disorderdetection: Disorder Detection data

In DiscreteDatasets: Example Data Sets for Use with Discrete Statistical Tests

Disorder Detection data

Description

For earlier recognition of diseases, multiple variations of the human base sequence get studied. The so-called coverage of each base is calculated to detect duplicates, deletions and insertions in the base sequence. To find these variations a hypothesis-test gets performed for each base in the tested area. The null-hypothesis being that the coverage of the base is as expected under the null-hypothesis (expected coverage Cb can be calculated using a given formula, following a poisson distribution). If the observed coverage is exceptionally high or low the null-hypothesis gets rejected. For each type of variation there is a different formula to calculate the expected coverages. The expected coverages in this data set were calculated using the formula for a local test without GC-correction.

Usage

data("disorderdetection")

Format

A data frame with 315 rows representing a base sequence with the following 2 columns:

observed frequencies

Observed coverage of each base

expected frequencies

Expected coverage of each base

Details

The data was collected from the "Goodness-of-fit tests for disorder detection in NGS experiments" published by the Biometrical Journal , by Jiménez-Otero, de Uña-Álvarez and Pardo-Fernández. See references for more details.

References

Jiménez-Otero N, de Uña-Álvarez J, Pardo-Fernández JC (2019). Goodness-of-fit tests for disorder detection in NGS experiments. Biometrical Journal, 61(2), pp. 424-441. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/bimj.201700284")}.

DiscreteDatasets documentation built on April 4, 2025, 2:22 a.m.