| Title: | Species Identity and Evolution in R |
|---|---|
| Description: | Analysis of species limits and DNA barcoding data. Included are functions for generating important summary statistics from DNA barcode data, assessing specimen identification efficacy, testing and optimizing divergence threshold limits, assessment of diagnostic nucleotides, and calculation of the probability of reciprocal monophyly. Additionally, a sliding window function offers opportunities to analyse information across a gene, often used for marker design in degraded DNA studies. Further information on the package has been published in Brown et al (2012) <doi:10.1111/j.1755-0998.2011.03108.x>. |
| Authors: | Samuel Brown, Rupert Collins, Stephane Boyer, Marie-Caroline Lefort, Jagoba Malumbres-Olarte, Cor Vink, Rob Cruickshank |
| Maintainer: | Rupert A. Collins <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.5.0.9000 |
| Built: | 2024-11-11 04:14:15 UTC |
| Source: | https://github.com/boopsboops/spider |
Spider: SPecies IDentity and Evolution in R, is an R package implementing a number of useful analyses for DNA barcoding studies and associated research into species delimitation and speciation. Included are functions for generating summary statistics from DNA barcode data, assessing specimen identification efficacy, and for testing and optimising divergence threshold limits. In terms of investigating evolutionary and taxonomic questions, techniques for sliding window, population aggregate, and nucleotide diagnostic analyses are also provided.
The complete list of functions can be displayed with
library(help=spider).
More information, including a tutorial on the use of spider can be found at
http://spider.r-forge.r-project.org.
| Package: | spider |
| Type: | Package |
| Version: | 1.4-2 |
| Date: | 2017-05-13 |
| License: | GPL |
| LazyLoad: | yes |
A few of the key functions provided by spider:
DNA barcoding: bestCloseMatch, nearNeighbour,
threshID, threshOpt, heatmapSpp.
Sliding window: slidingWindow, slideAnalyses,
slideBoxplots.
Nucleotide diagnostics: nucDiag, rnucDiag.
Morphological techniques: paa.
Samuel Brown, Rupert Collins, Stephane Boyer, Marie-Caroline Lefort, Jagoba Malumbres-Olarte, Cor Vink, Rob Cruickshank
Maintainer: Samuel Brown <[email protected]>
Brown S. D. J., Collins R. A., Boyer S., Lefort M.-C., Malumbres-Olarte J., Vink C. J., & Cruickshank R. H. 2012. SPIDER: an R package for the analysis of species identity and evolution, with particular reference to DNA barcoding. _Molecular Ecology Resources_ 12:562-565. doi: 10.1111/j.1755-0998.2011.03108.x
A set of 33 sequences of the mitochondrial protein-coding gene cytochrome oxidase I from 20 species of the New Zealand wolf spider genus Anoteropsis (Lycosidae) and two species of Artoria as outgroups. The sequences are available on GenBank as accession numbers AY059961 through AY059993.
A DNAbin object containing 33 sequences with a length of 409 base pairs stored as a matrix.
Vink, C. J., and Paterson, A. M. (2003). Combined molecular and morphological phylogenetic analyses of the New Zealand wolf spider genus _Anoteropsis_ (Araneae: Lycosidae). _Molecular Phylogenetics and Evolution_ *28* 576-587.
Tests of barcoding efficacy using distance-based methods.
bestCloseMatch(distobj, sppVector, threshold = 0.01, names = FALSE)bestCloseMatch(distobj, sppVector, threshold = 0.01, names = FALSE)
distobj |
A distance object (usually from |
sppVector |
Vector of species names. See |
threshold |
Distance cutoff for identifications. Default of 0.01 (1%). |
names |
Logical. Should the names of the nearest match be shown? Default of FALSE. |
These functions test barcoding efficacy. All sequences must be identified prior to testing. Each sequence is considered an unknown while the remaining sequences in the dataset constitute the DNA barcoding database that is used for identification. If the identification from the test is the same as the pre-considered identification, a correct result is returned.
bestCloseMatch conducts the "best close match" analysis of Meier et
al. (2006), considering the closest individual unless it is further than the
given threshold, which results in no identification. More than one species
tied for closest match results in an assignment of "ambiguous". When the
threshold is large, this analysis will return essentially the same result as
nearNeighbour. If names = TRUE, a list is returned containing
the names of all species represented by specimens within the threshold.
nearNeighbour finds the closest individual and returns if their names
are the same (TRUE) or different (FALSE). If names = TRUE, the name
of the closest individual is returned. Ties are decided by majority rule.
threshID conducts a threshold-based analysis, similar to that
conducted by the "Identify Specimen" tool provided by the Barcode of Life
Database (http://www.boldsystems.org/index.php/IDS_OpenIdEngine). It
is more inclusive than bestCloseMatch, considering ALL sequences
within the given threshold. If names = TRUE, a list is returned
containing the names of all species represented by specimens within the
threshold.
These functions are not recommended as identification tools, though they can
be used as such when names = TRUE.
bestCloseMatch and threshID return a character vector
giving the identification status of each individual.
"correct" |
The name of the closest match is the same |
"incorrect" |
The name of the closest match is different |
"ambiguous" |
More than one species is the
closest match ( |
"no id" |
No species are within the threshold distance |
nearNeighbour returns a logical vector or (if names = TRUE)
the name for the nearest individual.
Samuel Brown <[email protected]>
Meier, R., Shiyang, K., Vaidya, G., & Ng, P. (2006). DNA barcoding and taxonomy in Diptera: a tale of high intraspecific variability and low identification success. _Systematic Biology_ *55* (5) 715-728.
nearNeighbour, threshID, dist.dna, sppVector
Also as help, ~~~
data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split = "_"), function(x) paste(x[1], x[2], sep = "_")) bestCloseMatch(anoDist, anoSpp) bestCloseMatch(anoDist, anoSpp, threshold = 0.005) nearNeighbour(anoDist, anoSpp) nearNeighbour(anoDist, anoSpp, names = TRUE) threshID(anoDist, anoSpp) threshID(anoDist, anoSpp, threshold = 0.003) data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) bestCloseMatch(doloDist, doloSpp) bestCloseMatch(doloDist, doloSpp, threshold = 0.005) nearNeighbour(doloDist, doloSpp) nearNeighbour(doloDist, doloSpp, names=TRUE) threshID(doloDist, doloSpp) threshID(doloDist, doloSpp, threshold = 0.003)data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split = "_"), function(x) paste(x[1], x[2], sep = "_")) bestCloseMatch(anoDist, anoSpp) bestCloseMatch(anoDist, anoSpp, threshold = 0.005) nearNeighbour(anoDist, anoSpp) nearNeighbour(anoDist, anoSpp, names = TRUE) threshID(anoDist, anoSpp) threshID(anoDist, anoSpp, threshold = 0.003) data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) bestCloseMatch(doloDist, doloSpp) bestCloseMatch(doloDist, doloSpp, threshold = 0.005) nearNeighbour(doloDist, doloSpp) nearNeighbour(doloDist, doloSpp, names=TRUE) threshID(doloDist, doloSpp) threshID(doloDist, doloSpp, threshold = 0.003)
Coerces all sequences in a DNAbin object to the same length.
blockAlignment(DNAbin, mode = "shortest", range = NULL, fill = "")blockAlignment(DNAbin, mode = "shortest", range = NULL, fill = "")
DNAbin |
An object of class |
mode |
Character vector. Options of "shortest" or "longest" |
range |
Numeric vector of length 2. Index of the bases where the new alignment should begin and end |
fill |
Character to fill the extra bases in short sequences. Default of "" (blank). Recommend that only "-" (gap) or "?" be used |
When mode = "shortest", the alignment is truncated at the length of
the shortest sequence. When mode = "longest", the alignment is
extended to the end of the longest sequence, with shorter sequences filled
in with "fill"s.
A DNAbin object in matrix format.
Samuel Brown <[email protected]>
data(salticidae) salticidae blockAlignment(salticidae) blockAlignment(salticidae, mode = "longest") blockAlignment(salticidae, mode = NULL, range = c(200, 600)) graphics::image(blockAlignment(salticidae)) graphics::image(blockAlignment(salticidae, mode = "longest")) graphics::image(blockAlignment(salticidae, mode = NULL, range = c(200, 600)))data(salticidae) salticidae blockAlignment(salticidae) blockAlignment(salticidae, mode = "longest") blockAlignment(salticidae, mode = NULL, range = c(200, 600)) graphics::image(blockAlignment(salticidae)) graphics::image(blockAlignment(salticidae, mode = "longest")) graphics::image(blockAlignment(salticidae, mode = NULL, range = c(200, 600)))
Creates a complete graph for the given cloud of vertices.
cgraph(x, y = NULL, ...)cgraph(x, y = NULL, ...)
x |
X values, or a matrix with two columns containing X and Y values. |
y |
Y values. Can be left empty if |
... |
Other arguments to be passed to |
If y is not given, x is required to be a matrix containing
both x and y values.
Plots a complete graph between the given vertices.
Samuel Brown <[email protected]>
x <- runif(15) y <- runif(15) graphics::plot(x, y) cgraph(x, y) M <- cbind(x, y) cgraph(M[1:10,], col = "blue")x <- runif(15) y <- runif(15) graphics::plot(x, y) cgraph(x, y) M <- cbind(x, y) cgraph(M[1:10,], col = "blue")
Calculates the Chao1 estimate of the number of haplotypes in a population based on the total number of haplotypes present, and the number of singletons and doubletons in the dataset.
chaoHaplo(DNAbin)chaoHaplo(DNAbin)
DNAbin |
An object of class ‘DNAbin’. |
The function assumes a large number of specimens have been sampled and that duplicate haplotypes have not been removed. Interpretation becomes difficult when more than one species is included in the dataset.
An vector of length three, giving the estimated total number of haplotypes in the population, and lower and upper 95% confidence limits.
Samuel Brown <[email protected]>
Vink, C. J., McNeill, M. R., Winder, L. M., Kean, J. M., and Phillips, C. B. (2011). PCR analyses of gut contents of pasture arthropods. In: Paddock to PCR: Demystifying Molecular Technologies for Practical Plant Protection (eds. Ridgway, H. J., Glare, T. R., Wakelin, S. A., O'Callaghan, M.), pp. 125-134. New Zealand Plant Protection Society, Lincoln.
Chao, A. (1989). Estimating population size for sparse data in capture-recapture experimnets. _Biometrics_ *45* 427-438.
data(dolomedes) #Create dataset with multiple copies of Dolomedes haplotypes doloSamp <- dolomedes[sample(16, 100, replace=TRUE, prob=c(0.85, rep(0.01, 15))), ] chaoHaplo(doloSamp)data(dolomedes) #Create dataset with multiple copies of Dolomedes haplotypes doloSamp <- dolomedes[sample(16, 100, replace=TRUE, prob=c(0.85, rep(0.01, 15))), ] chaoHaplo(doloSamp)
This functions counts the number of bases in an alignment that are composed of missing data.
checkDNA(DNAbin, gapsAsMissing = TRUE)checkDNA(DNAbin, gapsAsMissing = TRUE)
DNAbin |
A DNA alignment of class ‘DNAbin’. |
gapsAsMissing |
Logical. Should gaps (coded as '-') be considered missing bases? Default of TRUE. |
This function considers bases coded as '?' and 'N' as missing data. By default, gaps (coded as '-') are also considered missing.
A numeric vector giving the number of missing bases in each sequence of the alignment.
Samuel Brown <[email protected]>
data(anoteropsis) checkDNA(anoteropsis) checkDNA(anoteropsis, gapsAsMissing=FALSE)data(anoteropsis) checkDNA(anoteropsis) checkDNA(anoteropsis, gapsAsMissing=FALSE)
Returns the numbers of species, genera and individuals in the dataset.
dataStat(sppVector, genVector, thresh = 5)dataStat(sppVector, genVector, thresh = 5)
sppVector |
Species vector (see |
genVector |
Genus vector that defines the genera of each individual, created in a similar manner to the species vector. |
thresh |
Threshold for adequate individual/species number. Default of 5. |
The value NULL can be passed to gen if genera are not of
interest in the dataset.
A table giving the number of genera and species in the dataset; giving the minimum, maximum, mean and median number of individuals per species, and the number of species below the given threshold.
