RFLPtools (version 2.0)

RFLPdist: Compute distances for RFLP data.

Description

Within each group containing RFLP-samples exhibiting a equal number of bands, the distance between the molecular weights is computed.

Usage

RFLPdist(x, distfun = dist, nrBands, LOD = 0)

Arguments

x

data.frame with RFLP data; see RFLPdata.

distfun

function computing the distance with default dist; cf. dist.

nrBands

if not missing, then only samples with the specified number of bands are considered.

LOD

threshold for low-bp bands.

Value

A named list with the distances; see dist.

In case nrBands is not missing, an object of S3 class dist.

Details

For each number of bands the given distance between the molecular weights is computed. The result is a named list of distances where the names correspond to the number of bands which occur in each group.

If nrBands is specified only samples with this number of bands are considered.

If LOD > 0 is specified, all values below LOD are removed before the distances are calculated.

References

Flessa, F., Kehl, A., Kohl, M. Analysing diversity and community structures using PCR-RFLP: a new software application. Molecular Ecology Resources 2013 Jul; 13(4):726-33.

Poussier, Stephane; Trigalet-Demery, Danielle; Vandewalle, Peggy; Goffinet, Bruno; Luisetti, Jacques; Trigalet, Andre. Genetic diversity of Ralstonia solanacearum as assessed by PCR-RFLP of the hrp gene region, AFLP and 16S rRNA sequence analysis, and identification of an African subdivision. Microbiology 2000 146:1679-1692

Matsumoto, Masaru; Furuya, Naruto; Takanami, Yoichi; Matsuyama, Nobuaki. RFLP analysis of the PCR-amplified 28S rDNA in Rhizoctonia solani. Mycoscience 1996 37:351 - 356

See Also

RFLPdata, dist

Examples

# NOT RUN {
## Euclidean distance
data(RFLPdata)
res <- RFLPdist(RFLPdata)
names(res) ## number of bands
res$"6"

RFLPdist(RFLPdata, nrBands = 6)

## Other distances
res1 <- RFLPdist(RFLPdata, distfun = function(x) dist(x, method = "manhattan"))
res2 <- RFLPdist(RFLPdata, distfun = function(x) dist(x, method = "maximum"))
res[[1]]
res1[[1]]
res2[[1]]

## cut dendrogram at height 50
clust4bd <- hclust(res[[2]])
cgroups50 <- cutree(clust4bd, h=50)
cgroups50

## or
library(MKomics)
res3 <- RFLPdist(RFLPdata, distfun = corDist)
res3$"9"

## hierarchical clustering
par(mfrow = c(2,2))
plot(hclust(res[[1]]), main = "Euclidean distance")
plot(hclust(res1[[1]]), main = "Manhattan distance")
plot(hclust(res2[[1]]), main = "Maximum distance")
plot(hclust(res3[[1]]), main = "Pearson correlation distance")


## Similarity matrix
library(MKomics)
myCol <- colorRampPalette(brewer.pal(8, "RdYlGn"))(128)
ord <- order.dendrogram(as.dendrogram(hclust(res[[1]])))
temp <- as.matrix(res[[1]])
simPlot(temp[ord,ord], col = rev(myCol), minVal = 0, 
        labels = colnames(temp), title = "(Dis-)Similarity Plot")


## or
library(lattice)
levelplot(temp[ord,ord], col.regions = rev(myCol),
          at = do.breaks(c(0, max(temp)), 128),
          xlab = "", ylab = "",
          ## Rotate label of x axis
          scales = list(x = list(rot = 90)),
          main = "(Dis-)Similarity Plot")

## multidimensional scaling
loc <- cmdscale(res[[5]])
x <- loc[,1]
y <- -loc[,2]
plot(x, y, type="n", xlab="", ylab="", xlim = 1.05*range(x), main="Multidemsional scaling")
text(x, y, rownames(loc), cex=0.8)
# }