normalizeCoverage {MADSEQ} | R Documentation

## correct coverage bias due to GC content

### Description

function to normalize coverage by GC content and quantile normalization

### Usage

    normalizeCoverage(object, ..., control = NULL, writeToFile = TRUE,
      destination = NULL, plot = TRUE)

### Arguments

`object` | A `GRanges` object returned from `prepareCoverageGC` function.
`...` | additional `GRanges` object to pass. Note1: If there is only one Granges object given, then coverage will be corrected by GC content. If there are more than one GRanges object from multiple samples are given, the function will first quantile normalize coverage across samples, then correct coverage by GC content in each sample. Note2: If more than one GRanges object provided, make sure they are different samples sequenced by the same protocol, which means the targeted region is the same Note3: If your input samples contain female and male, we suggest you separate them to get a more accurate normalization.
`control` | A `GRanges` object returned from `prepareCoverageGC` function. Default value: NULL. If you have a control normal sample, then put it here
`writeToFile` | `Boolean` Default: `TRUE`. If `TRUE`, normalized coverage table for each sample provided will be written to `destination` specified, the file will be named as "sample_normed_depth.txt". If set to `FALSE`, a `GRangesList` object will be returned
`destination` | A `character`, specify the path to the location where the normalized coverage table will be written. Default: `NULL`, the file will be written to current working directory
`plot` | `Boolean` Default: `TRUE`. If `TRUE`, the coverage vs. GC content plot before and after normalization will be plotted And the average coverage for each chromosome before and after normalization will be plotted

### Value

If `writeToFile` is set to TRUE, normalized coverage will be written to the `destination`. Otherwise, a `GRangesList` object containing each of input sample will be returned.

### Note

The normalize function works better when you have multiple samples sequenced using the same protocol, namely have the same targeted regions. And if you have female sample and male sample, the best way is to normalize them separately.

### Author(s)

Yu Kong

### References

C. Alkan, J. Kidd, T. Marques-Bonet et al (2009). Personalized copy number and segmental duplication maps using next-generation sequencing. Nature Genetics, 41(10):1061-7.

### See Also

`prepareCoverageGC`

### Examples

    ##------------------------------------
    ##if you deal with single sample
    ##------------------------------------
    ## 1. prepare coverage and gc
    ## specify the path to the location of bed file
    target = system.file("extdata","target.bed",package="MADSEQ")

    ## specify the path to the bam file
    aneuploidy_bam = system.file("extdata","aneuploidy.bam",package="MADSEQ")

    ## prepare coverage data for the aneuploidy sample
    aneuploidy_cov_gc = prepareCoverageGC(target,aneuploidy_bam,"hg19")

    ## normalize the coverage
    ##---- if not write to file ----
    aneuploidy_norm = normalizeCoverage(aneuploidy_cov_gc,writeToFile=FALSE)
    ## check the GRangesList and subset your sample
    aneuploidy_norm
    names(aneuploidy_norm)
    aneuploidy_norm["aneuploidy_cov_gc"]

    ##---- if write to file ----
    normalizeCoverage(aneuploidy_cov_gc,writeToFile=TRUE,destination=".")

    ##-----------------------------------------------------------
    ##if you deal with multiple samples without normal control
    ##-----------------------------------------------------------
    ## specify the path to the location of bed file
    target = system.file("extdata","target.bed",package="MADSEQ")

    ## specify the path to the bam file
    aneuploidy_bam = system.file("extdata","aneuploidy.bam",package="MADSEQ")
    normal_bam = system.file("extdata","normal.bam",package="MADSEQ")

    ## prepare coverage data for the samples
    aneuploidy_cov_gc = prepareCoverageGC(target,aneuploidy_bam,"hg19")
    normal_cov_gc = prepareCoverageGC(target,normal_bam,"hg19")

    ## normalize the coverage
    normed=normalizeCoverage(aneuploidy_cov_gc,normal_cov_gc,writeToFile=FALSE)
    names(normed)
    normed["aneuploidy_cov_gc"]
    normed["normal_cov_gc"]
    ## or
    normalizeCoverage(aneuploidy_cov_gc,normal_cov_gc,
                      writeToFile=TRUE,destination=".")

    ##-----------------------------------------------------------
    ##if you deal with multiple samples with a normal control
    ##-----------------------------------------------------------
    ## specify the path to the location of bed file
    target = system.file("extdata","target.bed",package="MADSEQ")

    ## specify the path to the bam file
    aneuploidy_bam = system.file("extdata","aneuploidy.bam",package="MADSEQ")
    normal_bam = system.file("extdata","normal.bam",package="MADSEQ")

    ## prepare coverage data for the samples
    aneuploidy_cov_gc = prepareCoverageGC(target,aneuploidy_bam,"hg19")
    normal_cov_gc = prepareCoverageGC(target,normal_bam,"hg19")

    ## normalize the coverage
    normed = normalizeCoverage(aneuploidy_cov_gc,
                               control=normal_cov_gc,writeToFile=FALSE)
    ## or
    normalizeCoverage(aneuploidy_cov_gc,control=normal_cov_gc,
                      writeToFile=TRUE,destination=".")

[Package MADSEQ version 1.16.0 Index]