Rupert Collins <[email protected]>
data(anoteropsis) #Species vector anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) #Genus vector anoGen <- sapply(strsplit(anoSpp, split="_"), function(x) x[1]) dataStat(anoSpp, anoGen)data(anoteropsis) #Species vector anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) #Genus vector anoGen <- sapply(strsplit(anoSpp, split="_"), function(x) x[1]) dataStat(anoSpp, anoGen)
A set of 37 sequences of the mitochondrial protein-coding gene cytochrome oxidase I from the 4 New Zealand species of the nursery-web spider genus Dolomedes (Pisauridae). These sequences are available on GenBank as accession numbers GQ337328 through GQ337385.
A DNAbin object containing 37 sequences with a length of 850 base pairs stored as a matrix.
Vink, C. J., and Duperre, N. (2010). Pisauridae (Arachnida: Araneae). _Fauna of New Zealand_ *64* 1-54.
haploAccum identifies the different haplotypes represented in a set
of DNA sequences and performs the calculations for plotting haplotype
accumulations curves (see plot.haploAccum).
haploAccum(DNAbin, method = "random", permutations = 100, ...)haploAccum(DNAbin, method = "random", permutations = 100, ...)
DNAbin |
A set of DNA sequences in an object of class ‘DNAbin’. |
method |
Method for haplotype accumulation. Method |
permutations |
Number of permutations for method |
... |
Other parameters to functions. |
Haplotype accumulation curves can be used to assess haplotype diversity in
an area or compare different populations, or to evaluate sampling effort.
``random'' calculates the mean accumulated number of haplotypes and
its standard deviation through random permutations (subsampling of
sequences), similar to the method to produce rarefaction curves (Gotelli and
Colwell 2001).
An object of class ‘haploAccum’ with items:
call |
Function call. |
method |
Method for accumulation. |
sequences |
Number of analysed sequences. |
n.haplotypes |
Accumulated number of haplotypes corresponding to each number of sequences. |
sd |
The standard deviation
of the haplotype accumulation curve. Estimated through permutations for
|
perm |
Results of the permutations for |
This function is based on the functions haplotype (E. Paradis)
from the package 'pegas' and specaccum (R. Kindt) from the
package'vegan'. Missing or ambiguous data will be detected and indicated by
a warning, as they may cause an overestimation of the number of haplotypes.
Jagoba Malumbres-Olarte <[email protected]>.
Gotellli, N.J. & Colwell, R.K. (2001). Quantifying biodiversity: procedures and pitfalls in measurement and comparison of species richness. _Ecology Letters_ *4*, 379–391.
data(dolomedes) #Generate multiple haplotypes doloHaplo <- dolomedes[sample(37, size = 200, replace = TRUE), ] dolocurv <- haploAccum(doloHaplo, method = "random", permutations = 100) dolocurv graphics::plot(dolocurv)data(dolomedes) #Generate multiple haplotypes doloHaplo <- dolomedes[sample(37, size = 200, replace = TRUE), ] dolocurv <- haploAccum(doloHaplo, method = "random", permutations = 100) dolocurv graphics::plot(dolocurv)
This function plots a heatmap of the distance matrix, with shorter distances indicated by darker colours.
heatmapSpp(distObj, sppVector, col = NULL, axisLabels = NULL, triangle = "both", showData = FALSE, dataRound = 3, dataCEX = 1)heatmapSpp(distObj, sppVector, col = NULL, axisLabels = NULL, triangle = "both", showData = FALSE, dataRound = 3, dataCEX = 1)
distObj |
A matrix or object of class |
sppVector |
The species vector. See |
col |
A vector giving the colours for the heatmap. |
axisLabels |
A character vector that provides the axis labels for the heatmap. By default the species vector is used. |
triangle |
Which triangle of the heatmap should be plotted. Possible values of "both", "upper" and "lower". Default of "both". |
showData |
Logical. Should the data be shown on the heatmap? Default of FALSE. |
dataRound |
The number of significant figures the printed data will show. Default of 3. |
dataCEX |
Size of text for printed data. Default of 1. |
The default palette has been taken from the colorspace package.
Plots a heatmap of the distance matrix. Darker colours indicate shorter distances, lighter colours indicate greater distances.
Samuel Brown <[email protected]>
data(dolomedes) doloDist <- ape::dist.dna(dolomedes, model = "raw") doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) heatmapSpp(doloDist, doloSpp) heatmapSpp(doloDist, doloSpp, axisLabels = dimnames(dolomedes)[[1]]) data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis, model = "raw") anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) heatmapSpp(anoDist, anoSpp) heatmapSpp(anoDist, anoSpp, showData = TRUE) heatmapSpp(anoDist, anoSpp, showData = TRUE, dataRound = 1, dataCEX = 0.4) heatmapSpp(anoDist, anoSpp, triangle = "upper") heatmapSpp(anoDist, anoSpp, triangle = "lower") heatmapSpp(anoDist, anoSpp, triangle = "lower", showData = TRUE, dataRound = 1, dataCEX = 0.4)data(dolomedes) doloDist <- ape::dist.dna(dolomedes, model = "raw") doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) heatmapSpp(doloDist, doloSpp) heatmapSpp(doloDist, doloSpp, axisLabels = dimnames(dolomedes)[[1]]) data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis, model = "raw") anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) heatmapSpp(anoDist, anoSpp) heatmapSpp(anoDist, anoSpp, showData = TRUE) heatmapSpp(anoDist, anoSpp, showData = TRUE, dataRound = 1, dataCEX = 0.4) heatmapSpp(anoDist, anoSpp, triangle = "upper") heatmapSpp(anoDist, anoSpp, triangle = "lower") heatmapSpp(anoDist, anoSpp, triangle = "lower", showData = TRUE, dataRound = 1, dataCEX = 0.4)
Checks what columns in an alignment have ambiguous bases or missing data.
is.ambig(DNAbin)is.ambig(DNAbin)
DNAbin |
A DNA alignment of class ‘DNAbin’. |
Ambiguous bases are bases that have been coded with any of the Union of Pure and Applied Chemistry (IUPAC) DNA codes that are not A, C, G, or T. Missing data are bases that have been coded with "-", "?" or "N".
A logical vector containing TRUE if ambiguous bases or missing data are present, FALSE if not. Does not differentiate between the two classes of data.
Samuel Brown <[email protected]>
data(woodmouse) is.ambig(woodmouse) #Columns with ambiguous bases which(is.ambig(woodmouse))data(woodmouse) is.ambig(woodmouse) #Columns with ambiguous bases which(is.ambig(woodmouse))
This function determines possible thresholds from the distance matrix for an alignment.
localMinima(distobj)localMinima(distobj)
distobj |
A distance object (usually from |
This function is based on the concept of the barcoding gap, where a dip in the density of genetic distances indicates the transition between intra- and inter-specific distances. Understanding your data is vital to correctly interpreting the output of this function, but as a start, the first local minimum is often a good place to start.
The value of this function is that it does not require prior knowledge of species identity to get an indication of potential threshold values.
An object of class ‘density’, which is a list containing the values
calculated by density. The element localMinima has been
added, which contains the values of the local minima of the density plot.
Samuel Brown <[email protected]>
dist.dna, density.
Also as help, ~~~
data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoThresh <- localMinima(anoDist) graphics::plot(anoThresh) anoThresh$localMinima #Often the first value is the one to go for: anoThresh$localMinima[1]data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoThresh <- localMinima(anoDist) graphics::plot(anoThresh) anoThresh$localMinima #Often the first value is the one to go for: anoThresh$localMinima[1]
These functions give the distances to the nearest non-conspecific and furthest conspecific representatives for each individual in the dataset.
maxInDist(distobj, sppVector = NULL, propZero = FALSE, rmNA = FALSE)maxInDist(distobj, sppVector = NULL, propZero = FALSE, rmNA = FALSE)
distobj |
Distance matrix. |
sppVector |
Species vector (see |
propZero |
Logical. TRUE gives the proportion of zero distances. |
rmNA |
Logical. TRUE ignores missing values in the distance matrix. Default of FALSE |
nonConDist returns the minimum inter-specific distance for each
individual.
maxInDist returns the maximum intra-specific distance for each
individual.
These two functions can be used to create a version of the barcoding gap.
minInDist returns the minimum intra-specific distance for each
individual.
If propZero=FALSE, a numeric vector giving the distance of
the closest non-conspecific individual (nonConDist) or the most
distant conspecific individual (maxInDist).
If propZero=TRUE, a single number giving the proportion of zero
distances.
Samuel Brown <[email protected]>
data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) nonConDist(anoDist, anoSpp) nonConDist(anoDist, anoSpp, propZero=TRUE) maxInDist(anoDist, anoSpp) maxInDist(anoDist, anoSpp, propZero=TRUE) #Barcoding gap inter <- nonConDist(anoDist, anoSpp) intra <- maxInDist(anoDist, anoSpp) graphics::hist(inter-intra) #An alternative way of plotting the gap bnd <- cbind(data.frame(inter, intra)) ord <- bnd[order(bnd$inter),] graphics::plot(ord$inter, type="n", ylab="Percent K2P distance", xlab="Individual") segCol <- rep("gray50", length(ord$inter)) segCol[ord$inter-ord$intra < 0] <- "red" graphics::segments(x0=1:length(ord$inter), y0=ord$inter, y1=ord$intra, col=segCol, lwd=6)data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) nonConDist(anoDist, anoSpp) nonConDist(anoDist, anoSpp, propZero=TRUE) maxInDist(anoDist, anoSpp) maxInDist(anoDist, anoSpp, propZero=TRUE) #Barcoding gap inter <- nonConDist(anoDist, anoSpp) intra <- maxInDist(anoDist, anoSpp) graphics::hist(inter-intra) #An alternative way of plotting the gap bnd <- cbind(data.frame(inter, intra)) ord <- bnd[order(bnd$inter),] graphics::plot(ord$inter, type="n", ylab="Percent K2P distance", xlab="Individual") segCol <- rep("gray50", length(ord$inter)) segCol[ord$inter-ord$intra < 0] <- "red" graphics::segments(x0=1:length(ord$inter), y0=ord$inter, y1=ord$intra, col=segCol, lwd=6)
These functions give the distances to the nearest non-conspecific and furthest conspecific representatives for each individual in the dataset.
minInDist(distobj, sppVector = NULL, propZero = FALSE, rmNA = FALSE)minInDist(distobj, sppVector = NULL, propZero = FALSE, rmNA = FALSE)
distobj |
Distance matrix. |
sppVector |
Species vector (see |
propZero |
Logical. TRUE gives the proportion of zero distances. |
rmNA |
Logical. TRUE ignores missing values in the distance matrix. Default of FALSE |
nonConDist returns the minimum inter-specific distance for each
individual.
maxInDist returns the maximum intra-specific distance for each
individual.
These two functions can be used to create a version of the barcoding gap.
minInDist returns the minimum intra-specific distance for each
individual.
If propZero=FALSE, a numeric vector giving the distance of
the closest non-conspecific individual (nonConDist) or the most
distant conspecific individual (maxInDist).
If propZero=TRUE, a single number giving the proportion of zero
distances.
Samuel Brown <[email protected]>
data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) nonConDist(anoDist, anoSpp) nonConDist(anoDist, anoSpp, propZero=TRUE) maxInDist(anoDist, anoSpp) maxInDist(anoDist, anoSpp, propZero=TRUE) #Barcoding gap inter <- nonConDist(anoDist, anoSpp) intra <- maxInDist(anoDist, anoSpp) graphics::hist(inter-intra) #An alternative way of plotting the gap bnd <- cbind(data.frame(inter, intra)) ord <- bnd[order(bnd$inter),] graphics::plot(ord$inter, type="n", ylab="Percent K2P distance", xlab="Individual") segCol <- rep("gray50", length(ord$inter)) segCol[ord$inter-ord$intra < 0] <- "red" graphics::segments(x0=1:length(ord$inter), y0=ord$inter, y1=ord$intra, col=segCol, lwd=6)data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) nonConDist(anoDist, anoSpp) nonConDist(anoDist, anoSpp, propZero=TRUE) maxInDist(anoDist, anoSpp) maxInDist(anoDist, anoSpp, propZero=TRUE) #Barcoding gap inter <- nonConDist(anoDist, anoSpp) intra <- maxInDist(anoDist, anoSpp) graphics::hist(inter-intra) #An alternative way of plotting the gap bnd <- cbind(data.frame(inter, intra)) ord <- bnd[order(bnd$inter),] graphics::plot(ord$inter, type="n", ylab="Percent K2P distance", xlab="Individual") segCol <- rep("gray50", length(ord$inter)) segCol[ord$inter-ord$intra < 0] <- "red" graphics::segments(x0=1:length(ord$inter), y0=ord$inter, y1=ord$intra, col=segCol, lwd=6)
Determines if the species given in sppVector form monophyletic groups
on a given tree.
monophyly(phy, sppVector, pp = NA, singletonsMono = TRUE)monophyly(phy, sppVector, pp = NA, singletonsMono = TRUE)
phy |
A tree of class ‘phylo’. |
sppVector |
Species vector. See |
pp |
Object of class ‘prop.part’. Assists in speeding up the function,
if it has been called already. Default of NA, calling
|
singletonsMono |
Logical. Should singletons (i.e. only a single specimen representing that species) be treated as monophyletic? Default of TRUE. Possible values of FALSE and NA. |
monophyly determines if each species is monophyletic.
monophylyBoot incorporates a bootstrap test to determine the support
for this monophyly. Species with a bootstrap support lower than
"thresh" are recorded as FALSE.
