Mercurial > repos > rnateam > methylkit
changeset 0:a8705df7c57f draft default tip
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/methylkit commit 14f0b39f64982773ef0367379b915f742eabcc1b
| author | rnateam |
|---|---|
| date | Wed, 21 Dec 2016 17:30:57 -0500 |
| parents | |
| children | |
| files | methylkit.xml test-data/input_control1.myCpG.txt test-data/input_control2.myCpG.txt test-data/input_test1.myCpG.txt test-data/input_test2.myCpG.txt test-data/output_MethPerChr.tsv test-data/output_MethylDiff.bedgraph test-data/output_PCA.pdf test-data/output_clustering.pdf test-data/output_correlation.pdf test-data/output_myDiff.bedgraph test-data/output_seg_diff.bed test-data/output_seg_raw.bed test-data/output_statistics.pdf test-data/test.r |
| diffstat | 15 files changed, 9678 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/methylkit.xml Wed Dec 21 17:30:57 2016 -0500 @@ -0,0 +1,527 @@ +<tool id="methylkit" name="methylKit" version="0.99.2"> + <description>DNA methylation analysis and annotation</description> + <macros> + <macro name="differential_methylation"> + <param name="overdispersion" type="select" + label="overdispersion" + help="if set to 'none' (default), no overdispersion + correction will be attempted. 'MN' applies a scaling + parameter to variance estimated by the model. + If set to 'shrinkMN' (experimental parameter), + scaling parameter will be shrunk towards a common value"> + <option value="none" selected="True"> + none + </option> + <option value="MN"> + MN + </option> + <option value="shrinkMN"> + shrinkMN + </option> + </param> + <param name="adjust" type="select" + label="adjust" + help="different methods to correct the p-values + for multiple testing (default: SLIM)."> + <option value="SLIM" selected="True"> + SLIM + </option> + <option value="holm"> + holm + </option> + <option value="hochberg"> + hochberg + </option> + <option value="hommel"> + hommel + </option> + <option value="bonferroni"> + bonferroni + </option> + <option value="BH"> + BH + </option> + <option value="BY"> + BY + </option> + <option value="fdr"> + fdr + </option> + <option value="none"> + none + </option> + <option value="qvalue"> + qvalue + </option> + </param> + <param name="effect" type="select" + label="effect" + help="method to calculate the mean methylation different + between groups using read coverage as weights (default: wmean). + When set to 'mean', the generic mean is applied and when + set to 'predicted', predicted means from the logistic + regression model is used for calculating the effect."> + <option value="wmean" selected="True"> + wmean + </option> + <option value="mean"> + mean + </option> + <option value="predicted"> + predicted + </option> + </param> + <param name="test" type="select" + label="test" + help="the statistical test used to determine + the methylation differences (default: Chisq-test). + The F-test can be chosen + if overdispersion control is applied."