waltlabtools¶
A collection of tools for biomedical research assay analysis in Python.
Key Features¶
Analysis for assays such as digital ELISA.
Calculation of calibration curves, concentrations, limits of detection, and more.
Free and open-source software under the GNU General Public License v3.
Getting Started¶
Installation:
waltlabtools
can be installed using conda or pip. In the command line,conda:
conda install -c tylerdougan waltlabtools
pip:
pip install waltlabtools
Dependencies:
waltlabtools
requires scipy ≥ 1.3.1, and either jax ≥ 0.1.64 or numpy ≥ 1.10.0. To make the best use ofwaltlabtools
, you may want to install pandas (for data import/export and organization), scikit-learn (for data analysis), matplotlib (for plotting), and JupyterLab (for writing code). These can all be installed using conda or pip, and may become dependencies in future releases.
Functions and Classes¶
API: waltlabtools
includes classes for mathematical models and
calibration curves, and a set of functions to make use of these objects
and others. These are covered in the
documentation.
- noindex
Functions
|
The average number of enzymes per bead. |
|
Factor c4 for unbiased estimation of the standard deviation. |
|
Flattens most data structures. |
|
The fraction of beads which are on. |
|
Geometric meandian. |
|
|
|
Compute the limit of detection (LOD). |
|
Performs a (nonlinear) regression and return coefficients. |
Classes
|
Calibration curve. |
|
Mathematical model for calibration curve fitting. |
- noindex
Functions
|
The identity function. |
Development of waltlabtools
is led by the
Walt Lab for Advanced Diagnostics
at Brigham and Women's Hospital,
Harvard Medical School, and the
Wyss Institute for Biologically Inspired Engineering.