Computational protocol: Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods

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[…] The PVCA approach (http://www.niehs.nih.gov/research/resources/software/pvca/index.cfm) was used to estimate sources of variability and compare batch effects before and after adjustment in the VAS and SMRI microarray data sets. We first selected the top PCs, enough to explain a proportion of overall variation larger than a pre-defined threshold (60%–90%, 60% in this case), and retained the corresponding eigenvalues. Each factor was treated as random in a mixed linear model and matched to a PC, then weighted by that PC's corresponding eigenvector. After we standardized the variation attributable to each factor, we calculated the proportion of total variance each factor explained. Including residuals, four factors' variations were estimated in the VAS data and ten factors' variations were estimated in SMRI data.Signal detection slopes were calculated using the spkTools R package. The significances of differences between slopes were assessed with a test for homogeneity of slope, which was done with the NCStats R package. The evaluation of overall performance was performed using the ROCR R package from Bioconductor. […]

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