title: Unifying gene expression measures from multiple platforms using factor analysis.
abstract: In the Cancer Genome Atlas (TCGA) project, gene expression of the same set of samples is measured multiple times on different microarray platforms. There are two main advantages to combining these measurements. First, we have the opportunity to obtain a more precise and accurate estimate of expression levels than using the individual platforms alone. Second, the combined measure simplifies downstream analysis by eliminating the need to work with three sets of expression measures and to consolidate results from the three platforms.We propose to use factor analysis (FA) to obtain a unified gene expression measure (UE) from multiple platforms. The UE is a weighted average of the three platforms, and is shown to perform well in terms of accuracy and precision. In addition, the FA model produces parameter estimates that allow the assessment of the model fit.The R code is provided in File S2. Gene-level FA measurements for the TCGA data sets are available from http://tcga-data.nci.nih.gov/docs/publications/unified_expression/.
authors: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America. vwang@jimmy.harvard.edu
authors: Xin Victoria
authors: XV
authors: Wang
authors: Roel G W
authors: RG
authors: Verhaak
authors: Elizabeth
authors: E
authors: Purdom
authors: Paul T
authors: PT
authors: Spellman
authors: Terence P
authors: TP
authors: Speed
mesh_terms: D003198:Computer Simulation; D005163:Factor Analysis, Statistical; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D052138:Genes, Neoplasm / Q000235:genetics; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics*; D012372:ROC Curve