NMFProfiler

Name

NMFProfiler: A multi-omics integration method for samples stratified in groups

Description

NMFProfiler method scheme

Installation

NMFProfiler can be installed from PiPy:

pip install nmfprofiler

In addition, various docker images are provided in the container registry.

Usage

Below is a short illustration of the method on a toy dataset.

from nmfprofiler.nmfprofiler import NMFProfiler
from nmfprofiler.toyexample import ToyExample

# Fix a seed (not mandatory)
seed = 240820

# Run NMFProfiler
model = NMFProfiler(
    omic1=ToyExample().omic1,
    omic2=ToyExample().omic2,
    y=ToyExample().y,
    seed=seed,
    as_sklearn=False,
    backtrack=True)
res = model.fit()

# Get a quick overview of the dataset and model used
print(res)

# Visualize analyzed datasets (samples x features)
ToyExample().y  # 2 groups
res.heatmap(obj_to_viz="omic1", width=15, height=6, path="")
res.heatmap(obj_to_viz="omic2", width=15, height=6, path="")

Note: NMFProfiler produces as many signatures as groups, i.e. levels in \(\mathbf{y}\) vector. Hence in this case we will obtain 2 signatures.

# Visualize contribution matrix W obtained (samples x 2)
res.heatmap(obj_to_viz="W", width=10, height=10, path="")

# Visualize signature matrices H1 and H2 obtained (2 x features)
res.heatmap(obj_to_viz="H1", width=15, height=6, path="")
res.heatmap(obj_to_viz="H2", width=15, height=6, path="")

# Monitor the size of each error term of the loss
res.barplot_error(width=15, height=6, path="")

Support

For questions or additional feature requests, use gitlab issues if possible. Authors can also be contacted by email (check authors’ webpages for email information).

Authors and acknowledgment

This work was supported by the ANRT (CIFRE no. 2022/0051).

License

GPL-3

Project status

Active