This example plots the co-occurrence data embeded in a sparse matrix. The data is obtained from a disease co-occurrence anaysis on pubmed articles that are related to Epilepsy. For more details see PubMedMiner.jl
using JLD
top_coo_sp, top_mesh_labels = jldopen("chord_data.jld", "r") do file
read(file, "top_coo_sp", "top_mesh_labels")
end
display(top_coo_sp)
display(top_mesh_labels)
49×49 SparseMatrixCSC{Int64,Int64} with 2187 stored entries:
[1 , 1] = 2455
[2 , 1] = 255
[3 , 1] = 156
[4 , 1] = 39
[5 , 1] = 298
[6 , 1] = 28
[7 , 1] = 38
[8 , 1] = 46
[9 , 1] = 51
[10, 1] = 62
⋮
[37, 49] = 2
[39, 49] = 12
[40, 49] = 1
[41, 49] = 4
[43, 49] = 14
[44, 49] = 11
[45, 49] = 2
[46, 49] = 2
[47, 49] = 1
[48, 49] = 1
[49, 49] = 292
49-element Array{String,1}:
"Intellectual Disability"
"Mental Disorders"
"Brain Diseases"
"Brain Neoplasms"
"Syndrome"
"Epilepsy, Temporal Lobe"
"Epilepsies, Partial"
"Pregnancy Complications"
"Cognition Disorders"
"Epilepsy, Absence"
"Epilepsy, Tonic-Clonic"
"Schizophrenia"
"Psychotic Disorders"
⋮
"Dementia"
"Depressive Disorder"
"Acute Disease"
"Learning Disorders"
"Malformations of Cortical Development"
"Neurotic Disorders"
"Personality Disorders"
"Bipolar Disorder"
"Hydrocephalus"
"Memory Disorders"
"Attention Deficit Disorder with Hyperactivity"
"Psychophysiologic Disorders"
using PlotlyJSFactory
using PlotlyJS
p = create_chord_plot(top_coo_sp, labels = top_mesh_labels)
relayout!(p, title="Co-occurrances between top 50 MeSH terms")
JupyterPlot(p)
Plotly javascript loaded.
To load again call
init_notebook(true)
WARNING: Method definition (::Type{PlotlyJS.SyncPlot{TD} where TD<:PlotlyJS.AbstractPlotlyDisplay})(PlotlyJS.Plot{TT} where TT<:PlotlyJS.AbstractTrace) in module PlotlyJS at /Users/isa/.julia/v0.6/PlotlyJS/src/display.jl:136 overwritten at /Users/isa/.julia/v0.6/PlotlyJS/src/displays/ijulia.jl:187.