Rerooting is done on the longest internal edge in the tree returned by
nj(dist.dna(DNAbin)).
monophyly returns a logical vector, stating if each species
is monophyletic. Values correspond to the species order given by
unique(sppVector).
monophylyBoot returns a list with the following elements:
results |
A logical vector, stating if each species is monophyletic with a bootstrap support higher than the given threshold. |
BSvalues |
A
numeric vector giving the bootstrap proportions for each node of
|
Samuel Brown <[email protected]>
#Random trees set.seed(16) tr <- ape::rtree(15) spp <- rep(LETTERS[1:5], rep(3,5)) monophyly(tr, spp) tr2 <- tr spp2 <- c(rep(LETTERS[1:4], rep(3,4)), LETTERS[5:7]) monophyly(tr2, spp2) #Empirical data ## Not run: data(anoteropsis) anoTree <- ape::nj(ape::dist.dna(anoteropsis)) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) monophyly(anoTree, anoSpp) monophyly(anoTree, anoSpp, singletonsMono=FALSE) unique(anoSpp) #To get score for each individual anoMono <- monophyly(anoTree, anoSpp) anoMono[match(anoSpp, unique(anoSpp))] data(woodmouse) woodTree <- ape::nj(ape::dist.dna(woodmouse)) woodSpp <- c("D", "C", "C", "A", "A", "E", "A", "F", "C", "F", "E", "D", "A", "A", "E") unique(woodSpp) monophyly(woodTree, woodSpp) woodMono <- monophylyBoot(woodTree, woodSpp, woodmouse) woodMono$results woodMono$BSvalues monophylyBoot(woodTree, woodSpp, woodmouse, reroot = FALSE) monophylyBoot(woodTree, woodSpp, woodmouse, thresh = 0.9, reroot = FALSE) ## End(Not run)#Random trees set.seed(16) tr <- ape::rtree(15) spp <- rep(LETTERS[1:5], rep(3,5)) monophyly(tr, spp) tr2 <- tr spp2 <- c(rep(LETTERS[1:4], rep(3,4)), LETTERS[5:7]) monophyly(tr2, spp2) #Empirical data ## Not run: data(anoteropsis) anoTree <- ape::nj(ape::dist.dna(anoteropsis)) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) monophyly(anoTree, anoSpp) monophyly(anoTree, anoSpp, singletonsMono=FALSE) unique(anoSpp) #To get score for each individual anoMono <- monophyly(anoTree, anoSpp) anoMono[match(anoSpp, unique(anoSpp))] data(woodmouse) woodTree <- ape::nj(ape::dist.dna(woodmouse)) woodSpp <- c("D", "C", "C", "A", "A", "E", "A", "F", "C", "F", "E", "D", "A", "A", "E") unique(woodSpp) monophyly(woodTree, woodSpp) woodMono <- monophylyBoot(woodTree, woodSpp, woodmouse) woodMono$results woodMono$BSvalues monophylyBoot(woodTree, woodSpp, woodmouse, reroot = FALSE) monophylyBoot(woodTree, woodSpp, woodmouse, thresh = 0.9, reroot = FALSE) ## End(Not run)
Determines if the species given in sppVector form monophyletic groups
on a given tree.
monophylyBoot(phy, sppVector, DNAbin, thresh = 0.7, reroot = TRUE, pp = NA, singletonsMono = TRUE, reps = 1000, block = 3)monophylyBoot(phy, sppVector, DNAbin, thresh = 0.7, reroot = TRUE, pp = NA, singletonsMono = TRUE, reps = 1000, block = 3)
phy |
A tree of class ‘phylo’. |
sppVector |
Species vector. See |
DNAbin |
An object of class 'DNAbin'. Required for calculating bootstrap values. |
thresh |
Numeric between 0 and 1. Bootstrap threshold under which potentially monophyletic species are negated. Default of 0.7. |
reroot |
Logical. Should the bootstrap replicates be rerooted on the longest edge? Default of TRUE. |
pp |
Object of class ‘prop.part’. Assists in speeding up the function,
if it has been called already. Default of NA, calling
|
singletonsMono |
Logical. Should singletons (i.e. only a single specimen representing that species) be treated as monophyletic? Default of TRUE. Possible values of FALSE and NA. |
reps |
Numeric. Number of bootstrap replications. Default of 1000. |
block |
The number of nucleotides that will be resampled together. Default of 3 to resample on the codon level. |
monophyly determines if each species is monophyletic.
monophylyBoot incorporates a bootstrap test to determine the support
for this monophyly. Species with a bootstrap support lower than
"thresh" are recorded as FALSE.
Rerooting is done on the longest internal edge in the tree returned by
nj(dist.dna(DNAbin)).
monophyly returns a logical vector, stating if each species
is monophyletic. Values correspond to the species order given by
unique(sppVector).
monophylyBoot returns a list with the following elements:
results |
A logical vector, stating if each species is monophyletic with a bootstrap support higher than the given threshold. |
BSvalues |
A
numeric vector giving the bootstrap proportions for each node of
|
Samuel Brown <[email protected]>
prop.part, root,
boot.phylo, monophyly.
#Random trees set.seed(16) tr <- ape::rtree(15) spp <- rep(LETTERS[1:5], rep(3,5)) monophyly(tr, spp) tr2 <- tr spp2 <- c(rep(LETTERS[1:4], rep(3,4)), LETTERS[5:7]) monophyly(tr2, spp2) #Empirical data ## Not run: data(anoteropsis) anoTree <- ape::nj(ape::dist.dna(anoteropsis)) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) monophyly(anoTree, anoSpp) monophyly(anoTree, anoSpp, singletonsMono=FALSE) unique(anoSpp) #To get score for each individual anoMono <- monophyly(anoTree, anoSpp) anoMono[match(anoSpp, unique(anoSpp))] data(woodmouse) woodTree <- ape::nj(ape::dist.dna(woodmouse)) woodSpp <- c("D", "C", "C", "A", "A", "E", "A", "F", "C", "F", "E", "D", "A", "A", "E") unique(woodSpp) monophyly(woodTree, woodSpp) woodMono <- monophylyBoot(woodTree, woodSpp, woodmouse) woodMono$results woodMono$BSvalues monophylyBoot(woodTree, woodSpp, woodmouse, reroot = FALSE) monophylyBoot(woodTree, woodSpp, woodmouse, thresh = 0.9, reroot = FALSE) ## End(Not run)#Random trees set.seed(16) tr <- ape::rtree(15) spp <- rep(LETTERS[1:5], rep(3,5)) monophyly(tr, spp) tr2 <- tr spp2 <- c(rep(LETTERS[1:4], rep(3,4)), LETTERS[5:7]) monophyly(tr2, spp2) #Empirical data ## Not run: data(anoteropsis) anoTree <- ape::nj(ape::dist.dna(anoteropsis)) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) monophyly(anoTree, anoSpp) monophyly(anoTree, anoSpp, singletonsMono=FALSE) unique(anoSpp) #To get score for each individual anoMono <- monophyly(anoTree, anoSpp) anoMono[match(anoSpp, unique(anoSpp))] data(woodmouse) woodTree <- ape::nj(ape::dist.dna(woodmouse)) woodSpp <- c("D", "C", "C", "A", "A", "E", "A", "F", "C", "F", "E", "D", "A", "A", "E") unique(woodSpp) monophyly(woodTree, woodSpp) woodMono <- monophylyBoot(woodTree, woodSpp, woodmouse) woodMono$results woodMono$BSvalues monophylyBoot(woodTree, woodSpp, woodmouse, reroot = FALSE) monophylyBoot(woodTree, woodSpp, woodmouse, thresh = 0.9, reroot = FALSE) ## End(Not run)
Tests of barcoding efficacy using distance-based methods.
nearNeighbour(distobj, sppVector, names = FALSE)nearNeighbour(distobj, sppVector, names = FALSE)
distobj |
A distance object (usually from |
sppVector |
Vector of species names. See |
names |
Logical. Should the names of the nearest match be shown? Default of FALSE. |
These functions test barcoding efficacy. All sequences must be identified prior to testing. Each sequence is considered an unknown while the remaining sequences in the dataset constitute the DNA barcoding database that is used for identification. If the identification from the test is the same as the pre-considered identification, a correct result is returned.
bestCloseMatch conducts the "best close match" analysis of Meier et
al. (2006), considering the closest individual unless it is further than the
given threshold, which results in no identification. More than one species
tied for closest match results in an assignment of "ambiguous". When the
threshold is large, this analysis will return essentially the same result as
nearNeighbour. If names = TRUE, a list is returned containing
the names of all species represented by specimens within the threshold.
nearNeighbour finds the closest individual and returns if their names
are the same (TRUE) or different (FALSE). If names = TRUE, the name
of the closest individual is returned. Ties are decided by majority rule.
threshID conducts a threshold-based analysis, similar to that
conducted by the "Identify Specimen" tool provided by the Barcode of Life
Database (http://www.boldsystems.org/index.php/IDS_OpenIdEngine). It
is more inclusive than bestCloseMatch, considering ALL sequences
within the given threshold. If names = TRUE, a list is returned
containing the names of all species represented by specimens within the
threshold.
These functions are not recommended as identification tools, though they can
be used as such when names = TRUE.
bestCloseMatch and threshID return a character vector
giving the identification status of each individual.
"correct" |
The name of the closest match is the same |
"incorrect" |
The name of the closest match is different |
"ambiguous" |
More than one species is the
closest match ( |
"no id" |
No species are within the threshold distance |
nearNeighbour returns a logical vector or (if names = TRUE)
the name for the nearest individual.
Samuel Brown <[email protected]>
Meier, R., Shiyang, K., Vaidya, G., & Ng, P. (2006). DNA barcoding and taxonomy in Diptera: a tale of high intraspecific variability and low identification success. _Systematic Biology_ *55* (5) 715-728.
nearNeighbour, threshID, dist.dna, sppVector
Also as help, ~~~
data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split = "_"), function(x) paste(x[1], x[2], sep = "_")) bestCloseMatch(anoDist, anoSpp) bestCloseMatch(anoDist, anoSpp, threshold = 0.005) nearNeighbour(anoDist, anoSpp) nearNeighbour(anoDist, anoSpp, names = TRUE) threshID(anoDist, anoSpp) threshID(anoDist, anoSpp, threshold = 0.003) data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) bestCloseMatch(doloDist, doloSpp) bestCloseMatch(doloDist, doloSpp, threshold = 0.005) nearNeighbour(doloDist, doloSpp) nearNeighbour(doloDist, doloSpp, names=TRUE) threshID(doloDist, doloSpp) threshID(doloDist, doloSpp, threshold = 0.003)data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split = "_"), function(x) paste(x[1], x[2], sep = "_")) bestCloseMatch(anoDist, anoSpp) bestCloseMatch(anoDist, anoSpp, threshold = 0.005) nearNeighbour(anoDist, anoSpp) nearNeighbour(anoDist, anoSpp, names = TRUE) threshID(anoDist, anoSpp) threshID(anoDist, anoSpp, threshold = 0.003) data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) bestCloseMatch(doloDist, doloSpp) bestCloseMatch(doloDist, doloSpp, threshold = 0.005) nearNeighbour(doloDist, doloSpp) nearNeighbour(doloDist, doloSpp, names=TRUE) threshID(doloDist, doloSpp) threshID(doloDist, doloSpp, threshold = 0.003)
These functions give the distances to the nearest non-conspecific and furthest conspecific representatives for each individual in the dataset.
nonConDist(distobj, sppVector = NULL, propZero = FALSE, rmNA = FALSE)nonConDist(distobj, sppVector = NULL, propZero = FALSE, rmNA = FALSE)
distobj |
Distance matrix. |
sppVector |
Species vector (see |
propZero |
Logical. TRUE gives the proportion of zero distances. |
rmNA |
Logical. TRUE ignores missing values in the distance matrix. Default of FALSE |
nonConDist returns the minimum inter-specific distance for each
individual.
maxInDist returns the maximum intra-specific distance for each
individual.
