Deprecate iTol and use taxize:: instead. Rewrite of tip re-ordering.
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# tocID <- "BIN-PHYLO-Tree_analysis.R"
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#
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# ---------------------------------------------------------------------------- #
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# PATIENCE ... #
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# Do not yet work wih this code. Updates in progress. Thank you. #
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# boris.steipe@utoronto.ca #
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# ---------------------------------------------------------------------------- #
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#
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# Purpose: A Bioinformatics Course:
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# R code accompanying the BIN-PHYLO-Tree_analysis unit.
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#
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# Version: 1.1
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# Version: 1.2
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#
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# Date: 2017 10 - 2019 01
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# Date: 2017-10 - 2020-09
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 1.2 2020 updates. Deprecate iTol and use taxize:: instead.
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# Rewrite of tip re-ordering. Better handling of
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# messages. pBar() for randomization.
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# 1.1 Change from require() to requireNamespace(),
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# use <package>::<function>() idiom throughout,
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# use Biocmanager:: not biocLite()
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@ -37,15 +34,16 @@
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#TOC> ==========================================================================
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#TOC>
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#TOC>
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#TOC> Section Title Line
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#TOC> --------------------------------------------------
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#TOC> 1 Preparation and Tree Plot 46
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#TOC> 2 Tree Analysis 86
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#TOC> 2.1 Rooting Trees 145
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#TOC> 2.2 Rotating Clades 190
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#TOC> 2.3 Computing tree distances 241
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#TOC>
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#TOC> 1 Preparation and Tree Plot 50
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#TOC> 2 SPECIES REFERENCE TREE 66
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#TOC> 3 Tree Analysis 117
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#TOC> 3.1 Rooting Trees 177
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#TOC> 3.2 Rotating Clades 222
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#TOC> 3.3 Computing tree distances 309
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#TOC>
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#TOC> ==========================================================================
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@ -60,36 +58,63 @@ if (! requireNamespace("ape", quietly = TRUE)) {
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# browseVignettes("ape") # available vignettes
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# data(package = "ape") # available datasets
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# We change the graphics parameters from time to time, let's define the
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# default so we can recreate a sane state:
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dev.off()
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PAR <- par()
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# Read the species tree that you have created at the phyloT Website:
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fungiTree <- ape::read.tree("fungiTree.txt")
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# = 2 SPECIES REFERENCE TREE ==============================================
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# Before we do any kind of phylogenetic analysis of genes from several species,
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# we MUST have a reference tree of the taxonomic relationships in hand. This
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# context is absolutely required for the interpretation of our tree.
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# We have the tax-ids in our database, and the NCBI has the species tree - we just need some way to extract the subtree that corresponds to our taxons of interest. Here's how to use the taxize:: package.
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if (! requireNamespace("taxize", quietly = TRUE)) {
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install.packages("taxize")
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}
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# Package information:
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# library(help = taxize) # basic information
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# browseVignettes("taxize") # available vignettes
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# data(package = "taxize") # available datasets
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( mySOI <- c(myDB$taxonomy$ID, "83333") )
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myClass <- taxize::classification(mySOI, db = "ncbi")
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str(myClass)
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myClass[[1]]
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fungiTree <- taxize::class2tree(myClass, check = TRUE)
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plot(fungiTree)
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# The tree produced by phyloT contains full length species names, but it would
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# be more convenient if it had bicodes instead.
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# The tree produced by taxize:: contains full length species names,
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# but it would be more convenient if it had bicodes instead. Also, the actual
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# tree is only part of the list(), which will cause problems later:
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str(fungiTree)
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# The species names are in a vector $tip.label of this list. We can use bicode()
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# to shorten them - but note that they have underscores as word separators. Thus
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# we will use gsub("-", " ", ...) to replace the underscores with spaces.
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# we therefor simplify
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fungiTree <- fungiTree$phylo
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str(fungiTree)
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for (i in seq_along(fungiTree$tip.label)) {
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fungiTree$tip.label[i] <- biCode(gsub("_", " ", fungiTree$tip.label[i]))
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}
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# The species names are in a vector $phylo$tip.label of this list.
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# We can use biCode() to shorten them.
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fungiTree$tip.label <- biCode(fungiTree$tip.label)
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# Plot the tree
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plot(fungiTree, cex = 1.0, root.edge = TRUE, no.margin = TRUE)
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nSP <- length(fungiTree$tip.label)
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plot(fungiTree, cex = 0.8, root.edge = TRUE, no.margin = TRUE)
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text(-1, nSP - 0.5, "Species Tree:\nFungi", pos = 4)
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ape::nodelabels(text = fungiTree$node.label,
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cex = 0.6,
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adj = 0.2,
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bg = "#D4F2DA")
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# Note that you can use the arrow buttons in the menu above the plot to scroll
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# back to plots you have created earlier - so you can reference back to the
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# species tree.
