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Microarray platform comparison study of hippocampal gene expression in DCLK1 transgenic and wild-type mice


ABSTRACT: The aim of the present study was to compare, on a statistical basis, the performance of different microarray platforms to detect differences in gene expression in a realistic and challenging biological setting. Gene expression profiles in the hippocampus of five wild-type and five transgenic δC-doublecortin-like kinase mice were evaluated with five microarray platforms: Applied Biosystems, Affymetrix, Agilent, Illumina and home-spotted oligonucleotide arrays. We observed considerable overlap between the different platforms, the overlap being better detectable with significance level-based ranking than with a p-value based cut-off. Confirming the qualitative agreement between platforms, Pathway analysis consistently demonstrated aberrances in GABA-ergic signalling in the transgenic mice, even though pathways were represented by only partially overlapping genes on the different platforms. Keywords: microarray platform comparison 5 biological replicates for each experimental group were analyzed on each of the five platforms. For two-color arrays a dye swap was performed. ABI_raw_data.csv contains the raw data for GSM206638 to GSM206647. Illumina_raw_data.csv contains the raw data for GSM206887 to GSM206896. In these files, the sample identifiers are included in the column headers.

ORGANISM(S): Mus musculus

SUBMITTER: P Pedotti 

PROVIDER: E-GEOD-8349 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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<h4>Background</h4>The comparability of gene expression data generated with different microarray platforms is still a matter of concern. Here we address the performance and the overlap in the detection of differentially expressed genes for five different microarray platforms in a challenging biological context where differences in gene expression are few and subtle.<h4>Results</h4>Gene expression profiles in the hippocampus of five wild-type and five transgenic deltaC-doublecortin-like kinase mi  ...[more]

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