Significance tests to identify regulated proteins based on a large number of small samples

Title:

Significance tests to identify regulated proteins based on a large number of small samples

Creator:

Klawonn, Frank

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:b449eb92-ffd3-4ed8-91b9-07e0c3f8f8b2
uuid:b449eb92-ffd3-4ed8-91b9-07e0c3f8f8b2

Subject:

MAD, standard deviation, small samples, and significance test

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Modern biology is interested in better understanding mechanisms within cells. For this purpose, products of cells like metabolites, peptides, proteins or mRNA are measured and compared under different conditions, for instance healthy cells vs. infected cells. Such experiments usually yield regulation or expression values - the abundance or absence of a cell product in one condition compared to another one - for a large number of cell products, but with only a few replicates. In order to distinguish random fluctuations and noise from true regulations, suitable significance tests are needed. Here we propose a simple model which is based on the assumption that the regulation factors follow normal distributions with different expected values, but with the same standard deviation. Before suitable significance tests can be derived from this model, a reliable estimation for the standard deviation in the context of many small samples is needed. We therefore also include a discussion on the properties of the sample MAD ({\bf M}edian {\bf A}bsolute {\bf D}eviation from the median) and the sample standard deviation for small samples sizes.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

[478]-493

Source:

Kybernetika | 2012 Volume:48 | Number:3

Harvested from:

CDK

Metadata only:

false