> + <option value="Chisq" selected="True"> + Chisq + </option> + <option value="F"> + F + </option> + </param> + <param name="qvalue_cutoff" type="float" + value="0.01" label="qvalue.cutoff" + help="cutoff for qvalue of differential methylation statistic (default:0.01)"> + <validator type="in_range" + message="Minimum 0 and maximum 1" min="0" max="1"/> + </param> + <param name="meth_cutoff" type="float" + value="25" label="meth.cutoff" + help="cutoff for absolute value of methylation percentage change between test and control (default:25)"> + <validator type="in_range" + message="Minimum 0 and maximum 100" min="0" max="100"/> + </param> + <param name="type" type="select" + label="type" + help="For retrieving + hyper-methylated regions/bases type='hyper', + for hypo-methylated type='hypo' (default:'all')"> + <option value="all" selected="True"> + all + </option> + <option value="hyper"> + hyper + </option> + <option value="hypo"> + hypo + </option> + </param> + </macro> + <macro name="clustering"> + <param name="dist" type="select" + label="dist" + help="the distance measure to be used. + (default: correlation)"> + <option value="correlation" selected="True"> + correlation + </option> + <option value="euclidean"> + euclidean + </option> + <option value="maximum"> + maximum + </option> + <option value="manhattan"> + manhattan + </option> + <option value="canberra"> + canberra + </option> + <option value="binary"> + binary + </option> + <option value="minkowski"> + minkowski + </option> + </param> + <param name="method" type="select" + label="method" + help="the agglomeration method to be used. + (default: ward)"> + <option value="ward" selected="True"> + ward + </option> + <option value="single"> + single + </option> + <option value="complete"> + complete + </option> + <option value="average"> + average + </option> + <option value="mcquitty"> + mcquitty + </option> + <option value="median"> + median + </option> + <option value="centroid"> + centroid + </option> + </param> + </macro> + </macros> + <requirements> + <requirement type="package" version="0.99.2">bioconductor-methylkit</requirement> + </requirements> + <stdio> + <regex match="Execution halted" + source="both" + level="fatal" + description="Execution halted." /> + <regex match="Input-Error 01" + source="both" + level="fatal" + description="Error in your input parameters: Make sure you only apply factors to selected samples." /> + <regex match="Error in" + source="both" + level="fatal" + description="An undefined error occured, please check your intput carefully and contact your administrator." /> + </stdio> + <command> +<![CDATA[ + Rscript $script_file +]]> + </command> + <configfiles> + <configfile name="script_file"> + library("methylKit") + + test_files = list() + control_files = list() + + test_ids = list() + control_ids = list() + + #for $i, $s in enumerate( $test_series ) + test_ids[${i}+1] = paste("test ", ${i}+1, sep="") + test_files[${i}+1] = "${s.input.file_name}" + #end for + + #for $i, $s in enumerate( $control_series ) + control_ids[${i}+1] = paste("control ", ${i}+1, sep="") + control_files[${i}+1] = "${s.input.file_name}" + #end for + + input_files = append(test_files, control_files) + sample_ids = append(test_ids, control_ids) + treatment_tag = c(rep.int(1, length(test_ids)), rep.int(0, length(control_ids))) + + myobj=methRead(input_files, sample.id=sample_ids, assembly="${assembly}", + pipeline="${pipeline}", treatment=treatment_tag) + + pdf('output_statistics.pdf') + for (obj in myobj){ + getMethylationStats(obj,plot=TRUE,both.strands=FALSE) + getCoverageStats(obj,plot=TRUE,both.strands=FALSE) + } + devname = dev.off() + + ## unite function + methidh=unite(myobj) + + pdf("output_correlation.pdf") + getCorrelation(object = methidh, plot=TRUE, method = "${correlation}") + devname = dev.off() + + #if $input_type.choice in ["all", "differential_methylation"]: + ## the last two arguments