Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi

Fortin, Jean Philippe; Triche, Timothy J.; Hansen, Kasper D.

doi:10.1093/bioinformatics/btw691

Cited by 678 publications

(550 citation statements)

References 16 publications

Supporting

Mentioning

549

Contrasting

Unclassified

Order By: Relevance

“…Unlike quantile normalization [Bolstad et al, 2003], which forces the histograms to be all the same across subjects, functional normalization only removes variation in the histograms that can be explained by a covariate. It has been successfully used to normalize cancer data and to normalize data from different genomic array technologies [Fortin et al, 2016b]. For multi-site DTI studies, we use site as a covariate.…”

Section: Methodsmentioning

confidence: 99%

Harmonization of multi-site diffusion tensor imaging data

et al. 2017

Self Cite

View full text Add to dashboard Cite

Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, may be counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies.

show abstract

Section: Methodsmentioning

confidence: 99%

Harmonization of multi-site diffusion tensor imaging data

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…The methylation data were processed with the R packages Minfi and RnBeads . The two cohorts were analyzed separately.…”

Section: Methodsmentioning

confidence: 99%

“…The two cohorts were analyzed separately. Background correction was performed with the Noob method in Minfi . Using RnBeads, probes were filtered out if they: (i) overlapped within 5 bp of a SNP (DC: 21 414; NSG: 21 358 probes); (ii) had a detection P‐ value > 0.01 or were considered unreliable measures based on RnBeads's greedy‐cut algorithm (DC: 26 065; NSG: 18 864 probes); or (iii) were context‐specific sites (DC: 2825; NSG: 2873 probes).…”

Section: Methodsmentioning

confidence: 99%

Genome‐wide DNA methylation investigation of glucocorticoid exposure within buccal samples

Braun

Tanaka-Sahker

Chan

et al. 2019

Psychiatry Clin Neurosci

View full text Add to dashboard Cite

Aim Glucocorticoids play a major role in regulating the stress response, and an imbalance of glucocorticoids has been implicated in stress‐related disorders. Within mouse models, CpGs across the genome have been shown to be differentially methylated in response to glucocorticoid treatment, and using the Infinium 27K array, it was shown that humans given synthetic glucocorticoids had DNA methylation (DNAm) changes in blood. However, further investigation of the extent to which glucocorticoids affect DNAm across a larger proportion of the genome is needed. Methods Buccal samples were collected before and after synthetic glucocorticoid treatment in the context of a dental procedure. This included 30 tooth extraction surgery patients who received 10 mg of dexamethasone. Genome‐wide DNAm was assessed with the Infinium HumanMethylationEPIC array. Results Five CpGs showed genome‐wide significant DNAm changes that were >10%. These differentially methylated CpGs were in or nearest the following genes: ZNF438, KLHDC10, miR‐544 or CRABP1, DPH5, and WDFY2. Using previously published datasets of human blood gene expression changes following dexamethasone exposure, a significant proportion of genes with false‐discovery‐rate‐adjusted significant CpGs were also differentially expressed. A pathway analysis of the genes with false‐discovery‐rate‐adjusted significant CpGs revealed significant enrichment of olfactory transduction, pentose and glucuronate interconversions, ascorbate and aldarate metabolism, and steroid hormone biosynthesis pathways. Conclusion High‐dose synthetic glucocorticoid administration in the setting of a dental procedure was significantly associated with DNAm changes within buccal samples. These findings are consistent with prior findings of an influence of glucocorticoids on DNAm in humans.

show abstract

“…After extension, arrays were fluorescently stained and scanned using an iScan array scanner (Illumina). Data analysis was performed in R studio version 1.1.423 and extraction of beta values was performed using the minfi package version 1.24.0 …”

Section: Methodsmentioning

confidence: 99%

“…Data analysis was performed in R studio version 1.1.423 and extraction of beta values was performed using the minfi package version 1.24.0. [26][27][28][29][30]…”

Section: Global Gene Expression Profilingmentioning

confidence: 99%

Somatic mutations and promotor methylation of the ryanodine receptor 2 is a common event in the pathogenesis of head and neck cancer

Schmitt

Molfenter

Laureano

et al. 2019

Intl Journal of Cancer

View full text Add to dashboard Cite

Genomic sequencing projects unraveled the mutational landscape of head and neck squamous cell carcinoma (HNSCC) and provided a comprehensive catalog of somatic mutations. However, the limited number of significant cancer‐related genes obtained so far only partially explains the biological complexity of HNSCC and hampers the development of novel diagnostic biomarkers and therapeutic targets. We pursued a multiscale omics approach based on whole‐exome sequencing, global DNA methylation and gene expression profiling data derived from tumor samples of the HIPO‐HNC cohort (n = 87), and confirmed new findings with datasets from The Cancer Genome Atlas (TCGA). Promoter methylation was confirmed by MassARRAY analysis and protein expression was assessed by immunohistochemistry and immunofluorescence staining. We discovered a set of cancer‐related genes with frequent somatic mutations and high frequency of promoter methylation. This included the ryanodine receptor 2 (RYR2), which showed variable promoter methylation and expression in both tumor samples and cell lines. Immunohistochemical staining of tissue sections unraveled a gradual loss of RYR2 expression from normal mucosa via dysplastic lesion to invasive cancer and indicated that reduced RYR2 expression in adjacent tissue and precancerous lesions might serve as risk factor for unfavorable prognosis and upcoming malignant conversion. In summary, our data indicate that impaired RYR2 function by either somatic mutation or epigenetic silencing is a common event in HNSCC pathogenesis. Detection of RYR2 expression and/or promoter methylation might enable risk assessment for malignant conversion of dysplastic lesions.

show abstract

Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi

Cited by 678 publications

References 16 publications

Harmonization of multi-site diffusion tensor imaging data

Harmonization of multi-site diffusion tensor imaging data

Genome‐wide DNA methylation investigation of glucocorticoid exposure within buccal samples

Somatic mutations and promotor methylation of the ryanodine receptor 2 is a common event in the pathogenesis of head and neck cancer

Contact Info

Product

Resources

About