Function reference • MethylIT

Data preprocessing
`MethylIT`	MethylIT: Methylation Analysis Based on Signal Detection and Machine Learning
`readCounts2GRangesList()`	Read files of methylation count tables
`poolFromGRlist()`	Methylation pool from a list of GRanges objects with methylation read counts
`estimateDivergence()`	Information Divergences of Methylation Levels
Signal detection
`nonlinearFitDist()`	Nonlinear fit of Information divergences distribution
`gofReport()` `print(<ProbDistrList>)`	Report the Best Fitted Probability Distribution Model
`getPotentialDIMP()`	Potential methylation signal
`selectDIMP()` `print(<InfDiv>)` `print(<pDMP>)` `print(<testDMP>)`	Selection of DMPs
`estimateCutPoint()`	Estimate cutpoints to distinguish the treatment methylation signal from the control
`evaluateDIMPclass()`	Evaluate DMPs Classification
Differentially methylated regions
`getDIMPatGenes()`	Count DMPs at gene-body
`getDMPatRegions()`	Count DMPs at Genomic Regions
`getGRangesStat()`	Statistic of Genomic Regions
`dmpClusters()`	DMP clustering
`dmrfinder()`	Find Differentially Methylated Regions (DMRs)
`glmDataSet()`	Data set constructor for class glmDataSet
`countTest2()`	Regression Test for Count
`getDMGs()`	DMGs Estimation on Specified Genomic Region
Base functions
`uniqueGRanges()`	Unique genomic ranges from a list of GRanges objects
`filterGRange()`	Filter methylation counts by coverage in a GRanges object
`estimateBayesianDivergence()`	Information divergence estimator
`estimateBetaDist()`	Select the beta distribution that fit specified quantiles
`beta_bin_meth()`	Beta-binomial Posterior Methylation Levels
`estimateHellingerDiv()`	Hellinger divergence of methylation levels
`estimateJDiv()`	J Information Divergence of Methylation Levels
`filterByCoverage()`	Filter methylation counts by coverage
`fitGGammaDist()`	Nonlinear fit of Generalized Gamma CDF (GGamma)
`fitGammaDist()`	Nonlinear fit of Gamma CDF (Gamma)
`fitLogNormDist()`	Nonlinear fit of Log-Normal CDF (LogNorm)
`pcaLDA()` `predict.pcaLDA()`	Linear Discriminant Analysis (LDA) using Principal Component Analysis (PCA)
`pcaLogisticR()` `predict.pcaLogisticR()`	Logistic Classification Model using Principal Component Analysis (PCA)
`pcaQDA()` `predict.pcaQDA()`	Quadratic Discriminant Analysis (QDA) using Principal Component Analysis (PCA)
`predict.LogisticR()`	Predict function for logistic regression model from 'LogisticR' class
`predictDIMPclass()`	Predict DIMP class
`sortBySeqnameAndStart()` `sortBySeqnameAndEnd()`	Sorting 'GRanges' objects
`uniqueGRfilterByCov()`	Unique `GRanges-class` of methylation read counts filtered by coverage.
Auxiliary functions
`boltzman_factor()`	Boltzmann's Factors
`coef(<cdfMODEL>)` `coef(<cdfMODELlist>)` `coef(<ProbDistrList>)`	Extract Model Coefficients
`FisherTest()`	Fisher's exact test for read counts on GRanges objects
`GeneUpDownStream()`	Get Genes plus Up and Down Stream Regions
`getGEOSuppFiles()`	Get Supplemental Files from GEO
`dggamma()` `pggamma()` `qggamma()` `rggamma()`	Generalized Gamma distribution
`gibb_entropy()`	Gibbs entropy of Generalized Gamma Distribution
`helmholtz_free_energy()`	Helmholtz Free Energy of Generalized Gamma Distribution
`machine_ent()`	Molecular Machine Entropy
`meth_levels()`	Compute methylation levels
`pjob_split()`	Parallel Job Split
`predict()`	Prediction Method of Random Forest for 'pDMP' Objects
`predict.cdfMODEL()` `predict.cdfMODELlist()`	Predict function for probability distributions in Methyl-IT
`pweibull3P()`	Weibull distribution with three parameters
`slidingGRegions()`	Generates intervals for a GRanges objects
`weibull3P()`	Nonlinear fit of Weibull CDF
Data
`HD`	Simulated dataset of Hellinger divergences used in the examples
`hdiv`	Simulated dataset of Information divergences used in the examples
`PS`	Simulated dataset of potential DMPs used in examples
`cutpoint`	Cutpoint of the 'PS' simulated dataset used in the examples
`dmps`	Simulated dataset of DMPs used in examples
`ds`	Simulated dataset of RangedGlmDataSet class object (DMPs counts)
`gof`	Simulated dataset of nonlinear fits used in the examples
`lda_perf`	Classification LDA model for simulated dataset of DMPs used in examples
`logit_perf`	Classification logistic model for simulated dataset of DMPs used in examples
`pcaLda_perf`	Classification PCA+LDA model for simulated dataset of DMPs used in examples
`pcaQda_perf`	Classification PCA+QDA model for simulated dataset of DMPs used in examples
`pcalogit_perf`	Classification logistic model for simulated dataset of DMPs used in examples
`qda_perf`	Classification LDA model for simulated dataset of DMPs used in examples
`rcounts`	Simulated dataset of read counts used in the examples

Reference

Data preprocessing

Signal detection

Differentially methylated regions

Base functions

Auxiliary functions

Data