The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster

Jin, Wei; Riley, Rebecca M.; Wolfinger, Russell D.; White, Kevin P.; Passador-Gurgel, Gisele; Gibson, Greg

doi:10.1038/ng766

Cited by 576 publications

(465 citation statements)

References 20 publications

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“…1A). Recent work has shown that sex-biased gene expression is significant in Drosophila (6)(7)(8). This first report of sex-biased expression of the full (predicted) genome also strongly indicates that there is significant sex-biased expression-especially in gonads (Fig.…”

mentioning

confidence: 88%

Paucity of Genes on the Drosophila X Chromosome Showing Male-Biased Expression

Parisi

Nuttall

Naiman

et al. 2003

Science

536

634

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Sex chromosomes are primary determinants of sexual dimorphism in many organisms. These chromosomes are thought to arise via the divergence of an ancestral autosome pair and are almost certainly influenced by differing selection in males and females. Exploring how sex chromosomes differ from autosomes is highly amenable to genomic analysis. We examined global gene expression in Drosophila melanogaster and report a dramatic underrepresentation of X-chromosome genes showing high relative expression in males. Using comparative genomics, we find that these same Xchromosome genes are exceptionally poorly conserved in the mosquito Anopheles gambiae. These data indicate that the X chromosome is a disfavored location for genes selectively expressed in males.

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mentioning

confidence: 88%

Paucity of Genes on the Drosophila X Chromosome Showing Male-Biased Expression

Parisi

Nuttall

Naiman

et al. 2003

Science

536

634

View full text Add to dashboard Cite

show abstract

“…ANOVA analysis has been used in several microarray studies (30)(31)(32) to account for the variations in gene expressions caused by different sources, and normalization was included as part of their models. Our analysis has separated the normalization procedure from significance inference, which identifies differentially expressed genes more reliably (33).…”

Section: Discussion Factorial Analysis Can Effectively Identify Relevmentioning

confidence: 99%

Factorial microarray analysis of zebrafish retinal development

Leung

Ma²,

Link³

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

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In a zebrafish recessive mutant young (yng), retinal cells are specified to distinct cell classes, but they fail to morphologically differentiate. A null mutation in a brahma-related gene 1 (brg1) is responsible for this phenotype. To identify retina-specific Brg1-regulated genes that control cellular differentiation, we conducted a factorial microarray analysis. Gene expression profiles were compared from wild-type and yng retinas and stage-matched whole embryos at 36 and 52 hours postfertilization (hpf). From our analysis, three categories of genes were identified: (i) Brg1-regulated retinal differentiation genes (731 probesets), (ii) retinaspecific genes independent of Brg1 regulation (3,038 probesets), and (iii) Brg1-regulated genes outside the retina (107 probesets). Biological significance was confirmed by further analysis of components of the Cdk5 signaling pathway and Irx transcription factor family, representing genes identified in category 1. This study highlights the utility of factorial microarray analysis to efficiently identify relevant regulatory pathways influenced by both specific gene products and normal developmental events.Brg1-regulated genes ͉ neuronal differentiation ͉ cellular differentiation ͉ signal transduction pathways ͉ transcription factors

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“…The ratio function method (Chen et al, 1997) assumes that the coefficients of variation of the two channels are the same. The ANOVA methods (Kerr et al, 2000;Jin et al, 2001;Wolfinger et al, 2001) adjust for overall effects of array and dye across genes. Since the background subtraction and normalization steps have been addressed elsewhere (Yang et al, 2002a;Quackenbush, 2001;Quackenbush, 2002), this paper will focus on the transformation step.…”

Section: Introductionmentioning

confidence: 99%

Transformations for cDNA Microarray Data

Cui

Kerr²,

Churchill³

2003

Statistical Applications in Genetics and Molecular Biology

117

101

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Two channel microarray data often contain systematic variations that can be minimized by data transformation prior to further analysis. The most commonly observed effects are revealed by viewing scatter plots of the logarithm of the ratio by the average logarithmic intensity of the two color channels (RI plots). In this paper we present a general model for signal intensity data with multiple error sources. We demonstrate how these sources of error influence the shape of an RI plot. We then compare some currently available transformation strategies in terms of their mechanism and performance on both simulated and real microarray data. A linlog transformation is proposed to stabilize the variance of the log ratios. We also propose a regional smoothing method to remove variation in log ratios due to spatial heterogeneity on the microarray surface. The discussed transformations represent an important initial step in microarray data analysis for both ratio-based and ANOVA methods.

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The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster

Cited by 576 publications

References 20 publications

Paucity of Genes on the Drosophila X Chromosome Showing Male-Biased Expression

Paucity of Genes on the Drosophila X Chromosome Showing Male-Biased Expression

Factorial microarray analysis of zebrafish retinal development

Transformations for cDNA Microarray Data

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