This analysis is an additional step of the ebioKit tutorials to visualize the results of 16S metabarcoding using unsupervised learning techniques such as t-distributed stochastic neighbor embedding(t-SNE) and Principal component analysis(PCA) -- You can even use a barplot to find the answer to this question or a frequency table.

Read more about MiSeq SOP the Schloss Lab uses to process their 16S rRNA gene sequences that are generated using Illumina's MiSeq platform using paired end reads here.

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Setting up your environment

• Download Anaconda for your operating system for Python 3 anaconda

• Create a conda environment like mine:

  conda env create -f environment.yml

  This creates an environment called py35. Activate it with this command in your terminal

  source activate py35

• In your terminal, in the directory where you cloned this repository. Run this command

  jupyter notebook otu_data_viz.ipynb

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A codebook is provided for the .csv file: I encourage you to go through the exercise here. to generate the 0.16.cons.taxonomy.csv dataset. Which was created by processing .fastq files obtained from Mus musculus with Mothur. Find the notebook here.