plot_tsr_metric: Plot TSR Metrics
In rpolicastro/tsrexplorer: TSS and TSR Analysis

Description Usage Arguments Details Value See Also Examples

Plot selected TSR metrics.

plot_tsr_metric(
  experiment,
  tsr_metrics,
  plot_type = "violin",
  samples = "all",
  log2_transform = FALSE,
  ncol = 1,
  use_normalized = FALSE,
  dominant = FALSE,
  threshold = NULL,
  data_conditions = NULL,
  ...
)

`experiment`	TSRexploreR object.
`tsr_metrics`	Names of metrics in TSRexploreR to plot.
`plot_type`	Output a 'violin', 'jitter', 'box', or 'boxjitter' plot.
`samples`	A vector of sample names to analyze.
`log2_transform`	Whether the metric should be log2 + 1 transformed prior to plotting.
`ncol`	Integer specifying the number of columns to arrange multiple plots.
`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.
`threshold`	TSSs or TSRs with a score below this value will not be considered.
`data_conditions`	Apply advanced conditions to the data.
`...`	Arguments passed to ggplot2 plotting functions.

Plot any TSR metric contained within the counts data.table. Metrics can be supplied as a character vector to 'tsr_metrics', and will be optionally log2 transformed if 'log2_transform' is TRUE. Valid plot types that can be supplied to 'plot_type' are 'violin', 'box', 'jitter', and 'boxjitter' (a combination of boxplot and jitterplot).

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.

ggplot2 object with TSR matrix plotted.

tsr_metrics to calculate additional TSR metrics.

data(TSSs_reduced)

exp <- TSSs_reduced %>%
  tsr_explorer %>%
  format_counts(data_type="tss") %>%
  tss_clustering(threshold=3)

p <- plot_tsr_metric(exp, "width")