network_visualization
generates an interactive graph from the provided
Mapper object.
network_visualization( obj_mapper, groups_ind, dat = NULL, folder = "", add_surv_analysis = FALSE, add_analysis_js = NULL, palette = "Set1", legend_ncol = 2, color_code = NULL, color_mix = FALSE )
obj_mapper | An object of class |
---|---|
groups_ind | A vector of group names each of the samples belongs to. |
dat, add_surv_analysis, add_analysis_js | Arguments passed to
|
folder | The name of the folder to save the generated networks. |
palette | A string giving the name of palette provided in
|
legend_ncol | Number of columns of legends. |
color_code | The dataframe of color codes for groups of samples. If not provided, the function will automatically assign colors to different groups. |
color_mix | Boolean. If to display the color of nodes as a mixer of the colors of samples within the nodes, where colors of samples are determined by their associated groups |
An HTML file and a set of pie plots will be saved under the location
given in folder
. The HTML file contains the interactive graph
generated based on the Mapper object, and the pie plots are for the
summerise of nodes.
network_visualization
generates an interactive graph based on the
provided Mapper object with Javascript tools from visNetwork
. It
accepts statistics summary from the stat_summery
function and
display them as tooltips. The tooltips can also be customized by the users by
passing Javascript codes with additional summerise of nodes to the argument
add_analysis_js
.
Nodes are colored with the colors associated with the dominated groups within
each of the nodes. The colors of groups can either be defined by users or by
function auto_set_colorcode
. Self defined color codes should
follow the format introduced in check_color_code
, and we
recommend reading color code files with read_color_code
.
The width of edges is propotional to the percentage of overlapping between connected nodes.
Feng, T., Davila, J.I., Liu, Y., Lin, S., Huang, S. and Wang, C., 2019. Semi-supervised Topological Analysis for Elucidating Hidden Structures in High-Dimensional Transcriptome Datasets. _IEEE/ACM transactions on computational biology and bioinformatics._
tp_data = chicken_generator(1) ff = filter_coordinate(tp_data[,-1], 2) tp_data_mapper = mapper.sta(dat = tp_data[,2:4], filter_values = ff, num_intervals = 10, percent_overlap = 70) network_visualization(tp_data_mapper, groups_ind = tp_data$Group, dat = tp_data[,2:4], folder = "Exp_network") # Add additional analysis to nodes add_analysis_js = paste0('Node Index:<b>', 1:length(tp_data_mapper$points_in_vertex), '</b><br>') network_visualization(tp_data_mapper, groups_ind = tp_data$Group, dat = tp_data[,2:4], folder = "Exp_network", add_analysis_js = add_analysis_js)