Running ONTraC on a MERFISH dataset¶
Download the data¶
Warning: The MERFISH dataset is quite large and will take a long time to run on CPU only.
Download merfish_dataset.csv from Zenodo
Running ONTraC¶
If your default shell is not Bash, please adjust this code.
ONTraC will run on CPU if CUDA is not available.
conda activate ONTraC
ONTraC --meta-input original_data.csv \
--NN-dir merfish_NN \
--GNN-dir merfish_GNN \
--NT-dir merfish_NT \
--device cuda --epochs 1000 --batch-size 10 -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 > log/merfish.log
Results visualization¶
Please see the Visualizations tutorials for details.
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)
Plotting preparation
import numpy as np
import pandas as pd
import matplotlib as mpl
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
mpl.rcParams['font.family'] = 'Arial'
import matplotlib.pyplot as plt
import seaborn as sns
Spatial cell type distribution
cell_types = ana_data.cell_type_codes['Cell_Type'].tolist()
selected_cell_types = ["VLMC", 'L2/3 IT', 'L4/5 IT', 'L5 IT',"L5 ET", "L5/6 NP" , 'L6 IT',"L6 CT","L6 IT Car3"]
rainbow_cmap = mpl.colormaps['gist_rainbow']
my_pal = {"VLMC": rainbow_cmap(0)}
my_pal.update({cell_type: rainbow_cmap( 0.3 + 0.7 * (i - 1) / (len(selected_cell_types) - 1)) for i, cell_type in enumerate(selected_cell_types[1:])})
my_pal.update({cell_type: 'gray' for cell_type in cell_types if cell_type not in selected_cell_types})
We only show two samples here
seleted_samples = ['mouse1_slice91', 'mouse1_slice131']
data_df = ana_data.meta_data_df[[x in seleted_samples for x in ana_data.meta_data_df['Sample']]]
with sns.axes_style('white', rc={
'xtick.bottom': True,
'ytick.left': True
}), sns.plotting_context('paper',
rc={
'axes.titlesize': 8,
'axes.labelsize': 8,
'xtick.labelsize': 6,
'ytick.labelsize': 6,
'legend.fontsize': 6
}):
N = len(seleted_samples)
fig, axes = plt.subplots(1, N, figsize = (4 * N, 4))
for i, sample in enumerate(seleted_samples):
sample_df = data_df.loc[data_df['Sample'] == sample]
ax = axes[i] if N > 1 else axes
sns.scatterplot(data = sample_df,
x = 'x',
y = 'y',
hue = 'Cell_Type',
palette = my_pal,
hue_order = selected_cell_types + [x for x in cell_types if x not in selected_cell_types],
edgecolor=None,
s = 4,
ax=ax)
ax.set_xticks([])
ax.set_yticks([])
ax.set_title(f"{sample}")
ax.legend(loc='upper left', bbox_to_anchor=(0,-0.2), ncol=4)
fig.tight_layout()
fig.savefig('figures/spatial_cell_type.png', dpi=300)
Cell-level NT score spatial distribution
N = len(seleted_samples)
fig, axes = plt.subplots(1, N, figsize = (3.5 * N, 3))
for i, sample in enumerate(seleted_samples):
sample_df = data_df.loc[data_df['Sample'] == sample]
sample_df = sample_df.join(ana_data.NT_score['Cell_NTScore'])
ax = axes[i] if N > 1 else axes
scatter = ax.scatter(sample_df['x'], sample_df['y'], c=1 - sample_df['Cell_NTScore'], cmap='rainbow', vmin=0, vmax=1, s=1) # substitute with following line if you don't need change the direction of NT score
# scatter = ax.scatter(sample_df['x'], sample_df['y'], c=sample_df['Cell_NTScore'], cmap='rainbow', vmin=0, vmax=1, s=1)
ax.set_xticks([])
ax.set_yticks([])
plt.colorbar(scatter)
ax.set_title(f"{sample} cell-level NT score")
fig.tight_layout()
fig.savefig('figures/cell_level_NT_score.png', dpi=300)