These two functions can be used to create a version of the barcoding gap.
minInDist returns the minimum intra-specific distance for each
individual.
If propZero=FALSE, a numeric vector giving the distance of
the closest non-conspecific individual (nonConDist) or the most
distant conspecific individual (maxInDist).
If propZero=TRUE, a single number giving the proportion of zero
distances.
Samuel Brown <[email protected]>
data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) nonConDist(anoDist, anoSpp) nonConDist(anoDist, anoSpp, propZero=TRUE) maxInDist(anoDist, anoSpp) maxInDist(anoDist, anoSpp, propZero=TRUE) #Barcoding gap inter <- nonConDist(anoDist, anoSpp) intra <- maxInDist(anoDist, anoSpp) graphics::hist(inter-intra) #An alternative way of plotting the gap bnd <- cbind(data.frame(inter, intra)) ord <- bnd[order(bnd$inter),] graphics::plot(ord$inter, type="n", ylab="Percent K2P distance", xlab="Individual") segCol <- rep("gray50", length(ord$inter)) segCol[ord$inter-ord$intra < 0] <- "red" graphics::segments(x0=1:length(ord$inter), y0=ord$inter, y1=ord$intra, col=segCol, lwd=6)data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) nonConDist(anoDist, anoSpp) nonConDist(anoDist, anoSpp, propZero=TRUE) maxInDist(anoDist, anoSpp) maxInDist(anoDist, anoSpp, propZero=TRUE) #Barcoding gap inter <- nonConDist(anoDist, anoSpp) intra <- maxInDist(anoDist, anoSpp) graphics::hist(inter-intra) #An alternative way of plotting the gap bnd <- cbind(data.frame(inter, intra)) ord <- bnd[order(bnd$inter),] graphics::plot(ord$inter, type="n", ylab="Percent K2P distance", xlab="Individual") segCol <- rep("gray50", length(ord$inter)) segCol[ord$inter-ord$intra < 0] <- "red" graphics::segments(x0=1:length(ord$inter), y0=ord$inter, y1=ord$intra, col=segCol, lwd=6)
Determines the diagnostic nucleotides for each species given in
sppVector.
nucDiag(DNAbin, sppVector)nucDiag(DNAbin, sppVector)
DNAbin |
An object of class 'DNAbin'. |
sppVector |
The species vector (see |
These functions provide a means for evaluating the presence of diagnostic
nucleotides that distinguish species within an alignment. nucDiag
returns the positions of bases corresponding to the definition of pure,
simple diagnostic nucleotides given by Sarkar et al (2008).
rnucDiag runs a bootstrapping-style resampling test to evaluate the
numbers of diagnostic nucleotides that might be expected by random
assortment of specimens.
nucDiag returns a list giving the pure, simple diagnostic
nucleotides (i.e. those nucleotides that are fixed within species and
different from all other species) for each species in the species vector. A
result of integer(0) indicates there are no diagnostic nucleotides
for those species.
rnucDiag returns a list containing the following elements:
min |
The minimum number of diagnostic nucleotides in the sample. |
mean |
The mean number of diagnostic nucleotides in the sample. |
median |
The median number of diagnostic nucleotides in the sample. |
max |
The maximum number of diagnostic nucleotides in the sample. |
rndFreq |
A list of frequency distributions of the number of diagnostic nucleotides in groups formed by 1 sequence, 2 sequences, etc. |
Samuel Brown <[email protected]>
Sarkar, I., Planet, P., & DeSalle, R. (2008). CAOS software for use in character- based DNA barcoding. _Molecular Ecology Resources_ *8* 1256-1259
data(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) nucDiag(anoteropsis, anoSpp) #To view the nucleotide values anoNuc <- nucDiag(anoteropsis, anoSpp) as.character(anoteropsis[ ,anoNuc[[1]][1] ]) data(sarkar) sarkarSpp <- substr(dimnames(sarkar)[[1]], 1, 3) nucDiag(sarkar, sarkarSpp) ## Not run: rnucDiag(anoteropsis, anoSpp, n = 100) ## End(Not run)data(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) nucDiag(anoteropsis, anoSpp) #To view the nucleotide values anoNuc <- nucDiag(anoteropsis, anoSpp) as.character(anoteropsis[ ,anoNuc[[1]][1] ]) data(sarkar) sarkarSpp <- substr(dimnames(sarkar)[[1]], 1, 3) nucDiag(sarkar, sarkarSpp) ## Not run: rnucDiag(anoteropsis, anoSpp, n = 100) ## End(Not run)
Calculates Principical Coonrdinates Analysis on a matrix of genetic distances and plots an ordination of the first two major axes.
ordinDNA(distobj, sppVector, ...)ordinDNA(distobj, sppVector, ...)
distobj |
A distance matrix. |
sppVector |
The species vector (see |
... |
Other arguments to be passed to |
This function is a wrapper for cmdscale, which performs a
Principal Coordinates Analysis on the distance matrix given. In addition, it
plots an ordination of the genetic distance matrix given, showing the
relative distance between each of the species in the dataset. It is
presented as an alternative to the neighbour-joining trees which are
frequently used for the visualisation of DNA barcoding data. NJ trees show
hypotheses of relationships, which are inappropriate for the questions
usally asked in DNA barcoding studies.
The distance between the centroids of the clusters are roughly proportional to the genetic distances between the species. NOTE: it is important to remember that the plot shows only one plane of a multi-dimensional space. Species with overlapping circles are not necessarily conspecific. Further exploration is required.
Plots an ordination of the first two major axes showing the positions of each individual (squares), the centroid of each species (circular bullet and name of species), and the variation in the species (large circle, the radius of which is the distance to the furthest individual from the centroid).
Additionally returns a list of class "ordinDNA" with the following
elements:
pco |
Output of the Principal Coordinates Analysis. |
sppVector |
Character vector giving the species vector. |
Samuel Brown <[email protected]>
data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) doloOrd <- ordinDNA(doloDist, doloSpp) doloOrddata(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) doloOrd <- ordinDNA(doloDist, doloSpp) doloOrd
Conducts population aggregate analysis over a matrix of characters of interest.
paa(data, sppVector)paa(data, sppVector)
data |
A data matrix with columns as characters and rows as individuals. |
sppVector |
The species vector. See |
When used on DNA sequences, the function treats gaps as seperate characters.
A matrix with species as rows and characters as columns. Cells give the character state of each species if fixed, or "poly" if the character is polymorphic.
Samuel Brown <[email protected]>
Sites, J. W. J., & Marshall, J. C. (2003). Delimiting species: a Renaissance issue in systematic biology. _Trends in Ecology and Evolution_ *18* (9), 462-470.
#Create some exemplar data u <- sample(c(0,1), 16, replace=TRUE) v <- rep(c(0,1), rep(8,2)) x <- rep(c(1,0), rep(8,2)) y <- sample(c(0,1), 16, replace=TRUE) z <- rep(c(1,0), rep(8,2)) dat <- cbind(u,v,x,y,z) popn <- rep(c("A","B", "C", "D"), rep(4,4)) paa(dat, popn) #Use on DNA sequences data(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) paa(as.character(anoteropsis), anoSpp)#Create some exemplar data u <- sample(c(0,1), 16, replace=TRUE) v <- rep(c(0,1), rep(8,2)) x <- rep(c(1,0), rep(8,2)) y <- sample(c(0,1), 16, replace=TRUE) z <- rep(c(1,0), rep(8,2)) dat <- cbind(u,v,x,y,z) popn <- rep(c("A","B", "C", "D"), rep(4,4)) paa(dat, popn) #Use on DNA sequences data(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) paa(as.character(anoteropsis), anoSpp)
Plots the accumulation curves calculated by haploAccum.
## S3 method for class 'haploAccum' plot(x, add = FALSE, ci = 2, ci.type = c("bar", "line", "polygon"), col = par("fg"), ci.col = col, ci.lty = 1, xlab, ylab = "Haplotypes", ylim, main = paste(x$method, "method of haplotype accumulation", sep = " "), ...)## S3 method for class 'haploAccum' plot(x, add = FALSE, ci = 2, ci.type = c("bar", "line", "polygon"), col = par("fg"), ci.col = col, ci.lty = 1, xlab, ylab = "Haplotypes", ylim, main = paste(x$method, "method of haplotype accumulation", sep = " "), ...)
x |
A ‘haploAccum’ object obtained from |
add |
Add graph to an existing graph. |
ci |
Multiplier for the calculation of confidence intervals from
standard deviation. |
ci.type |
Type of confidence intervals: |
col |
Colour for curve line. |
ci.col |
Colour for lines or shaded area when |
ci.lty |
Line type for confidence interval lines or border of the
|
xlab |
Label for the X-axis. |
ylab |
Label for the Y-axis. |
ylim |
Y-axis limits. |
main |
Title of the plot. |
... |
Other parameters to pass to plot. |
Plots a haplotype accumulation curve and confidence intervals
depending on the options given to haploAccum.
Jagoba Malumbres-Olarte <[email protected]>.
Gotellli, N.J. & Colwell, R.K. (2001). Quantifying biodiversity: procedures and pitfalls in measurement and comparison of species richness. _Ecology Letters_ *4* 379–391.
data(dolomedes) #Generate multiple haplotypes doloHaplo <- dolomedes[sample(37, size = 200, replace = TRUE), ] dolocurv <- haploAccum(doloHaplo, method = "random", permutations = 100) graphics::plot(dolocurv) graphics::plot(dolocurv, add = FALSE, ci = 2, ci.type = "polygon", col = "blue", ci.col = "red", ci.lty = 1)data(dolomedes) #Generate multiple haplotypes doloHaplo <- dolomedes[sample(37, size = 200, replace = TRUE), ] dolocurv <- haploAccum(doloHaplo, method = "random", permutations = 100) graphics::plot(dolocurv) graphics::plot(dolocurv, add = FALSE, ci = 2, ci.type = "polygon", col = "blue", ci.col = "red", ci.lty = 1)
Plots an ordination of the Principal Components Analysis conducted by
ordinDNA.
## S3 method for class 'ordinDNA' plot(x, majorAxes = c(1, 2), plotCol = "default", trans = "CC", textcex = 0.7, pchCentroid = FALSE, sppBounds = "net", sppNames = TRUE, namePos = "top", ptPch = 21, ptCex = 0.5, netWd = 1, ...)## S3 method for class 'ordinDNA' plot(x, majorAxes = c(1, 2), plotCol = "default", trans = "CC", textcex = 0.7, pchCentroid = FALSE, sppBounds = "net", sppNames = TRUE, namePos = "top", ptPch = 21, ptCex = 0.5, netWd = 1, ...)
x |
An object of class ‘ordinDNA’. |
majorAxes |
Numeric. Gives the numbers of the major axes that should be
plotted. Default of the first two major axes ( |
plotCol |
A vector of RGB colours giving the colours of the points and
circles. Must be in the form of a character vector with elements "#XXXXXX"
where XXXXXX gives the hexadecimal value for the colours desired. Default of
|
trans |
A character vector giving the hexadecimal value for the transparency of the circles. Default of "CC". |
textcex |
Numeric. Controls the size of the text giving the species value of the circles. |
pchCentroid |
Numeric. Controls the shape of the point showing the centroid of the circle for each species. Default of FALSE, no plotting of centroid position. |
sppBounds |
Option to determine the method of visualising conspecific
points. Options of |
sppNames |
Logical. Should species names be plotted? Default of TRUE. |
namePos |
Character vector of length 1 giving the position where the species names should be plotted. Possible values are: "top" and "bottom", anything else plots the names at the centroid. |
ptPch |
Numeric. Number of the symbol to be used for plotting. see
|
ptCex |
Numeric. Number governing the size of the points. Default of 0.5. |
netWd |
Numeric. Number governing the width of the lines in the netowk. Default of 1. |
... |
Other arguments to be passed to |
plot.ordinDNA calculates the centroid and radius of the most variable
individual for each species in the multivariate space of the Principal
Components Analysis object given.
majorAxes plots the axes in the form c(x, y). The maximum
number of axes calculated is the number of specimens in the dataset minus
one.
sppBounds has the following options: "net" (the default)
creates a complete graph between all individuals within a species. If
"circles" is specified, a circle is drawn with a center fixed on the
centroid, and a radius of the length to the maximally distant individual.