slim, weighted.mean + ## have the redundant counterparts in effect and adjust, + ## so turning them off to avoide the possible conflict. + myDiff = calculateDiffMeth(methidh, overdispersion="${input_type.overdispersion}", + adjust="${input_type.adjust}", effect="${input_type.effect}", test="${input_type.test}", + slim=FALSE, weighted.mean=FALSE) + + bedgraph(myDiff, file.name="output_myDiff.bedgraph", col.name="meth.diff", + unmeth=FALSE, log.transform=FALSE, negative=FALSE, add.on="") + + MethPerChr = diffMethPerChr(myDiff, plot=FALSE, + qvalue.cutoff=${input_type.qvalue_cutoff}, + meth.cutoff=${input_type.meth_cutoff}) + write.table(MethPerChr, sep="\t", row.names=FALSE, quote=FALSE, file="output_MethPerChr.tsv") + + MethylDiff = getMethylDiff(myDiff, difference=${input_type.meth_cutoff}, + qvalue=${input_type.qvalue_cutoff}, type="${input_type.type}") + bedgraph(MethylDiff, file.name="output_MethylDiff.bedgraph", col.name="meth.diff", + unmeth=FALSE,log.transform=FALSE,negative=FALSE,add.on="") + #end if + + #if $input_type.choice in ["all", "clustering"]: + pdf( "output_clustering.pdf" ) + methClust = clusterSamples(methidh, dist="${input_type.dist}", method="${input_type.method}") + devname = dev.off() + + pdf( "output_PCA.pdf" ) + PCASamples(methidh) + devname = dev.off() + #end if + + #if $input_type.choice in ["all", "segmentation"]: + ## methSeg works for methylRaw or methylDiff with resolution region, + ## so methylBase has to be tiled before + tileRaw = tileMethylCounts(myobj[[1]]) + tileBase = tileMethylCounts(methidh) + tileDiff = calculateDiffMeth(tileBase) + + ## methseg generates Granges + segRaw = methSeg(tileRaw, diagnostic.plot = FALSE) + segDiff = methSeg(tileDiff, diagnostic.plot = FALSE) + + ## and can be exported as BED + methSeg2bed(segments = segRaw, filename = "output_seg_raw.bed") + methSeg2bed(segments = segDiff, filename = "output_seg_diff.bed") + #end if + </configfile> + </configfiles> + <inputs> + <repeat name="test_series" title="Test samples" min="1"> + <param name="input" type="data" format="tabular" label="Add a file" + help="Such input file may be obtained from AMP pipeline for aligning RRBS reads."> + <validator type="unspecified_build" /> + </param> + </repeat> + <repeat name="control_series" title="Control samples" min="1"> + <param name="input" type="data" format="tabular" label="Add a file" + help="Such input file may be obtained from AMP pipeline for aligning RRBS reads." > + <validator type="unspecified_build" /> + </param> + </repeat> + <param name="assembly" type="text" + value="hg18" label="assembly" + help="A string that defines the genome assembly such as + hg18, mm9 (default: hg18)."> + </param> + <param name="correlation" type="select" + label="correlation" + help="correlation method (default: pearson)"> + <option value="pearson" selected="True"> + pearson + </option> + <option value="kendall"> + kendall + </option> + <option value="spearman"> + spearman + </option> + </param> + <param name="pipeline" type="select" + label="pipeline" + help="name of the alignment pipeline (default: amp)"> + <option value="amp" selected="True"> + amp + </option> + <option value="bismark"> + bismark + </option> + <option value="bismarkCoverage"> + bismarkCoverage + </option> + <option value="bismarkCytosineReport"> + bismarkCytosineReport + </option> + </param> + <conditional name="input_type"> + <param name="choice" type="select" + label="analysis to carry out:" + help="The analysis you wish to carry out."