@@ -1,7 +1,7 @@

 Package: scmeth

 Type: Package

 Title: Functions to conduct quality control analysis in methylation data

-Version: 0.99.14

+Version: 0.99.15

 Author: Divy Kangeyan <[email protected]>

 Depends: R (>= 3.5.0)

 Imports:

@@ -25,7 +25,6 @@ coverage <- function(bs,subSample=1e6,offset=50000) {

     covMatrix<-bsseq::getCoverage(bs)

     covVec<- DelayedArray::colSums(covMatrix>0,na.rm=TRUE)

-

     covVec<-covVec*(nCpGs/subSample)

     return(covVec)

 }

@@ -23,7 +23,7 @@ cpgDensity<-function(bs,organism,windowLength=1000){

     cpgd<-Repitools::cpgDensityCalc(gr, organism, window = windowLength)

     maxcpgd<-max(cpgd)

-    cpgdCov<-sapply(1:ncol(cov), function(i) {

+    cpgdCov<-sapply(seq_len(ncol(cov)), function(i) {

         cv = as.vector(cov[,i])

         cpgdCell<-cpgd[cv>0 ]

         tab <- table(cpgdCell)

@@ -32,13 +32,6 @@ cpgDensity<-function(bs,organism,windowLength=1000){

         x

     })

-

     rownames(cpgdCov)<-1:maxcpgd

-    #cpgdBin<-cut(cpgd,seq(0,max(cpgd),5))

-

-    # Need to find a better way to conduct this on-disk

-    #cpgdCov <- by(cov>0, cpgd, colSums)

-    #cpgdCov <- do.call("rbind", cpgdCov)

     return(cpgdCov)

-

 }

@@ -45,13 +45,11 @@ cpgDiscretization<-function(bs,subSample=1e6,offset=50000,coverageVec=NULL){

     }else{

         covVec<-coverageVec

     }

-    #methMatrix[methMatrix>=0.8]<-1

-    #methMatrix[methMatrix<=0.2]<-0

     # Only consider methylation between 0.2 and 0.8

     methCutOff<-c(0.01,0.19,0.79,1.0)

-    methylationDistMatrix<-sapply(1:nSamples, function(i) {

+    methylationDistMatrix<-sapply(seq_len(nSamples), function(i) {

         mv = as.vector(methMatrix[,i])

         mv<-mv[!is.na(mv)]

         mvBin<-cut(mv,methCutOff)

@@ -60,7 +58,6 @@ cpgDiscretization<-function(bs,subSample=1e6,offset=50000,coverageVec=NULL){

         x

     })

-

     removedCpGs<-methylationDistMatrix[2,]*(nCpGs/subSample)

     removedCpGFrac<-(removedCpGs/(covVec))*100

     returnList<-list('discard' = removedCpGs,

@@ -37,17 +37,15 @@ downsample <-function(bs,dsRates = c(0.01,0.02,0.05, seq(0.1,0.9,0.1)),subSample

     nSamples<-dim(covMatrix)[2]

     downSampleMatrix<-matrix(nrow=length(dsRates)+1,ncol=nSamples)

     maxCov<-20

-

     nonZeroProbMatrix<-matrix(nrow=(length(dsRates)+1),ncol=maxCov)

-    for (i in 1:length(dsRates)){

-        nonZeroProbMatrix[i,]<-1 - dbinom(0,1:maxCov,dsRates[i])

+    for (i in seq_len(length(dsRates))){

+        nonZeroProbMatrix[i,]<-1 - dbinom(0,seq_len(maxCov),dsRates[i])

     }

     nonZeroProbMatrix[(length(dsRates)+1),]<-1

-

-    countMatrix<-sapply(1:ncol(covMatrix), function(i) {

+    countMatrix<-sapply(seq_len(ncol(covMatrix)), function(i) {

         cv = as.vector(covMatrix[,i])

         cv[cv>maxCov ] <- maxCov

         tab <- table(cv)

@@ -60,16 +58,6 @@ downsample <-function(bs,dsRates = c(0.01,0.02,0.05, seq(0.1,0.9,0.1)),subSample

     downSampleMatrix<-round(nonZeroProbMatrix %*% countMatrix)

     downSampleMatrix<-downSampleMatrix*(nCpGs/subSample)

-    #for (i in 1:length(dsRates)){

-    #    for (j in 1:nSamples){

-    #    cellCoverage<-as.vector(covMatrix[,j])

-    #    cellNonZeroCoverage<-cellCoverage[cellCoverage>0]

-    #    covSubList<-lapply(cellNonZeroCoverage,rbinom,n=1,prob=dsRates[i])

-    #    downSampleMatrix[i,j]<- sum(covSubList>0)

-

-    #    }

-    #}

-    #downSampleMatrix[length(dsRates)+1,]<-DelayedArray::colSums(covMatrix>0)

     rownames(downSampleMatrix)<-c(dsRates,1)

     return(downSampleMatrix)

