This code is associated with the paper from Zanini et al., "Single-cell transcriptional dynamics of flavivirus infection". eLife, 2018. http://dx.doi.org/10.7554/eLife.32942

Singlet

Single cell RNA-Seq analysis with quantitative phenotypes.

Requirements

Python 3.4+ is required. Moreover, you will need:

• pyyaml
• numpy
• scipy
• pandas
• xarray
• scikit-learn
• matplotlib
• seaborn
• bhtsne (for t-SNE dimensionality reduction)

Get those from pip or conda.

Install

To get the latest stable version, use pip:

pip install singlet

To get the latest development version, clone the git repo and then call:

python3 setup.py install

Usage example

You can have a look inside the test folder for examples. To start using the example dataset:

• Set the environment variable SINGLET_CONFIG_FILENAME to the location of the example YAML file
• Open a Python/IPython shell and type:

from singlet.dataset import Dataset
ds = Dataset(samplesheet='example_sheet_tsv', counts_table='example_table_tsv')

ds.counts = ds.counts.iloc[:200]
vs = ds.dimensionality.tsne(
        n_dims=2,
        transform='log10',
        theta=0.5,
        perplexity=0.8)
ax = ds.plot.scatter_reduced_samples(
        vs,
        color_by='quantitative_phenotype_1_[A.U.]')
plt.show()

This will calculate a t-SNE embedding of the first 200 features and then show your samples in the reduced space. It should look like this: