ONTraC 2.0.9.post1

A niche-centered, machine learning method for constructing spatially continuous trajectories

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

Please see ONTraC website for details.

Run ONTraC with original cell type

Input File

An example metadata input file is provided. This file contains 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 the IO files explanation.

Run ONTraC

The required options for running ONTraC are the paths to the input file and the three output directories:

• NN-dir: This directory stores preprocessed data and other intermediary datasets for analysis.
• GNN-dir: This directory stores output from he GNN algorithm.
• NT-dir: This directory stores NT output.

For detailed description about all parameters, please see the Parameters explanation.

ONTraC --meta-input simulated_dataset.csv --NN-dir simulation_NN --GNN-dir simulation_GNN --NT-dir simulation_NT --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 the Zenodo Dataset Repository.

We recommand running ONTraC on GPU, it may take much more time on your own laptop with CPU only.

Run ONTraC with kernel-based cell type adjustment

Input Files

• Metadata file

An example metadata input file is provided.

• Embeddings file

An example embeddings input file is provided.

For detailed information about input and output file, please see the IO files explanation.

Run ONTraC

ONTraC --meta-input ./examples/V2/data/merfish_meta.csv --embedding-input ./examples/V2/data/merfish_embedding.csv --NN-dir ./examples/V2_example_embedding_NN --GNN-dir ./examples/V2_example_embedding_GNN --NTScore-dir ./examples/V2_example_embedding_NT  --embedding-adjust -s 42 --equal-space 2>&1 | tee log/V2_example_embedding_input.log

Output

The example ouputs could be found in the Zenodo Dataset Repository. The intermediate and final results are located in NN-dir, GNN-dir, and NT-dir directories. Please see IO files explanation for detailed infromation.

Visualization

Please see the Visualization Tutorial.

Interoperability

ONTraC has been incorporated with Giotto Suite.

Citation

Wang, W.*, Zheng, S.*, Shin, C. S., Chávez-Fuentes J. C. & Yuan, G.-C.$. ONTraC characterizes spatially continuous variations of tissue microenvironment through niche trajectory analysis. Genome Biol, 2025.

If you are using kernel-based cell type adjustment or working on low-resolution data, please also cite: Wang, W., Shin, C. S., Chávez-Fuentes J. C. & Yuan, G.-C.$. A Robust Kernel-Based Workflow for Niche Trajectory Analysis. Small Methods, 2025 (inner cover story).

Other Resources

• Reproducible codes for our Genome Biology paper
• Dataset/output used in our Genome Biology paper
• Reproducible codes for our Small Methods paper