 }

@@ -28,13 +28,11 @@

 featureCoverage <-function(bs,features,genomebuild){

-

     annotationFeatures<-c()

     for (i in features){

     annotationFeatures<-c(paste0(genomebuild,'_',i),annotationFeatures)

     }

-

     annots_gr = annotatr::build_annotations(genome = genomebuild,

                                         annotations = annotationFeatures)

     GenomeInfoDb::seqlevelsStyle(bs)<-"UCSC"

@@ -43,7 +41,7 @@ featureCoverage <-function(bs,features,genomebuild){

     sumAnnotMatrix<-matrix(nrow=length(features),ncol=nSamples)

     featureLabel<-rep(NA,length(features))

-    for (i in 1:nSamples){

+    for (i in seq_len(nSamples)){

         bsCell<-bs[,i]

         # CpGs that are observed

@@ -59,7 +59,6 @@ mbiasplot<-function(dir=NULL,mbiasFiles=NULL){

     mt<-reshape2::melt(mbiasTableList,

                         id.vars=c('position', 'X..methylation', 'read'))

-

     mt$read_rep <- paste(mt$read, mt$L1, sep="_")

     sum_mt <- mt %>% dplyr::select('read','position','X..methylation','L1') %>%

                         dplyr::group_by(position,read) %>%

@@ -79,7 +78,5 @@ mbiasplot<-function(dir=NULL,mbiasFiles=NULL){

                                                 alpha=0.4)

     g<-g+ggplot2::ylim(0,100)+ggplot2::ggtitle('Mbias Plot')

     g<-g+ggplot2::ylab('methylation')

-

-

     return(g)

 }

@@ -40,7 +40,7 @@ methylationDist<-function(bs,subSample=1e6, offset=50000,coverageVec=NULL){

     }

     #totCpGs<-DelayedArray::colSums(covMatrix>0)

-    methylationDistMatrix<-sapply(1:nSamples, function(i) {

+    methylationDistMatrix<-sapply(seq_len(nSamples), function(i) {

         mv = as.vector(methMatrix[,i])

         mv<-mv[!is.na(mv)]

         mvBin<-cut(mv,methCutOff)

@@ -52,17 +52,12 @@ methylationDist<-function(bs,subSample=1e6, offset=50000,coverageVec=NULL){

     methylationDistMatrix<-t(methylationDistMatrix)

     methylationDistMatrix<-methylationDistMatrix*(nCpGs/subSample)

-

     methylationDistMatrix<-apply(methylationDistMatrix,2,function(x) x/totCpGs)

     orderdMeth<-order(methylationDistMatrix[,1])

     methylationDistMatrix<-methylationDistMatrix[orderdMeth,]

     colnames(methylationDistMatrix)<-c('[0,0.2]','(0.2,0.4]','(0.4,0.6]','(0.6,0.8]','(0.8,1]')

     meltedMDistMatrix<-reshape2::melt(methylationDistMatrix)

-

-

-

     return(meltedMDistMatrix)

-

 }

@@ -38,7 +38,5 @@ readmetrics<-function(bs){

                         axis.text.y=ggplot2::element_blank(),

                         axis.ticks=ggplot2::element_blank())

     return(g)

-

-

 }

@@ -16,7 +16,6 @@

 repMask<-function(bs,organism,genome){

-

     GenomeInfoDb::seqlevelsStyle(bs)<-"UCSC"

     hub <- AnnotationHub::AnnotationHub()

     repeatGr <- hub[[names(AnnotationHub::query(hub,

@@ -25,5 +24,4 @@ repMask<-function(bs,organism,genome){

     cov<-bsseq::getCoverage(bs)

     covDf <- data.frame(coveredCpgs=DelayedArray::colSums(cov[!rep,]>=1))

     return(covDf)

-

 }

@@ -23,11 +23,10 @@ scmeth::coverage(bsObject)

 ## ----fig.width=6,fig.height=3--------------------------------------------

 scmeth::readmetrics(bsObject)

-## ---- warning=FALSE,message=FALSE,eval=FALSE-----------------------------

-#  library(BSgenome.Mmusculus.UCSC.mm10)

-#  #load(system.file("extdata",'bsObject.rda',package='scmeth'))

-#  load('bsObject.rda')

-#  scmeth::repMask(bs,Mmusculus,"mm10")

+## ---- warning=FALSE,message=FALSE----------------------------------------

+library(BSgenome.Mmusculus.UCSC.mm10)

+load(system.file("extdata",'bsObject.rda',package='scmeth'))

+scmeth::repMask(bs,Mmusculus,"mm10")

 ## ---- warning=FALSE------------------------------------------------------

 scmeth::chromosomeCoverage(bsObject)

@@ -160,10 +160,9 @@ mask regions of the genome **repmask** function will require the organism and

 the genome build information.

