Gender study gene expression data from Vawter et al . [ 2004 ]

Vawter et al. [2004] studied differences in gene expression between male and female patients. This gender study is an interesting benchmark for methods aiming at removing unwanted variation as it expected to be affected by several technical and biological factors: two microarray platforms, three different labs, three tissue localizations in the brain. Most of the 10 patients involved in the study had samples taken from the anterior cingulate cortex (a), the dorsolateral prefontal cortex (d) and the cerebellar hemisphere (c). Most of these samples were sent to three independent labs: UC Irvine (I), UC Davis (D) and University of Michigan, Ann Arbor (M). Gene expression was measured using either HGU-95A or HGU-95Av2 Affymetrix arrays with 12, 600 genes shared between the two platforms (12, 626 on the HG-U95A and 12, 625 on the HGu-95Av2). Six of the 10×3×3 combinations were missing, leading to 84 samples. Gagnon-Bartsch and Speed [2012] used the resulting dataset to study the performances of RUV-2: the number of genes from the X and Y chromosomes which were among the most differentially expressed genes between male and female patients was used to assess how much each correction method helped. Following this paper, we pre-processed each array using RMA, and log transformed the probe intensities. This data package also provides negative control probeset indices. These indices correspond to the 799 housekeeping probesets which were provided in Eisenberg and Levanon [2003] and used in Gagnon-Bartsch and Speed [2012]. The data in this package is used in the vignette and examples of the RUVnormalize package. RUVnormalize implements normalization methods from Jacob et al. [2012], intended for the case where neither the unwanted variation sources nor the factors of interest are observed. This situation arises when performing unsupervised estimation tasks such as clustering or PCA, in the presence of unwanted variation. It can also be the case that one needs to normalize a dataset without knowing which factors of interest will be studied. The objective is then to correct the gene expression by estimating and removing the unwanted variation, without removing the — unobserved — variation of interest.