GitLocker: The Coding Marketplace

Description:

ONTraC 1.0.4

ONTraC

ONTraC (Ordered Niche Trajectory Construction) is a niche-centered, machine learning
method for constructing spatially continuous trajectories. ONTraC differs from existing tools in
that it treats a niche, rather than an individual cell, as the basic unit for spatial trajectory
analysis. In this context, we define niche as a multicellular, spatially localized region where
different cell types may coexist and interact with each other. ONTraC seamlessly integrates
cell-type composition and spatial information by using the graph neural network modeling
framework. Its output, which is called the niche trajectory, can be viewed as a one dimensional
representation of the tissue microenvironment continuum. By disentangling cell-level and niche-
level properties, niche trajectory analysis provides a coherent framework to study coordinated
responses from all the cells in association with continuous tissue microenvironment variations.

Installation
pip install ONTraC

For details and alternative approches, please see the installation tutorial
Tutorial
Input File
A example input file is provided in examples/stereo_seq_brain/original_data.csv.
This file contains all input formation with five columns: Cell_ID, Sample, Cell_Type, x, and y.

Cell_ID
Sample
Cell_Type
x
y

E12_E1S3_100034
E12_E1S3
Fibro
15940
18584

E12_E1S3_100035
E12_E1S3
Fibro
15942
18623

...
...
...
...
...

E16_E2S7_326412
E16_E2S7
Fibro
32990.5
14475

For detailed information about input and output file, please see IO files explanation.
Running ONTraC
The required options for running ONTraC are the paths to the input file and the three output directories:

preprocessing-dir: This directory stores preprocessed data and other intermediary datasets for analysis.
GNN-dir: This directory stores output from running the GP (Graph Pooling) algorithm.
NTScore-dir: This directory stores NTScore output.

For detailed description about all parameters, please see Parameters explanation.
ONTraC -d simulated_dataset.csv --preprocessing-dir simulation_preprocessing_dir --GNN-dir simulation_GNN --NTScore-dir simulation_NTScore --hidden-feats 4 -k 6 --modularity-loss-weight 0.3 --purity-loss-weight 300 --regularization-loss-weight 0.1 --beta 0.03 2>&1 | tee simulation.log

The input dataset and output files could be downloaded from Zenodo.
We recommand running ONTraC on GPU, it may take much more time on your own laptop with CPU only.
Output
The intermediate and final results are located in preprocessing-dir, GNN-dir, and NTScore-dir directories. Please see IO files explanation for detailed infromation.
Visualization
Please see post analysis tutorial.
Citation
Wang, W.*, Zheng, S.*, Shin, C. S. & Yuan, G. C.$. Characterizing Spatially Continuous Variations in Tissue Microenvironment through Niche Trajectory Analysis. bioRxiv, 2024.