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Statistical evaluation of differential expression on cDNA nylon arrays with replicated experiments.

ABSTRACT: In this paper we focus on the detection of differentially expressed genes according to changes in hybridization signals using statistical tests. These tests were applied to 14 208 zebrafish cDNA clones that were immobilized on a nylon support and hybridized with radioactively labeled target mRNA from wild-type and lithium-treated zebrafish embryos. The methods were evaluated with respect to 16 control clones that correspond to eight different genes which are known to be involved in dorso-ventral axis specification. Moreover, 4608 Arabidopsis thaliana clones on the same array were used to judge statistical significance of expression changes and to control the false positive rates of the test decisions. Utilizing this special array design we show that differential expression of a high proportion of cDNA clones (15/16) and the respective genes (7/8) were identified, with a false positive error of <5% using the constant control data. Furthermore, we investigated the influence of the number of repetitions of experiments on the accuracy of the procedures with experimental and simulated data. Our results suggest that the detection of differential expression with repeated hybridization experiments is an accurate and sensitive way of identifying even small expression changes (1:1.5) of a large number of genes in parallel.

SUBMITTER: Herwig R

PROVIDER: S-EPMC96709 | biostudies-literature | 2001 Dec

REPOSITORIES: biostudies-literature

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Statistical evaluation of differential expression on cDNA nylon arrays with replicated experiments.

Herwig R R Aanstad P P Clark M M Lehrach H H

Nucleic acids research 20011201 23

In this paper we focus on the detection of differentially expressed genes according to changes in hybridization signals using statistical tests. These tests were applied to 14 208 zebrafish cDNA clones that were immobilized on a nylon support and hybridized with radioactively labeled target mRNA from wild-type and lithium-treated zebrafish embryos. The methods were evaluated with respect to 16 control clones that correspond to eight different genes which are known to be involved in dorso-ventral ...[more]

PMID: 11726700

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Project description:UnlabelledBackgroundBecause of the large volume of data and the intrinsic variation of data intensity observed in microarray experiments, different statistical methods have been used to systematically extract biological information and to quantify the associated uncertainty. The simplest method to identify differentially expressed genes is to evaluate the ratio of average intensities in two different conditions and consider all genes that differ by more than an arbitrary cut-off value to be differentially expressed. This filtering approach is not a statistical test and there is no associated value that can indicate the level of confidence in the designation of genes as differentially expressed or not differentially expressed. At the same time the fold change by itself provide valuable information and it is important to find unambiguous ways of using this information in expression data treatment.ResultsA new method of finding differentially expressed genes, called distributional fold change (DFC) test is introduced. The method is based on an analysis of the intensity distribution of all microarray probe sets mapped to a three dimensional feature space composed of average expression level, average difference of gene expression and total variance. The proposed method allows one to rank each feature based on the signal-to-noise ratio and to ascertain for each feature the confidence level and power for being differentially expressed. The performance of the new method was evaluated using the total and partial area under receiver operating curves and tested on 11 data sets from Gene Omnibus Database with independently verified differentially expressed genes and compared with the t-test and shrinkage t-test. Overall the DFC test performed the best - on average it had higher sensitivity and partial AUC and its elevation was most prominent in the low range of differentially expressed features, typical for formalin-fixed paraffin-embedded sample sets.ConclusionsThe distributional fold change test is an effective method for finding and ranking differentially expressed probesets on microarrays. The application of this test is advantageous to data sets using formalin-fixed paraffin-embedded samples or other systems where degradation effects diminish the applicability of correlation adjusted methods to the whole feature set.

Dataset Information

Statistical evaluation of differential expression on cDNA nylon arrays with replicated experiments.

Publications

Statistical evaluation of differential expression on cDNA nylon arrays with replicated experiments.

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