Dataset Information

Purity estimation from differentially methylated sites using Illumina Infinium methylation microarray data.

ABSTRACT: Solid tissues collected from patient-driven clinical settings are composed of both normal and cancer cells, which often precede complications in data analysis and epigenetic findings. The Purity estimation of samples is crucial for reliable genomic aberration identification and uniform inter-sample and inter-patient comparisons as well. Here, an effective and flexible method has been developed and designed to estimate the level of methylation, which infers tumor purity without prior knowledge from the other datasets. The comprehensive analysis of our approach on Illumina Infinium 450 k methylation microarray explains that TCGA Breast Cancer data exhibits improved performance for purity assessment. This assessment has a strong correlation with other advanced methods.

SUBMITTER: Azim R

PROVIDER: S-EPMC7469651 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Purity estimation from differentially methylated sites using Illumina Infinium methylation microarray data.

Azim Riasat R Wang Shulin S Zhou Su S Zhong Xing X

Cell cycle (Georgetown, Tex.) 20200705 16

Solid tissues collected from patient-driven clinical settings are composed of both normal and cancer cells, which often precede complications in data analysis and epigenetic findings. The Purity estimation of samples is crucial for reliable genomic aberration identification and uniform inter-sample and inter-patient comparisons as well. Here, an effective and flexible method has been developed and designed to estimate the level of methylation, which infers tumor purity without prior knowledge fr ...[more]

PMID: 32627651

Similar Datasets

Project description:The Illumina Infinium MethylationEPIC provides an efficient platform for profiling DNA methylation in humans at over 850,000 CpGs. Model organisms such as mice do not currently benefit from an equivalent array. Here we used this array to measure DNA methylation in mice. We defined probes targeting conserved regions and performed differential methylation analysis and compared between the array-based assay and affinity-based DNA sequencing of methyl-CpGs (MBD-seq) and reduced representation bisulfite sequencing. Mouse samples consisted of 11 liver DNA from two strains, C57BL/6J (B6) and DBA/2J (D2), that varied widely in age. Linear regression was applied to detect differential methylation. In total, 13,665 probes (1.6% of total probes) aligned to conserved CpGs. Beta-values (?-value) for these probes showed a distribution similar to that in humans. Overall, there was high concordance in methylation signal between the EPIC array and MBD-seq (Pearson correlation r = 0.70, p-value < 0.0001). However, the EPIC probes had higher quantitative sensitivity at CpGs that are hypo- (?-value < 0.3) or hypermethylated (?-value > 0.7). In terms of differential methylation, no EPIC probe detected a significant difference between age groups at a Benjamini-Hochberg threshold of 10%, and the MBD-seq performed better at detecting age-dependent change in methylation. However, the top most significant probe for age (cg13269407; uncorrected p-value = 1.8 x 10-5) is part of the clock CpGs used to estimate the human epigenetic age. For strain, 219 EPIC probes detected significant differential methylation (FDR cutoff 10%) with ~80% CpGs associated with higher methylation in D2. This higher methylation profile in D2 compared to B6 was also replicated by the MBD-seq data. To summarize, we found only a small subset of EPIC probes that target conserved sites. However, for this small subset the array provides a reliable assay of DNA methylation and can be effectively used to measure differential methylation in mice.

Project description:BACKGROUND: Genome-wide methylation profiling has led to more comprehensive insights into gene regulation mechanisms and potential therapeutic targets. Illumina Human Methylation BeadChip is one of the most commonly used genome-wide methylation platforms. Similar to other microarray experiments, methylation data is susceptible to various technical artifacts, particularly batch effects. To date, little attention has been given to issues related to normalization and batch effect correction for this kind of data. METHODS: We evaluated three common normalization approaches and investigated their performance in batch effect removal using three datasets with different degrees of batch effects generated from HumanMethylation27 platform: quantile normalization at average ? value (QN?); two step quantile normalization at probe signals implemented in "lumi" package of R (lumi); and quantile normalization of A and B signal separately (ABnorm). Subsequent Empirical Bayes (EB) batch adjustment was also evaluated. RESULTS: Each normalization could remove a portion of batch effects and their effectiveness differed depending on the severity of batch effects in a dataset. For the dataset with minor batch effects (Dataset 1), normalization alone appeared adequate and "lumi" showed the best performance. However, all methods left substantial batch effects intact in the datasets with obvious batch effects and further correction was necessary. Without any correction, 50 and 66 percent of CpGs were associated with batch effects in Dataset 2 and 3, respectively. After QN?, lumi or ABnorm, the number of CpGs associated with batch effects were reduced to 24, 32, and 26 percent for Dataset 2; and 37, 46, and 35 percent for Dataset 3, respectively. Additional EB correction effectively removed such remaining non-biological effects. More importantly, the two-step procedure almost tripled the numbers of CpGs associated with the outcome of interest for the two datasets. CONCLUSION: Genome-wide methylation data from Infinium Methylation BeadChip can be susceptible to batch effects with profound impacts on downstream analyses and conclusions. Normalization can reduce part but not all batch effects. EB correction along with normalization is recommended for effective batch effect removal.

Project description:Deoxyribonucleic acid (DNA) methylation is an epigenetic alteration crucial for regulating stress responses. Identifying large-scale DNA methylation at single nucleotide resolution is made possible by whole genome bisulfite sequencing. An essential task following the generation of bisulfite sequencing data is to detect differentially methylated cytosines (DMCs) among treatments. Most statistical methods for DMC detection do not consider the dependency of methylation patterns across the genome, thus possibly inflating type I error. Furthermore, small sample sizes and weak methylation effects among different phenotype categories make it difficult for these statistical methods to accurately detect DMCs. To address these issues, the wavelet-based functional mixed model (WFMM) was introduced to detect DMCs. To further examine the performance of WFMM in detecting weak differential methylation events, we used both simulated and empirical data and compare WFMM performance to a popular DMC detection tool methylKit. Analyses of simulated data that replicated the effects of the herbicide glyphosate on DNA methylation in Arabidopsis thaliana show that WFMM results in higher sensitivity and specificity in detecting DMCs compared to methylKit, especially when the methylation differences among phenotype groups are small. Moreover, the performance of WFMM is robust with respect to small sample sizes, making it particularly attractive considering the current high costs of bisulfite sequencing. Analysis of empirical Arabidopsis thaliana data under varying glyphosate dosages, and the analysis of monozygotic (MZ) twins who have different pain sensitivities-both datasets have weak methylation effects of <1%-show that WFMM can identify more relevant DMCs related to the phenotype of interest than methylKit. Differentially methylated regions (DMRs) are genomic regions with different DNA methylation status across biological samples. DMRs and DMCs are essentially the same concepts, with the only difference being how methylation information across the genome is summarized. If methylation levels are determined by grouping neighboring cytosine sites, then they are DMRs; if methylation levels are calculated based on single cytosines, they are DMCs.

Dataset Information

Purity estimation from differentially methylated sites using Illumina Infinium methylation microarray data.

Publications

Purity estimation from differentially methylated sites using Illumina Infinium methylation microarray data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets