inst/doc/snpClust.R

In adjclust: Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix

## ----skipNoSNPSTATS-----------------------------------------------------------
# IMPORTANT: this vignette is not created if snpStats is not installed
if (!require("snpStats")) {
  knitr::opts_chunk$set(eval = FALSE)
}

## ----loadLib, message=FALSE---------------------------------------------------
library("adjclust")

## ----loadData, results="hide", message=FALSE----------------------------------
data("ld.example", package = "snpStats")

## ----preData------------------------------------------------------------------
geno <- ceph.1mb[, -316]  ## drop one SNP leading to one missing LD value
p <- ncol(geno)
nSamples <- nrow(geno)
geno

## ----LD-----------------------------------------------------------------------
ld.ceph <- snpStats::ld(geno, stats = "R.squared", depth = p-1)
image(ld.ceph, lwd = 0)

## ----snpClust-----------------------------------------------------------------
fit <- snpClust(geno, stats = "R.squared")

## ----snpClust-sparse----------------------------------------------------------
fitH <- snpClust(geno, h = 100, stats = "R.squared")
fitH

## ----dendro-------------------------------------------------------------------
plot(fitH, type = "rectangle", leaflab = "perpendicular")

## ----objectDesc---------------------------------------------------------------
head(cbind(fitH$merge, fitH$gains))

## ----snpClust-LD--------------------------------------------------------------
h <- 100
ld.ceph <- snpStats::ld(geno, stats = "R.squared", depth = h, symmetric = TRUE)
image(ld.ceph, lwd = 0)

## ----snpClust-sMatrix---------------------------------------------------------
fitL <- snpClust(ld.ceph, h)

## ----snpClust-matrix, warning=FALSE-------------------------------------------
gmat <- as(geno, "matrix")
fitM <- snpClust(geno, h, stats = "R.squared")

## ----session------------------------------------------------------------------
sessionInfo()