Usage

Basic usage

The simplest use of triku is with a common pipeline of scanpy. We show the example with the pbmc3k dataset from scanpy:

import scanpy as sc
import triku as tk

pbmc = sc.datasets.pbmc3k()

sc.pp.filter_cells(pbmc, min_genes=50)
sc.pp.filter_genes(pbmc, min_cells=10)
sc.pp.log1p(pbmc)

sc.pp.pca(pbmc)
sc.pp.neighbors(pbmc)

tk.tl.triku(pbmc)

This is a basic preprocessing of a dataset. You can run triku either after or before sc.pp.log1p. It usually works better after log transformation.

After running triku, results are stored in adata.var (triku_distance, highly_variable), and in adata.uns['triku_params'][None].

Advanced usage

When using triku, there are more some parameters that can be changed. All of them can be found at the API Reference.

  • n_features: The number of features to be selected. For instance, tk.tl.triku(adata, n_features=500) would select the first 500 features.

  • use_raw: Uses counts from adata.raw. This, for instance, can be used to select non log-transformed counts. This can be set as tk.tl.triku(adata, use_raw=True).

  • name: Saves the results with a custom name. For instance, if the name is sample, then the results would be stored in adata.var['triku_distance_sample'], and in adata.uns['triku_params']['sample'].