Abstract
Factor analysis is applied to microarray data in order to relate gene networks to complex traits and identifies a factor associated with body size in Drosophila simulans.
Highlights
A sample size of 100 individuals was examined. This sample size is large for a microarray experiment, but is in the low range of the minimum sample size suggested in factor analysis methodology [34]
Using simulated data to estimate factors, we found that when correlation among genes is strong, the number of factors and their structure can be estimated, even in the case where genes unrelated to the factor structure are included
We found that when noise genes were included hierarchical cluster analysis was unable to separate the noise genes from the signal, or to correctly identify the number of clusters
Summary
University Medical Center, Department of Biostatistics and Bioinformatics, Durham, NC 27710, USA. Factor analysis is an analytic approach that describes the covariation among a set of genes through the estimation of factors. The resulting factor model represents sets of coordinately expressed genes. Factor analysis is the extension of Sewell Wright's work on the correspondence among traits [19], and as such is perfectly suited for modeling the relationships among transcript levels for a set of crosses. The high dimensionality of genome-wide expression data presents special challenges This challenge, primarily the illconditioned matrices resulting from such studies, has been well described and explicitly acknowledged in much of the literature on the analysis of gene-expression data [20,21,22]. Previous applications of factor analyses to array data [25,26] dealt with this issue by an initial reduction of dimensionality through the use of cluster analysis
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