Principal Coordinate Analysis (PCoA) is used in microbiome research for summarizing the compositional differences in the microbial community between samples. In the few papers I read, the analysis was done in R. I found one paper which had used python for doing PCoA. Here I tried to compare, how PCoA works on both platforms. I wanted to test if I can keep on working in Python which I am comfortable with or will I need to get comfortable with R for microbiome analysis.
Here we will see how we can perform a principal coordinate analysis (PCoA) in R. I have used a microbiome data from a gut microbiome study. This is just to demonstrate the workflow of how to perform the PCoA. This is not an attempt to do any meaningful scientific analysis as it requires sufficient expertise in the field of microbiome research.
Accuracy of machine learning models trained to classify data into discreet categories is the proportion of samples the model is correctly able to classify. For example, in data that contains two categories, if the model is able to correctly predict 45 out of 50 samples, then the accuracy of the model is 90%.