SMILE: Detection of a subpopulation under selection

In MathieuEmily/SMILE: Detecting subpopulation under selection in a genomic region

Description

SMILE allows the detection of a subpopulation under selection in a genomic region.

Usage

SMILE(x,plot=TRUE)

Arguments

x	A nxp matrix or data.frame.
plot	A boolean flag indicating whether an graphical output of the clustering should be displayed or not.

Details

SMILE is an implementation of the statistical procedure proposed in Emily et al. (2016). The SMILE method selects the smallest subset of conditional profiles that coalesce at the final step of a single-linkage hierarchical clustering constructed with the d2s dissimilarity.

Value

A list containing the following components:

selected	A vector that stored the subset of individuals selected by SMILE
hc	An object of class hclust corresponding to the hierarchical clustering tree obtaining with SMILE
d2s	An object of class dist corresponding to the dissimilarity matrix computed with the dissimilarity measure d2s.

References

Emily, M., Hitte, C. and Mom, A. SMILE: a novel Dissimilarity-based Procedure for Detecting Sparse-Specific Profiles in Sparse Contingency Tables. Computational Statistics and Data Analysis, Vol. 99, pages 171-188, 2016.

Examples

## Example of a matrix of haplotype counts for dog breeds
data(GenomicRegion)

## Computation of the SMILE procedure with a graphical output
res <- SMILE(GenomicRegion)

## Identification of the individuals selected with SMILE
res$selected

## Drawing of the hierarchical clustering tree
plot(res$hc)

## Printing of the dissimilarity matrix
res$d2s

MathieuEmily/SMILE documentation built on May 7, 2019, 4:33 p.m.