plot_heatmap: Plot Heatmap

In zentnerlab/TSRexploreR: TSS and TSR Analysis

Plot Heatmap

Description

Plot heatmap from count matrix generated by tss_heatmap_matrix or tsr_heatmap_matrix

Usage

plot_heatmap(
  experiment,
  samples = "all",
  data_type = c("tss", "tsr"),
  upstream = 1000,
  downstream = 1000,
  threshold = NULL,
  use_normalized = FALSE,
  dominant = FALSE,
  remove_antisense = TRUE,
  rasterize = FALSE,
  raster_dpi = 150,
  max_value = NULL,
  low_color = "white",
  high_color = "blue",
  log2_transform = TRUE,
  x_axis_breaks = 100,
  ncol = 3,
  filtering = NULL,
  ordering = score,
  order_descending = TRUE,
  order_fun = sum,
  order_samples = NULL,
  quantiling = NULL,
  quantile_fun = sum,
  n_quantiles = 5,
  quantile_samples = NULL,
  split_by = NULL,
  diff_heatmap_list = NULL,
  ...
)

Arguments

experiment	TSRexploreR object.
samples	A vector of sample names to analyze.
data_type	Plot TSS ('tss') or TSR ('tsr') scores.
upstream	Bases upstream to consider
downstream	bases downstream to consider
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.
remove_antisense	Remove antisense reads.
rasterize	Rasterize a ggplot.
raster_dpi	If rasterization is set, this controls the rasterization DPI.
max_value	Truncate heatmap scale at this value.
low_color	Color for minimum value.
high_color	Color for maximum value.
log2_transform	Log2 + 1 transform values for plotting.
x_axis_breaks	The distance breaks to show values on the x-axis.
ncol	Integer specifying the number of columns to arrange multiple plots.
filtering	Logical statement by which to filter data.
ordering	Symbol/name specifying the column by which to order.
order_descending	Whether to order in descending (TRUE) order.
order_fun	Function to aggregate variable by before ordering.
order_samples	Samples that are used to calculate ordering.
quantiling	Character specifying column by which to quantile..
quantile_fun	Functiont o aggregate variable by before quantiling.
n_quantiles	Number of quantiles.
quantile_samples	Samples to use for quantiling.
split_by	Named list with split group as name and vector of genes, or data.frame with columns 'feature' and 'split_group'.
diff_heatmap_list	Named list if sample pairs. The name will be the comparison name,
...	Additional arguments passed to Heatmap

Details

This plotting function generates a ggplot2 heatmap of TSS or TSR signal surrounding the annotated TSSs of genes or transcripts. Whether genes or transcripts are used depends on the feature type chosen when annotating the TSSs with the 'annotate_features' function.

The region around the annotated TSS used for plotting is controlled by 'upstream' and 'downstream', which should be positive integers.

A set of arguments to control data structure for plotting are included. 'use_normalized' will use the normalized scores as opposed to raw read counts. 'threshold' definites the minimum number of reads a TSS or TSR must have to be considered. 'dominant' specifies whether only the dominant TSS or TSR is considered from the 'mark_dominant' function. For TSSs this can be either dominant per TSR or gene, and for TSRs it is just the dominant TSR per gene.

A set of arguments for data conditions are supplied seperatly from the 'conditionals' function used in many other core functions. This is because each row (feature) can have multiple TSSs or TSRs, which is unique to this type of plot. 'filtering' can be supplied with a logical statement to filter TSSs and TSRs by the given condition(s). 'ordering' can be supplied with a symbol/name of the variable to order by, and 'order_descending' controls ordering direction. 'order_fun' is the function used to aggregate the variable score for each row/feature, and 'order_sample' controls the samples used to order from these aggregated variables. 'quantiling' is a character specifying the numeric variable to quantile by, and 'n_quantiles' controls the number of quantiles to split the data into. Just as with ordering, 'quantiles_fun' is the function to aggregate the numeric variable by per feature/row, and 'quantile_samples' are the samples used to determine the order. Finally, 'split_by' can be given either a two column data.frame ('feature' and 'split_group'), or a named list, where the names are the split category and the list contents are a vector of genes.

An option to rasterize the heatmaps using ggrastr is provided with the 'rasterize' argument, and the DPI (resolution) is controlled by 'raster_dpi'.

If diff_heatmap_list is given, the heatmaps will represent the subtracted score between the sample pairs provided in the list. If this argument is given the only data conditionals that will work are ordering related.

Value

ggplot2 object of TSS or TSR heatmap

See Also

annotate_features to annotate the TSSs or TSRs.

Examples

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_heatmap(exp, data_type="tss")

zentnerlab/TSRexploreR documentation built on Dec. 30, 2022, 10:27 p.m.