njab is a collection of some python function building on top of
pandas, scikit-learn, statsmodels, pingoin, numpy and more...
It aims to formalize a procedure for biomarker discovery which was first developed for a paper on alcohol-related liver disease, based on mass spectrometry-based proteomics measurements of blood plasma samples:
Niu, L., Thiele, M., Geyer, P. E., Rasmussen, D. N., Webel, H. E.,
Santos, A., Gupta, R., Meier, F., Strauss, M., Kjaergaard, M., Lindvig,
K., Jacobsen, S., Rasmussen, S., Hansen, T., Krag, A., & Mann, M. (2022).
“Noninvasive Proteomic Biomarkers for Alcohol-Related Liver Disease.”
Nature Medicine 28 (6): 1277–87.
nature.com/articles/s41591-022-01850-y
The approach was formalized for an analysis of inflammation markers of a cohort of patients with alcohol related cirrhosis, based on OLink-based proteomics measurments of blood plasma samples:
Mynster Kronborg, T., Webel, H., O’Connell, M. B., Danielsen, K. V., Hobolth, L., Møller, S., Jensen, R. T., Bendtsen, F., Hansen, T., Rasmussen, S., Juel, H. B., & Kimer, N. (2023).
Markers of inflammation predict survival in newly diagnosed cirrhosis: a prospective registry study.
Scientific Reports, 13(1), 1–11.
nature.com/articles/s41598-023-47384-2
Install using pip from PyPi version.
pip install njab
or directly from github
pip install git+https://github.com/RasmussenLab/njab.git
The tutorial can be found on the documentation of the project with output or can be run directly in colab.
The tutorial builds on a dataset example of survival of prostatic cancer.
The main steps in the tutorial are:
- Data loading and inspection
- Uncontrolled binary and t-tests for binary and continous variables respectively
- ANCOVA analysis controlling for age and weight, corrected for multiple testing
- Kaplan-Meier plots of for significant features
All steps are describe in the tutorial, where you could load your own data with minor adaptions. The tutorial build on an curated Alzheimer dataset from omiclearn. See the Alzheimer Data section for more information.
The main steps in the tutorial are:
- Load and prepare data for machine learning
- Find a good set of features using cross validation
- Evaluate and inspect your model retrained on the entire training data
Please find the documentation under njab.readthedocs.io