> + <option value="all" selected="True"> + All provided analysis + </option> + <option value="differential_methylation"> + Differential methylation + </option> + <option value="clustering"> + Clustering + </option> + <option value="segmentation"> + Segmentation + </option> + </param> + <when value="all"> + <expand macro="differential_methylation" /> + <expand macro="clustering" /> + </when> + <when value="differential_methylation"> + <expand macro="differential_methylation" /> + </when> + <when value="clustering"> + <expand macro="clustering" /> + </when> + <when value="segmentation" /> + </conditional> + </inputs> + <outputs> + <data name="output_statistics" format="pdf" + from_work_dir="output_statistics.pdf" + label="${tool.name} on ${on_string}: CpG statistics"> + </data> + + <data name="output_correlation" format="pdf" + from_work_dir="output_correlation.pdf" + label="${tool.name} on ${on_string}: correlation between samples"> + </data> + + <data name="output_myDiff" format="bedgraph" + from_work_dir="output_myDiff.bedgraph" + label="${tool.name} on ${on_string}: differential methylation"> + <filter>input_type['choice'] in ['all', 'differential_methylation']</filter> + </data> + + <data name="output_MethylDiff" format="bedgraph" + from_work_dir="output_MethylDiff.bedgraph" + label="${tool.name} on ${on_string}: differential methylation - subset"> + <filter>input_type['choice'] in ['all', 'differential_methylation']</filter> + </data> + + <data name="output_MethPerChr" format="tabular" + from_work_dir="output_MethPerChr.tsv" + label="${tool.name} on ${on_string}: number of hyper/hypo sites"> + <filter>input_type['choice'] in ['all', 'differential_methylation']</filter> + </data> + + <data name="output_clustering" format="pdf" + from_work_dir="output_clustering.pdf" + label="${tool.name} on ${on_string}: hierarchical clustering"> + <filter>input_type['choice'] in ['all', 'clustering']</filter> + </data> + + <data name="output_PCA" format="pdf" + from_work_dir="output_PCA.pdf" + label="${tool.name} on ${on_string}: PCA"> + <filter>input_type['choice'] in ['all', 'clustering']</filter> + </data> + + <data name="output_seg_raw" format="bed" + from_work_dir="output_seg_raw.bed" + label="${tool.name} on ${on_string}: methylation segment"> + <filter>input_type['choice'] in ['all', 'segmentation']</filter> + </data> + + <data name="output_seg_diff" format="bed" + from_work_dir="output_seg_diff.bed" + label="${tool.name} on ${on_string}: differential methylation segment"> + <filter>input_type['choice'] in ['all', 'segmentation']</filter> + </data> + </outputs> + <tests> + <test> + <repeat name="test_series"> + <param name="input" value="input_test1.myCpG.txt" dbkey="hg18" ftype="tabular" /> + </repeat> + <repeat name="test_series"> + <param name="input" value="input_test2.myCpG.txt" dbkey="hg18" ftype="tabular" /> + </repeat> + <repeat name="control_series"> + <param name="input" value="input_control1.myCpG.txt" dbkey="hg18" ftype="tabular" /> + </repeat> + <repeat name="control_series"> + <param name="input" value="input_control2.myCpG.txt" dbkey="hg18" ftype="tabular" /> + </repeat> + <param name="assembly" value="hg18" /> + <param name="correlation" value="pearson" /> + <param name="pipeline" value="amp" /> + <param name="choice" value="all" /> + <param name="overdispersion" value="none" /> + <param name="adjust" value="SLIM" /> + <param