GMQN: A Reference-Based Method for Correcting Batch Effects and Probe Bias in HumanMethylation BeadChip.
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ABSTRACT:
SUBMITTER: Xiong Z
PROVIDER: S-EPMC8777061 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationThe Illumina HumanMethylation450 BeadChip has been extensively utilized in epigenome-wide association studies. This array and its successor, the MethylationEPIC array, use two types of probes-Infinium I (type I) and Infinium II (type II)-in order to increase genome coverage but differences in probe chemistries result in different type I and II distributions of methylation values. Ignoring the difference in distributions between the two probe types may bias downstream analysis.ResultsHere, we developed a novel method, called Regression on Correlated Probes (RCP), which uses the existing correlation between pairs of nearby type I and II probes to adjust the beta values of all type II probes. We evaluate the effect of this adjustment on reducing probe design type bias, reducing technical variation in duplicate samples, improving accuracy of measurements against known standards, and retention of biological signal. We find that RCP is statistically significantly better than unadjusted data or adjustment with alternative methods including SWAN and BMIQ.AvailabilityWe incorporated the method into the R package ENmix, which is freely available from the Bioconductor website (https://www.bioconductor.org/packages/release/bioc/html/ENmix.html).Contactniulg@ucmail.uc.eduSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC5013906 | biostudies-literature
Project description:The Illumina Infinium HumanMethylation450 Beadchips have been widely utilized in epigenome-wide association studies (EWAS). However, the existing two types of probes (type I and type II), with the distribution of measurements of probes and dynamic range different, may bias downstream analyses. Here, we propose a method, MGMIN (M-values Gaussian-MIxture Normalization), to correct the probe designs based on M-values of DNA methylation. Our strategy includes fitting Gaussian mixture distributions to type I and type II probes separately, a transformation of M-values into quantiles and finally a dilation transformation based on M-values of DNA methylation to maintain the continuity of the data. Our method is validated on several public datasets on reducing probe design bias, reducing the technical variation and improving the ability to find biologically differential methylation signals. The results show that MGMIN achieves competitive performances compared to BMIQ which is a well-known normalization method on β-values of DNA methylation.
| S-EPMC7652792 | biostudies-literature
Project description:High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in case samples are converted to percentiles of the equivalent features in control samples within a study prior to pooling data across studies. We look at how this percentile-normalization method compares to traditional meta-analysis methods for combining independent p-values and to limma and ComBat, widely used batch-correction models developed for RNA microarray data. Overall, we show that percentile-normalization is a simple, non-parametric approach for correcting batch effects and improving sensitivity in case-control meta-analyses.
| S-EPMC5940237 | biostudies-literature
Project description:BACKGROUND:The Illumina Infinium HumanMethylation450 BeadChip and its successor, Infinium MethylationEPIC BeadChip, have been extensively utilized in epigenome-wide association studies. Both arrays use two fluorescent dyes (Cy3-green/Cy5-red) to measure methylation level at CpG sites. However, performance difference between dyes can result in biased estimates of methylation levels. RESULTS:Here we describe a novel method, called REgression on Logarithm of Internal Control probes (RELIC) to correct for dye bias on whole array by utilizing the intensity values of paired internal control probes that monitor the two color channels. We evaluate the method in several datasets against other widely used dye-bias correction methods. Results on data quality improvement showed that RELIC correction statistically significantly outperforms alternative dye-bias correction methods. We incorporated the method into the R package ENmix, which is freely available from the Bioconductor website ( https://www.bioconductor.org/packages/release/bioc/html/ENmix.html ). CONCLUSIONS:RELIC is an efficient and robust method to correct for dye-bias in Illumina Methylation BeadChip data. It outperforms other alternative methods and conveniently implemented in R package ENmix to facilitate DNA methylation studies.
| S-EPMC5209853 | biostudies-literature
Project description:MotivationThe Illumina Infinium 450 k DNA Methylation Beadchip is a prime candidate technology for Epigenome-Wide Association Studies (EWAS). However, a difficulty associated with these beadarrays is that probes come in two different designs, characterized by widely different DNA methylation distributions and dynamic range, which may bias downstream analyses. A key statistical issue is therefore how best to adjust for the two different probe designs.ResultsHere we propose a novel model-based intra-array normalization strategy for 450 k data, called BMIQ (Beta MIxture Quantile dilation), to adjust the beta-values of type2 design probes into a statistical distribution characteristic of type1 probes. The strategy involves application of a three-state beta-mixture model to assign probes to methylation states, subsequent transformation of probabilities into quantiles and finally a methylation-dependent dilation transformation to preserve the monotonicity and continuity of the data. We validate our method on cell-line data, fresh frozen and paraffin-embedded tumour tissue samples and demonstrate that BMIQ compares favourably with two competing methods. Specifically, we show that BMIQ improves the robustness of the normalization procedure, reduces the technical variation and bias of type2 probe values and successfully eliminates the type1 enrichment bias caused by the lower dynamic range of type2 probes. BMIQ will be useful as a preprocessing step for any study using the Illumina Infinium 450 k platform.AvailabilityBMIQ is freely available from http://code.google.com/p/bmiq/.Contacta.teschendorff@ucl.ac.ukSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3546795 | biostudies-literature
Project description:Scanline observation is known to introduce an angular bias into the probability distribution of orientation in three-dimensional space. In this paper, numerical solutions expressing the functional relationship between the scanline-observational distribution (in one-dimensional space) and the inherent distribution (in three-dimensional space) are derived using probability theory and calculus under the independence hypothesis of dip direction and dip angle. Based on these solutions, a novel method for obtaining the inherent distribution (also for correcting the bias) is proposed, an approach which includes two procedures: 1) Correcting the cumulative probabilities of orientation according to the solutions, and 2) Determining the distribution of the corrected orientations using approximation methods such as the one-sample Kolmogorov-Smirnov test. The inherent distribution corrected by the proposed method can be used for discrete fracture network (DFN) modelling, which is applied to such areas as rockmass stability evaluation, rockmass permeability analysis, rockmass quality calculation and other related fields. To maximize the correction capacity of the proposed method, the observed sample size is suggested through effectiveness tests for different distribution types, dispersions and sample sizes. The performance of the proposed method and the comparison of its correction capacity with existing methods are illustrated with two case studies.
