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ReFACTor / Reference-Free Adjustment for Cell-Type composition
A method based on principal component analysis (PCA) and designed for the correction of cell type heterogeneity in epigenome-wide association studies (EWAS). ReFACTor tool is based on a variant of PCA and can be applied to any tissue. It selects the sites that can be reconstructed with low error using a low-rank approximation of the original methylation matrix. Moreover, ReFACTor does not use the phenotype in the selection process, making ReFACTor useful as part of a quality control step in EWAS.
EWAS / Epigenome-Wide Association study Software
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Identifies the disease-related methylecomtypes. EWAS is a standalone application initially developed for computing DNA methylation disequilibrium (MD) coefficient between two CpG loci, identifying MD blocks across the whole genome or carrying out case-control association study of methylecomtypes. It can also use normalized methylation data to perform epigenome-wide association studies for single marker and methylation haplotype (meplotype) as well as epigenome-wide association meta-analysis.
Maximizes power while properly controlling the false positive rate. Bacon is based on the estimation of the empirical null distribution using Bayesian statistics. The utility of the tool was illustrated through the application of meta-analysis by performing epigenome- and transcriptome-wide association studies (EWASs/TWASs) on age and smoking which highlighted an overlap in differential methylation and expression of associated genes. It can be used to remove inflation and bias often observed in EWAS/TWAS.
RefFreeEWAS / RefFree Epigenome-Wide Association Studies
Permits reference-free deconvolution. RefFreeEWAS offers a method for evaluating the extent to which the underlying reflects specific types of cells. It differs from widely used principal components analysis (PCA)-based methods such as Surrogate Variable Analysis (SVA) in imposing biologically based constraints, thus resulting in mixture coefficients having greater biological interpretation and placing greater emphasis on coordinated cellular processes.
eFORGE / experimentally derived Functional element Overlap analysis of ReGions from EWAS
Identifies the cell type-specific regulatory component of a set of epigenome wide association studies (EWAS)-identified differentially methylated positions. eFORGE generates a background of 1,000 random probe sets with matching properties regarding their location within genes and CpG islands. It shows rigorous performance assessment with regards to false-positive rates and speed to ensure ability to cope with high user demand. All results produced by the tool are highly reproducible.
EDec / Epigenomic Deconvolution
Provides accurate platform-independent estimation of cell type proportions, DNA methylation profiles and gene expression profiles of constituent cell type. EDec enables deconvolution of complex tumor tissues where highly accurate reference are enables. EDec reveals layers of biological information about distinct cell types within solid tumors and about their heterotypic interactions that were previously inaccessible at such large scale due to tissue heterogeneity.
Provides an assortment of methods for methylome-wide association studies (MWAS). RaMWAS includes multiples functions: (i) sex check via read count on chr. X and Y, (ii) fragment size distribution estimation, (iii) enrichment diagnostic via average score by CpG density plot, (iv) quantile-quantile plot for major depression disorder phenotype, (v) principal component analysis and (vi) methlylation risk score for age. The application can also be applied to alternative datatypes or platforms such as arrays.
A web-based plotting tool and R-based package to visualize EWAS (epigenome-wide association scan) results in a genomic region of interest. coMET provides a plot of the EWAS association signal and visualisation of the methylation correlation between CpG sites (co-methylation). The coMET package also provides the option to annotate the region using functional genomic information, including both user-defined features and pre-selected features based on the Encode project. The plot can be customized with different parameters, such as plot labels, colours, symbols, heatmap colour scheme, significance thresholds, and including reference CpG sites. The software package is designed for epigenetic data, but can also be applied to genomic and functional genomic datasets in any species.
BayesCCE / Bayesian Cell Count Estimation
Encodes experimentally obtained cell count information as a prior on the distribution of the cell type composition in the data. BayesCCE is a Bayesian semi-supervised method that leverages prior knowledge on the cell type composition distribution in the studied tissue. It can generate components such that each component corresponds to a linear transformation of a single cell type. These components can allow researchers to perform downstream analysis that is not possible using existing reference-free methods.
BECon / Blood-Brain Epigenetic Concordance
Provides a resource to the community to aid interpretation of blood based DNA methylation results in the context of brain tissue. BECon provides metrics data on: the variability of CpGs in blood and brain samples, the concordance of CpGs between blood and brain, and estimations of how strongly a CpG is affected by cell composition in both blood and brain. This data come from paired samples from 16 individuals from three brain regions and whole blood, run on the Illumina 450K Human Methylation Array to quantify the concordance of DNA methylation between tissues. It is expected that BECon will be useful to users who need to interpret blood DNAm results from a study of brain function and health. This application may also help guide blood based surrogate studies toward candidate gene approaches or post-hoc selection of CpGs for validation.
EpiDISH / Epigenetic Dissection of Intra-Sample Heterogeneity
Uses DNAse Hypersensitive Site (DHS) data from the NIH Roadmap and ENCODE to construct a reference DNAm database. EpiDISH is a reference-based algorithm, for in-silico deconvolution of DNA methylation data, which compares very favorably in relation to the current gold-standard. The use of EpiDISH is recommended for dissection of intra-sample heterogeneity in Epigenome-Wide Association Studies (EWAS). It infers the proportions of a priori known cell subtypes present in a sample representing a mixture of such cell-types.
NEpiC / Network-assisted algorithm for Epigenetic studies that uses mean and variance Combined signals
Incorporates combined signals in mean and variance differences of DNA methylation data. In the proposed NEpiC algorithm, we first compute mean and variance signal scores for a CpG site and then summarize the two scores with weights to create a combined score for the CpG site. We then extract the gene-level score out of all CpG sites on a gene. Finally, using a protein protein interction (PPI) network, we search for dense modules that are enriched for genes with large gene-level scores with a greedy search algorithm. Results from both simulations and real data applications demonstrate a much better performance of the NEpiC algorithm compared to several other methods that ignore either the biological network information or variance signals in DNA methylation data.
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