ssvSignalHeatmap: 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

See ssvSignalHeatmap.ClusterBars for an alternative with more control over where the cluster bars appear.

Usage

ssvSignalHeatmap(
  bw_data,
  nclust = 6,
  perform_clustering = c("auto", "yes", "no")[1],
  row_ = "id",
  column_ = "x",
  fill_ = "y",
  facet_ = "sample",
  cluster_ = "cluster_id",
  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,
  show_cluster_bars = TRUE,
  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".
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.
show_cluster_bars	if TRUE, show bars indicating cluster membership.
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.

Value

ggplot heatmap of signal profiles, facetted by sample

Examples

#the simplest use
ssvSignalHeatmap(CTCF_in_10a_profiles_gr)
ssvSignalHeatmap(CTCF_in_10a_profiles_gr, show_cluster_bars = FALSE)

#clustering can be done manually beforehand
clust_dt = ssvSignalClustering(CTCF_in_10a_profiles_gr, nclust = 3)
ssvSignalHeatmap(clust_dt)

ssvSignalHeatmap(clust_dt, max_rows = 20, max_cols = 7)

# aggregation, when facet_ is shared by multiple samples
prof_gr = CTCF_in_10a_profiles_gr
prof_gr$mark = "CTCF"
clust_gr = ssvSignalClustering(
  prof_gr,
  facet_ = "mark",
  fun.aggregate = function(x)as.numeric(x > 10)
)
table(clust_gr$y)
ssvSignalHeatmap(prof_gr, facet_ = "mark",
  fun.aggregate = function(x)as.numeric(x > 10))
ssvSignalHeatmap(prof_gr, facet_ = "mark",
  fun.aggregate = max)
ssvSignalHeatmap(prof_gr, facet_ = "mark",
  fun.aggregate = min)

jrboyd/seqsetvis documentation built on March 17, 2024, 3:14 p.m.