ssvSignalHeatmap {seqsetvis} | R Documentation |
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 |
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)