Selecting the option of "none" means the individuals are not
connected in any way.
Plots an ordination of the first two major axes showing the positions of each individual (squares), the centroid of each species (circular bullet and name of species), and the variation in the species (large circle, the radius of which is the distance to the furthest individual from the centroid).
Samuel Brown <[email protected]>
data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) doloOrd <- ordinDNA(doloDist, doloSpp) graphics::plot(doloOrd) graphics::plot(doloOrd, majorAxes = c(1,3)) graphics::plot(doloOrd, textcex = 0.001) graphics::plot(doloOrd, plotCol = c("#FF0000", "#00FF00", "#0000FF")) graphics::plot(doloOrd, namesPos = "bottom") graphics::plot(doloOrd, namesPos = "centre") data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) anoOrd <- ordinDNA(anoDist, anoSpp) plot(anoOrd, sppBounds = "circles")data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) doloOrd <- ordinDNA(doloDist, doloSpp) graphics::plot(doloOrd) graphics::plot(doloOrd, majorAxes = c(1,3)) graphics::plot(doloOrd, textcex = 0.001) graphics::plot(doloOrd, plotCol = c("#FF0000", "#00FF00", "#0000FF")) graphics::plot(doloOrd, namesPos = "bottom") graphics::plot(doloOrd, namesPos = "centre") data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) anoOrd <- ordinDNA(anoDist, anoSpp) plot(anoOrd, sppBounds = "circles")
Graphical representation of the summary statistics derived from
slideAnalyses and slideBoxplots
## S3 method for class 'slidWin' plot(x, outliers = FALSE, ...)## S3 method for class 'slidWin' plot(x, outliers = FALSE, ...)
x |
An object of class ‘slidWin’. |
outliers |
Logical. When the results of |
... |
Other arguments to be passed to |
When boxplots of methods nonCon and interAll, the y-axis
limits are constrained to the midpoint of the range covered by the boxplots,
so that the intra-specific variation can be seen.
Plots graphs depending on the options given to
slideAnalyses or slideBoxplots.
Samuel Brown <[email protected]>
data(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) doloSlide <- slideAnalyses(dolomedes, doloSpp, 200, interval=10, treeMeasures=TRUE) graphics::plot(doloSlide) doloBox <- slideBoxplots(dolomedes, doloSpp, 200, interval=10, method="overall") graphics::plot(doloBox) data(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) anoBox <- slideBoxplots(anoteropsis, anoSpp, 200, interval=10, method="interAll") graphics::plot(anoBox) graphics::plot(anoBox, outliers=TRUE)data(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) doloSlide <- slideAnalyses(dolomedes, doloSpp, 200, interval=10, treeMeasures=TRUE) graphics::plot(doloSlide) doloBox <- slideBoxplots(dolomedes, doloSpp, 200, interval=10, method="overall") graphics::plot(doloBox) data(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) anoBox <- slideBoxplots(anoteropsis, anoSpp, 200, interval=10, method="interAll") graphics::plot(anoBox) graphics::plot(anoBox, outliers=TRUE)
This function computes the numbers of descendants for each dichotomous branch of a phylogenetic tree.
polyBalance(phy)polyBalance(phy)
phy |
A tree of class ‘phylo’. |
The function extends balance to allow the balance of a tree
with polytomies to be calculated. When the tree is fully dichotomous, the
result is identical to balance.
A numeric matrix with two columns and one row for each node of the tree. The columns give the numbers of descendants on each node. Non-dichotomous nodes are reported as 'NA'.
Samuel Brown <[email protected]>
set.seed(55) tr <- ape::rtree(15) tr2 <- ape::di2multi(tr, tol=0.02) polyBalance(tr) polyBalance(tr2)set.seed(55) tr <- ape::rtree(15) tr2 <- ape::di2multi(tr, tol=0.02) polyBalance(tr) polyBalance(tr2)
Display the highest ranking windows measured by slideAnalyses.
rankSlidWin(slidWin, criteria = "mean_distance", num = 10)rankSlidWin(slidWin, criteria = "mean_distance", num = 10)
slidWin |
An object of class ‘slidWin’, made using
|
criteria |
Name of criteria to sort by. Can be any of the following:
|
num |
Number of windows to return. Default of 10. |
The criteria for rankSlidWin correspond to the variables outputted by
slideAnalyses and are sorted in the following manner:
rankSlidWin criterion: |
slideAnalyses output: |
Sorting method: |
"mean_distance" |
"dist_mean_out" |
Ascending |
"monophyly" |
"win_mono_out" |
Ascending |
"clade_comparison" |
"comp_out" |
Ascending |
"clade_comp_shallow" |
"comp_depth_out" |
Ascending |
"zero_noncon" |
"noncon_out" |
Descending |
"zero_distances" |
"zero_out" |
Descending |
"diag_nuc" |
"nd_out" |
Ascending |
Given a sequence of 1:10, the ascending method of sorting considers 10 as high. The descending method considers 1 as high.
The "all" criterion returns the windows that have the highest
cumulative total score over all criteria.
A data frame giving the values of the measures calculated by
slideAnalyses, ranked to show the top 10 positions based on
the criterion given.
Samuel Brown <[email protected]>
data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) doloSlide <- slideAnalyses(dolomedes, doloSpp, 200, interval = 10, treeMeasures = TRUE) rankSlidWin(doloSlide) rankSlidWin(doloSlide, criteria = "zero_distances") doloSlide2 <- slideAnalyses(dolomedes, doloSpp, 200, interval = 10, treeMeasures = FALSE) rankSlidWin(doloSlide2) doloSlide3 <- slideAnalyses(dolomedes, doloSpp, 200, interval = 10, distMeasures = FALSE, treeMeasures = TRUE) rankSlidWin(doloSlide3)data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) doloSlide <- slideAnalyses(dolomedes, doloSpp, 200, interval = 10, treeMeasures = TRUE) rankSlidWin(doloSlide) rankSlidWin(doloSlide, criteria = "zero_distances") doloSlide2 <- slideAnalyses(dolomedes, doloSpp, 200, interval = 10, treeMeasures = FALSE) rankSlidWin(doloSlide2) doloSlide3 <- slideAnalyses(dolomedes, doloSpp, 200, interval = 10, distMeasures = FALSE, treeMeasures = TRUE) rankSlidWin(doloSlide3)
These functions allow DNA sequences to be downloaded from the Barcode of Life Database (BOLD).
read.BOLD(IDs)read.BOLD(IDs)
IDs |
A character vector containing BOLD process ID numbers. |
search.BOLD retrieves BOLD process identification numbers for any
given taxon using the API for BOLD version 3.0. By default, it only returns
the first 500 process IDs for the given taxon. By selecting the option
exhaustive = TRUE, the function can be made to search for more than
500 process IDs, but is much slower.
stats.BOLD retrieves the total number of records for the given taxon.
read.BOLD downloads the sequences associated with the process
identification numbers using a brute force method of downloading the
specimen record, then searching and splitting the HTML code to remove the
relevant information. This process is likely to make the function fairly
unstable if BOLD make any changes to their website.
Previous versions of read.BOLD used the eFetch web service offered by
BOLD to enable batch retrieval of records, however from October 2012 BOLD
deprecated eFetch without providing a replacement service.
search.BOLD returns a character vector giving the process
identification numbers of the specimens found by the search.
read.BOLD returns an object of class ‘DNAbin’. This object has the
attributes "species", "accession_num", and "gene".
On 26 Oct 2011, attempts to access records using the eFetch system through a web browser resulted in an error, saying that eFetch and eSearch are offline for maintainance.
As of 7 March 2012, both functions have been modified to interface with the new BOLD architecture, and work as expected.
29 Oct 2012: It appears that BOLD has taken eFetch offline permanently,
rendering read.BOLD as it currently stands useless. While we may be
able to work out something, this will require a complete rewrite of the
function. search.BOLD continues to work as intended.
17 Dec 2012: A new version of read.BOLD has been released that
appears to work (for the time being).
15 Feb 2018: 'read.BOLD' is deprecated. Please use the rOpenSci 'bold' package for better functionality.
Samuel Brown <[email protected]>
BOLD web services: http://www.boldsystems.org/index.php/resources/api?type=webservices.
BOLD version 3.0 http://v3.boldsystems.org/.
stats.BOLD, search.BOLD, read.GB.
help, ~~~
## Not run: stats.BOLD("Pisauridae") search.BOLD(c("Danio kyathit", "Dolomedes", "Sitona discoideus")) nn <- search.BOLD("Pisauridae") pisaurid <- read.BOLD(nn) ape::write.dna(pisaurid, "filename.fas", format="fasta") ## End(Not run)## Not run: stats.BOLD("Pisauridae") search.BOLD(c("Danio kyathit", "Dolomedes", "Sitona discoideus")) nn <- search.BOLD("Pisauridae") pisaurid <- read.BOLD(nn) ape::write.dna(pisaurid, "filename.fas", format="fasta") ## End(Not run)
Downloads sequences associated with the given accession numbers into a ‘DNAbin’ class.
read.GB(access.nb, seq.names = access.nb, species.names = TRUE, gene = TRUE, access = TRUE, as.character = FALSE)read.GB(access.nb, seq.names = access.nb, species.names = TRUE, gene = TRUE, access = TRUE, as.character = FALSE)
access.nb |
A character vector giving the GenBank accession numbers to download. |
seq.names |
A character vector giving the names to give to each sequence. Defaults to "accession number | species name". |
species.names |
Logical. Should species names be downloaded? Default of TRUE. |
gene |
Logical. Should the name of the gene region be downloaded? Default of TRUE. |
access |
Logical. Should the accession number be downloaded? Default of TRUE. |
as.character |
Logical. Should the sequences be returned as character vector? Default of FALSE, function returns sequences as a ‘DNAbin’ object. |
This function is a modification of
read.GenBank to include metadata with each
sequence. Additional data currently implemented are the species names and
the gene region from which sequences were derived.
A 'DNAbin' object with the following attributes: "species",
"gene", and "accession_num".
15 Feb 2018: 'read.GB' is deprecated. Please use the rOpenSci packages 'rentrez' and 'traits', or 'ape' for better functionality.
Samuel Brown <[email protected]>
## Not run: read.GB("AY059961") #Download the sequences making data(anoteropsis) from Genbank nums <- 59961:59993 seqs <- paste("AY0", nums, sep="") dat <- read.GB(seqs) attr(dat, "species") attr(dat, "gene") attr(dat, "accession_num") ## End(Not run)## Not run: read.GB("AY059961") #Download the sequences making data(anoteropsis) from Genbank nums <- 59961:59993 seqs <- paste("AY0", nums, sep="") dat <- read.GB(seqs) attr(dat, "species") attr(dat, "gene") attr(dat, "accession_num") ## End(Not run)
A utility to detect and remove species represented only by singletons.
rmSingletons(sppVector, exclude = TRUE)rmSingletons(sppVector, exclude = TRUE)
sppVector |
Vector of species names. (see |
exclude |
Logical. Should singletons be removed? Default of TRUE. |
When exclude = TRUE (the default), singletons are excluded and the
vector returns the index of all non-singletons in the dataset. When
exclude = FALSE, the indices of the singletons are presented.
Returns a numeric vector giving the indices of the selected individuals.
