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Series GSE25410 Query DataSets for GSE25410
Status Public on Nov 17, 2010
Title The suitability of statistical tests according to characteristics in variation of expression microarray data
Sample organism Mus musculus
Experiment type Third-party reanalysis
Expression profiling by array
Summary Microarray technology has enabled the measurement of comprehensive transcriptomic information. However, each data entry may reflect trivial individual differences among samples and also contain technical noise. Therefore, the certainty of each observed difference should be confirmed at earlier steps of the analyses, and statistical tests are frequently used for this purpose. Since a microarray measures a huge number of genes and the results are processed simultaneously, concerns regarding problems of multiplicity have been raised to the tests. To deal with these problems, several methodologies have been proposed, making the tests very conservative. Indeed, arbitrary tuning of the test threshold has also been introduced to relax the test conditions. However, the appropriateness of the multiplicity problems as well as the compensation methods has not been confirmed.

The appropriateness of the compensation methods was checked by means of coincidence of the premises of the methodologies with the observed characteristics found in real data of two typical platforms of microarray analysis. Normality was observed in within-group data variations, supporting applications of parametric tests. However, genes displayed their own tendencies in the magnitude of variations, and the distributions of P-values were rather complex and varied; these characteristics are inconsistent with the premises of the compensation methodologies. Additionally, the appropriateness of the proposed multiplicities is reconsidered. When we observed differences in the transcriptome, the family-wise error rate should not be considered, since analyses at higher levels would not be influenced by a few false positives among the huge numbers of true information. Likely, concerns for a false discovery rate are not suitable for the point null hypotheses on expression levels, since the rate of true null hypotheses should be rare in contradiction to the premise of the methodology.

Although compensation methods have been recommended to deal with the problem of multiplicity, the compensations are actually inappropriate for many of the applications of transcriptome analyses. Compensations are not only unnecessary, but will increase the occurrence of false negative errors, and arbitrary adjustment of the threshold damages the objectivity of the tests. Rather, the results of parametric tests should be evaluated directly.

This SuperSeries is composed of the SubSeries listed below.
Overall design Refer to individual Series.
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Submission date Nov 16, 2010
Last update date Dec 28, 2012
Contact name Tomokazu Konishi
Phone +81-18-872-1603
Organization name Akita Prefectural University
Department Bioresource Sciences
Lab Molecular Genetics
Street address Shimoshinjyo Nishi
City Akita
State/province Akita
ZIP/Postal code 010-0195
Country Japan
This SuperSeries is composed of the following SubSeries:
GSE24948 Renormalized GSE3889 by using the parametric method
GSE24950 Renormalized GSE6089 by using the parametric method
Reanalysis of GSM88872
Reanalysis of GSM88873
Reanalysis of GSM88874
Reanalysis of GSM88875
Reanalysis of GSM88876
Reanalysis of GSM88877
Reanalysis of GSM88878
Reanalysis of GSM88879
Reanalysis of GSM88880
Reanalysis of GSM88881
Reanalysis of GSM88882
Reanalysis of GSM88883
Reanalysis of GSM88884
Reanalysis of GSM88885
Reanalysis of GSM88886
Reanalysis of GSM88887
Reanalysis of GSM88888
Reanalysis of GSM88889
Reanalysis of GSM88890
Reanalysis of GSM88891
Reanalysis of GSM140954
Reanalysis of GSM140955
Reanalysis of GSM140956
Reanalysis of GSM140957
Reanalysis of GSM140958
Reanalysis of GSM140959
Reanalysis of GSM140960
Reanalysis of GSM140961
Reanalysis of GSM140962
Reanalysis of GSM140963
Reanalysis of GSM140964
Reanalysis of GSM140965
Reanalysis of GSM140966
Reanalysis of GSM140967
BioProject PRJNA134153

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