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

 Package: scmeth

 Type: Package

 Title: Functions to conduct quality control analysis in methylation data

-Version: 0.99.17

+Version: 0.99.18

 Author: Divy Kangeyan <[email protected]>

 Maintainer: Divy Kangeyan <[email protected]>

 Depends: R (>= 3.5.0)

@@ -22,6 +22,7 @@ coverage <- function(bs,subSample=1e6,offset=50000) {

     if (nCpGs<(subSample+offset)){

         bs <- bs

+        subSample <- nCpGs

     }else{

         bs <- bs[offset:(subSample+offset)]

     }

@@ -2,7 +2,7 @@

 #'of CpGs observed in certain base pair long region.

 #'@param bs bsseq object

 #'@param organism scientific name of the organism of interest,

-#'e.g. Mus musculus or Homo sapiens

+#'e.g. Mmusculus or Hsapiens

 #'@param windowLength Length of the window to calculate the density

 #'Default value for window length is 1000 basepairs.

 #'@return Data frame with sample name and coverage in repeat masker regions

@@ -34,6 +34,7 @@ cpgDiscretization <- function(bs,subSample=1e6,offset=50000,coverageVec=NULL){

     if (nCpGs<(subSample+offset)){

         bs <- bs

+        subSample <- nCpGs

     }else{

         bs <- bs[offset:(subSample+offset)]

     }

@@ -30,6 +30,7 @@ downsample <- function(bs,dsRates = c(0.01,0.02,0.05, seq(0.1,0.9,0.1)),subSampl

     if (nCpGs<(subSample+offset)){

         bs <- bs

+        subSample <- nCpGs

     }else{

         bs <- bs[offset:(subSample+offset)]

     }

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

 #'Provides Coverage metrics in the repeat masker region

 #'@param bs bsseq object

 #'@param organism scientific name of the organism of interest,

-#'e.g. Mus musculus or Homo sapiens

+#'e.g. Mmusculus or Hsapiens

 #'@param genome reference alignment, i.e. mm10 or hg38

 #'@return Data frame with sample name and coverage in repeat masker regions

 #'@examples

@@ -11,7 +11,7 @@ cpgDensity(bs, organism, windowLength = 1000)

 \item{bs}{bsseq object}

 \item{organism}{scientific name of the organism of interest,

-e.g. Mus musculus or Homo sapiens}

+e.g. Mmusculus or Hsapiens}

 \item{windowLength}{Length of the window to calculate the density

 Default value for window length is 1000 basepairs.}

@@ -10,7 +10,7 @@ repMask(bs, organism, genome)

 \item{bs}{bsseq object}

 \item{organism}{scientific name of the organism of interest,

-e.g. Mus musculus or Homo sapiens}

+e.g. Mmusculus or Hsapiens}

 \item{genome}{reference alignment, i.e. mm10 or hg38}

 }

new file mode 100644

@@ -0,0 +1,20 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/scmeth.R

+\docType{package}

+\name{scmeth}

+\alias{scmeth}

+\alias{scmeth-package}

+\title{scmeth: a package to conduct quality control analysis for methylation data.

+Most functions can be applied to both bulk and single-cell methylation

+while other functions are specific to single-cell methylation data.

+scmeth is especially customized to use the output from the FireCloud

+implementation of methylation pipeline to produce comprehensive

+quality control report}

+\description{

+scmeth: a package to conduct quality control analysis for methylation data.

+Most functions can be applied to both bulk and single-cell methylation

+while other functions are specific to single-cell methylation data.

+scmeth is especially customized to use the output from the FireCloud

+implementation of methylation pipeline to produce comprehensive

+quality control report

+}

@@ -31,23 +31,22 @@ Contents

 Though a small chemical change in the genome, DNA methylation has significant

 impact in several diseases, developmental processes and other biological 

 changes. Hence methylation data should be analyzed carefully to gain 

-biological insights. **scmeth** package offers a few functions to asseess

+biological insights. **scmeth** package offers a few functions to assess

 the quality of the methylation data. 

 </p>

 <p style="text-align: justify;">

 This bioconductor package contains functions to perform quality control and 

 preprocessing analysis for single-cell methylation data. *scmeth* is 

-especially customized to use the output from the fireCloud implementation of 

-methylation pipeline. For now only human and mouse genomes are supported in 

-this package but in the future we will expand to other organisms. In addition 

-to individual functions, **report** function in the package provides all 

-inclusive report using most of the functions. If users prefer

-they can just use the **report** function to gain summary of their data.

+especially customized to use the output from the FireCloud implementation of 

+methylation pipeline. In addition to individual functions, **report** function

+in the package provides all inclusive report using most of the functions. If

+users prefer they can just use the **report** function to gain summary of 

+their data.