 </p>

-```{r, warning=FALSE,message=FALSE,eval=FALSE}

+```{r, warning=FALSE,message=FALSE}

 library(BSgenome.Mmusculus.UCSC.mm10)

 load(system.file("extdata",'bsObject.rda',package='scmeth'))

-#load('bsObject.rda')

 scmeth::repMask(bs,Mmusculus,"mm10")

 ```

@@ -166,9 +166,12 @@ bsObject&lt;-HDF5Array<span class="op">::</span><span class="kw">loadHDF5Summari

 CpG Islands are characterized by their high GC content, high level of observed to expected ratio of CpGs and length over 500 bp. However some repeat regions in the genome also fit the same criteria although they are not bona fide CpG Island. Therefore it is important to see how many CpGs are observed in the non repeat regions of the genome. <strong>repMask</strong> functions provide information on the CpG coverage in non repeat regions of the genome. In order to build the repeat mask regions of the genome <strong>repmask</strong> function will require the organism and the genome build information.

 </p>

 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(BSgenome.Mmusculus.UCSC.mm10)

-<span class="co">#load(system.file(&quot;extdata&quot;,'bsObject.rda',package='scmeth'))</span>

-<span class="kw">load</span>(<span class="st">'bsObject.rda'</span>)

+<span class="kw">load</span>(<span class="kw">system.file</span>(<span class="st">&quot;extdata&quot;</span>,<span class="st">'bsObject.rda'</span>,<span class="dt">package=</span><span class="st">'scmeth'</span>))

 scmeth<span class="op">::</span><span class="kw">repMask</span>(bs,Mmusculus,<span class="st">&quot;mm10&quot;</span>)</code></pre></div>

+<pre><code>##                     coveredCpgs

+## sc-RRBS_zyg_01_chr1       12208

+## sc-RRBS_zyg_02_chr1        3056

+## sc-RRBS_zyg_03_chr1        6666</code></pre>

 </div>

 <div id="coverage-by-chromosome" class="section level3">

 <h3>Coverage by Chromosome</h3>

@@ -191,8 +194,8 @@ Another way to observe the distribution of CpGs is to classify them by the genom

 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(annotatr)

 featureList&lt;-<span class="kw">c</span>(<span class="st">'genes_exons'</span>,<span class="st">'genes_introns'</span>)

 DT<span class="op">::</span><span class="kw">datatable</span>(scmeth<span class="op">::</span><span class="kw">featureCoverage</span>(bsObject,<span class="dt">features=</span>featureList,<span class="st">&quot;hg38&quot;</span>))</code></pre></div>

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 </p>

 </div>

 <div id="cpgdensity" class="section level3">

@@ -202,8 +205,8 @@ CpGs are not distributed across the genome uniformly. Most of the genome contain

 </p>

 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(BSgenome.Hsapiens.NCBI.GRCh38)

 DT<span class="op">::</span><span class="kw">datatable</span>(scmeth<span class="op">::</span><span class="kw">cpgDensity</span>(bsObject,Hsapiens,<span class="dt">windowLength=</span><span class="dv">1000</span>))</code></pre></div>

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 </div>

 <div id="downsample" class="section level3">

 <h3>downsample</h3>

@@ -211,8 +214,8 @@ DT<span class="op">::</span><span class="kw">datatable</span>(scmeth<span class=

 In addition to the CpG coverage, methylation data can be assessed via down sampling analysis, methylation bias plot and methylation distribution. Down sampling analysis is a technique to assess whether the sequencing process achieved the saturation level in terms of CpG capture. In order to perform down sampling analysis CpGs that are covered at least will be sampled via binomial random sampling with given probability. At each probability level the number of CpGs captured is assessed. If the number of CpG captured attains a plateau then the sequencing was successful. <strong>downsample</strong> function provides a matrix of CpG coverage for each sample at various down sampling rates. The report renders this information into a plot. Downsampling rate ranges from 0.01 to 1, however users can change the downsampling rates.

 </p>

 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT<span class="op">::</span><span class="kw">datatable</span>(scmeth<span class="op">::</span><span class="kw">downsample</span>(bsObject))</code></pre></div>

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 </div>

 <div id="mbiasplot" class="section level3">

 <h3>mbiasPlot</h3>

@@ -253,7 +256,7 @@ scmeth<span class="op">::</span><span class="kw">mbiasplot</span>(<span class="d

 Another important metric in methylation analysis is the bisulfite conversion rate. Bisulfite conversion rate indicates out of all the Cytosines in the non CpG context what fraction of them were methylated. Ideally this number should be 1 or 100% indicating none of the non CpG context cytosines are methylated. However in real data this will not be the case, yet bisulfite conversion rate below 95% indicates some problem with sample preparation. <strong>bsConversionPlot</strong> function generates a plot showing this metric for each sample.

 </p>

 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">scmeth<span class="op">::</span><span class="kw">bsConversionPlot</span>(bsObject)</code></pre></div>

-<p><img 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" 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" 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