ArticlePloS one2012
Analyzing illumina gene expression microarray data from different tissues: methodological aspects of data analysis in the metaxpress consortium.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 7 of them syntheses that pooled it.
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Who cites it
56 citing papers in PubMed, 7 syntheses or guidelines pooled it, 86 citations in OpenAlex.
- Meta-analyses identify DNA methylation associated with kidney function and damage.Nature communications · 2021Pooled it
- Blood DNA methylation sites predict death risk in a longitudinal study of 12, 300 individuals.Aging · 2020Pooled it
- A meta-analysis of two high-risk prospective cohort studies reveals autism-specific transcriptional changes to chromatin, autoimmune, and environmental response genes in umbilical cord blood.Molecular autism · 2019Pooled it
- A whole-blood transcriptome meta-analysis identifies gene expression signatures of cigarette smoking.Human molecular genetics · 2016Pooled it
- Genomewide meta-analysis identifies loci associated with IGF-I and IGFBP-3 levels with impact on age-related traits.Aging cell · 2016Pooled it
- Gene transcripts associated with muscle strength: a CHARGE meta-analysis of 7,781 persons.Physiological genomics · 2016Pooled it
- A meta-analysis of gene expression signatures of blood pressure and hypertension.PLoS genetics · 2015Pooled it
- DNA methylation of lipid-related genes affects blood lipid levels.Circulation. Cardiovascular genetics · 2015Trial
- Systematic investigation of interindividual variation of DNA methylation in human whole blood.Genome biology · 2026Article
- Interpretable multimodal machine learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy.Communications medicine · 2024Article
- RIOK2 transcriptionally regulates TRiC and dyskerin complexes to prevent telomere shortening.Nature communications · 2024Article
- Distinct genetic liability profiles define clinically relevant patient strata across common diseases.Nature communications · 2024Article
- Unveiling Prognostic RNA Biomarkers through a Multi-Cohort Study in Colorectal Cancer.International journal of molecular sciences · 2024Article
- Methylation Patterns of the FKBP5 Gene in Association with Childhood Maltreatment and Depressive Disorders.International journal of molecular sciences · 2024Article
- The interplay between genetic variation and gene expression of the glucocorticoid receptor gene NR3C1 and blood cortisol levels on verbal memory and hippocampal volumes.European archives of psychiatry and clinical neuroscience · 2022Article
- Article
- Limited evidence for blood eQTLs in human sexual dimorphism.Genome medicine · 2022Article
- Associations of carotid intima media thickness with gene expression in whole blood and genetically predicted gene expression across 48 tissues.Human molecular genetics · 2022Article
- Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome.Nature communications · 2021Article
- Mycolactone toxin induces an inflammatory response by targeting the IL-1β pathway: Mechanistic insight into Buruli ulcer pathophysiology.PLoS pathogens · 2020Article
Corrections and comments
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Authors and funding
31 authors at 12 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microarray profiling of gene expression is widely applied in molecular biology and functional genomics. Experimental and technical variations make meta-analysis of different studies challenging. In a total of 3358 samples, all from German population-based cohorts, we investigated the effect of data preprocessing and the variability due to sample processing in whole blood cell and blood monocyte gene expression data, measured on the Illumina HumanHT-12 v3 BeadChip array.Gene expression signal intensities were similar after applying the log(2) or the variance-stabilizing transformation. In all cohorts, the first principal component (PC) explained more than 95% of the total variation. Technical factors substantially influenced signal intensity values, especially the Illumina chip assignment (33-48% of the variance), the RNA amplification batch (12-24%), the RNA isolation batch (16%), and the sample storage time, in particular the time between blood donation and RNA isolation for the whole blood cell samples (2-3%), and the time between RNA isolation and amplification for the monocyte samples (2%). White blood cell composition parameters were the strongest biological factors influencing the expression signal intensities in the whole blood cell samples (3%), followed by sex (1-2%) in both sample types. Known single nucleotide polymorphisms (SNPs) were located in 38% of the analyzed probe sequences and 4% of them included common SNPs (minor allele frequency >5%). Out of the tested SNPs, 1.4% significantly modified the probe-specific expression signals (Bonferroni corrected p-value<0.05), but in almost half of these events the signal intensities were even increased despite the occurrence of the mismatch. Thus, the vast majority of SNPs within probes had no significant effect on hybridization efficiency.In summary, adjustment for a few selected technical factors greatly improved reliability of gene expression analyses. Such adjustments are particularly required for meta-analyses.
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