Microbiome Data Analysis

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• The default alpha diversity metrics are: Shannon’s diversity index (a quantitative measure of community richness); Observed Features (a qualitative measure of community richness); Faith’s Phylogenetic Diversity (a qualitative measure of community richness that incorporates phylogenetic relationships between the features); Evenness (or Pielou’s Evenness; a measure of community evenness).

• The default beta diversity metrics are: Jaccard distance (a qualitative measure of community dissimilarity); Bray-Curtis distance (a quantitative measure of community dissimilarity); unweighted UniFrac distance (a qualitative measure of community dissimilarity that incorporates phylogenetic relationships between the features); weighted UniFrac distance (a quantitative measure of community dissimilarity that incorporates phylogenetic relationships between the features).

• The first plot is an alpha rarefaction plot, and is primarily used to determine if the richness of the samples has been fully observed or sequenced. If the lines in the plot appear to “level out” (i.e., approach a slope of zero) at some sampling depth along the x-axis, that suggests that collecting additional sequences beyond that sampling depth would not be likely to result in the observation of additional features. If the lines in a plot don’t level out, this may be because the richness of the samples hasn’t been fully observed yet (because too few sequences were collected), or it could be an indicator that a lot of sequencing error remains in the data (which is being mistaken for novel diversity). The second plot in this visualization is important when grouping samples by metadata. It illustrates the number of samples that remain in each group when the feature table is rarefied to each sampling depth. If a given sampling depth d is larger than the total frequency of a sample s (i.e., the number of sequences that were obtained for sample s), it is not possible to compute the diversity metric for sample s at sampling depth d. If many of the samples in a group have lower total frequencies than d, the average diversity presented for that group at d in the first plot will be unreliable because it will have been computed on relatively few samples. When grouping samples by metadata, it is therefore essential to look at the bottom plot to ensure that the data presented in the top plot is reliable.