Comparative MTP Analyzer

Comparative MTP Analyzer consists of the following functionalities.

Composition: Compare relative taxonomic abundances among sets. E.g., find the overall difference in taxonomic compositions between the healthy and diseased subjects. 
Alpha-diversity: Compare alpha-diversity indices among sets. E.g., find if a set of diseased subjects shows lesser species diversity than that of healthy subjects.
 Beta-diversity: Compare beta-diversity indices among sets.
 Taxonomic biomarker discovery:  Find differentially present species that are characteristic of a disease.


Three visualization methods are provided here.

Stacked bar

Compostional analysis in Comparative MTP Analyzer

  1. Select a taxonomic rank here.
  2. The names of MTP sets. Please note that the order of displayed sets was set when you select the sets.
  3. Download the chart.
  4. Download the taxonomic profiles.
  5. Click this small box to go to the page of the taxon (Fusobacteria phylum in this example).
  6. Click on the box or taxon name that you are interested to highlight (as shown as the below figure).

“Firmicutes” phylum was selected and highlighted.

Double pie chart

This module visualizes a pie chart showing two different taxonomic ranks simultaneously.

Double pie chart in Comparative MTP Analyzer

  1. Select a taxonomic rank for the inner circle.
  2. Select a taxonomic rank for the outer circle.
  3. Name of the MTP set
  4. Taxonomic composition of the outer circle, as selected in (b).
  5. Taxonomic composition of the inner circle, as selected in (a).

Selected taxa

In this module, the composition of the selected taxa can be shown as box plots [Learn more]. In addition, predefined sets are provided to quickly browse their compositional information.

Selected taxa in Comparative MTP Analyzer

  1. A predefined group, “Human gut taxa”, is selected.
  2. Among them, “Enterobacteriaceae” is selected. This family contains Escherichia coli and is known to be associated with inflammation.
  3. Name of an MTP set. In this example, CDI set contain C. difficile infection-positive seniors.
  4. Wilcoxon rank-sum test” is used to test the null hypothesis.
  5. Indicating the p-value of the statistical test used in (d). N.S. means “not significant” (p-value is lower than 0.05). In this example, the portion of the family Enterobacteriaceae in MTPs in the “CDI” set is statistically significantly different from both “Control” and “Antibiotics” sets.
  6. By default, outliers are not displayed. Click this button to show outliers. Please note that only scale of the Y axis is changed.
  7. An image file can be downloaded here.

You can also analyze using a taxon of your choice.

Stacked bar visualization of an MTP set

  1. Click here to start.
  2. Enter a taxon name here (any rank).
  3. Box plots and results of statistical tests are displayed instantly.


In this module, various alpha-diversity measures are compared among multiple MTP sets.

Species richness is the number of different species/OTUs represented in an MTP/microbiome sample. Species richness values among MTP sets are compared and statistically tested to see if they are different.

Comparing alpha-diversity in Comparative MTP Analyzer

  1. Apply “normalization” if you wish.
  2. Type of the alpha-diversity measure
  3. Box plots to describe the distributions among the MTP sets [Learn more]
  4. p-values resulted from the statistical test
  5. The statistical test applied (Wilcoxon rank-sum test)
  6. Download the chart as an image.


In this module, beta-diversity or relationship among MTPs are visualized and statistically evaluated. All calculations are based on a distance between two MTPs (microbial communities), for which several algorithms are available in EzBioCloud 16S-based MTP app.

Beta set-significance analysis

Using beta set-significance analysis, you can test if there is a significant difference between MTP sets using distance measures of beta diversity. The results are displayed in two sections. To change the types and parameters of beta diversity distance measures for this analysis, click the desired options on the left <Beta diversity distance> panel. The pages will be refreshed with new results instantly.


Permutational multivariate analysis of variance (PERMANOVA), a non-parametric multivariate statistical test, is used to test the null hypothesis that the centroids and dispersion of the MTP sets as defined by measured space, are equivalent for all sets. A rejection of the null hypothesis means that either the centroid and/or the spread of the MTPs is different between the MTP sets.

PERMANOVA test is first performed with all MTP sets, then for each pair of MTP sets.

It is equivalent to the “beta-group-significance” command in the QIIME2 package.

Inter-set distances

In this visualization, beta diversity distances between a reference MTP set and a target MTP set are calculated and plotted as boxplots. When the reference set is identical to the target set, the calculations are made within a set (=intra-set distances). Otherwise, inter-set distances are calculated.

PERMANOVA results and Inter-set distances

  1. The reference MTP set
  2. The target MTP sets. In this example, MTPs in the “Control” (reference) set showed larger distances to “CDI” than “Antibiotics”. Also, the distances within “Control” and between “Control” and “Antibiotics” are similar, meaning that there is no significant difference between “Control” and “Antibiotics”. Please note that PERMANOVA test between these two sets also indicated the rejection of the null hypothesis (N.S. p=0.209).

Biomarker discovery

There are three methods for biomarker discovery. <Kruskal-Wallis H test> is the default method. <XOR analysis> is simple calculation of OTU presence/absence between(among) the sets. <LefSe analysis> is more rigorous method than <Kruskal-Wallis test>.

Taxonomic biomarker discovery

  1. Discovered biomarker taxa are listed in the order of p value.
  2. OTU frequencies of Control, Antibiotics and CDI sets are shown in the last three columns.
  3. Click to see more details on each taxon in individual bar charts or box plot.
  4. Apply “Normalization” function as you need.
  5. Click the headers to re-sort the Table.
  6. Click the taxon name to see the taxonomic information on EzBioCloud.

The EzBioCloud team / Last edited on Feb. 8, 2019