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# Note that you can use the arrow buttons in the menu above the plot pane to
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# scroll back to plots you have created earlier - so you can reference back to
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# this species tree in your later analysis.
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# = 2 Tree Analysis =======================================================
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# = 3 Tree Analysis =======================================================
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# 1.1 Visualizing your tree
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@ -110,7 +135,7 @@ ape::nodelabels(text = fungiTree$node.label,
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# We load the APSES sequence tree that you produced in the
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# BIN-PHYLO-Tree_building unit:
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apsTree <- readRDS(file = "APSEStreeRproml.rds")
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apsTree <- readRDS(file = "data/APSEStreeRproml.rds")
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plot(apsTree) # default type is "phylogram"
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plot(apsTree, type = "unrooted")
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@ -144,11 +169,12 @@ ape::Nnode(apsTree)
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ape::Nedge(apsTree)
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ape::Ntip(apsTree)
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par(PAR) # reset graphics state
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# Finally, write the tree to console in Newick format
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ape::write.tree(apsTree)
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# == 2.1 Rooting Trees =====================================================
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# == 3.1 Rooting Trees =====================================================
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# In order to analyse the tree, it is helpful to root it first and reorder its
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# clades. Contrary to documentation, Rproml() returns an unrooted tree.
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@ -163,7 +189,7 @@ plot(apsTree)
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ape::nodelabels(cex = 0.5, frame = "circle")
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ape::tiplabels(cex = 0.5, frame = "rect")
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# The outgroup of the tree is tip "11" in my sample tree, it may be a different
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# The outgroup of the tree (KILA ESCCO) is tip "11" in my sample tree, it may be a different
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# number in yours. Substitute the correct node number below for "outgroup".
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apsTree <- ape::root(apsTree, outgroup = 11, resolve.root = TRUE)
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plot(apsTree)
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@ -193,7 +219,7 @@ plot(apsTree, cex = 0.7, root.edge = TRUE)
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ape::nodelabels(text = "MRCA", node = 12, cex = 0.5, adj = 0.8, bg = "#ff8866")
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# == 2.2 Rotating Clades ===================================================
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# == 3.2 Rotating Clades ===================================================
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# To interpret the tree, it is useful to rotate the clades so that they appear
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# in the order expected from the cladogram of species.
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@ -206,30 +232,66 @@ plot(ape::rotate(apsTree, node = 13), no.margin = TRUE, root.edge = TRUE)
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ape::nodelabels(node = 13, cex = 0.7, bg = "#88ff66")
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# Note that the species at the bottom of the clade descending from node
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# 17 is now plotted at the top.
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layout(matrix(1), widths = 1.0, heights = 1.0)
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# ... or we can plot the tree so it corresponds as well as possible to a
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# predefined tip ordering. Here we use the ordering that phyloT has returned
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# for the species tree.
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par(PAR) # reset graphics state
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# (Nb. we need to reverse the ordering for the plot. This is why we use the
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# expression [nOrg:1] below instead of using the vector directly.)
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# ... or we can rearrange the tree so it corresponds as well as possible to a
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# predefined tip ordering. Here we use the ordering that taxize:: has inferred
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# from the NCBI taxonomic classification.
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nOrg <- length(apsTree$tip.label)
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layout(matrix(1:2, 1, 2))
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plot(fungiTree,
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no.margin = TRUE, root.edge = TRUE)
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no.margin = FALSE, root.edge = TRUE)
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ape::nodelabels(text = fungiTree$node.label,
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cex = 0.5,
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adj = 0.2,
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bg = "#D4F2DA")
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plot(ape::rotateConstr(apsTree, apsTree$tip.label[nOrg:1]),
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# These are the fungi tree tips ...
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fungiTree$tip.label
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# ... and their order is determined by the edge-list that is stored in
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fungiTree$edge
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# which edges join the tips?
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ape::tiplabels(cex = 0.5, frame = "rect")
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# as you can see, the tips (range [1:nOrg] ) are in column 2 and they are
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# ordered from bottom to top.
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# And each tip number is the index of the species in the tip.label vector. So we can take column 2, subset it, and use it to get a list of species in the order of the tree ...
