PAC_nbias | R Documentation |
PAC_nbias
analyses nucleotide bias.
PAC_nbias(
PAC,
position = 1,
norm = NULL,
range = NULL,
anno_target = NULL,
pheno_target = NULL,
summary_target = NULL,
colors = NULL,
ymax = NULL,
data_only = FALSE
)
PAC |
PAC-list object containing an Anno data.frame with sequences as row names and a count table with raw counts. |
position |
Integer indicating the nucleotide position from 3' to 5' position (default=1). |
norm |
Character indicating what type of data to be used. If
type="counts" the plots will be based on the raw Counts. If type="cpm" the
analysis will be done on cpm values returned from |
range |
Integer vector indicating the sequence size range (default=c(min, max)). |
anno_target |
List with: 1st object being character vector of target column(s) in Anno, 2nd object being a character vector of the target biotypes(s) in the target column (1st object). (default=NULL) |
pheno_target |
List with: 1st object being character vector of target column(s) in Pheno, 2nd object being a character vector of the target group(s) in the target column (1st object). (default=NULL) |
summary_target |
List with: 1st object being character vector of target object in summary(PAC), 2nd object being a character vector of the target column(s) in the target summary object (1st object). (default=NULL) |
colors |
Character vector with RGB color codes to be parsed to ggplot2. |
ymax |
Integer that sets the maximum y for all plots (all plots gets the same y-axes). If ymax=NULL, then ggplot2 will automatically set ymax for each plot individually (different y-axes). |
data_only |
logical. If data_only=TRUE a data.frame a simple Anno object is returned with a Size and a Nucleotide bias column. As default, data_only=FALSE then graphs are returned in addition to data. |
Given a PAC object the function will attempt to extract the ratios of specific nucleotides at a given position in sequences in the Anno data.frame in relation to the sequence counts in Counts.
A list of objects: 1st object (Histograms::Samples): Individual histograms showing the nucleotide ratios per sample over the specified range. 2nd object (Data::Samples): Data used to generate the plots.
https://github.com/Danis102 for updates on the current package.
Other PAC analysis:
PAC_covplot()
,
PAC_deseq()
,
PAC_filter()
,
PAC_filtsep()
,
PAC_gtf()
,
PAC_jitter()
,
PAC_mapper()
,
PAC_norm()
,
PAC_pca()
,
PAC_pie()
,
PAC_saturation()
,
PAC_sizedist()
,
PAC_stackbar()
,
PAC_summary()
,
PAC_trna()
,
as.PAC()
,
filtsep_bin()
,
map_rangetype()
,
tRNA_class()
# Load a PAC-object
load(system.file("extdata", "drosophila_sRNA_pac_filt_anno.Rdata",
package = "seqpac", mustWork = TRUE))
output_nbias <- PAC_nbias(pac)
cowplot::plot_grid(plotlist=output_nbias$Histograms)
# Only miRNA (Oops, heavy T-bias on 1st nt; are they piRNA?)
table(anno(pac)$Biotypes_mis0)
output_nbias <- PAC_nbias(pac, anno_target = list("Biotypes_mis0", "miRNA") )
cowplot::plot_grid(plotlist=output_nbias$Histograms)
# Switch to 10:th nt bias
output_nbias <- PAC_nbias(pac, position=10,
anno_target = list("Biotypes_mis0", "miRNA"))
cowplot::plot_grid(plotlist=output_nbias$Histograms)
# Summarized over group cpm means
pac_test <- PAC_summary(pac, norm = "cpm", type = "means",
pheno_target=list("stage"), merge_pac=TRUE)
output_nbias <- PAC_nbias(pac_test, summary_target = list("cpmMeans_stage") )
cowplot::plot_grid(plotlist=output_nbias$Histograms)
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