plotReadsGC-methods {exomePeak2} | R Documentation

## Method plotReadsGC

### Description

`plotReadsGC` visualizes the local regression curves between the normalized reads abundance and the local GC content.

### Usage

    plotReadsGC(
      sep,
      bsgenome = NULL,
      txdb = NULL,
      save_pdf_prefix = NULL,
      fragment_length = 100,
      binding_length = 25,
      effective_GC = FALSE,
      pool_replicates = FALSE,
      save_dir = "."
    )

    ## S4 method for signature 'SummarizedExomePeak'
    plotReadsGC(
      sep,
      bsgenome = NULL,
      txdb = NULL,
      save_pdf_prefix = NULL,
      fragment_length = 100,
      binding_length = 25,
      effective_GC = FALSE,
      pool_replicates = FALSE,
      save_dir = "."
    )

### Arguments

`sep` | a `SummarizedExomePeak` object.
`bsgenome` | a `BSgenome` object for the genome sequence, it could be the name of the reference genome recognized by `getBSgenome`.
`txdb` | a `TxDb` object for the transcript annotation, it could be the name of the reference genome recognized by `makeTxDbFromUCSC`.
`save_pdf_prefix` | a `character`, if provided, a pdf file with the given name will be saved under the current directory; Default `= NULL`.
`fragment_length` | a `numeric` value for the expected fragment length in the RNA-seq library; Default `= 100`.
`binding_length` | a `numeric` value for the expected antibody binding length in IP samples; Default `= 25`.
`effective_GC` | a `logical` value of whether to calculate the weighted GC content by the probability of reads alignment; default `= FALSE`.
`pool_replicates` | a `logical` value of whether to pool the replicates in the local regression fit; default `= FALSE`.
`save_dir` | a `character` for the directory to save the plot; default ".".

### Details

The read abundances of both the control and the modification site regions are plotted, the read counts are normalized using the following method:

normalized feature abundance = ( ( read count / size factor) / region length ) * 500

By default, it will use the sequencing depth size factor defined in the `SummarizedExomePeak` object, if the sequencing depth size factor is not found, new size factors will be estimated with the default method in `estimateSeqDepth`.

### Value

a `ggplot` object

### Examples

    ### Load the example SummarizedExomPeak object
    f1 = system.file("extdata", "sep_ex_mod.rds", package="exomePeak2")

    sep <- readRDS(f1)

    ### Visualize the linear relationships between GC content and normalized reads count
    plotReadsGC(sep)

[Package exomePeak2 version 0.99.98 Index]