View source: R/genomic_distribution.R
| plot_genomic_distribution | R Documentation | 
Get genomic distributions of TSSs and TSRs.
plot_genomic_distribution(
  experiment,
  data_type = c("tss", "tsr", "shift"),
  samples = "all",
  threshold = NULL,
  use_normalized = FALSE,
  dominant = FALSE,
  data_conditions = NULL,
  return_table = FALSE,
  ...
)
| experiment | TSRexploreR object. | 
| data_type | Whether to get distribution of TSSs ('tss') or TSRs ('tsr'). | 
| samples | A vector of sample names to analyze. | 
| threshold | TSSs or TSRs with a score below this value will not be considered. | 
| use_normalized | Whether to use the normalized (TRUE) or raw (FALSE) counts. | 
| dominant | If TRUE, will only consider the highest-scoring TSS per gene, transcript, or TSR or highest-scoring TSR per gene or transcript. | 
| data_conditions | Apply advanced conditions to the data. | 
| return_table | Return a table of results instead of a plot. | 
| ... | Arguments passed to geom_col. | 
This plotting function will create a stacked barplot of the proportion of TSSs or TSRs containing within several types of genomic feature: exons, introns, intergenic, downstream, antisense, and promoter regions. The promoter region is user-defined during annotation.
A set of functions to control data structure for plotting are included. 'use_normalized' will use normalized scores, which only matters if 'consider_score' is TRUE. 'threshold' defines the minimum number of raw counts a TSS or TSR must have to be considered. dominant' specifies whether only the dominant TSS or TSR (determined using the 'mark_dominant' function) is considered. For TSSs, this can be either dominant TSS per TSR or gene/transcript, and for TSRs it is the dominant TSR per gene/transcript. 'data_conditions' can be used to filter, quantile, order, and/or group data for plotting.
If 'return_table' is TRUE, a data.frame containing the underlying data for the plot is returned.
ggplot2 plot with TSS or TSR genomic distribution. If 'return_table' is TRUE returns a data.frame of underlying stats.
annotate_features to annotate TSSs or TSRs.
data(TSSs_reduced)
annotation <- system.file("extdata", "S288C_Annotation.gtf", package="TSRexploreR")
exp <- TSSs_reduced %>%
  tsr_explorer(genome_annotation=annotation) %>%
  format_counts(data_type="tss") %>%
  annotate_features(data_type="tss")
p <- plot_genomic_distribution(exp, data_type="tss")
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