| S-EPMC4785530 | biostudies-literature
Project description:Sensitivity and specificity are common measures of the accuracy of a diagnostic test. The usual estimators of these quantities are unbiased if data on the diagnostic test result and the true disease status are obtained from all subjects in an appropriately selected sample. In some studies, verification of the true disease status is performed only for a subset of subjects, possibly depending on the result of the diagnostic test and other characteristics of the subjects. Estimators of sensitivity and specificity based on this subset of subjects are typically biased; this is known as verification bias. Methods have been proposed to correct verification bias under the assumption that the missing data on disease status are missing at random (MAR), that is, the probability of missingness depends on the true (missing) disease status only through the test result and observed covariate information. When some of the covariates are continuous, or the number of covariates is relatively large, the existing methods require parametric models for the probability of disease or the probability of verification (given the test result and covariates), and hence are subject to model misspecification. We propose a new method for correcting verification bias based on the propensity score, defined as the predicted probability of verification given the test result and observed covariates. This is estimated separately for those with positive and negative test results. The new method classifies the verified sample into several subsamples that have homogeneous propensity scores and allows correction for verification bias. Simulation studies demonstrate that the new estimators are more robust to model misspecification than existing methods, but still perform well when the models for the probability of disease and probability of verification are correctly specified.
| S-EPMC3276270 | biostudies-literature
Project description:Many biological data acquisition platforms suffer from inadvertent inclusion of biologically irrelevant variance in analyzed data, collectively termed batch effects. Batch effects can lead to difficulties in downstream analysis by lowering the power to detect biologically interesting differences and can in certain instances lead to false discoveries. They are especially troublesome in predictive modelling where samples in training sets and test sets are often completely correlated with batches. In this article, we present BARA, a normalization method for adjusting batch effects in predictive modelling. BARA utilizes a few reference samples to adjust for batch effects in a compressed data space spanned by the training set. We evaluate BARA using a collection of publicly available datasets and three different prediction models, and compare its performance to already existing methods developed for similar purposes. The results show that data normalized with BARA generates high and consistent prediction performances. Further, they suggest that BARA produces reliable performances independent of the examined classifiers. We therefore conclude that BARA has great potential to facilitate the development of predictive assays where test sets and training sets are correlated with batch.
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Project description:In two-color microarray experiments, well-known differences exist in the labeling and hybridization efficiency of Cy3 and Cy5 dyes. Previous reports have revealed that these differences can vary on a gene-by-gene basis, an effect termed gene-specific dye bias. If uncorrected, this bias can influence the determination of differentially expressed genes.We show that the magnitude of the bias scales multiplicatively with signal intensity and is dependent on which nucleotide has been conjugated to the fluorescent dye. A method is proposed to account for gene-specific dye bias within a maximum-likelihood error modeling framework. Using two different labeling schemes, we show that correcting for gene-specific dye bias results in the superior identification of differentially expressed genes within this framework. Improvement is also possible in related ANOVA approaches.A software implementation of this procedure is freely available at http://cellcircuits.org/VERA
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Project description:Chromatin Interaction Analysis by Paired-End Tag sequencing (ChIA-PET) is an established method for detecting genome-wide looping interactions at high resolution. Current ChIA-PET analysis software packages either fail to correct for non-specific interactions due to genomic proximity or only address a fraction of the steps required for data processing. We present Mango, a complete ChIA-PET data analysis pipeline that provides statistical confidence estimates for interactions and corrects for major sources of bias including differential peak enrichment and genomic proximity.Comparison to the existing software packages, ChIA-PET Tool and ChiaSig revealed that Mango interactions exhibit much better agreement with high-resolution Hi-C data. Importantly, Mango executes all steps required for processing ChIA-PET datasets, whereas ChiaSig only completes 20% of the required steps. Application of Mango to multiple available ChIA-PET datasets permitted the independent rediscovery of known trends in chromatin loops including enrichment of CTCF, RAD21, SMC3 and ZNF143 at the anchor regions of interactions and strong bias for convergent CTCF motifs.Mango is open source and distributed through github at https://github.com/dphansti/mango.mpsnyder@standford.eduSupplementary data are available at Bioinformatics online.
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