Getting Started with methyldeconv
+Welcome to methyldeconv! This vignette will help you get up and running with cell-type deconvolution for DNA methylation data using the package’s unified interface and included methods.
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+Installation
+You can install methyldeconv from GitHub using the pak package manager:
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+Example Usage
+methyldeconv can be applied directly to a methylSet from the minfi package, or you can apply each method separately on a beta matrix with Illumina CpG IDs.
+Both cases are demonstrated below using example data from minfi:
+ +## Loading required package: FlowSorted.Blood.450k## Loading required package: minfi## Loading required package: BiocGenerics##
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+## Position, rank, rbind, Reduce, rownames, sapply, saveRDS, setdiff,
+## table, tapply, union, unique, unsplit, which.max, which.min## Loading required package: GenomicRanges## Loading required package: stats4## Loading required package: S4Vectors##
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+## rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
+## rowWeightedSds, rowWeightedVars## Loading required package: Biobase## Welcome to Bioconductor
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+## Vignettes contain introductory material; view with
+## 'browseVignettes()'. To cite Bioconductor, see
+## 'citation("Biobase")', and for packages 'citation("pkgname")'.##
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+## strsplit## Loading required package: bumphunter## Loading required package: foreach## Loading required package: iterators## Loading required package: parallel## Loading required package: locfit## locfit 1.5-9.12 2025-03-05## Setting options('download.file.method.GEOquery'='auto')## Setting options('GEOquery.inmemory.gpl'=FALSE)## → checking methyldeconv environment and dependencies## Loading required package: IlluminaHumanMethylation450kmanifest## Loading required package: IlluminaHumanMethylation450kanno.ilmn12.hg19# use example data from Minfi
+methyl_set <- minfiData::MsetEx
+ratio_set <- minfi::ratioConvert(methyl_set)
+beta_matrix <- minfi::getBeta(ratio_set)
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+# run EpiDISH for deconvolution of example data
+result <- methyldeconv::deconvolute(methyl_set = methyl_set, method = 'epidish')## Warning in check_input_beta(beta_matrix): 12 NA values detected in your beta
+## matrix. Replacing them with 0.5.## RPC was chosen as default for "mode"## blood was chosen as default for "reference"## Starting EpiDISH deconvolution with mode RPC ...## Warning in check_input_beta(beta_matrix): 12 NA values detected in your beta
+## matrix. Replacing them with 0.5.## blood was chosen as default for "reference"
+## Starting EpiDISH deconvolution with mode RPC ...# you can also run multiple methods at the same time and get their results + aggregated results:
+result_multiple <- methyldeconv::deconvolute_combined(methyl_set = methyl_set,
+ methods = c('epidish','houseman'),
+ array = '450k')## Warning in check_input_beta(beta_matrix): 12 NA values detected in your beta
+## matrix. Replacing them with 0.5.## RPC was chosen as default for "mode"## blood was chosen as default for "reference"## Starting EpiDISH deconvolution with mode RPC ...## 450k was chosen as default for "array"## Blood was chosen as default for "compositeCellType"## IlluminaHumanMethylationEPIC was chosen as default for "referencePlatform"## IDOL was chosen as default for "probeSelect"## [estimateCellCounts2] The function will assume that no preprocessing has been performed. Using 'preprocessQuantile' in prenormalized data is experimental and it should only be run under the user responsibility## Loading required package: FlowSorted.Blood.EPIC## Loading required package: ExperimentHub## Loading required package: AnnotationHub## Loading required package: BiocFileCache## Loading required package: dbplyr##
+## Attaching package: 'AnnotationHub'## The following object is masked from 'package:Biobase':
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+## cache## see ?FlowSorted.Blood.EPIC and browseVignettes('FlowSorted.Blood.EPIC') for documentation## loading from cache## Loading required package: IlluminaHumanMethylationEPICmanifest## [convertArray] Casting as IlluminaHumanMethylationEPIC## [estimateCellCounts2] Combining user data with reference (flow sorted) data.## Warning in asMethod(object): NAs introduced by coercion## [estimateCellCounts2] Processing user and reference data together.## [preprocessQuantile] Mapping to genome.## Loading required package: IlluminaHumanMethylationEPICanno.ilm10b4.hg19##
+## Attaching package: 'IlluminaHumanMethylationEPICanno.ilm10b4.hg19'## The following objects are masked from 'package:IlluminaHumanMethylation450kanno.ilmn12.hg19':
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+## Islands.UCSC, Locations, Manifest, Other, SNPs.132CommonSingle,
+## SNPs.135CommonSingle, SNPs.137CommonSingle, SNPs.138CommonSingle,
+## SNPs.141CommonSingle, SNPs.142CommonSingle, SNPs.144CommonSingle,
+## SNPs.146CommonSingle, SNPs.147CommonSingle, SNPs.Illumina## [preprocessQuantile] Fixing outliers.## [preprocessQuantile] Quantile normalizing.## [estimateCellCounts2] Using IDOL L-DMR probes for composition estimation.## [estimateCellCounts2] Estimating proportion composition (prop), if you provide cellcounts those will be provided as counts in the composition estimation.With methyldeconv you can either get the original result object of each respective method (methyldeconv::run_XXX) or a table with cell-type fractions that has a unified format between methods (methyldeconv::deconvolute). The unified results can be visualized using the methyldeconv functions results_barplot() or results_boxplot().
Results from a run with more than one method (methyldeconv::deconvolute_combined) can be easily visualized as well with results_aggregated_boxplot:
For more details, see the package documentation and other vignettes!
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