Publication

Sizing Studies for Detecting Graphical Models

Stephan Morgenthaler
2012
Working Papers

Abstract

The ability to identify reliably a positive or negative partial correlation between the expression levels of two genes is determined by the number p of genes, the number n of analyzed samples, and the statistical properties of the measurements. Classical statistical theory teaches us that the product of the root sample size multiplied by the size of the partial correlation is the crucial quantity. But this has to be combined with some adjustment for multiplicity depending on p, which makes the classical analysis somewhat arbitrary. We investigate this problem through the lens of the Kullback-Leibler divergence, which is a measure of the average information for detecting an effect. We conclude that commonly sized studies in genetical epidemiology are not able to reliably detect moderately strong links.

Official source

https://infoscience.epfl.ch/entities/publication/c86c666f-b686-4721-99c9-e824cd81ccca

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Stephan Morgenthaler
2012
Working Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/c86c666f-b686-4721-99c9-e824cd81ccca

About this result

Related concepts (32)

Sample size determination

Sample size determination is the act of choosing the number of observations or replicates to include in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power.

Pearson correlation coefficient

In statistics, the Pearson correlation coefficient (PCC) is a correlation coefficient that measures linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationships or correlations.

Kullback–Leibler divergence

In mathematical statistics, the Kullback–Leibler divergence (also called relative entropy and I-divergence), denoted , is a type of statistical distance: a measure of how one probability distribution P is different from a second, reference probability distribution Q. A simple interpretation of the KL divergence of P from Q is the expected excess surprise from using Q as a model when the actual distribution is P.

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Sizing Studies for Detecting Graphical Models

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