GenePicker: replicate analysis of Affymetrix gene expression microarrays | Zendy

Giacomo Finocchiaro | Zendy; Paola Parise | Zendy; Simone Paolo Minardi | Zendy; Myriam Alcalay | Zendy; Heiko Müller | Zendy

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GenePicker: replicate analysis of Affymetrix gene expression microarrays

Author(s) -

Giacomo Finocchiaro,

Paola Parise,

Simone Paolo Minardi,

Myriam Alcalay,

Heiko Müller

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bth416

Subject(s) - replicate , false positive paradox , normalization (sociology) , dna microarray , database normalization , fold change , gene expression profiling , computer science , microarray analysis techniques , computational biology , gene , data mining , gene expression , statistics , biology , pattern recognition (psychology) , genetics , artificial intelligence , mathematics , sociology , anthropology

GenePicker allows efficient analysis of Affymetrix gene expression data performed in replicate, through definition of analysis schemes, data normalization, t-test/ANOVA, Change-Fold Change-analysis and yields lists of differentially expressed genes with high confidence. Comparison of noise and signal analysis schemes allows determining a signal-to-noise ratio in a given experiment. Change Call, Fold Change and Signal mean ratios are used in the analysis. While each parameter alone yields gene lists that contain up to 30% false positives, the combination of these parameters nearly eliminates the false positives as verified by northern blotting, quantitative PCR in numerous independent experiments as well as by the analysis of spike-in data.

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