cross-platform and cross-species analysis, improve clustering and class prediction, and provide a computational means to detect and remove sample contamination.
Tamayo
Pablo
P
Eli and Edythe L. Broad Institute, Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02141, USA.
Scanfeld
Daniel
D
Ebert
Benjamin L
BL
Gillette
Michael A
MA
Roberts
Charles W M
CW
Mesirov
Jill P
JP
D000818:Animals; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D004195:Disease Models, Animal; D020869:Gene Expression Profiling; D006801:Humans; D007938:Leukemia / Q000145:classification / Q000235:genetics* / Q000473:pathology; D008175:Lung Neoplasms / Q000145:classification / Q000235:genetics* / Q000473:pathology; D051379:Mice; D018345:Mice, Knockout; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013045:Species Specificity; D014158:Transcription, Genetic