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.
pip install ONTraCFor details and alternative approches, please see the installation tutorial
A example input file is provided in examples/stereo_seq_brain/meta_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.
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 Parameters explanation.
ONTraC --meta-input simulated_dataset.csv --NN-dir simulation_niche_net --GNN-dir simulation_GNN --NT-dir simulation_niche_trajectory --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.
The intermediate and final results are located in NN-dir, GNN-dir, and NT-dir directories. Please see IO files explanation for detailed infromation.
Please see post analysis tutorial.
ONTraC has been incorporated with Giotto Suite.
Wang, W.*, Zheng, S.*, Shin, C. S. & Yuan, G. C.$. Characterizing Spatially Continuous Variations in Tissue Microenvironment through Niche Trajectory Analysis. bioRxiv, 2024.