Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction - PubMed (original) (raw)

Comparative Study

Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction

Hanga C Galfalvy et al. BMC Bioinformatics. 2003.

Abstract

Background: Genomic studies of complex tissues pose unique analytical challenges for assessment of data quality, performance of statistical methods used for data extraction, and detection of differentially expressed genes. Ideally, to assess the accuracy of gene expression analysis methods, one needs a set of genes which are known to be differentially expressed in the samples and which can be used as a "gold standard". We introduce the idea of using sex-chromosome genes as an alternative to spiked-in control genes or simulations for assessment of microarray data and analysis methods.

Results: Expression of sex-chromosome genes were used as true internal biological controls to compare alternate probe-level data extraction algorithms (Microarray Suite 5.0 [MAS5.0], Model Based Expression Index [MBEI] and Robust Multi-array Average [RMA]), to assess microarray data quality and to establish some statistical guidelines for analyzing large-scale gene expression. These approaches were implemented on a large new dataset of human brain samples. RMA-generated gene expression values were markedly less variable and more reliable than MAS5.0 and MBEI-derived values. A statistical technique controlling the false discovery rate was applied to adjust for multiple testing, as an alternative to the Bonferroni method, and showed no evidence of false negative results. Fourteen probesets, representing nine Y- and two X-chromosome linked genes, displayed significant sex differences in brain prefrontal cortex gene expression.

Conclusion: In this study, we have demonstrated the use of sex genes as true biological internal controls for genomic analysis of complex tissues, and suggested analytical guidelines for testing alternate oligonucleotide microarray data extraction protocols and for adjusting multiple statistical analysis of differentially expressed genes. Our results also provided evidence for sex differences in gene expression in the brain prefrontal cortex, supporting the notion of a putative direct role of sex-chromosome genes in differentiation and maintenance of sexual dimorphism of the central nervous system. Importantly, these analytical approaches are applicable to all microarray studies that include male and female human or animal subjects.

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Figures

Figure 1

Figure 1

MAS5.0, MBEI and RMA signal variability. The variability in signal intensity measurement obtained with three different probe-level data extraction methods is represented by the lowess curves of the coefficient of variation. The X-axis represent increasing signal intensities, as measured by the percentage of arrays on which this gene is detected as present (% present calls). Presence calls were obtained with MBEI (BA9, n = 39; BA47, n = 36). Note that curves for the two brain areas are very close to each other for all three methods.

Figure 2

Figure 2

Y-chromosome-linked probesets: male-female expression comparisons. RMA-based averaged values (± STDEV) are displayed. A) Probesets with significant differences in expression levels for male and female samples in BA9 and/or BA47. All male-female comparisons were statistically significant with the exception of #11 in BA9 and # 12 and 13 in BA47 (See also Table 2). Probesets are organized according to order of y-linked genes in Table 2. B) Selected Y-linked probesets without sex-differences. All these genes were detected as ''absent'' by MAS5.0 or MBEI. Signal level represent background estimates. Probesets are: 1, 201909_at; 2, 204409_s_at; 3, 204410_at; 4, 205000_at; 5, 205001_s_at; 6, 206624_at; 7, 206700_s_at; 8, 207063_at; 9, 207246_at; 10, 214983_at; 11, 211149_at; 12, 208067_x_at, 13, 211227_s_at; 14, 214983_at; 15, 217261_at; 16, 217162_at; 17, 221179_at; 18, 211461_at; 19, 209596_at; 20, 210322_x_at; 21, 216376_x_at; 22, 216922_x_at; 23, 211462_s_at; 24, 207909_x_at; 25, 207918_s_at; 26, 207912_s_at.

Figure 3

Figure 3

Distribution of the t-tests p-values for sex differences or random group labels. Distribution of the p-values from the t-tests comparing males and females. These p-values are slightly lower than expected from a uniform distribution, representing a mixture distribution of p-values from differentially expressed and not affected genes.

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