Characterization of Variability in Large-Scale Gene Expression Data: Implications for Study Design

Novák, Jaroslav; Sladek, Robert; Hudson, Thomas J.

doi:10.1006/geno.2001.6675

Cited by 171 publications

(164 citation statements)

References 15 publications

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“…The comparison of noise across these biological studies agrees with the microarray studies where biological noise in cell cultures is less than that found in inbred mouse populations (32). It is easy to anticipate that biological variability in human population studies will be larger still.…”

Section: Figsupporting

confidence: 74%

Experimental and Statistical Considerations to Avoid False Conclusions in Proteomics Studies Using Differential In-gel Electrophoresis

Karp

McCormick²,

Russell

et al. 2007

Molecular & Cellular Proteomics

151

137

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In quantitative proteomics, the false discovery rate (FDR) can be defined as the number of false positives within statistically significant changes in expression. False positives accumulate during the simultaneous testing of expression changes across hundreds or thousands of protein or peptide species when univariate tests such as the Student's t test are used. Currently most researchers rely solely on the estimation of p values and a significance threshold, but this approach may result in false positives because it does not account for the multiple testing effect. For each species, a measure of significance in terms of the FDR can be calculated, producing individual q values. The q value maintains power by allowing the investigator to achieve an acceptable level of true or false positives within the calls of significance. The q value approach relies on the use of the correct statistical test for the experimental design. In this situation, a uniform p value frequency distribution when there are no differences in expression between two samples should be obtained. Here we report a bias in p value distribution in the case of a three-dye DIGE experiment where no changes in expression are occurring. The bias was shown to arise from correlation in the data from the use of a common internal standard. With a two-dye schema, where each sample has its own internal standard, such bias was removed, enabling the application of the q value to two different proteomics studies. In the case of the first study, we demonstrate that 80% of calls of significance by the more traditional method are false positives. In the second, we show that calculating the q value gives the user control over the FDR. These studies demonstrate the power and ease of use of the q value in correcting for multiple testing. This work also highlights the need for robust experimental design that includes the appropriate application of statistical procedures. Molecular & Cellular Proteomics 6: 1354 -1364, 2007.Quantitative proteomics, the study of global changes in protein expression, is a rapidly growing field where two-dimensional (2D) 1 gel electrophoresis (1, 2), differential labeling of protein and peptides with stable isotopes (3), and label-free mass spectrometric peak intensity measurements (4) are pivotal approaches to measuring changes in the expression level of proteins. The output of large scale genomic sequencing and gene expression studies have driven the need to globally assess protein behavior, which is central to our understanding of cellular function and disease processes. In quantitative studies, the quantitation of the protein or peptide signal allows the comparison of protein levels from one state with another. Regardless of the technique used, the data generated can be analyzed with a variety of statistical approaches. In a simple comparison of two samples, changes in protein expression are considered to be significant when above a specified threshold (5). More rigorous studies incorporating replicates use univariate (1, 6) or multivari...

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Section: Figsupporting

confidence: 74%

Experimental and Statistical Considerations to Avoid False Conclusions in Proteomics Studies Using Differential In-gel Electrophoresis

Karp

McCormick²,

Russell

et al. 2007

Molecular & Cellular Proteomics

151

137

View full text Add to dashboard Cite

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“…Probes for the microarray analysis were generated from isolated RNA obtained at days 1, 3, 5, and 8 of cocultures from three independent experiments. Affymetrix GeneChip Rat Genome 230 2.0 arrays were screened with the 12 generated probes via the microarray platform of McGill University and Génome Québec Innovation Center (http://genomequebec.mcgill) exactly as described previously (35). To test for statistically significant changes in signal intensity (P values of Յ0.05), compiled data (robust multichip averaging analysis) were screened by using the software available on the microarray platform website.…”

Section: Methodsmentioning

confidence: 99%

Hepatocyte nuclear factor-4α promotes differentiation of intestinal epithelial cells in a coculture system

Lussier

Babeu²,

Auclair³

et al. 2008

American Journal of Physiology-Gastrointestinal and Liver Physi

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Normal cellular models able to efficiently recapitulate intestinal epithelial cell differentiation in culture are not yet available. The aim of this work was to establish and genetically characterize a mesenchymal-epithelial coculture system to identify transcriptional regulators involved in this process. The deposition of rat intestinal epithelial cells on human intestinal mesenchymal cells led to the formation of clustered structures that expanded shortly after seeding. These structures were composed of polarized epithelial cells with brush borders and cell junction complexes. A rat GeneChip statistical analysis performed at different time points during this process identified hepatocyte nuclear factor-4α (HNF-4α) and hepatocyte nuclear factor-1α (HNF-1α) as being induced coincidently with the apparition of polarized epithelial structures. Stable introduction of HNF-4α in undifferentiated epithelial cells alone led to the rapid induction of HNF-1α and several intestinal-specific markers and metabolism-related genes for which mRNA was identified to be upregulated during epithelial differentiation. HNF-4α was capable to transactivate the calbindin 3 gene promoter, a process that was synergistically increased in the presence of HNF-1α. When HNF-4α-expressing cells were plated on mesenchymal cells, an epithelial monolayer formed rapidly with the apparition of dome structures that are characteristics of vectorial ion transport. Forced expression of HNF-1α alone did not result in dome structures formation. In sum, this novel coculture system functionally identified for the first time HNF-4α as an important modulator of intestinal epithelial differentiation and offers an innovative opportunity to investigate molecular mechanisms involved in this process.

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“…All data in the current study were obtained from at least three separate hybridizations per RNA sample and the genes were identified as expressed if at least two hybridizations provided a positive signal. For every probe, the signals obtained under two different conditions (i.e., A vs. B) were expressed as the difference (normalized signal in condition A Ϫ normalized signal in condition B) and considered to be significantly induced or repressed (folds) if this difference was outside a CI (P Ͻ .05) calculated 20 from the entire data set. To select the liver mRNAs that were regulated in patients with an acute inflammation versus controls, the value for any given mRNA from every individual with an inflammation was compared with the mean value obtained from the control set.…”

Section: Methodsmentioning

confidence: 99%

Altered gene expression in acute systemic inflammation detected by complete coverage of the human liver transcriptome

et al. 2004

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The goal of the current study was to provide complete coverage of the liver transcriptome with human probes corresponding to every gene expressed in embryonic, adult, and/or cancerous liver. We developed dedicated tools, namely, the Liverpool nylon array of complementary DNA (cDNA) probes for approximately 10,000 nonredundant genes and the LiverTools database. Inflammation-induced transcriptome changes were studied in liver tissue samples from patients with an acute systemic inflammation and from control subjects. One hundred and fifty-four messenger RNAs (

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Characterization of Variability in Large-Scale Gene Expression Data: Implications for Study Design

Cited by 171 publications

References 15 publications

Experimental and Statistical Considerations to Avoid False Conclusions in Proteomics Studies Using Differential In-gel Electrophoresis

Experimental and Statistical Considerations to Avoid False Conclusions in Proteomics Studies Using Differential In-gel Electrophoresis

Hepatocyte nuclear factor-4α promotes differentiation of intestinal epithelial cells in a coculture system

Altered gene expression in acute systemic inflammation detected by complete coverage of the human liver transcriptome

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