Running ONTraC on simulated dataset¶

Notes¶

This notebook will show you the process of running ONTraC on simulation data.

We assume that you have installed ONTraC according to the installation tutorial and open this notebook using installed Python kernel (Python 3.11 (ONTraC)).

Running ONTraC on simulated data¶

If your default shell is not Bash, please adjust this code.

ONTraC will run on CPU if CUDA is not available.

%%bash

source ~/.bash_profile
conda activate ONTraC
ONTraC --meta-input full_simulation_data_with_noise.csv --NN-dir simulation_NN --GNN-dir simulation_GNN --NT-dir simulation_NT --device cuda --epochs 1000 -s 42 --lr 0.03 --hidden-feats 4 -k 6 --modularity-loss-weight 0.3 --regularization-loss-weight 0.1 --purity-loss-weight 300 --beta 0.03 2>&1 | tee simulation.log

Results visualization¶

Please see post analysis tutorial for details.

Install required packages¶

If you default sh is not bash, please adjust this code

%%bash

source ~/.bash_profile
conda activate ONTraC

pip install ONTraC[analysis]

Loading results¶

from ONTraC.analysis.data import AnaData
from optparse import Values

options = Values()
options.NN_dir = 'simulation_NN'
options.GNN_dir = 'simulation_GNN'
options.NT_dir = 'simulation_NT'
options.log = 'simulation.log'
options.reverse = True  # Set it to False if you don't want reverse NT score
options.output = None  # We save the output figure by our self here
ana_data = AnaData(options)

Spatial cell type distribution¶

from ONTraC.analysis.cell_type import plot_spatial_cell_type_distribution_dataset_from_anadata


cell_type_pal = {'A': '#7CAE00',
                 'B': '#00BC5A',
                 'C': '#00C0B3',
                 'D': '#00B4F0',
                 'E': '#8E92FF',
                 'F': '#EA6AF1',
                 'G': '#FF64B0',
                 'H': '#C42F5D',
                 'I': '#A45900',
                 'J': '#6A7300'}



fig, axes = plot_spatial_cell_type_distribution_dataset_from_anadata(ana_data = ana_data,
                palette=cell_type_pal)
fig.savefig('figures/Spatial_cell_type.png', dpi=150)

spatial cell type distribution

Cell-level NT score spatial distribution¶

from ONTraC.analysis.spatial import plot_cell_NT_score_dataset_from_anadata

fig, ax = plot_cell_NT_score_dataset_from_anadata(ana_data=ana_data)
fig.savefig('cell_level_NT_score.png', dpi=300)

cell-level NT score