plot_ame_heatmap: Plot AME heatmap clustered by similarity in detected motifs

In snystrom/memes: motif matching, comparison, and de novo discovery using the MEME Suite

Plot AME heatmap clustered by similarity in detected motifs

Description

Plot AME heatmap clustered by similarity in detected motifs

Usage

plot_ame_heatmap(
  ame,
  id = motif_id,
  group = NULL,
  value = -log10(adj.pvalue),
  group_name = NULL,
  scale_max = NA
)

Arguments

ame	ame results data.frame
id	column of motif ids to use (default: motif_id).
group	grouping column if comparing across multiple ame runs (optional, default: NULL).
value	value to display as heatmap intensity. Default: -log10(adj.pvalue). Takes function or column name as input. If set to "normalize", will use normalized rank within 'group' as the heatmap values. **If in doubt**, prefer the -log10(adj.pvalue) plot potentially in conjunction with adjusting 'scale_max'. (See "Normalized rank visualization" section below for more details on how to interpret these data)
group_name	when group = NULL, name to use for input regions. Ignored if group is set.
scale_max	max heatmap value to limit upper-value of scale. Useful if distribution of 'value's vary greatly between groups. Usually a better to tweak this option than to use value = "normalize". The cumulative distribution plot generated by 'ame_compare_heatmap_methods()' can be useful for selecting this value, try to pick a value which captures the largest fraction of hits across all groups while excluding outliers.

Details

Normalized rank visualization **NOTE:** The normalized rank visualization eliminates all real values related to statistical significance! Instead, this visualization represents the relative ranks of hits within an AME run, which already pass a significance threshold set during 'runAME()'. This means that even if several motifs have similar or even identical pvalues, their heatmap representation will be a different color value based on their ranked order in the results list. This also means that using the normalized rank visualization will be misleading if there are only a few AME hits; it is only worth using if the number of hits is very large (>100). Both visualizations can be useful and reveal different properties of the data to the user during data exploration. Use 'ame_compare_heatmap_methods()' to help assess differences in the two visualizations. **If in doubt**, prefer the '-log10(adj.pvalue)' representation.

Common mistake: if 'value' is set to a string that is not "normalize", it will return: "Error: Discrete value supplied to continuous scale". To use a column by name, do not quote the column name.

Value

'ggplot' object

Examples

data("example_ame", package = "memes")

# Plot a single category heatmap
plot_ame_heatmap(example_ame$Decreasing)

# Plot a multi category heatmap
grouped_ame <- dplyr::bind_rows(example_ame, .id = "category")
plot_ame_heatmap(grouped_ame, group = category)

snystrom/memes documentation built on Oct. 12, 2024, 2:42 a.m.