Samuel Brown <[email protected]>
data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) rmSingletons(anoSpp) rmSingletons(anoSpp, exclude=FALSE) data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) rmSingletons(doloSpp) rmSingletons(doloSpp, exclude=FALSE)data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) rmSingletons(anoSpp) rmSingletons(anoSpp, exclude=FALSE) data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) rmSingletons(doloSpp) rmSingletons(doloSpp, exclude=FALSE)
Determines the diagnostic nucleotides for each species given in
sppVector.
rnucDiag(DNAbin, sppVector, n = 100)rnucDiag(DNAbin, sppVector, n = 100)
DNAbin |
An object of class 'DNAbin'. |
sppVector |
The species vector (see |
n |
The number of pseudoreplicates to perform. Default of 100 |
These functions provide a means for evaluating the presence of diagnostic
nucleotides that distinguish species within an alignment. nucDiag
returns the positions of bases corresponding to the definition of pure,
simple diagnostic nucleotides given by Sarkar et al (2008).
rnucDiag runs a bootstrapping-style resampling test to evaluate the
numbers of diagnostic nucleotides that might be expected by random
assortment of specimens.
nucDiag returns a list giving the pure, simple diagnostic
nucleotides (i.e. those nucleotides that are fixed within species and
different from all other species) for each species in the species vector. A
result of integer(0) indicates there are no diagnostic nucleotides
for those species.
rnucDiag returns a list containing the following elements:
min |
The minimum number of diagnostic nucleotides in the sample. |
mean |
The mean number of diagnostic nucleotides in the sample. |
median |
The median number of diagnostic nucleotides in the sample. |
max |
The maximum number of diagnostic nucleotides in the sample. |
rndFreq |
A list of frequency distributions of the number of diagnostic nucleotides in groups formed by 1 sequence, 2 sequences, etc. |
Samuel Brown <[email protected]>
Sarkar, I., Planet, P., & DeSalle, R. (2008). CAOS software for use in character- based DNA barcoding. _Molecular Ecology Resources_ *8* 1256-1259
data(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) nucDiag(anoteropsis, anoSpp) #To view the nucleotide values anoNuc <- nucDiag(anoteropsis, anoSpp) as.character(anoteropsis[ ,anoNuc[[1]][1] ]) data(sarkar) sarkarSpp <- substr(dimnames(sarkar)[[1]], 1, 3) nucDiag(sarkar, sarkarSpp) ## Not run: rnucDiag(anoteropsis, anoSpp, n = 100) ## End(Not run)data(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) nucDiag(anoteropsis, anoSpp) #To view the nucleotide values anoNuc <- nucDiag(anoteropsis, anoSpp) as.character(anoteropsis[ ,anoNuc[[1]][1] ]) data(sarkar) sarkarSpp <- substr(dimnames(sarkar)[[1]], 1, 3) nucDiag(sarkar, sarkarSpp) ## Not run: rnucDiag(anoteropsis, anoSpp, n = 100) ## End(Not run)
This function computes Rosenberg's probability of reciprocal monophyly for each dichotomous node of a phylogenetic tree.
rosenberg(phy)rosenberg(phy)
phy |
A tree of class ‘phylo’. |
Because ape plots node labels in a different manner to the method in
which they are stored, when plotting the node labels made by
rosenberg, make sure the node argument is given as shown in
the examples below.
A numeric vector with names giving the node numbers of phy.
Samuel Brown <[email protected]>
Rosenberg, N. A. (2007). Statistical tests for taxonomic distinctiveness from observations of monophyly. _Evolution_ *61* (2), 317-323.
data(anoteropsis) anoTr <- ape::nj(ape::dist.dna(anoteropsis)) anoLab <- rosenberg(anoTr) ape::plot.phylo(anoTr) ape::nodelabels(round(anoLab,3), node=as.numeric(names(anoLab))) data(dolomedes) doloTr <- ape::nj(ape::dist.dna(dolomedes)) doloRose <- rosenberg(doloTr) ape::plot.phylo(doloTr) ape::nodelabels(round(doloRose, 3)) #Colour circles for nodes with a probability < 0.005 doloNodes <- doloRose < 0.005 doloLabs <- doloRose doloLabs[doloNodes] <- "blue" doloLabs[!doloNodes] <- "red" ape::plot.phylo(doloTr, cex=0.7) ape::nodelabels(pch=21, bg=doloLabs, node=as.numeric(names(doloLabs)), cex=2) graphics::legend(x=0.015, y=16.13, legend=c("significant", "not significant"), pch=21, pt.bg=c("blue", "red"), bty="n", pt.cex=2)data(anoteropsis) anoTr <- ape::nj(ape::dist.dna(anoteropsis)) anoLab <- rosenberg(anoTr) ape::plot.phylo(anoTr) ape::nodelabels(round(anoLab,3), node=as.numeric(names(anoLab))) data(dolomedes) doloTr <- ape::nj(ape::dist.dna(dolomedes)) doloRose <- rosenberg(doloTr) ape::plot.phylo(doloTr) ape::nodelabels(round(doloRose, 3)) #Colour circles for nodes with a probability < 0.005 doloNodes <- doloRose < 0.005 doloLabs <- doloRose doloLabs[doloNodes] <- "blue" doloLabs[!doloNodes] <- "red" ape::plot.phylo(doloTr, cex=0.7) ape::nodelabels(pch=21, bg=doloLabs, node=as.numeric(names(doloLabs)), cex=2) graphics::legend(x=0.015, y=16.13, legend=c("significant", "not significant"), pch=21, pt.bg=c("blue", "red"), bty="n", pt.cex=2)
A set of 41 sequences of the mitochondrial protein-coding gene cytochrome oxidase I from 41 species of the jumping spider family Salticidae.The sequences are available on GenBank as accession numbers AY297360 through AY297400.
A DNAbin object containing 41 sequences with a length of 409 base pairs stored as a list.
Maddison, W. P., and Hedin, M. C. (2003). Jumping spider phylogeny (Araneae: Salticidae). _Invertebrate Systematics_ *17* 529-549.
A set of 8 dummy sequences published in Sarkar et al 2008 to illustrate the different categories of diagnostic nucleotides.
A DNAbin object containing 8 sequences with a length of 18 base pairs stored as a matrix.
Sarkar, I., Planet, P., & DeSalle, R. (2008). CAOS software for use in character- based DNA barcoding. _Molecular Ecology Resources_ *8* 1256-1259
These functions allow DNA sequences to be downloaded from the Barcode of Life Database (BOLD).
search.BOLD(taxon, exhaustive = FALSE)search.BOLD(taxon, exhaustive = FALSE)
taxon |
A character vector of the names of the taxa of interest. |
exhaustive |
Logical. Should the function search for more than 500 process IDs? Default of FALSE. |
search.BOLD retrieves BOLD process identification numbers for any
given taxon using the API for BOLD version 3.0. By default, it only returns
the first 500 process IDs for the given taxon. By selecting the option
exhaustive = TRUE, the function can be made to search for more than
500 process IDs, but is much slower.
stats.BOLD retrieves the total number of records for the given taxon.
read.BOLD downloads the sequences associated with the process
identification numbers using a brute force method of downloading the
specimen record, then searching and splitting the HTML code to remove the
relevant information. This process is likely to make the function fairly
unstable if BOLD make any changes to their website.
Previous versions of read.BOLD used the eFetch web service offered by
BOLD to enable batch retrieval of records, however from October 2012 BOLD
deprecated eFetch without providing a replacement service.
search.BOLD returns a character vector giving the process
identification numbers of the specimens found by the search.
read.BOLD returns an object of class ‘DNAbin’. This object has the
attributes "species", "accession_num", and "gene".
On 26 Oct 2011, attempts to access records using the eFetch system through a web browser resulted in an error, saying that eFetch and eSearch are offline for maintainance.
As of 7 March 2012, both functions have been modified to interface with the new BOLD architecture, and work as expected.
29 Oct 2012: It appears that BOLD has taken eFetch offline permanently,
rendering read.BOLD as it currently stands useless. While we may be
able to work out something, this will require a complete rewrite of the
function. search.BOLD continues to work as intended.
17 Dec 2012: A new version of read.BOLD has been released that
appears to work (for the time being).
15 Feb 2018: 'search.BOLD' is deprecated. Please use the rOpenSci 'bold' package for better functionality.
Samuel Brown <[email protected]>
BOLD web services: http://www.boldsystems.org/index.php/resources/api?type=webservices.
BOLD version 3.0 http://v3.boldsystems.org/.
stats.BOLD, search.BOLD, read.GB.
help, ~~~
## Not run: stats.BOLD("Pisauridae") search.BOLD(c("Danio kyathit", "Dolomedes", "Sitona discoideus")) nn <- search.BOLD("Pisauridae") pisaurid <- read.BOLD(nn) ape::write.dna(pisaurid, "filename.fas", format="fasta") ## End(Not run)## Not run: stats.BOLD("Pisauridae") search.BOLD(c("Danio kyathit", "Dolomedes", "Sitona discoideus")) nn <- search.BOLD("Pisauridae") pisaurid <- read.BOLD(nn) ape::write.dna(pisaurid, "filename.fas", format="fasta") ## End(Not run)
This function plots an illustrative barcode consisting of vertical bands in four colours corresponding to the DNA bases adenine (A), cytosine (C), guanine (G) and thiamine (T).
seeBarcode(seq, col = c("green", "blue", "black", "red"))seeBarcode(seq, col = c("green", "blue", "black", "red"))
seq |
A single sequence of class ‘DNAbin’. |
col |
A character vector of length 4 giving colours to represent A, G, C and T respectively. |
Green, blue, black and red are the standard colours representing A, G, C and T respectively.
Plots an illustrative barcode.
Samuel Brown <[email protected]>
graphics::layout(matrix(1:6, ncol=1)) graphics::par(mar=c(0.5, 0, 0.5, 0)) data(woodmouse) seeBarcode(woodmouse[1,]) seeBarcode(woodmouse[1,], col=c("pink", "orange", "steelblue", "yellow")) seeBarcode(woodmouse[1,], col=c("black", "white", "white", "black")) apply(woodmouse[1:3,], MARGIN=1, FUN=seeBarcode)graphics::layout(matrix(1:6, ncol=1)) graphics::par(mar=c(0.5, 0, 0.5, 0)) data(woodmouse) seeBarcode(woodmouse[1,]) seeBarcode(woodmouse[1,], col=c("pink", "orange", "steelblue", "yellow")) seeBarcode(woodmouse[1,], col=c("black", "white", "white", "black")) apply(woodmouse[1:3,], MARGIN=1, FUN=seeBarcode)
Utility that produces a table giving summary statistics for a ‘DNAbin’ object.
seqStat(DNAbin, thresh = 500)seqStat(DNAbin, thresh = 500)
DNAbin |
Alignment of class ‘DNAbin’. |
thresh |
Threshold sequence length. Default of 500 (minimum length for official DNA barcodes). |
This function considers bases coded as '?', 'N' and '-' as missing data.
A table giving the minimum, maximum, mean and median sequence lengths, and the number of sequences with lengths below the given threshold.
Rupert Collins <[email protected]>
data(anoteropsis) seqStat(anoteropsis)data(anoteropsis) seqStat(anoteropsis)
Wraps a number of measures used in sliding window analyses into one easy-to-use function.
slideAnalyses(DNAbin, sppVector, width, interval = 1, distMeasures = TRUE, treeMeasures = FALSE)slideAnalyses(DNAbin, sppVector, width, interval = 1, distMeasures = TRUE, treeMeasures = FALSE)
DNAbin |
A DNA alignment of class ‘DNAbin’. |
sppVector |
Species vector (see |
width |
Desired width of windows in number of nucleotides. |
interval |
Distance between each window in number of nucleotides. Default of 1. Giving the option of 'codons' sets the size to 3. |
distMeasures |
Logical. Should distance measures be calculated? Default of TRUE. |
treeMeasures |
Logical. Should tree-based measures be calculated? Default of FALSE. |
Distance measures include the following: proportion of zero non-conspecific distances, number of diagnostic nucleotides, number of zero-length distances, and overall mean distance.
Tree-based measures include the following: proportion of species that are
monophyletic, proportion of clades that are identical between the neighbour
joining tree calculated for the window and the tree calculated for the full
dataset, and the latter with method="shallow".
Tree-based measures are a lot more time-intensive than distance measures. When dealing with lots of taxa and short windows, this part of the function can take hours.
Both distance and tree measures are calculated from a K2P distance matrix
created from the data with the option pairwise.deletion = TRUE. When
sequences with missing data are compared with other sequences, a NA
distance results. These are ignored in the calculation of
slideAnalyses distance metrics. However, the tree measures cannot
cope with this missing data, and so no result is returned for windows where
some sequences solely contain missing data.
