Gut microbial communities tend to be like the second example. The first estimate of two is more accurate, since there are actually only two types of microbes in that community. Back to our example, if one person picked five individuals, and one picked 20, to rarefy we would then pick five individuals from the original 20 that were picked by the second person. You can imagine that two communities like these might have similar richness and evenness but they contain different species. However, the beta-diversity was positively correlated with alpha-diversity (P < 0.01, data not shown) suggesting that beta-diversity increased over time as well. For the first community, because it's simpler, you don't need to sample as much to get good data. If we did this, a community would be more diverse, if its organisms were less closely related. The second estimate of four is inaccurate, since there are actually 15 types of microbes in that community.
Alpha diversity describes the diversity within a particular sample or environment (for more info on the statistics of alpha diversity have a look at Amy D. Willis As a next step, we can explore the taxonomy that gives information on what kind of microbes are there based on the I like to use bubble charts as an alternative to stacked bar charts in which the size of the bubbles represent taxa abundances. I hope you found this tutorial useful and that it will help you with your own data analysis and visualization. You can see this here.
Let's try this. The last point I want to make is that when we're talking about alpha diversity we are not taking the identity of each species into consideration. The other species can keep doing the same job. Alpha-diversity; Beta-diversity; Differential abundance testing; Predicting class labels; Additional resources. Evenness describes how similar the abundance is of the different organisms are. What if you pulled out ten? Alpha diversity describes the diversity within a particular sample or environment (for more info on the statistics of alpha diversity have a look at Amy D. Willis paper). Sequencing cost money, so we want to only do as much as we need to answer the questions we have. The richness would increase, by a lot. Remember, both communities have 15 types of microbes. In ecology, the concepts of alpha diversity and beta diversity are frequently used to characterize habitats.
This module will help you understand data plots in later modules, but do not get discouraged if the material here is too technical, a full understanding is not required to complete the course! Again, pass the plateau. As you can see the more microbes I pull out the more accurate my conclusion becomes. Imagine if there were an organ in your body that weighed as much as your brain, that affected your health, your weight, and even your behavior. We are nearly reporting the richness and evenness. We'll talk about that in the next lecture. In this lecture, we'll start talking about the diversity of microbes in a community. We need to be able to produce many sequences to accurately describe the microbial communities we are interested in. Based on that I would say that there are three kinds of microbes in this community. The key is to get enough 16S sequences, to properly describe the community, but not too many sequences.
#' Alpha-diversity analysis #' #' Calculate alpha-diversity indices for each sample and combines with the metadata.
But it depends on how complex you think the community is. That way, if one goes extinct, it's not that big a deal. This is tricky, though, because it means we're not sampling either community as well. Think of habitats like rainforests. This is typically shown in form of a scatter plot or PCoA/NMDS plot (Principal Coordinates Analysis/Non-metric Multidimensional Scaling) in which samples are separated based on their similarity or dissimilarity and arranged in a low-dimensional 2D or 3D space. Even if I picked 20 I would keep coming up with the same number of microbes. Different indices can be used to calculate a dissimilarity matrix. An R package for analysis of microbiome relative abundance data using zero inflated beta GAMLSS and meta-analysis across microbiome studies using random effects models - nhanhocu/metamicrobiomeR. At a certain point though, I can't improve my results even if I sample more.
Faith's phylogenetic distance, is one index that takes biologeny into account, when we're talking about diversity.
This is why higher throughput sequencing is so important to microbiome research. If you’re already familiar with microbiome data you can skip this next section.This table alongside information on taxonomy and metadata can then be used for statistical analysis and visualization. Even though the two communities are exactly the same and both have 15 types of microbes. The food log assignments - reflections & final project are immensely beneficial.
Like these ones pictured here.