name="effect" value="wmean" /> + <param name="test" value="Chisq" /> + <param name="qvalue_cutoff" value="0.01" /> + <param name="meth_cutoff" value="25" /> + <param name="type" value="all" /> + <param name="dist" value="correlation" /> + <param name="method" value="ward" /> + <output name="output_statistics" file="output_statistics.pdf" + ftype="pdf" compare="sim_size"/> + <output name="output_correlation" file="output_correlation.pdf" + ftype="pdf" compare="sim_size"/> + <output name="output_myDiff" file="output_myDiff.bedgraph" + ftype="bedgraph"/> + <output name="output_MethPerChr" file="output_MethPerChr.tsv" + ftype="tabular"/> + <output name="output_MethylDiff" file="output_MethylDiff.bedgraph" + ftype="bedgraph"/> + <output name="output_clustering" file="output_clustering.pdf" + ftype="pdf" compare="sim_size"/> + <output name="output_PCA" file="output_PCA.pdf" + ftype="pdf" compare="sim_size"/> + <output name="output_seg_raw" file="output_seg_raw.bed" + ftype="bed"/> + <output name="output_seg_diff" file="output_seg_diff.bed" + ftype="bed"/> + </test> + </tests> +<help> +<![CDATA[ +.. class:: infomark + +**What it does** + +`methylKit`_ is an R package for DNA methylation analysis and annotation +from high-throughput bisulfite sequencing. +The package is designed to deal with sequencing data from RRBS and +its variants, but also target-capture methods such as Agilent SureSelect +methyl-seq. In addition, methylKit can deal with base-pair resolution data +for 5hmC obtained from Tab-seq or oxBS-seq. It can also handle whole-genome +bisulfite sequencing data if proper input format is provided. + +.. _methylKit: https://github.com/al2na/methylKit + +The Galaxy tool enables three types of analysis: + * differential methylation + * clustering + * segmentation + +The user can choose to run all provided analysis or run an individual one. + +.. class:: infomark + +**Input** + +Typically, bisulfite converted reads are aligned to the genome and % +methylation value per base is calculated by processing alignments. +methylKit takes that % methylation value per base information as input. +Such input file may be obtained from `AMP`_ pipeline +for aligning RRBS reads. A typical input file looks like this:: + + + chrBase chr base strand coverage freqC freqT + + chr21.9764539 chr21 9764539 R 12 25.00 75.00 + + chr21.9764513 chr21 9764513 F 12 0.00 100.00 + + +.. _AMP: http://code.google.com/p/amp-errbs/ + +.. class:: infomark + +**Output** + +The outputs from differential methylation + * ``differential methylation``: The `bedgraph`_ file contains differentially methylated bases/regions and the corresponding statistics. + * ``differential methylation - subset``: The bedgraph file contains the subset of differentially methylated bases/regions that satisfies the user defined thresholds with qvalue.cutoff and meth.cutoff. + * ``number of hyper/hypo sites``: The tabular file contains number of hyper/hypo methylated regions/bases. + +.. _bedgraph: https://genome.ucsc.edu/goldenpath/help/bedgraph.html + +The outputs from clustering + * ``hierarchical clustering``: The figure shows hierarchical clustering using methylation data. + * ``PCA``: The figure shows principal components analysis of methylation data. + +The output from segmentation + * ``methylation segment``: The `bed`_ file contains the profile of methylation segment. + * ``differential methylation segment``: The bed file contains