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sel <- fungiTree$edge[ , 2 ] <= nOrg
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( oSp <- fungiTree$tip.label[fungiTree$edge[sel , 2 ]] )
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# Now, here are the genes of the apsTree tips ...
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apsTree$tip.label
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# ... and the "constraint" we need for reordering, according to the help page
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# of ape::rotateConstr(), is "a vector specifying the order of the tips as they
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# should appear (from bottom to top)". Thus we need to add the "MBP1_" prefix to our vector
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oSp <- gsub("^", "MBP1_", oSp)
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( oSp <- gsub("MBP1_ESSCO", "KILA_ESCCO", oSp) )
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# Then we can plot the two trees to compare: the fungi- tree
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par(PAR) # reset graphics state
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layout(matrix(1:2, 1, 2))
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plot(fungiTree,
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no.margin = TRUE,
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root.edge = TRUE)
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ape::nodelabels(text = fungiTree$node.label,
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cex = 0.5,
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adj = 0.2,
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bg = "#D4F2DA")
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# and the re-organized apsesTree ...
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plot(ape::rotateConstr(apsTree, constraint = oSp[]),
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no.margin = TRUE,
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root.edge = TRUE)
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ape::add.scale.bar(length = 0.5)
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layout(matrix(1), widths = 1.0, heights = 1.0)
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par(PAR) # reset graphics state
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# As you can see, the reordering is not perfect, since the topologies are
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# different, mostly due to the unresolved nodes in the reference tree. One
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# could play with that ...
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# Task: Study the two trees and consider their similarities and differences.
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# What do you expect? What do you find? Note that this is not a "mixed"
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@ -240,11 +302,11 @@ layout(matrix(1), widths = 1.0, heights = 1.0)
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# branches would you need to remove and reinsert elsewhere to get the
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# same topology as the species tree?
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# In order to quantiofy how different these tow trees are, we need to compute
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# In order to quantify how different these two trees are, we need to compute
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# tree distances.
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# == 2.3 Computing tree distances ==========================================
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# == 3.3 Computing tree distances ==========================================
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# Many superb phylogeny tools are contributed by the phangorn package.
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@ -262,6 +324,7 @@ if (! requireNamespace("phangorn", quietly = TRUE)) {
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apsTree2 <- apsTree
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apsTree2$tip.label <- gsub("(MBP1_)|(KILA_)", "", apsTree2$tip.label)
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# phangorn provides several functions to compute tree-differences (and there
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# is a _whole_ lot of theory on how to compare trees). treedist() returns the
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# "symmetric difference"
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@ -280,33 +343,41 @@ ape::rtree(n = length(apsTree2$tip.label), # number of tips
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# fungiTree has none, so we can't
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# compare them anyway.
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# (Note the warning message about non-binary trees; we'll suppress that later
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# by wrapping the function call in supressMessages(); we don't want to
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# print it 10,000 times :-)
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# Let's compute some random trees this way, calculate the distances to
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# fungiTree, and then compare the values we get for apsTree2. The random
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# trees are provided by ape::rtree().
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N <- 10000 # takes about 15 seconds
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N <- 10000 # takes about 15 seconds, and we'll use the pBar function,
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# defined in .utilities.R to keep track of where we are at:
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myTreeDistances <- matrix(numeric(N * 2), ncol = 2)
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colnames(myTreeDistances) <- c("symm", "path")
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set.seed(112358)
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for (i in 1:N) {
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pBar(i, N)
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xTree <- ape::rtree(n = length(apsTree2$tip.label),
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rooted = TRUE,
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tip.label = apsTree2$tip.label,
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br = NULL)
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myTreeDistances[i, ] <- phangorn::treedist(fungiTree, xTree)
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myTreeDistances[i, ] <- suppressMessages(phangorn::treedist(fungiTree, xTree))
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}
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set.seed(NULL) # reset the random number generator
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table(myTreeDistances[, "symm"])
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(symmObs <- phangorn::treedist(fungiTree, apsTree2)[1])
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( symmObs <- phangorn::treedist(fungiTree, apsTree2)[1] )
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# Random events less-or-equal to observation, divided by total number of
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# events gives us the empirical p-value.
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cat(sprintf("\nEmpirical p-value for symmetric diff. of observed tree is %1.4f\n",
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(sum(myTreeDistances[ , "symm"] <= symmObs) + 1) / (N + 1)))
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par(PAR) # reset graphics state
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hist(myTreeDistances[, "path"],
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col = "aliceblue",
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main = "Distances of random Trees to fungiTree")
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