Analyzing Radioligand Binding Data

A radioligand is a radioactively labeled drug that can associate with a receptor, transporter, enzyme, or any protein of interest. Measuring the rate and extent of binding provides information on the number of binding sites, and their affinity and accessibility for various drugs. Radioligand binding experiments are easy to perform, and provide useful data in many fields. For example, radioligand binding studies are used to study receptor regulation, investigate receptor localization in different organs or regions using autoradiography, categorize receptor subtypes, and probe mechanisms of receptor signaling. This unit reviews the theory of receptor binding and explains how to analyze experimental data. Since binding data are usually best analyzed using nonlinear regression, this unit also explains the principles of curve fitting with nonlinear regression.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143671/1/cppsa03h.pd

Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate

BACKGROUND: Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads to the familiar goal of regression: to minimize the sum of the squares of the vertical or Y-value distances between the points and the curve. Outliers can dominate the sum-of-the-squares calculation, and lead to misleading results. However, we know of no practical method for routinely identifying outliers when fitting curves with nonlinear regression. RESULTS: We describe a new method for identifying outliers when fitting data with nonlinear regression. We first fit the data using a robust form of nonlinear regression, based on the assumption that scatter follows a Lorentzian distribution. We devised a new adaptive method that gradually becomes more robust as the method proceeds. To define outliers, we adapted the false discovery rate approach to handling multiple comparisons. We then remove the outliers, and analyze the data using ordinary least-squares regression. Because the method combines robust regression and outlier removal, we call it the ROUT method. When analyzing simulated data, where all scatter is Gaussian, our method detects (falsely) one or more outlier in only about 1–3% of experiments. When analyzing data contaminated with one or several outliers, the ROUT method performs well at outlier identification, with an average False Discovery Rate less than 1%. CONCLUSION: Our method, which combines a new method of robust nonlinear regression with a new method of outlier identification, identifies outliers from nonlinear curve fits with reasonable power and few false positives

Intuitive Biostatistics: Choosing a Statistical Test

Created by Harvey Motulsky for GraphPad Software, this page provides a table for selecting an appropriate statistical method based on type of data and what information is desired from the data. It also compares parametric and nonparametric tests, one-sided and two-sided p-values, paired and unpaired tests, Fisher's test and the Chi-square test, and regression and correlation. It comes from Chapter thirty-seven of the textbook, "Intuitive Biostatistics"

Analyzing Radioligand Binding Data

Radioligand binding experiments are easy to perform, and provide useful data in many fields. They can be used to study receptor regulation, discover new drugs by screening for compounds that compete with high affinity for radioligand binding to a particular receptor, investigate receptor localization in different organs or regions using autoradiography, categorize receptor subtypes, and probe mechanisms of receptor signaling, via measurements of agonist binding and its regulation by ions, nucleotides, and other allosteric modulators. This unit reviews the theory of receptor binding and explains how to analyze experimental data. Since binding data are usually best analyzed using nonlinear regression, this unit also explains the principles of curve fitting with nonlinear regression.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152946/1/cpns0705.pd

CALCULATING RECEPTOR NUMBER FROM BINDING EXPERIMENTS USING SAME COMPOUND AS RADIOLIGAND AND COMPETITOR

[No abstract available