the profile of differential methylation segment. + +.. _bed: https://genome.ucsc.edu/FAQ/FAQformat#format1 +]]> +</help> +<citations> + <citation type="doi">10.1186/gb-2012-13-10-r87</citation> +</citations> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input_control1.myCpG.txt Wed Dec 21 17:30:57 2016 -0500 @@ -0,0 +1,2378 @@ +chrBase chr base strand coverage freqC freqT +chr21.9826907 chr21 9826907 F 96 18.75 81.25 +chr21.9853326 chr21 9853326 F 16 87.50 12.50 +chr21.9853296 chr21 9853296 F 18 88.89 11.11 +chr21.9860126 chr21 9860126 F 83 100.00 0.00 +chr21.9906663 chr21 9906663 R 14 92.86 7.14 +chr21.9906677 chr21 9906677 R 14 78.57 21.43 +chr21.9906700 chr21 9906700 F 30 26.67 73.33 +chr21.9906954 chr21 9906954 F 30 86.67 13.33 +chr21.9906644 chr21 9906644 F 23 95.65 4.35 +chr21.9906873 chr21 9906873 F 15 26.67 73.33 +chr21.9906634 chr21 9906634 F 23 69.57 30.43 +chr21.9906704 chr21 9906704 F 18 94.44 5.56 +chr21.9906655 chr21 9906655 R 14 92.86 7.14 +chr21.9906645 chr21 9906645 R 13 100.00 0.00 +chr21.9906926 chr21 9906926 R 17 64.71 35.29 +chr21.9906619 chr21 9906619 F 22 81.82 18.18 +chr21.9906949 chr21 9906949 F 30 53.33 46.67 +chr21.9906910 chr21 9906910 R 16 56.25 43.75 +chr21.9906907 chr21 9906907 R 17 11.76 88.24 +chr21.9906681 chr21 9906681 F 37 37.84 62.16 +chr21.9906955 chr21 9906955 R 13 69.23 30.77 +chr21.9906724 chr21 9906724 F 18 72.22 27.78 +chr21.9906822 chr21 9906822 R 14 100.00 0.00 +chr21.9906808 chr21 9906808 R 13 100.00 0.00 +chr21.9906847 chr21 9906847 R 14 71.43 28.57 +chr21.9906660 chr21 9906660 R 14 64.29 35.71 +chr21.9906839 chr21 9906839 R 13 69.23 30.77 +chr21.9906846 chr21 9906846 F 15 93.33 6.67 +chr21.9906928 chr21 9906928 R 17 64.71 35.29 +chr21.9906657 chr21 9906657 R 14 0.00 100.00 +chr21.9906641 chr21 9906641 R 14 0.00 100.00 +chr21.9906676 chr21 9906676 F 39 100.00 0.00 +chr21.9906934 chr21 9906934 R 17 100.00 0.00 +chr21.9906890 chr21 9906890 R 17 0.00 100.00 +chr21.9906884 chr21 9906884 F 13 15.38 84.62 +chr21.9906841 chr21 9906841 R 14 71.43 28.57 +chr21.9906864 chr21 9906864 F 14 71.43 28.57 +chr21.9906714 chr21 9906714 F 25 76.00 24.00 +chr21.9906824 chr21 9906824 R 14 100.00 0.00 +chr21.9906635 chr21 9906635 R 13 53.85 46.15 +chr21.9906952 chr21 9906952 F 30 83.33 16.67 +chr21.9906640 chr21 9906640 F 23 0.00 100.00 +chr21.9906957 chr21 9906957 F 30 86.67 13.33 +chr21.9906604 chr21 9906604 F 23 78.26 21.74 +chr21.9906992 chr21 9906992 R 13 92.31 7.69 +chr21.9906950 chr21 9906950 R 13 61.54 38.46 +chr21.9906902 chr21 9906902 R 17 0.00 100.00 +chr21.9906805 chr21 9906805 R 13 61.54 38.46 +chr21.9906889 chr21 9906889 F 12 0.00 100.00 +chr21.9906933 chr21 9906933 F 29 89.66 10.34 +chr21.9906694 chr21 9906694 F 39 41.03 58.97 +chr21.9906885 chr21 9906885 R 16 0.00 100.00 +chr21.9906869 chr21 9906869 F 15 0.00 100.00 +chr21.9906958 chr21 9906958 R 13 100.00 0.00 +chr21.9906616 chr21 9906616 F 23 60.87 39.13 +chr21.9906931 chr21 9906931 R 17 35.29 64.71 +chr21.9906827 chr21 9906827 R 14 35.71 64.29 +chr21.9906953 chr21 9906953 R 13 84.62 15.38 +chr21.9911447 chr21 9911447 R 10 100.00 0.00 +chr21.9911433 chr21 9911433 R 10 90.00 10.00 +chr21.9911345 chr21 9911345 F 10 90.00 10.00 +chr21.9913543 chr21 9913543 F 34 91.18 8.82 +chr21.9913575 chr21 9913575 F 33 93.94 6.06 +chr21.9917414 chr21 9917414 R 11 100.00 0.00 +chr21.9917398 chr21 9917398 R 11 90.91 9.09 +chr21.9917607 chr21 9917607 F 49 77.55 22.45 +chr21.9917608 chr21 9917608 R 12 