CODEWITHSUNDEEP
pythonpandasmatplotlibseaborn
Michael Berry
Michael Berry

Reputation: 1003

Setting col_colors in seaborn clustermap from pandas

I have a clustermap generated from a pandas dataframe. Two of the columns are used to generate the clustermap and I need to use a 3rd column to generate a col_colors bar using sns.palplot(sns.light_palette('red')) palette (values will be from 0 - 1, light - dark colors).

The pseudo-code looks something like this: 

df=pd.DataFrame(input, columns = ['Source', 'amplicon', 'coverage', 'GC'])
tiles = df.pivot("Source", "amplicon", "coverage")
col_colors = [values from df['GC']]
sns.clustermap(tiles, vmin=0, vmax=2, col_colors=col_colors)

I'm battling to find details on how to setup the col_colors so the correct values are linked to the appropriate tiles. Some direction would be greatly appreciated.

Upvotes: 3

Views: 8465

Answers (1)

johnchase
johnchase

Reputation: 13705

This example will be much easier to explain with sample data. I don't know what your data looks like, but say you had a bunch of GC content measurements For instance:

import seaborn as sns
import numpy as np
import pandas as pd
data = {'16S':np.random.normal(.52, 0.05, 12),
        'ITS':np.random.normal(.52, 0.05, 12),
        'Source':np.random.choice(['soil', 'water', 'air'], 12, replace=True)}
df=pd.DataFrame(data)
df[:3]

    16S         ITS         Source
0   0.493087    0.460066    air
1   0.607229    0.592945    water
2   0.577155    0.440726    water

So data is GC content, and then there is a column describing the source. Say we want to plot a cluster map of the GC content where we use the Source column to define the network

#create a color palette with the same number of colors as unique values in the Source column
network_pal = sns.light_palette('red', len(df.Source.unique()))

#Create a dictionary where the key is the category and the values are the
#colors from the palette we just created
network_lut = dict(zip(df.Source.unique(), network_pal))

#get the series of all of the categories
networks = df.Source

#map the colors to the series. Now we have a list of colors the same
#length as our dataframe, where unique values are mapped to the same color
network_colors = pd.Series(networks).map(network_lut)

#plot the heatmap with the 16S and ITS categories with the network colors
#defined by Source column
sns.clustermap(df[['16S', 'ITS']], row_colors=network_colors, cmap='BuGn_r')

enter image description here Basically what most of the above code is doing is creating a vector of colors that corrospond to the Source column of the data frame. You could of course create this manually, where the first color in the list would be mapped to the first row in the dataframe and the second color would be mapped to the second row and so on (this order will change when you plot it), however that would be a lot of work. I used a red color palette as that is what you mentioned in your question though I might recommend using a different palette. I colored by rows, however you can do the same thing for columns. Hope this helps!

Upvotes: 7

Related Questions