Welcome to DTNE’s documentation!

DTNE (Diffusive Topology Neighbor Embedding) is a Python tool that implements a novel manifold learning framework. By leveraging a diffusive process, DTNE constructs a manifold distance matrix, enabling key analyses such as dimensionality reduction, pseudotime ordering, and cluster identification, providing valuable insights into complex datasets.

Features

  • Dimensionality Reduction: By preserving manifold geodesic distances, DTNE provides accurate low-dimensional projections of high-dimensional single-cell data.

  • Pseudotime Inference: Infer developmental trajectories by leveraging the manifold distance matrix, improving the identification of lineage progression.

  • Clustering: DTNE enables clustering by utilizing the manifold distance matrix, which captures both local and global data structures.

Installation

DTNE is uploaded to PyPI, and can be downloaded using pip: pip install dtne. Alternatively, you can clone our github repository, and install the package by running the following command in the terminal: pip install ..

Quickstart

DTNE requires input data in the form of a high-dimensional matrix. Suppose you have loaded a single-cell data X in Python.

  1. Dimensionality Reduction

    DTNE can be used for dimensionality reduction:

    from dtne import *

dtne_operator = DTNE(k_neighbors = 10,l=2)
Y = dtne_operator.fit_transform(X)

  2. Pseudotime Inference

    DTNE allows for pseudotime inference to analyze cell development trajectories:

    dtne_pseudotime = dtne_operator.order_cells(root_cells=[0])

  3. Clustering

    DTNE supports clustering based on the computed manifold distances:

    dtne_cluster = dtne_operator.cluster_cells(n_clusters=8)

Tutorials

API