75.00 25.00 +chr21.9917582 chr21 9917582 F 61 85.25 14.75 +chr21.9917587 chr21 9917587 F 59 77.97 22.03 +chr21.9917630 chr21 9917630 R 14 85.71 14.29 +chr21.9917629 chr21 9917629 F 28 0.00 100.00 +chr21.9927676 chr21 9927676 R 18 100.00 0.00 +chr21.9927687 chr21 9927687 R 18 100.00 0.00 +chr21.9927527 chr21 9927527 F 32 100.00 0.00 +chr21.9944663 chr21 9944663 R 45 77.78 22.22 +chr21.9944505 chr21 9944505 F 86 91.86 8.14 +chr21.9959569 chr21 9959569 R 40 100.00 0.00 +chr21.9959577 chr21 9959577 R 40 100.00 0.00 +chr21.9959644 chr21 9959644 R 59 88.14 11.86 +chr21.9959541 chr21 9959541 R 39 94.87 5.13 +chr21.9959407 chr21 9959407 F 52 94.23 5.77 +chr21.9959650 chr21 9959650 R 59 86.44 13.56 +chr21.9967634 chr21 9967634 R 93 66.67 33.33 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input_control2.myCpG.txt Wed Dec 21 17:30:57 2016 -0500 @@ -0,0 +1,1698 @@ +chrBase chr base strand coverage freqC freqT +chr21.9737358 chr21 9737358 F 13 30.77 69.23 +chr21.9736774 chr21 9736774 R 10 10.00 90.00 +chr21.9746025 chr21 9746025 R 11 72.73 27.27 +chr21.9764151 chr21 9764151 R 11 9.09 90.91 +chr21.9796637 chr21 9796637 F 12 25.00 75.00 +chr21.9797182 chr21 9797182 F 22 54.55 45.45 +chr21.9797133 chr21 9797133 R 16 43.75 56.25 +chr21.9797132 chr21 9797132 F 15 66.67 33.33 +chr21.9797212 chr21 9797212 F 15 13.33 86.67 +chr21.9798664 chr21 9798664 F 10 100.00 0.00 +chr21.9802590 chr21 9802590 F 19 73.68 26.32 +chr21.9802574 chr21 9802574 F 18 72.22 27.78 +chr21.9802620 chr21 9802620 F 14 64.29 35.71 +chr21.9804873 chr21 9804873 F 11 100.00 0.00 +chr21.9804899 chr21 9804899 F 11 27.27 72.73 +chr21.9804890 chr21 9804890 F 11 27.27 72.73 +chr21.9808158 chr21 9808158 F 14 57.14 42.86 +chr21.9808138 chr21 9808138 F 14 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input_test1.myCpG.txt Wed Dec 21 17:30:57 2016 -0500 @@ -0,0 +1,1905 @@ +chrBase chr base strand coverage freqC freqT +chr21.9764539 chr21 9764539 R 12 25.00 75.00 +chr21.9764513 chr21 9764513 R 12 0.00 100.00 +chr21.9820622 chr21 9820622 F 13 0.00 100.00 +chr21.9837545 chr21 9837545 F 11 0.00 100.00 +chr21.9849022 chr21 9849022 F 124 72.58 27.42 +chr21.9853326 chr21 9853326 F 17 70.59 29.41 +chr21.9853296 chr21 9853296 F 17 58.82 41.18 +chr21.9860126 chr21 9860126 F 39 97.44 2.56 +chr21.9874553 chr21 9874553 F 12 100.00 0.00 +chr21.9882434 chr21 9882434 F 10 0.00 100.00 +chr21.9882409 chr21 9882409 F 10 70.00 30.00 +chr21.9906663 chr21 9906663 R 37 67.57 32.43 +chr21.9906677 chr21 9906677 R 37 75.68 24.32 +chr21.9906700 chr21 9906700 F 13 46.15 53.85 +chr21.9906954 chr21 9906954 F 20 75.00 25.00 +chr21.9906644 chr21 9906644 F 68 92.65 7.35 +chr21.9906873 chr21 9906873 F 12 66.67 33.33 +chr21.9906634 chr21 9906634 F 68 82.35 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input_test2.myCpG.txt Wed Dec 21 17:30:57 2016 -0500 @@ -0,0 +1,2007 @@ +chrBase chr base strand coverage freqC freqT +chr21.9764513 chr21 9764513 R 20 75.00 25.00 +chr21.9764539 chr21 9764539 R 20 95.00 5.00 +chr21.9767701 chr21 9767701 F 10 10.00 90.00 +chr21.9804584 chr21 9804584 F 10 90.00 10.00 +chr21.9804595 chr21 9804595 F 10 100.00 0.00 +chr21.9802620 chr21 9802620 F 13 61.54 38.46 +chr21.9802590 chr21 9802590 F 13 76.92 23.08 +chr21.9809998 chr21 9809998 F 150 85.33 14.67 +chr21.9809957 chr21 9809957 F 15 20.00 80.00 +chr21.9796127 chr21 9796127 F 10 100.00 0.00 +chr21.9809695 chr21 9809695 F 12 91.67 8.33 +chr21.9797216 chr21 9797216 F 13 38.46 61.54 +chr21.9809684 chr21 9809684 F 12 83.33 16.67 +chr21.9797212 chr21 9797212 F 21 71.43 28.57 +chr21.9799590 chr21 9799590 F 12 58.33 41.67 +chr21.9797182 chr21 9797182 F 23 69.57 26.09 +chr21.9809664 chr21 9809664 F 10 80.00 20.00 +chr21.9817117 chr21 9817117 F 28 17.86 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output_seg_diff.bed Wed Dec 21 17:30:57 2016 -0500 @@ -0,0 +1,4 @@ +track name="meth segments" description="meth segments" itemRgb=on +chr21 9853000 13991000 1 -10.9674674784423 . 