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01.12.2017 | Research article | Ausgabe 1/2017 Open Access

BMC Neurology 1/2017

Concordance analysis of microarray studies identifies representative gene expression changes in Parkinson’s disease: a comparison of 33 human and animal studies

Zeitschrift:
BMC Neurology > Ausgabe 1/2017
Autoren:
Erin Oerton, Andreas Bender
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12883-017-0838-x) contains supplementary material, which is available to authorized users.
A correction to this article is available online at https://​doi.​org/​10.​1186/​s12883-019-1240-7.

Abstract

Background

As the popularity of transcriptomic analysis has grown, the reported lack of concordance between different studies of the same condition has become a growing concern, raising questions as to the representativeness of different study types, such as non-human disease models or studies of surrogate tissues, to gene expression in the human condition.

Methods

In a comparison of 33 microarray studies of Parkinson’s disease, correlation and clustering analyses were used to determine the factors influencing concordance between studies, including agreement between different tissue types, different microarray platforms, and between neurotoxic and genetic disease models and human Parkinson’s disease.

Results

Concordance over all studies is low, with correlation of only 0.05 between differential gene expression signatures on average, but increases within human patients and studies of the same tissue type, rising to 0.38 for studies of human substantia nigra. Agreement of animal models, however, is dependent on model type. Studies of brain tissue from Parkinson’s disease patients (specifically the substantia nigra) form a distinct group, showing patterns of differential gene expression noticeably different from that in non-brain tissues and animal models of Parkinson’s disease; while comparison with other brain diseases (Alzheimer’s disease and brain cancer) suggests that the mixed study types display a general signal of neurodegenerative disease. A meta-analysis of these 33 microarray studies demonstrates the greater ability of studies in humans and highly-affected tissues to identify genes previously known to be associated with Parkinson’s disease.

Conclusions

The observed clustering and concordance results suggest the existence of a ‘characteristic’ signal of Parkinson’s disease found in significantly affected human tissues in humans. These results help to account for the consistency (or lack thereof) so far observed in microarray studies of Parkinson’s disease, and act as a guide to the selection of transcriptomic studies most representative of the underlying gene expression changes in the human disease.
Zusatzmaterial
Additional file 1: List of Reactome pathways identified as significant in human and animal studies by gene set enrichment analysis. (XLSX 12 kb)
12883_2017_838_MOESM1_ESM.xlsx
Additional file 2: Concordance results for different subgroups using biological pathway enrichment analysis. (PDF 64 kb)
12883_2017_838_MOESM2_ESM.pdf
Additional file 3: Plots of average concordance against sample size for gene expression and biological pathway enrichment. (PDF 101 kb)
12883_2017_838_MOESM3_ESM.pdf
Additional file 4: Gene lists used for each analysis. (XLSX 154 kb)
12883_2017_838_MOESM4_ESM.xlsx
Additional file 5: Principal component analysis of studies based on differential expression signatures, principal components 1 and 2. (PDF 110 kb)
12883_2017_838_MOESM5_ESM.pdf
Additional file 6: Heatmap of differential gene expression in Parkinson’s disease studies. (PDF 454 kb)
12883_2017_838_MOESM6_ESM.pdf
Additional file 7: Principal component analysis of studies based on sign of differential expression signatures. (PDF 190 kb)
12883_2017_838_MOESM7_ESM.pdf
Additional file 8: Hierarchical clustering of studies based on the most highly differentially expressed genes in each study, complete linkage. (PDF 348 kb)
12883_2017_838_MOESM8_ESM.pdf
Additional file 9: Heatmap of differential gene expression in Parkinson’s disease, Alzheimer’s disease, and brain tumor studies. (PDF 348 kb)
12883_2017_838_MOESM9_ESM.pdf
Additional file 10: GEO accession numbers of Parkinson’s disease studies included in the analysis. (PDF 121 kb)
12883_2017_838_MOESM10_ESM.pdf
Additional file 11: Concordance calculated over the base set of genes, consisting of the 2,372 genes recorded in all studies, vs concordance calculated over the larger sets of genes shared between subsets of studies. (PDF 72 kb)
12883_2017_838_MOESM11_ESM.pdf
Additional file 12: Significance thresholds of concordance for different subgroup sizes. (PDF 70 kb)
12883_2017_838_MOESM12_ESM.pdf
Additional file 13: Principal component analysis of differential gene expression in Parkinson’s disease, Alzheimer’s disease and brain tumors, principal components 1 and 2. (PDF 83 kb)
12883_2017_838_MOESM13_ESM.pdf
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