plotReadsGC-methods: Method plotReadsGC
In exomePeak2: Bias Awared Peak Calling and Quantification for MeRIP-Seq

Description Usage Arguments Details Value Examples

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

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 = "."
)

`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 ".".

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.

a ggplot object

### 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)