Alpha diversity is used to measure the diversity within a sample and answers the question "how many?". It allows you to look at number of different taxa within each sample separately. If a sample has high alpha diversity it contains many organisms. There are several methods that can be used to look at and understand alpha diversity.
When talking about alpha diversity, we are looking at two things:
Species richness - a count of the number of different species present in a sample. It does not take into account the abundance of the species or their relative distributions.
Species evenness - a measure of relative abundance of different species that make up the richness.
The input metric for Alpha Diversity is Abundance Score. Abundance score is a normalized abundance metric that reflects the underlying microbiome composition of the community.
The aggregation level of the input data for Comparative Analysis has been set to species level.
CHAO1 index - appropriate for abundance data - assumes that the number of organisms identified for a taxa has a poisson distribution and corrects for variance. It is useful for data sets skewed toward low-abundance calls, as if often the case with microbes.
The Simpson diversity index is used to calculate a measure of diversity taking into account the number of taxa as well as the abundance. The simpson index gives more weight to common or dominant species which means a few rare species with only a few representatives will not affect the diversity of the sample.
The Shannon index summarizes the diversity in the population while assuming all species are represented in a sample and they are randomly sampled. The Shannon index increases as both the richness and evenness of the community increase.
Users have the option to visualize the alpha diversity distribution using box plot for each sample cohort selected using labels when creating comparative analysis: Wilcoxon rank sum test can also be overlayed on the boxplot chart by turning on the add wilcoxon overlay toggle
How does this test work and what do the results mean?
Above the alpha diversity chart the results of a Wilcoxon rank sum test can be explored in a table.
This nonparametric statistical test can be used to investigate whether two independent cohorts consist of samples that were selected from populations having the same alpha diversity distribution. The null hypothesis thereby is that the probability that a randomly selected value from one cohort is less than a randomly selected value from a second cohort is equal to the probability of being greater.
P-values below e.g. 0.05 suggest that the null hypothesis can be rejected, confirming that the samples of two cohorts are selected from populations with different alpha diversity distributions.
Test results are shown in table listing the test statistic and p-value for the different possible cohort combinations. A negative (positive) test statistic for a cohort pair Cohort 1 ↔︎ Cohort 2 thereby means that the median alpha diversity of Cohort 1 is lower (higher) than the median alpha diversity for Cohort
Above and to the right of the alpha diversity charts you can find a Result Switcher and a Cohort Menu.
The Result Switcher allows you to hide the results (using the NONE setting, which is selected by default).
Clicking on SIGNIFICANT will limit the results to only those cohort combinations for which alpha diversity differs with statistical significance, i.e. for which the p-value is equal or lower than 0.05. The ALL setting displays test statistics and p-values for all cohort combinations.
The Cohort Menu to the right of the Result Switcher offers another way to filter the displayed cohort combinations. A pulldown menu lists all possible cohort combinations. Selecting checkboxes for the cohort pairs of interest will reduce the rows with test statistics and p-values in the table accordingly.
It is possible to export the test results to TSV.
Wilcoxon rank sum test can also be visualized on the boxplot by turning on the add wilcoxon overlay toggle
Updated 4 months ago