31 Streptococcus mutans genomes reconstructed from metagenomic data
15 Streptococcus sobrinus genomes reconstructed from metagenomic data
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 |
tar vxzf "Dataset S3.tar.gz"
- Install required libraries:
pip install -r requirements.txt
python SPARSE_ml.py --help
python SPARSE_curve.py --help
python SPARSE_dist.py --help
To obtain detailed help on the scripts.