Understanding the Problem and Data
The problem presented involves analyzing a dataset of OTUs (observations) and plant species to visualize the shared and unique characteristics among the plant species. The dataset provided includes two variables: .OTU.ID, which represents the identification number of each OTU observation, and various columns representing different plant species.
Introduction to Vegan Package
To address this problem, we will utilize the vegan package in R, a popular statistical programming language for data analysis. The vegan package provides several tools for analyzing and visualizing ecological and biological data.
One specific function that can be used to solve this problem is rarecurve(), which generates a rarefaction curve of the observed OTUs (observations) across different plant species. A rarefaction curve is a graphical representation that shows how many OTU observations are found in each plant species, with the x-axis representing the number of OTU observations and the y-axis representing the abundance or frequency of the OTU observation.
Setting Up the Data
To use the rarecurve() function from the vegan package, we need to first load the necessary libraries and set up our dataset. The code provided includes a sample dataset:
dat <- read.table(header=TRUE, text="
.OTU.ID T..kraussiana R..venulosa T..africanum T..repens I..evansiana Z..capensis V..unguiculata E..cordatum
1 Glomus 6150 207 0 111 1144 945 9112 8862
2 Claroi~ 638 71 706 55 1415 510 10798 573
3 Glomus~ 9232 1757 269 1335 0 0 87 1210
4 Glomus~ 133 0 0 0 0 0 6764 3415
5 Glomus 5292 0 1997 0 16 857 66 6562
6 Glomus~ 1596 2675 1167 1800 27 552 0 21
7 Glomus~ 119 179 544 148 0 792 24967 2471
8 Glomus~ 10493 0 0 0 175 0 0 357
9 Glomus~ 4011 0 0 0 0 0 0 477
10 Glomus 2099 1012 15 902 0 726 0 28
")
Generating the Rarefaction Curve
Now that we have our dataset, let’s use the rarecurve() function to generate the rarefaction curve. Here is how you can do it:
library(vegan)
# Set the number of OTU observations for each plant species
sample <- min(rowSums(m))
# Generate the rarefaction curve
rarecurve(dat[-1], sample=sample, col="#F48024", lwd=2, main="Rearfication Curve")
Understanding and Interpreting the Results
The resulting rarefaction curve graphically represents how many OTU observations are found in each plant species. The x-axis of the curve shows the number of OTU observations, while the y-axis represents the abundance or frequency of the OTU observation.
By examining this rarefaction curve, we can visualize the shared and unique characteristics among different plant species. For example, if a plant species has a high abundance of OTU observations, it is likely to be more diverse in terms of its microbial community structure compared to species with lower abundances.
The vegan package offers other tools for analyzing ecological data, such as multivariate analysis techniques, which allow us to better understand the relationships between different variables. However, the rarecurve() function provides a simple yet effective way to visualize the diversity patterns among plant species based on their OTU observations.
Conclusion
In conclusion, this post has demonstrated how to use the vegan package in R to generate a rarefaction curve of observed OTUs (observations) across different plant species. By examining the resulting graph, we can visualize shared and unique characteristics among the plant species, gaining insights into their microbial community structures. This approach provides a straightforward yet effective method for analyzing ecological data and exploring diversity patterns in microbial communities.
Last modified on 2024-01-29