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zheminzhou / OralMicrobiome

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Dataset S1.tar.gz

31 Streptococcus mutans genomes reconstructed from metagenomic data

Dataset S2.tar.gz

15 Streptococcus sobrinus genomes reconstructed from metagenomic data

Dataset S3.tar.gz

Python Scripts
1. SPARSE_ml.py Fit machine learning models on SPARSE results
2. SPARSE_curve.py Calculate rarefaction curve on SPARSE results
3. SPARSE_dist.py Calculate Euclidian distances of samples and species
Source Files
1. SPARSE.species.profile SPARSE results
2. SPARSE.samples Oral sources of samples
Batch workflow
1. requirements.txt Required python libraries
2. commands.bash All the commands to generate results
Outputs
1. SPARSE.species.profile.SVM Support Vector Machine results. Figure 2
2. SPARSE.species.profile.PCA PCA results. Figure S1
3. SPARSE.species.profile.UMAP UMAP & K-mean clustering. Figures 1A & S1
4. SPARSE.species.profile.curves Rarefaction curves. Figure 5
5. SPARSE.species.profile.sample.dist Abundance distances of samples for NJ tree. Figure 1B
6. SPARSE.species.profile.taxon.dist Abundance distances of species for NJ tree. Figures 4 & S2
Tested on Python >= 3.6
Use:
 python SPARSE_ml.py --help
 python SPARSE_curve.py --help
 python SPARSE_dist.py --help

To obtain detailed help on the scripts.