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The LVS algorithm normalizes the data set based on the least variant probe sets and replaces the quantile normalization implemented in GCRMA.
In our case the least variant endogenous controls were sno135 and miR-25, and these were used to normalize the data.
Overrepresentation of biological themes among most and least variant genes was performed in DAVID using functional annotation clustering analysis [ 11].
To identify genes with multiple instances of low overall variance, the 200 least variant probe sets in each tissue-array set were intersected by probe identifier.
This involved analysing the expression of all 667 miRNAs across all 48 samples in the discovery cohort allowing us to choose one of the least variant miRNAs.
Interestingly, this list contains 8 genes expressed in both liver and kidney (Cyp2d26, Tf, C3, F2, ApoE, Spp2, Rbp4, Rn.107069) that were highly variant in control kidney, but among the least variant in control liver samples.
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Individual 10036 yielded the most SNPs and individual 10034 showed the least variants of all samples (Table 2).
The measurements were done using Affymetrix GeneChip HG-U133plus2, and probe set data was generated using two normalization algorithms: MAS5.0 and GCRMA with least-variant set (LVS).
To measure gene expression, probe set data (cell intensity files) were generated using two standard normalization algorithms: Affymetrix Microarray Suite v.5 (MAS5.0) and GCRMA with least-variant set (LVS) probe sets.
Array normalization was performed using the least-variant-set method [ 11].
In addition, we also include the modified least-variant set (LVS) [ 15] in the set of methods being evaluated.
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