Example: t-SNE

t-SNE [tsne] is a commonly used algorithm to reduce dimensionality in single cell data.

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

ds.counts.normalize('counts_per_million', inplace=True)
ds.counts = ds.counts.iloc[:200]

print('Calculate t-SNE')
vs = ds.dimensionality.tsne(
        n_dims=2,
        transform='log10',
        theta=0.5,
        perplexity=0.8)

print('Plot t-SNE')
ax = ds.plot.scatter_reduced_samples(
        vs,
        color_by='quantitative_phenotype_1_[A.U.]')

plt.show()

You should get figures similar to the following ones:

[tsne]

L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008.