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A python3 bokeh based categorical dendrogram and heatmap plotting library.

Project description

# BokehHeat

## Abstract

Bokehheat provides a python3, bokeh based, interactive
categorical dendrogram and heatmap plotting implementation.

+ Minimal requirement: python 3.6
+ Dependencies: bokeh, pandas, scipy
+ Programmer: bue, jenny
+ Date origin: 2018-08
+ License: >= GPLv3
+ User manual: this README file
+ Source code: [](

Available bokehheat plots are:
+ heat.cdendro: an interactive categorical dendrogram plot implementation.
+ heat.cabar: an interactive categorical bar plot implementation.
+ heat.qabar: an interactive quantitative bar plot implementation.
+ heat.heatmap: an interactive heatmap implementation.
+ heat.clustermap: an interactive cluster heatmap implementation which combines
heat.cdendro, heat.cabar, heat.qabar and heat.heatmap under the hood.

## Example Results

For the real interactive experience please clone or download this repository
and open theclustermap.html file with your favorite web browser
(we recommend [FireFox](

![heat.clustermap image](theclustermap.png)

**Figure:** This is a poor, static heat.clustermap html result screenshot.

## HowTo Guide

How to install bokehheat?
pip install bokehheat

How to load the bokehheat library?
from bokehheat import heat

Howto get reference information about how to use each bokehheat module?
from bokehheat import heat


Howto integrate bokehheat plots into [pweave](
from pweave.bokeh import output_pweave, show

o_clustermap, ls_xaxis, ls_yaxis = heat.clustermap(...)

## Tutorial
This tutorial guides you through a cluster heatmap generation process.

1. Load libraries needed for this tutorial:
# library
from bokehheat import heat
from bokeh.palettes import Reds9, YlGn8, Colorblind8
import numpy as np
import pandas as pd

1. Prepare data:
# generate test data
ls_sample = ['sampleA','sampleB','sampleC','sampleD','sampleE','sampleF','sampleG','sampleH']
ls_variable = ['geneA','geneB','geneC','geneD','geneE','geneF','geneG','geneH', 'geneI']
ar_z = np.random.rand(8,9)
df_matrix = pd.DataFrame(ar_z)
df_matrix.index = ls_sample
df_matrix.columns = ls_variable = 'y' = 'x'

# generate some sample annotation
df_sample = pd.DataFrame({
'y': ls_sample,
'age_year': list(np.random.randint(0,101, 8)),
'sampletype': ['LumA','LumA','LumA','LumB','LumB','Basal','Basal','Basal'],
'sampletype_color': ['Cyan','Cyan','Cyan','Blue','Blue','Red','Red','Red'],
df_sample.index = df_sample.y

# generate some gene annotation
df_variable = pd.DataFrame({
'x': ls_variable,
'genereal': list(np.random.random(9) * 2 - 1),
'genetype': ['Lig','Lig','Lig','Lig','Lig','Lig','Rec','Rec','Rec'],
'genetype_color': ['Yellow','Yellow','Yellow','Yellow','Yellow','Yellow','Brown','Brown','Brown'],
df_variable.index = df_variable.x

1. Generate categorical and quantitative sample and gene
annotation tuple of tuples:
t_ycat = (df_sample, ['sampletype'], ['sampletype_color'])
t_yquant = (df_sample, ['age_year'], [0], [128], [YlGn8])
t_xcat = (df_variable, ['genetype'], ['genetype_color'])
t_xquant = (df_variable, ['genereal'], [-1], [1], [Colorblind8])
tt_catquant = (t_ycat, t_yquant, t_xquant, t_xcat)

1. Generate the cluster heatmap:
s_file = "theclustermap.html"
o_clustermap, ls_xaxis, ls_yaxis = heat.clustermap(
df_matrix = df_matrix,
ls_color_palette = Reds9,
r_low = 0,
r_high = 1,
s_z = "log2",
tt_axis_annot = tt_catquant,
b_ydendo = True,
b_xdendo = True,
#i_px = 80,
s_filetitel="the Clustermap",

1. Display the result:
print(f"check out: {s_file}")
print(f"y axis is: {ls_yaxis}")
print(f"x axis is: {ls_xaxis}")

The resulting clustermap should look something like the example result
in the section above.

## Discussion

In bioinformatics a clustered heatmap is a common plot to present
gene expression data from many patient samples.
There are well established open source clustering software kits like
[Cluster and TreeView](
for producing and investigating such heatmaps.

### Static cluster heaptmap implementations

There exist a wealth of
[R]( and R/[bioconductor](
packages with static cluster heatmaps functions (e.g. heatmap.2 from the gplots library),
each one with his own pros and cons.

In Python the static cluster heatmap landscape looks much more deserted.
There are some ancient [mathplotlib]( based implementations
like this [active state recipe](
or the [heatmapcluster]( library.
There is the [seaborn clustermap]( implementation,
which looks good but might need hours of tweaking to get an agreeable plot with all the needed information out.

So, static heatmaps are not really a tool for exploring data.

### Interactive cluster heatmap implementations

There exist d3heatmap a R/d3.js based interactive cluster heatmap packages.
And heatmaply, a R/plotly based package.
Or on a more basic level R/plotly based cluster heatmaps can be written
with the ggdendro and ggplot2 library.

But I have not found a full fledged python based interactive cluster heatmap library.
Neither Python/[plottly]( nor Python/[bokeh]( based.
The only Python/bokeh based cluster heatmap implementation I found was this
from Daniel Russo.

### Synopsis

All in all, all of this implementations were not really what I was looking for.
That is why I rolled my own.
Bokehheat is a Python/[bokeh]( based interactive cluster heatmap library.

The challenges this implementation tried to solve are,
the library should be:
+ easy to use with [pandas]( datafarmes.
+ interactive, this means the results should be hover and zoomable plots.
+ output should be in computer platform independent and easy accessible format,
like java script spiced up html file, which can be opened in any webbrowser.
+ possibility to add as many categorical and quantitative annotation bars on y and x axis as wished.
+ possibility to cluster y and/or x axis.
+ snappy interactivity, even with big datasets with thousands of samples and genes
(this is actually still a bit an issue).

#### Future directions

An [altair]( based cluster heatmap implementation.
I think that this will be the future. Check out Jake VanderPlas talk
[Python Visualization Landscape](
from the PyCon 2017 in Portland Oregon (USA).

## Contributions

+ Implementation: Elmar Bucher
+ Documentation: Jennifer Eng, Elmar Bucher
+ Helpfull discussion: Mark Dane, Daniel Derrick, Hongmei Zhang,
Annette Kolodize, Jim Korkola, Laura Heiser

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