# Compute distances for RFLP data. If gel image quality is low, faint bands may be disregarded and may lead to wrong conclusions. This function computes the distance between the molecular weights of RFLP samples, including samples containing one or more additional bands. Thus, failures during band detection could be identified. Visualisation of band patterns using this method can be done by `RFLPplot` using the argument `nrMissing`. ```r RFLPdist2(x, distfun = dist, nrBands, nrMissing, LOD = 0, diag = FALSE, upper = FALSE) ``` ## Arguments - `x`: data.frame with RFLP data; see `RFLPdata`. - `distfun`: function computing the distance with default `dist`; cf. `dist`. - `nrBands`: samples with number of bands equal to `nrBands` are to be considered. - `nrMissing`: number of bands that might be missing. - `LOD`: threshold for low-bp bands. - `diag`: see `dist` - `upper`: see `dist` ## Details For a given number of bands the given distance between the molecular weights is computed. It is assumed that a number of bands might be missing. Hence all samples with number of bands in nrBands, nrBands+1, ..., nrBands+nrMissing are compared. If `LOD > 0` is specified, it is assumed that missing bands can only occur for molecular weights smaller than `LOD`. As a consequence only samples which have `nrBands` bands with molecular weight larger or equal to `LOD` are selected. For computing the distance between the molecular weight of a sample S1 with x bands and a Sample S2 with x+y bands the distances between the molecular weight of sample S1 and the molecular weight of all possible subsets of S2 with x bands are computed. The distance between S1 and S2 is then defined as the minimum of all these distances. If `LOD > 0` is specified, only all combinations of values below `LOD` are considered. This option may be useful, if gel image quality is low, and the detection of bands is doubtful. ## Returns An object of class `"dist"` returned; cf. `dist`. ## 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. Ian A. Dickie, Peter G. Avis, David J. McLaughlin, Peter B. Reich. Good-Enough RFLP Matcher (GERM) program. Mycorrhiza 2003, 13:171-172. ## Author(s) Fabienne Flessa Fabienne.Flessa@uni-bayreuth.de , Alexandra Kehl Alexandra.Kehl@uni-tuebingen.de , Matthias Kohl Matthias.Kohl@stamats.de ## See Also `RFLPdata`, `nrBands`, `RFLPdist`, `dist` ## Examples ```r ## Euclidean distance data(RFLPdata) nrBands(RFLPdata) res0 <- RFLPdist(RFLPdata, nrBands = 4) res1 <- RFLPdist2(RFLPdata, nrBands = 4, nrMissing = 1) res2 <- RFLPdist2(RFLPdata, nrBands = 4, nrMissing = 2) res3 <- RFLPdist2(RFLPdata, nrBands = 4, nrMissing = 3) ## assume missing bands only below LOD res1.lod <- RFLPdist2(RFLPdata, nrBands = 4, nrMissing = 1, LOD = 60) ## hierarchical clustering par(mfrow = c(2,2)) plot(hclust(res0), main = "0 bands missing") plot(hclust(res1), main = "1 band missing") plot(hclust(res2), main = "2 bands missing") plot(hclust(res3), main = "3 bands missing") ## missing bands only below LOD par(mfrow = c(1,2)) plot(hclust(res0), main = "0 bands missing") plot(hclust(res1.lod), main = "1 band missing below LOD") ## Similarity matrix library(MKomics) myCol <- colorRampPalette(brewer.pal(8, "RdYlGn"))(128) ord <- order.dendrogram(as.dendrogram(hclust(res1))) temp <- as.matrix(res1) simPlot(temp[ord,ord], col = rev(myCol), minVal = 0, labels = colnames(temp), title = "(Dis-)Similarity Plot") ## missing bands only below LOD ord <- order.dendrogram(as.dendrogram(hclust(res1.lod))) temp <- as.matrix(res1.lod) simPlot(temp[ord,ord], col = rev(myCol), minVal = 0, labels = colnames(temp), title = "(Dis-)Similarity Plot\n1 band missing below LOD") ## 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") ## Other distances res11 <- RFLPdist2(RFLPdata, distfun = function(x) dist(x, method = "manhattan"), nrBands = 4, nrMissing = 1) res12 <- RFLPdist2(RFLPdata, distfun = corDist, nrBands = 4, nrMissing = 1) res13 <- RFLPdist2(RFLPdata, distfun = corDist, nrBands = 4, nrMissing = 1, LOD = 60) par(mfrow = c(2,2)) plot(hclust(res1), main = "Euclidean distance\n1 band missing") plot(hclust(res11), main = "Manhattan distance\n1 band missing") plot(hclust(res12), main = "Pearson correlation distance\n1 band missing") plot(hclust(res13), main = "Pearson correlation distance\n1 band missing below LOD") ```

RFLPdist2 function