plot_tsr_metric.RdPlot 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. |
ggplot2 object with TSR matrix plotted.
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.
tsr_metrics to calculate additional TSR metrics.
data(TSSs_reduced) exp <- TSSs_reduced %>% tsr_explorer %>% format_counts(data_type="tss") %>% tss_clustering(threshold=3)#> Warning: Arguments in '...' ignored#> Warning: Arguments in '...' ignoredp <- plot_tsr_metric(exp, "width")