An object of class 'slidWin' which is a list containing the following elements:
win_mono_out |
Proportion of species that are monophyletic. |
comp_out |
Proportion of clades that are identical between the NJ tree calculated for the window and the tree calculated for the full dataset. |
comp_depth_out |
Proportion of shallow clades that are identical. |
pos_tr_out |
Index of window position for tree-based analyses. |
noncon_out |
Proportion of zero non-conspecific distances. |
nd_out |
The sum of diagnostic nucleotides for each species. |
zero_out |
The number of zero-length distances. |
dist_mean_out |
Overall mean K2P distance of each window. |
pos_out |
Index of window position. |
dat_zero_out |
Number of zero inter-specific distances in the full dataset. |
boxplot_out |
Always
FALSE. Required for |
distMeasures |
Value
of argument. Required for |
treeMeasures |
Value of argument. Required for
|
Samuel Brown <[email protected]>
dist.dna, plot.slidWin,
rankSlidWin, slideNucDiag.
## Not run: data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) slideAnalyses(dolomedes, doloSpp, 200, interval=10, treeMeasures=TRUE) ## End(Not run)## Not run: data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) slideAnalyses(dolomedes, doloSpp, 200, interval=10, treeMeasures=TRUE) ## End(Not run)
Calculates boxplots of genetic distances using sliding windows.
slideBoxplots(DNAbin, sppVector, width, interval = 1, method = "nonCon")slideBoxplots(DNAbin, sppVector, width, interval = 1, method = "nonCon")
DNAbin |
A DNA alignment of class ‘DNAbin’. |
sppVector |
A species vector (see |
width |
Width of windows. |
interval |
Distance between each window in number of base pairs.
Default of 1. Giving the option of |
method |
Options of |
Giving method="overall" calculates the boxplot for the distance
matrix of each window.
Giving method="interAll" calculates boxplots for the inter- and
intra-specific distances of each window, showing the result for ALL
inter-specific distances.
Giving method="nonCon" calculates boxplots for the inter- and
intra-specific distances of each window, showing the result for only the
nearest-conspecific distances for each individual.
A list with
treeMeasures |
Logical. Tree measures calculated? Always FALSE. |
distMeasures |
Logical. Distance measures calculated? Always FALSE. |
bp_out |
If |
bp_InterSpp_out |
If |
bp_IntraSpp_out |
If |
bp_range_out |
range of y-axis values. |
pos_out |
x-axis values. |
boxplot_out |
Logical. Boxplots calculated? Always TRUE. |
method |
The method used for calculating boxplots. |
Samuel Brown <[email protected]>
boxplot, plot.slidWin,
slideAnalyses, slidingWindow.
data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) doloNonCon <- slideBoxplots(dolomedes, doloSpp, 200, interval=10) graphics::plot(doloNonCon) doloOverall <- slideBoxplots(dolomedes, doloSpp, 200, interval=10, method="overall") graphics::plot(doloOverall) doloInterall <- slideBoxplots(dolomedes, doloSpp, 200, interval=10, method="interAll") graphics::plot(doloInterall)data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) doloNonCon <- slideBoxplots(dolomedes, doloSpp, 200, interval=10) graphics::plot(doloNonCon) doloOverall <- slideBoxplots(dolomedes, doloSpp, 200, interval=10, method="overall") graphics::plot(doloOverall) doloInterall <- slideBoxplots(dolomedes, doloSpp, 200, interval=10, method="interAll") graphics::plot(doloInterall)
Calculates the number of diagnostic nucleotides in sliding windows.
slideNucDiag(DNAbin, sppVector, width, interval = 1)slideNucDiag(DNAbin, sppVector, width, interval = 1)
DNAbin |
A DNA alignment of class ‘DNAbin’. |
sppVector |
Species vector (see |
width |
Desired width of windows in number of base pairs. |
interval |
Distance between each window in number of base pairs.
Default of 1. Giving the option of |
Determines the number of diagnostic nucleotides for each species in each window.
A matrix giving the number of diagnostic nucleotides for each species (rows) in each window (columns).
Samuel Brown <[email protected]>
slideAnalyses, slideBoxplots,
slidingWindow.
data(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) slideNucDiag(dolomedes, doloSpp, 200, interval = 3) slidND <- slideNucDiag(dolomedes, doloSpp, 200, interval = 3) #Number of basepairs for each species graphics::matplot(t(slidND), type = "l") #Number of basepairs for a single species graphics::plot(slidND[4, ], type = "l") #Total number of basepairs per window graphics::plot(colSums(slidND), type = "l")data(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) slideNucDiag(dolomedes, doloSpp, 200, interval = 3) slidND <- slideNucDiag(dolomedes, doloSpp, 200, interval = 3) #Number of basepairs for each species graphics::matplot(t(slidND), type = "l") #Number of basepairs for a single species graphics::plot(slidND[4, ], type = "l") #Total number of basepairs per window graphics::plot(colSums(slidND), type = "l")
Creates windows of a specified width along a DNA alignment.
slidingWindow(DNAbin, width, interval = 1)slidingWindow(DNAbin, width, interval = 1)
DNAbin |
A DNA alignment of class ‘DNAbin’. |
width |
Width of each window. |
interval |
Numeric or option of |
Sliding window analyses are often used to determine the variability along sequences. This can be useful for investigating whether there is evidence for recombination, developing shorter genetic markers, or for determining variation within a gene.
Analyses can be conducted on each window using lapply.
A list of ‘DNAbin’ objects, with each alignment being width
bases in length. The list has length of the DNA alignment minus the width.
The positions covered by each window can be retreived with attr(x,
"window").
Samuel Brown <[email protected]>
lapply, slideAnalyses,
slideBoxplots.
data(woodmouse) woodmouse <- woodmouse[,1:20] win1 <- slidingWindow(woodmouse, width = 10) length(win1) win2 <- slidingWindow(woodmouse, width = 10, interval = 2) length(win2) win3 <- slidingWindow(woodmouse, width = 10, interval = "codons") length(win3) win4 <- slidingWindow(woodmouse, width = 15) length(win4) attr(win4[[1]], "window") attr(win4[[2]], "window")data(woodmouse) woodmouse <- woodmouse[,1:20] win1 <- slidingWindow(woodmouse, width = 10) length(win1) win2 <- slidingWindow(woodmouse, width = 10, interval = 2) length(win2) win3 <- slidingWindow(woodmouse, width = 10, interval = "codons") length(win3) win4 <- slidingWindow(woodmouse, width = 15) length(win4) attr(win4[[1]], "window") attr(win4[[2]], "window")
Separates a distance matrix into its inter- and intra-specific components.
sppDist(distobj, sppVector)sppDist(distobj, sppVector)
distobj |
A distance matrix. |
sppVector |
The species vector (see |
This function can be used to produce histograms and other charts exploring the ‘barcode gap’, such as in the examples below.
A list with two elements:
inter |
A numeric vector containing ALL inter-specific pairwise distances. |
intra |
A numeric vector containing ALL intra-specific pairwise distances. |
Samuel Brown <[email protected]>
data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) doloSpDist <- sppDist(doloDist, doloSpp) doloSpDist #Histogram of the barcode gap transGreen <- rgb(0, 1, 0, 0.5) #Make a slightly transparent colour to see some overlap graphics::hist(doloSpDist$inter, col="grey") graphics::hist(doloSpDist$intra, col=transGreen, add=TRUE) #Boxplot of the same graphics::boxplot(doloSpDist)data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) doloSpDist <- sppDist(doloDist, doloSpp) doloSpDist #Histogram of the barcode gap transGreen <- rgb(0, 1, 0, 0.5) #Make a slightly transparent colour to see some overlap graphics::hist(doloSpDist$inter, col="grey") graphics::hist(doloSpDist$intra, col=transGreen, add=TRUE) #Boxplot of the same graphics::boxplot(doloSpDist)
Creates a matrix giving the mean distances within and between species.
sppDistMatrix(distobj, sppVector)sppDistMatrix(distobj, sppVector)
distobj |
A distance matrix. |
sppVector |
The species vector (see |
A square matrix with dimensions length(sppVector). It
contains the mean intra specific distances down the diagonal, and the mean
pairwise distance between the species in the triangles. The two triangles
are identical.
Samuel Brown <[email protected]>
data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) sppDistMatrix(doloDist, doloSpp)data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) sppDistMatrix(doloDist, doloSpp)
A grouping variable that gives an identity to the individuals in various analyses.
Species vectors are the key concept behind a lot of spider's functionality. They are the method used to group data from individuals into species. It is important to note that "species" in this context can mean any cluster (real or otherwise) that is of interest. Populations, demes, subspecies and genera could be the taxa segregated by "species vectors".
The two characteristics of a species vector are UNIQUENESS between species and CONSISTENCY within them. R recognises differences of a single character between elements, leading to spider considering these elements to represent different species.
There is an easy way and a hard way to create species vectors. The hard way is to type out each element in the vector, making sure no typos or alignment errors are made.
The easy way is to add species designations into your data matrix from the beginning in such a way that it is easy to use R's data manipulation tools to create a species vector from the names of your data. See the examples for a few ways to do this.
Samuel Brown <[email protected]>
Functions for creating species vectors:
strsplit, substr, sapply.
Functions that use species vectors:
nearNeighbour, monophyly, nonConDist, nucDiag, rmSingletons, slideAnalyses, slideBoxplots, sppDist, sppDistMatrix, threshOpt.
data(dolomedes) #Dolomedes species vector doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) data(anoteropsis) #Anoteropsis species vector anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_"))data(dolomedes) #Dolomedes species vector doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) data(anoteropsis) #Anoteropsis species vector anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_"))
These functions allow DNA sequences to be downloaded from the Barcode of Life Database (BOLD).
stats.BOLD(taxon)stats.BOLD(taxon)
taxon |
A character vector of the names of the taxa of interest. |
search.BOLD retrieves BOLD process identification numbers for any
given taxon using the API for BOLD version 3.0. By default, it only returns
the first 500 process IDs for the given taxon. By selecting the option
exhaustive = TRUE, the function can be made to search for more than
500 process IDs, but is much slower.
stats.BOLD retrieves the total number of records for the given taxon.
read.BOLD downloads the sequences associated with the process
identification numbers using a brute force method of downloading the
specimen record, then searching and splitting the HTML code to remove the
relevant information. This process is likely to make the function fairly
unstable if BOLD make any changes to their website.
Previous versions of read.BOLD used the eFetch web service offered by
BOLD to enable batch retrieval of records, however from October 2012 BOLD
deprecated eFetch without providing a replacement service.
search.BOLD returns a character vector giving the process
identification numbers of the specimens found by the search.
read.BOLD returns an object of class ‘DNAbin’. This object has the
attributes "species", "accession_num", and "gene".
On 26 Oct 2011, attempts to access records using the eFetch system through a web browser resulted in an error, saying that eFetch and eSearch are offline for maintainance.
As of 7 March 2012, both functions have been modified to interface with the new BOLD architecture, and work as expected.
29 Oct 2012: It appears that BOLD has taken eFetch offline permanently,
rendering read.BOLD as it currently stands useless. While we may be
able to work out something, this will require a complete rewrite of the
function. search.BOLD continues to work as intended.
17 Dec 2012: A new version of read.BOLD has been released that
appears to work (for the time being).
15 Feb 2018: 'stats.BOLD' is deprecated. Please use the rOpenSci 'bold' package for better functionality.
Samuel Brown <[email protected]>
BOLD web services: http://www.boldsystems.org/index.php/resources/api?type=webservices.
BOLD version 3.0 http://v3.boldsystems.org/.
stats.BOLD, search.BOLD, read.GB.
help, ~~~
## Not run: stats.BOLD("Pisauridae") search.BOLD(c("Danio kyathit", "Dolomedes", "Sitona discoideus")) nn <- search.BOLD("Pisauridae") pisaurid <- read.BOLD(nn) ape::write.dna(pisaurid, "filename.fas", format="fasta") ## End(Not run)## Not run: stats.BOLD("Pisauridae") search.BOLD(c("Danio kyathit", "Dolomedes", "Sitona discoideus")) nn <- search.BOLD("Pisauridae") pisaurid <- read.BOLD(nn) ape::write.dna(pisaurid, "filename.fas", format="fasta") ## End(Not run)
Calculates Tajima's K index of divergence.
tajima.K(DNAbin, prop = TRUE)tajima.K(DNAbin, prop = TRUE)
DNAbin |
An object of class ‘DNAbin’. |
prop |
Logical. Should the function report the number of substitutions per nucleotide? Default of TRUE. |
A vector of length 1. If prop = FALSE, the mean number of
substitutions between any two sequences is returned. If prop = TRUE
(the default), this number is returned as the mean number of substitutions
per nucleotide (i.e. the above divided by the length of the sequences).
