Plot

Heatmap

A heatmap can be used to show relative abundances oftaxa in samples or groups of samples. The plot.heatmap function can be used like this.

import qdiv
obj = qdiv.MicrobiomeData.load_example("Saheb-Alam_DADA2") #First we load example data
obj.rename_features(inplace=True, name_type="ASV") #This is the name the features ASV1, ASV2
obj.tax_prefix(add=True, inplace=True) #This is to add prefix to the taxonomic classified, i.e., d__ for domain, p__ for phylum, etc.
print(obj.meta) #Let's have a look at the meta data before plotting the heatmap

       location     feed mfc
sample
S4        anode  acetate   B
S5        anode  acetate   B
S6        anode  acetate   B
S7        anode  acetate   B
S10     cathode  acetate   B
S11     cathode  acetate   B
S12     cathode  acetate   B
S13     cathode  acetate   B
S20       anode  glucose   D
S21       anode  glucose   D
S22       anode  glucose   D
S23       anode  glucose   D
S26     cathode  glucose   D
S27     cathode  glucose   D
S28     cathode  glucose   D
S29     cathode  glucose   D

fig, ax, data = qdiv.plot.heatmap(obj, group_by=["feed", "location"], levels=["Phylum", "Genus"])

Here I chose to group the samples based on the meta data columns ‘feed’ and ‘location’. I also specified that I want to show phylum and genus levels on the y-axis. The features are grouped based on the lowest taxonomic level chosen (i.e. genus in this case).

Alpha diversity profiles

The plot.alpha_diversity_profile function let’s us visualize how alpha diversity depends on diversity order.

import qdiv
obj = qdiv.MicrobiomeData.load_example("Saheb-Alam_DADA2")
obj.rename_features(inplace=True, name_type="ASV") #This is the name the features ASV1, ASV2
obj.tax_prefix(add=True, inplace=True) #This is to add prefix to the taxonomic classified, i.e., d__ for domain, p__ for phylum, etc.
obj.rarefy(inplace=True) #Rarefy to make comparison of alpha diversity between samples easier
fig, ax, data = qdiv.plot.alpha_diversity_profile(obj, color_by="location")

Here we can see the cathode samples tend to have higher diversity than anode samples for all diversity orders.

Beta diversity

Similarities and differences in community composition between samples if often visualized using an ordination. First, we calculate pairwise dissimilarities between samples using the diversity.naive_beta function.

import qdiv
obj = qdiv.MicrobiomeData.load_example("Saheb-Alam_DADA2")
obj.rename_features(inplace=True, name_type="ASV") #This is the name the features ASV1, ASV2
obj.tax_prefix(add=True, inplace=True) #This is to add prefix to the taxonomic classified, i.e., d__ for domain, p__ for phylum, etc.
obj.rarefy(inplace=True)

dis = qdiv.diversity.naive_beta(obj, q=1) #Here I calculate for q=1

Next, we plot a principal coordinate analysis using the plot.ordination function.

fig, ax, data1, data2 = qdiv.plot.ordination(dis, obj, color_by="location", shape_by="feed")