9853000 13991000 190,190,190 +chr21 13991000 14019000 2 17.5358121475575 . 13991000 14019000 0,100,0 +chr21 14057000 19944000 1 -7.21990925670131 . 14057000 19944000 140,207,140
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output_seg_raw.bed Wed Dec 21 17:30:57 2016 -0500 @@ -0,0 +1,6 @@ +track name="meth segments" description="meth segments" itemRgb=on +chr21 9764000 14382000 2 68.3360848593166 . 9764000 14382000 0,227,0 +chr21 14510000 14774000 1 37.2301737330093 . 14510000 14774000 162,199,162 +chr21 14791000 15242000 3 93.231961836613 . 14791000 15242000 0,100,0 +chr21 15286000 15583000 1 32.8700430825969 . 15286000 15583000 190,190,190 +chr21 15583000 19944000 2 67.8414022933781 . 15583000 19944000 0,230,0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/test.r Wed Dec 21 17:30:57 2016 -0500 @@ -0,0 +1,60 @@ +library("methylKit") + +file.list = list("input_test1.myCpG.txt", "input_test2.myCpG.txt", "input_control1.myCpG.txt", "input_control2.myCpG.txt") + +myobj=methRead( file.list, + sample.id=list("test 1","test 2","control 1","control 2"),assembly="hg18",pipeline="amp",treatment=c(1,1,0,0)) + +pdf('output_statistics.pdf') +for (obj in myobj){ + getMethylationStats(obj,plot=TRUE,both.strands=FALSE) + getCoverageStats(obj,plot=TRUE,both.strands=FALSE) +} +devname = dev.off() + +# unite function +methidh = unite(myobj) + +pdf("output_correlation.pdf") +getCorrelation(object = methidh, plot=TRUE, method = "pearson") +devname = dev.off() + +# differential methylation +myDiff = calculateDiffMeth(methidh, overdispersion="none", + adjust="SLIM", effect="wmean", test="Chisq", + slim=FALSE, weighted.mean=FALSE) + +bedgraph(myDiff, file.name="output_myDiff.bedgraph", col.name="meth.diff", + unmeth=FALSE, log.transform=FALSE, negative=FALSE, add.on="") + +MethPerChr = diffMethPerChr(myDiff, plot=FALSE, + qvalue.cutoff=0.01, + meth.cutoff=25) +write.table(MethPerChr, sep="\t", row.names=FALSE, quote=FALSE, file="output_MethPerChr.tsv") + +MethylDiff = getMethylDiff(myDiff, difference=25, + qvalue=0.01, type="all") +bedgraph(MethylDiff, file.name="output_MethylDiff.bedgraph", col.name="meth.diff", + unmeth=FALSE,log.transform=FALSE,negative=FALSE,add.on="") + +pdf( "output_clustering.pdf" ) +methClust = clusterSamples(methidh, dist="correlation", method="ward") +devname = dev.off() + +pdf( "output_PCA.pdf" ) +PCASamples(methidh) +devname = dev.off() + +## methSeg works for methylRaw or methylDiff with resolution region, +## so methylBase has to be tiled before +tileRaw = tileMethylCounts(myobj[[1]]) +tileBase = tileMethylCounts(methidh) +tileDiff = calculateDiffMeth(tileBase) + +## methseg generates Granges +segRaw = methSeg(tileRaw, diagnostic.plot = FALSE) +segDiff = methSeg(tileDiff, diagnostic.plot = FALSE) + +## and can be exported as BED +methSeg2bed(segments = segRaw, filename = "output_seg_raw.bed") +methSeg2bed(segments = segDiff, filename = "output_seg_diff.bed")