plotReadsGC-methods {exomePeak2} | R Documentation |
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 |
bsgenome |
a |
txdb |
a |
save_pdf_prefix |
a |
fragment_length |
a |
binding_length |
a |
effective_GC |
a |
pool_replicates |
a |
save_dir |
a |
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)