ssvSignalHeatmap: heatmap style representation of membership table. instead of...
In seqsetvis: Set Based Visualizations for Next-Gen Sequencing Data

Description Usage Arguments Value Examples

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

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,
  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
)

`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 peak id or gene name for ngs data
`column_`	varaible mapped to column, likely bp position for ngs data
`fill_`	numeric variable to map to fill
`facet_`	variable name to facet horizontally by
`cluster_`	variable name to use for cluster info
`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
`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.

ggplot heatmap of signal profiles, facetted by sample

#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)