ssvSignalHeatmap.ClusterBars: heatmap style representation of membership table. instead of...

In jrboyd/seqsetvis: Set Based Visualizations for Next-Gen Sequencing Data

heatmap style representation of membership table. instead of clustering, each column is sorted starting from the left.

Description

Compared to ssvSignalHeatmap, cluster_bars are displayed on the left once instead of for each facet

Usage

ssvSignalHeatmap.ClusterBars(
  bw_data,
  nclust = 6,
  perform_clustering = c("auto", "yes", "no")[1],
  row_ = "id",
  column_ = "x",
  fill_ = "y",
  facet_ = "sample",
  cluster_ = "cluster_id",
  FUN_format_heatmap = NULL,
  max_rows = 500,
  max_cols = 100,
  fill_limits = NULL,
  clustering_col_min = -Inf,
  clustering_col_max = Inf,
  within_order_strategy = c("hclust", "sort")[2],
  dcast_fill = NA,
  return_data = FALSE,
  return_unassembled_plots = FALSE,
  rel_widths = c(1, 9),
  rect_colors = c("black", "gray"),
  text_colors = rev(rect_colors),
  show_labels = TRUE,
  label_angle = 0,
  fun.aggregate = "mean",
  ...
)

Arguments

bw_data	a GRanges or data.table of bigwig signal. As returned from ssvFetchBam and ssvFetchBigwig
nclust	number of clusters
perform_clustering	should clustering be done? default is auto. auto considers if row_ has been ordered by being a factor and if cluster_ is a numeric.
row_	variable name mapped to row, likely id or gene name for ngs data. Default is "id" and works with ssvFetch* output.
column_	varaible mapped to column, likely bp position for ngs data. Default is "x" and works with ssvFetch* output.
fill_	numeric variable to map to fill. Default is "y" and works with ssvFetch* output.
facet_	variable name to facet horizontally by. Default is "sample" and works with ssvFetch* output. Set to "" if data is not facetted.
cluster_	variable name to use for cluster info. Default is "cluster_id".
FUN_format_heatmap	optional function to modify main ggplot (labels, themes, scales, etc.). Take a ggplot and returns a ggplot. Default is NULL.
max_rows	for speed rows are sampled to 500 by default, use Inf to plot full data
max_cols	for speed columns are sampled to 100 by default, use Inf to plot full data
fill_limits	limits for fill legend. values will be cropped to this range if set. Default of NULL uses natural range of fill_.
clustering_col_min	numeric minimum for col range considered when clustering, default in -Inf
clustering_col_max	numeric maximum for col range considered when clustering, default in Inf
within_order_strategy	one of "hclust" or "sort". if hclust, hierarchical clustering will be used. if sort, a simple decreasing sort of rosSums.
dcast_fill	value to supply to dcast fill argument. default is NA.
return_data	logical. If TRUE, return value is no longer ggplot and is instead the data used to generate that plot. Default is FALSE.
return_unassembled_plots	logical. If TRUE, return list of heatmap and cluster-bar ggplots. Can be customized and passed to assemble_heatmap_cluster_bars
rel_widths	numeric of length 2. Passed to cowplot::plot_grid. Default is c(1, 9).
rect_colors	colors of rectangle fill, repeat to match number of clusters. Default is c("black", "gray").
text_colors	colors of text, repeat to match number of clusters. Default is reverse of rect_colors.
show_labels	logical, shoud rectangles be labelled with cluster identity. Default is TRUE.
label_angle	angle to add clusters labels at. Default is 0, which is horizontal.
fun.aggregate	Function to aggregate when multiple values present for facet_, row_, and column_. Affects both clustering and plotting. The function should accept a single vector argument or be a character string naming such a function.
...	addtional arguments passed to cowplot::plot_grid

Value

ggplot heatmap of signal profiles, facetted by sample

Examples

data(CTCF_in_10a_profiles_gr)

#the simplest use
ssvSignalHeatmap.ClusterBars(CTCF_in_10a_profiles_gr)
ssvSignalHeatmap.ClusterBars(CTCF_in_10a_profiles_gr, rel_widths = c(1, 5))

#clustering can be done manually beforehand
clust_dt = ssvSignalClustering(data.table::as.data.table(CTCF_in_10a_profiles_gr), nclust = 3)
ssvSignalHeatmap.ClusterBars(clust_dt)

# aggregation, when facet_ is shared by multiple samples
prof_gr = CTCF_in_10a_profiles_gr
prof_gr$mark = "CTCF"
ssvSignalHeatmap.ClusterBars(prof_gr, facet_ = "mark", fun.aggregate = mean)
ssvSignalHeatmap.ClusterBars(prof_gr, facet_ = "mark", fun.aggregate = "sum")

jrboyd/seqsetvis documentation built on Dec. 10, 2024, 11:23 a.m.