Sigmaplot Whitepapers

Exploratory Enzyme Inhibition Analysis

Enzyme inhibition data is analyzed with the Exploratory Enzyme Kinetics option in SigmaPlot. The direct linear plot, secondary plots and a numeric report are created to help determine if Michaelis-Menten kinetics are satisfied and to elicit the type of inhibition. This analysis provides excellent qualitative and quantitative information prior to fitting multiple candidate inhibition models using the Enzyme Kinetics module. Link to white paper

ROC Curves Analysis

Receiver operating characteristic (ROC) curves are used in medicine to determine a cutoff value for a clinical test. For example, the cutoff value of 4.0 ng/ml was determined for the prostate specific antigen (PSA) test for prostate cancer. A test value below 4.0 is considered to be normal and above 4.0 to be abnormal. Clearly there will be patients with PSA values below 4.0 that are abnormal (false negative) and those above 4.0 that are normal (false positive). The goal of an ROC curve analysis is to determine the cutoff value. Link to white paper

Standard Curves Analysis

A standard curve is used to calibrate an instrument or assay. The Standard Curves macro in SigmaPlot provides five equations that may be fit to your data. Link to white paper

Using Global Curve Fitting to Determine Dose Response Parallelism

Dose response curves are parallel if they are only shifted right or left on the concentration (X) axis. So if you were to fit a 4 parameter logistic function to multiple dose response curves then, for curves which are parallel, only the EC50 parameters would be significantly different. If the data is normalized then the min, max and Hill slope parameters would not be significantly different. Link to white paper

Engineering Application: Particle Behavior

A three-dimensional computational fluid dynamic (CFD) analysis was conducted on the in-flight particle behavior during the plasma spraying process with external injection. Link to white paper

Data Transformations in Biology Using SigmaPlot

Many variables in biology do not meet the assumptions of parametric statistical tests: they are not normally distributed, the variances are not homogeneous, or both. Using a parametric statistical test such as an ANOVA or linear regression on such data may give a misleading result. Link to white paper