Data Calibration

This page is completely unrelated to Smoldyn, except that it was also written by Steve Andrews.

It performs data calibration using a new method, called the 1-step method. This contrasts the conventional 2-step method, in which the first step is to use measurements of standards to estimate a calibration curve and the second step is to calibrate the data using the calibration curve. The 1-step method uses information from repeated measurements of unknown samples to reduce the effects of measurement error, especially in standard measurements. This method is particularly useful when samples are analyzed in "batches," where a batch might be a protein gel or a series of analyses; by definition, calibration parameters are the same for all measurements in a single batch and different for measurements in different batches.

This method is published as: Steven S. Andrews and Suzannah Rutherford, "A Method and On-line Tool for Maximum Likelihood Calibration of Immunoblots and Other Measurements that are Quantified in Batches", PLoS ONE 11:e0149575 (2016). The software provided on this webpage was updated from Python2 to Python3 in August, 2022.

The measurements are assumed to be accurately represented by the model: y_i,j,k = α_i + β_ix_j + ε_i,j,k, where y_i,j,k is an uncalibrated measurement, α_i and β_i are batch-specific calibration parameters for batch i, x_j is the amount of analyte in sample j, and ε_i,j,k is Gaussian-distributed measurement noise with standard deviation σ for that particular measurement.

You can try this method here using our data, random data, or your own data. You can also download the Python source code from here. If you encounter problems, e-mail Steve Andrews.

Data for calibration

Example data		Very simple example data set. These data are in Figure 1 of the PLoS ONE submission.
Western blot data		Some of our Western blot data. View the data here. Calibrate with only the β parameter and the standard set to "STD-pERK" with value 1.
Random data	Batches: α mean: α s.d.: β mean: β s.d.: Samples: x mean: x s.d.: Measurements: σ: Stds. in all batches: no yes	This creates a synthetic data set of your requested size, with randomly chosen αi and βi parameters, and randomly chosen xj analyte amounts.
Text input	batch,sample,measurement	Enter data in rows, where each row has a batch name (text or number), a sample name (text or number), and a measurement value (number). Separate these with just commas; do not add any white space.
Upload file		This needs to be a plain text CSV file. Linux/Mac line terminations work; I don't know about Windows. Format the data as above, where each row includes a batch name, sample name, and measurement value.

Sensitivity parameters

β only		Choose this if all of the α parameters equal zero. This requires only 1 standard.
α and β		Choose this if the α parameters are non-zero. This requires at least 2 standards.

Standards

First sample		The first sample is made the standard with value 1.
Text input	standard,measurement	Enter standard data in rows, where each row has a sample name (text or number) and a measurement value (number). Separate these with just commas; do not add any white space.
Upload file		This needs to be a plain text CSV file. Linux/Mac line terminations work; I don't know about Windows. Format the data as above, where each row includes a sample name and measurement value.

Amount of output

Low		Only final results.
Medium		Intermediate and final results.
High		All computations during calibration process.

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