 </p>

-2. Installation

+2. Installation and package loading

+------------------------------------

 **scmeth** is available in bioconductor and can be downloaded using the 

 following commands

 ```{r, eval=FALSE}

@@ -55,14 +54,19 @@ source("http://bioconductor.org/biocLite.R")

 biocLite("scmeth")

 ```

+Load the package

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

+library(scmeth)

+```

+

 3. Input files

 ---------------------

 <p style="text-align: justify;">

-Main input for most of the function is a *bsseq* object. In the fireCloud 

+Main input for most of the function is a *bsseq* object. In the FireCloud 

 implementation it is stored as hdf5 file which can be read via 

-*loadHDF5SummarizedExperiment* function in *SummarizedExperiment* package.

+*loadHDF5SummarizedExperiment* function in *HDF5Array* package.

 Code chunk below shows how it can be loaded.

 </p>

@@ -99,13 +103,12 @@ when subsetting from the beginning of the data.

 </p>

 ```{r, eval=FALSE}

-library(scmeth)

-scmeth::report(bsObject, '~/Documents',Hsapiens,"hg38")

+report(bsObject, '~/Documents',Hsapiens,"hg38")

 ```

 <p style="text-align: justify;">

-Command above generayed an html report named *qcReport.html*. It will be stored

+Command above generated an html report named *qcReport.html*. It will be stored

 in the indicated directory. 

 </p>

@@ -129,8 +132,6 @@ sample. **coverage** function can be used to get this information.

 </p>

 Loading the data

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

-#

-library(scmeth)

 directory <- system.file("extdata","bismark_data",package='scmeth')

 bsObject <- HDF5Array::loadHDF5SummarizedExperiment(directory)

 ```

@@ -145,7 +146,7 @@ succeeded. **readmetrics** function outputs a visualization showing number

 of reads seen in each samples and of those reads what proportion of 

 them were mapped to the reference genome. 

 ```{r,fig.width=6,fig.height=3}

-scmeth::readmetrics(bsObject)

+readmetrics(bsObject)

 ```

 ### repmask

@@ -163,7 +164,7 @@ the genome build information.

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

 library(BSgenome.Mmusculus.UCSC.mm10)

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

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

+repMask(bs,Mmusculus,"mm10")

 ```

 ### Coverage by Chromosome

@@ -177,7 +178,7 @@ only the CpGs covered in chromosome 1 is shown.)

 </p>

 ```{r, warning=FALSE}

-scmeth::chromosomeCoverage(bsObject)

+chromosomeCoverage(bsObject)

 ```

 ### featureCoverage

@@ -195,7 +196,7 @@ that region.

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

 library(annotatr)

 featureList <- c('genes_exons','genes_introns')

-DT::datatable(scmeth::featureCoverage(bsObject,features=featureList,"hg38"))

+DT::datatable(featureCoverage(bsObject,features=featureList,"hg38"))

 ```

 </p>

@@ -217,7 +218,7 @@ obtained uniformly across the regions.

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

 library(BSgenome.Hsapiens.NCBI.GRCh38)

-DT::datatable(scmeth::cpgDensity(bsObject,Hsapiens,windowLength=1000))

+DT::datatable(cpgDensity(bsObject,Hsapiens,windowLength=1000))

 ```

 ### downsample

@@ -236,7 +237,7 @@ report renders this information into a plot. Downsampling rate ranges from

 </p>

 ```{r,warning=FALSE}

-DT::datatable(scmeth::downsample(bsObject))

+DT::datatable(downsample(bsObject))

 ```

@@ -248,13 +249,13 @@ However there could be fluctuations in the beginning or the end of the read due

 to the quality of the bases. Single cell sequencing samples also can show 

 jagged trend in the methylation bias plot due to low read count. Methylation

 bias can be assessed via **mbiasPlot** function. This function takes the mbias

-file generated from firecloud pipeline and generates the methylation bias plot.

+file generated from FireCloud pipeline and generates the methylation bias plot.

 </p>

 ```{r,warning=FALSE,message=FALSE,fig.width=6,fig.height=6}

 methylationBiasFile <- '2017-04-21_HG23KBCXY_2_AGGCAGAA_TATCTC_pe.M-bias.txt'

-scmeth::mbiasplot(mbiasFiles=system.file("extdata",methylationBiasFile,

+mbiasplot(mbiasFiles=system.file("extdata",methylationBiasFile,

                                          package='scmeth'))

 ```

 </p>

@@ -272,7 +273,7 @@ are large number of intermediate methylation this indicates there might be

 some error in sequencing. 

 </p>

 ```{r,warning=FALSE,message=FALSE,fig.width=6,fig.height=3}

-scmeth::methylationDist(bsObject)

+methylationDist(bsObject)

 ```

@@ -288,7 +289,7 @@ rate below 95% indicates some problem with sample preparation.

 sample.

 </p>

 ```{r,warning=FALSE,message=FALSE,fig.width=4,fig.height=6}

-scmeth::bsConversionPlot(bsObject)

+bsConversionPlot(bsObject)

 ```

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