Samuel Brown <[email protected]>
Tajima, F. (1983). Evolutionary relationship of DNA sequences in finite populations. _Genetics_ *105*, 437-460.
data(anoteropsis) tajima.K(anoteropsis) tajima.K(anoteropsis, prop = FALSE)data(anoteropsis) tajima.K(anoteropsis) tajima.K(anoteropsis, prop = FALSE)
Identifies clusters, excluding individuals greater than the threshold from any member.
tclust(distobj, threshold = 0.01)tclust(distobj, threshold = 0.01)
distobj |
A distance object (usually from |
threshold |
Distance cutoff for clustering. Default of 0.01 (1%). |
If two individuals are more distant than threshold from each other,
but both within threshold of a third, all three are contained in a
single cluster.
A list with each element giving the index of the individuals contained in each cluster.
Samuel Brown <[email protected]>
dist.dna, localMinima.
See Also as help, ~~~
data(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) anoDist <- ape::dist.dna(anoteropsis) tclust(anoDist) #Names of individuals anoClust <- tclust(anoDist) lapply(anoClust, function(x) anoSpp[x])data(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) anoDist <- ape::dist.dna(anoteropsis) tclust(anoDist) #Names of individuals anoClust <- tclust(anoDist) lapply(anoClust, function(x) anoSpp[x])
Tests of barcoding efficacy using distance-based methods.
threshID(distobj, sppVector, threshold = 0.01, names = FALSE)threshID(distobj, sppVector, threshold = 0.01, names = FALSE)
distobj |
A distance object (usually from |
sppVector |
Vector of species names. See |
threshold |
Distance cutoff for identifications. Default of 0.01 (1%). |
names |
Logical. Should the names of the nearest match be shown? Default of FALSE. |
These functions test barcoding efficacy. All sequences must be identified prior to testing. Each sequence is considered an unknown while the remaining sequences in the dataset constitute the DNA barcoding database that is used for identification. If the identification from the test is the same as the pre-considered identification, a correct result is returned.
bestCloseMatch conducts the "best close match" analysis of Meier et
al. (2006), considering the closest individual unless it is further than the
given threshold, which results in no identification. More than one species
tied for closest match results in an assignment of "ambiguous". When the
threshold is large, this analysis will return essentially the same result as
nearNeighbour. If names = TRUE, a list is returned containing
the names of all species represented by specimens within the threshold.
nearNeighbour finds the closest individual and returns if their names
are the same (TRUE) or different (FALSE). If names = TRUE, the name
of the closest individual is returned. Ties are decided by majority rule.
threshID conducts a threshold-based analysis, similar to that
conducted by the "Identify Specimen" tool provided by the Barcode of Life
Database (http://www.boldsystems.org/index.php/IDS_OpenIdEngine). It
is more inclusive than bestCloseMatch, considering ALL sequences
within the given threshold. If names = TRUE, a list is returned
containing the names of all species represented by specimens within the
threshold.
These functions are not recommended as identification tools, though they can
be used as such when names = TRUE.
bestCloseMatch and threshID return a character vector
giving the identification status of each individual.
"correct" |
The name of the closest match is the same |
"incorrect" |
The name of the closest match is different |
"ambiguous" |
More than one species is the
closest match ( |
"no id" |
No species are within the threshold distance |
nearNeighbour returns a logical vector or (if names = TRUE)
the name for the nearest individual.
Samuel Brown <[email protected]>
Meier, R., Shiyang, K., Vaidya, G., & Ng, P. (2006). DNA barcoding and taxonomy in Diptera: a tale of high intraspecific variability and low identification success. _Systematic Biology_ *55* (5) 715-728.
nearNeighbour, threshID, dist.dna, sppVector
Also as help, ~~~
data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split = "_"), function(x) paste(x[1], x[2], sep = "_")) bestCloseMatch(anoDist, anoSpp) bestCloseMatch(anoDist, anoSpp, threshold = 0.005) nearNeighbour(anoDist, anoSpp) nearNeighbour(anoDist, anoSpp, names = TRUE) threshID(anoDist, anoSpp) threshID(anoDist, anoSpp, threshold = 0.003) data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) bestCloseMatch(doloDist, doloSpp) bestCloseMatch(doloDist, doloSpp, threshold = 0.005) nearNeighbour(doloDist, doloSpp) nearNeighbour(doloDist, doloSpp, names=TRUE) threshID(doloDist, doloSpp) threshID(doloDist, doloSpp, threshold = 0.003)data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split = "_"), function(x) paste(x[1], x[2], sep = "_")) bestCloseMatch(anoDist, anoSpp) bestCloseMatch(anoDist, anoSpp, threshold = 0.005) nearNeighbour(anoDist, anoSpp) nearNeighbour(anoDist, anoSpp, names = TRUE) threshID(anoDist, anoSpp) threshID(anoDist, anoSpp, threshold = 0.003) data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) bestCloseMatch(doloDist, doloSpp) bestCloseMatch(doloDist, doloSpp, threshold = 0.005) nearNeighbour(doloDist, doloSpp) nearNeighbour(doloDist, doloSpp, names=TRUE) threshID(doloDist, doloSpp) threshID(doloDist, doloSpp, threshold = 0.003)
Determines the positive, negative, false positive and false negative rates of identification accuracy for a given threshold.
threshOpt(distobj, sppVector, threshold = 0.01)threshOpt(distobj, sppVector, threshold = 0.01)
distobj |
Distance matrix. |
sppVector |
Species vector (see |
threshold |
Threshold distance for delimiting intra- and inter-specific variation. Default of 0.01. |
When run over a range of thresholds, this function allows the optimisation of threshold values based on minimising the identification error rates. See the example below for more details.
A table giving the threshold and number of negative and positive identifications, number of false negative and false positive identifications, and the cumulative error.
Rupert Collins <[email protected]>
Meyer, C. P., and Paulay, G. (2005). DNA barcoding: error rates based on comprehensive sampling. _PLoS Biology_ *3* (12), 2229-2238.
data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) threshOpt(anoDist, anoSpp) data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) threshOpt(doloDist, doloSpp) #Conduct the analysis over a range of values to determine the optimum threshold threshVal <- seq(0.001,0.02, by = 0.001) opt <- lapply(threshVal, function(x) threshOpt(doloDist, doloSpp, thresh = x)) optMat <- do.call(rbind, opt) graphics::barplot(t(optMat)[4:5,], names.arg=optMat[,1], xlab="Threshold values", ylab="Cumulative error") graphics::legend(x = 2.5, y = 29, legend = c("False positives", "False negatives"), fill = c("grey75", "grey25"))data(anoteropsis) anoDist <- ape::dist.dna(anoteropsis) anoSpp <- sapply(strsplit(dimnames(anoteropsis)[[1]], split="_"), function(x) paste(x[1], x[2], sep="_")) threshOpt(anoDist, anoSpp) data(dolomedes) doloDist <- ape::dist.dna(dolomedes) doloSpp <- substr(dimnames(dolomedes)[[1]], 1, 5) threshOpt(doloDist, doloSpp) #Conduct the analysis over a range of values to determine the optimum threshold threshVal <- seq(0.001,0.02, by = 0.001) opt <- lapply(threshVal, function(x) threshOpt(doloDist, doloSpp, thresh = x)) optMat <- do.call(rbind, opt) graphics::barplot(t(optMat)[4:5,], names.arg=optMat[,1], xlab="Threshold values", ylab="Cumulative error") graphics::legend(x = 2.5, y = 29, legend = c("False positives", "False negatives"), fill = c("grey75", "grey25"))
Provides an ordered vector of tip labels, corresponding to their position on the tree.
tiporder(phy, labels = TRUE)tiporder(phy, labels = TRUE)
phy |
A tree of class ‘phylo’. |
labels |
Logical. Should labels be printed? If FALSE, the indices are given. Default of TRUE. |
A character or numeric vector giving the names of the tip in the
order of their position on the tree. The order is that from top to bottom
when the tree is plotted with direction = "rightwards".
Samuel Brown <[email protected]>
data(anoteropsis) anoTree <- ape::nj(ape::dist.dna(anoteropsis)) tiporder(anoTree) tiporder(anoTree, labels = FALSE) data(woodmouse) woodTree <- ape::nj(ape::dist.dna(woodmouse)) tiporder(woodTree) tiporder(ape::ladderize(woodTree))data(anoteropsis) anoTree <- ape::nj(ape::dist.dna(anoteropsis)) tiporder(anoTree) tiporder(anoTree, labels = FALSE) data(woodmouse) woodTree <- ape::nj(ape::dist.dna(woodmouse)) tiporder(woodTree) tiporder(ape::ladderize(woodTree))
Calculates the number of pairwise transitions and transversions between sequences.
titv(DNAbin)titv(DNAbin)
DNAbin |
A DNA alignment of class ‘DNAbin’. |
A square matrix with dimensions of length(dat). The upper
triangle contains the number of transversions. The lower triangle contains
the number of transitions.
Samuel Brown <[email protected]>
data(dolomedes) subs <- titv(dolomedes) #Transversions subs[upper.tri(subs)] tv <- t(subs) tv <- tv[lower.tri(tv)] #Transitions ti <- subs[lower.tri(subs)] #Saturation plot doloDist <- ape::dist.dna(dolomedes) graphics::plot(doloDist, ti, type="p", pch=19, col="blue", main="Saturation plot of number of transitions and transversions\n against K2P distance. Red: transversions. Blue: transitions") graphics::points(doloDist, tv, pch=19, col="red")data(dolomedes) subs <- titv(dolomedes) #Transversions subs[upper.tri(subs)] tv <- t(subs) tv <- tv[lower.tri(tv)] #Transitions ti <- subs[lower.tri(subs)] #Saturation plot doloDist <- ape::dist.dna(dolomedes) graphics::plot(doloDist, ti, type="p", pch=19, col="blue", main="Saturation plot of number of transitions and transversions\n against K2P distance. Red: transversions. Blue: transitions") graphics::points(doloDist, tv, pch=19, col="red")
Compares the clades between two trees.
tree.comp(phy1, phy2, method = "prop")tree.comp(phy1, phy2, method = "prop")
phy1, phy2
|
Trees of class ‘phylo’ to compare. |
method |
One of the following options:
|
This function is a modification of the dist.topo function in
ape to give similarity between the two trees as a proportion, and to
account for the unreliable resolution of deeper nodes that affect some
methods of tree construction (such as NJ).
It is important that the tip labels of the two trees are the same. If the tip labels are different between the two trees, the method will not recognise any similarity between them.
This function does not take into account differences in branch length. The
"score" method in dist.topo does this if desired.
Numeric vector of length 1.
If method = "prop", the number returned is the proportion of nodes in
the first tree for which there is a node in the second that contains the
same tips. Higher number represents greater similarity. If it is 1, the
trees are identical. If 0, the trees have no similarity whatsoever.
When method = "shallow", only those nodes tipwards of the median node
depth are taken into account. This will not be useful for small trees, but
may be helpful with larger datasets.
"PH85" is the Penny and Hendy (1985) distance. This measure is the
default of dist.topo. In this measure, the smaller the number,
the closer the trees are. If the trees are identical, this results in 0.
Samuel Brown <[email protected]>
Penny, D. and Hendy, M. D. (1985) The use of tree comparison metrics. _Systematic Zoology_ *34* 75-82.
set.seed(15) tr <- ape::rtree(15) set.seed(22) tr2 <- ape::rtree(15) tree.comp(tr, tr2) tree.comp(tr, tr2, method="PH85") tree.comp(tr, tr2, method="shallow")set.seed(15) tr <- ape::rtree(15) set.seed(22) tr2 <- ape::rtree(15) tree.comp(tr, tr2) tree.comp(tr, tr2, method="PH85") tree.comp(tr, tr2, method="shallow")
This is a set of 15 sequences of the mitochondrial gene cytochrome b of the woodmouse (Apodemus sylvaticus) which is a subset of the data analysed by Michaux et al. (2003). The full data set is available through GenBank (accession numbers AJ511877 to AJ511987). Dataset from the ape package.
A DNAbin object containing 8 sequences with a length of 18 base pairs stored as a matrix.
Michaux, J. R., Magnanou, E., Paradis, E., Nieberding, C. and Libois, R. (2003) Mitochondrial phylogeography of the Woodmouse (Apodemus sylvaticus) in the Western Palearctic region. _Molecular Ecology_ *12*, 685-697