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.

Installation¶

Download waltlabtools from PyPI.
Dependencies: waltlabtools requires scipy ≥ 1.3.1, and either jax ≥ 0.1.64 or numpy ≥ 1.10.0. To make the best use of waltlabtools, you may want to install pandas (for data import/export and organization), matplotlib (for plotting), and JupyterLab (for writing code). These can all be installed using conda, and may become dependencies in future releases.

Functions and Classes¶

Functions

`aeb`(fon)	The average number of enzymes per bead.
`c4`(n)	Factor c4 for unbiased estimation of the standard deviation.
`flatten`(data[, on_bad_data])	Flattens most data structures.
`fon`(aeb)	The fraction of beads which are on.
`gmnd`(data)	Geometric meandian.
`jit`(fun)
`lod`(blank_signal[, inverse_fun, sds, corr])	Compute the limit of detection (LOD).
`regress`(model, x, y[, use_inverse, weights, ...])	Performs a (nonlinear) regression and return coefficients.

Classes

`CalCurve`([model, coefs, lod, lod_sds, force_lod])	Calibration curve.
`Model`([fun, inverse, name, params, xscale, ...])	Mathematical model for calibration curve fitting.

Functions

`Id`(x)	The identity function.
`isiterable`(data)	Determines whether an object is iterable.

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.