Updates, and add treedist()
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#
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# Version: 1.0
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#
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# Date: 2017 10. 31
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# Date: 2017 10 31
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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@ -24,6 +24,20 @@
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#
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# ==============================================================================
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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 Preparations 41
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#TOC> 2 Fetching sequences 78
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#TOC> 3 Multiple Sequence Alignment 119
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#TOC> 4 Reviewing and Editing Alignments 136
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#TOC> 4.1 Masking workflow 152
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#TOC>
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#TOC> ==========================================================================
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# = 1 Preparations ========================================================
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@ -61,18 +75,7 @@ if (! require(msa, quietly=TRUE)) {
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# data(package = "msa") # available datasets
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if (! require(stringr, quietly=TRUE)) {
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install.packages("stringr")
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library(stringr)
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}
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# Package information:
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# library(help=stringr) # basic information
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# browseVignettes("stringr") # available vignettes
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# data(package = "stringr") # available datasets
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# = 1 Fetching sequences
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# = 2 Fetching sequences ==================================================
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# myDB contains the ten Mbp1 orthologues from the reference species and the Mbp1
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@ -113,7 +116,7 @@ names(APSI)[length(APSI)] <- "KILA_ESCCO"
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tail(APSI)
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# = 1 Multiple Sequence Alignment
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# = 3 Multiple Sequence Alignment =========================================
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# This vector of sequences with named elements fulfills the requirements to be
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# imported as a Biostrings object - an AAStringSet - which we need as input for
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@ -130,7 +133,7 @@ writeALN(APSESMsa)
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# What do you think? Is this a good alignment for phylogenetic inference?
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# = 1 Reviewing and Editing Alignments
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# = 4 Reviewing and Editing Alignments ====================================
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# Head back to the Wiki page for this unit and read up on the background
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@ -146,7 +149,7 @@ writeALN(APSESMsa)
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# go through the matrix, column by column and decide
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# whether we want to include that column.
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# = 1.1 Masking workflow
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# == 4.1 Masking workflow ==================================================
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# get the length of the alignment
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(lenAli <- APSESMsa@unmasked@ranges@width[1])
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@ -24,7 +24,21 @@
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#
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# ==============================================================================
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# = 1 ___Section___
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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 ___Section___ 38
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#TOC> 2 Tree Analysis 77
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#TOC> 2.1 Rooting Trees 136
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#TOC> 2.2 Rotating Clades 182
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#TOC> 2.3 Computing tree distances 229
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#TOC>
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#TOC> ==========================================================================
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# = 1 ___Section___ =======================================================
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if (!require(Rphylip, quietly=TRUE)) {
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@ -63,7 +77,7 @@ nodelabels(text=orgTree$node.label, cex=0.6, adj=0.2, bg="#D4F2DA")
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# species tree.
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# = 1 Tree Analysis
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# = 2 Tree Analysis =======================================================
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# 1.1 Visualizing your tree
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@ -122,7 +136,7 @@ Ntip(apsTree)
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# Finally, write the tree to console in Newick format
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write.tree(apsTree)
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# = 1.1 Rooting Trees
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# == 2.1 Rooting Trees =====================================================
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# In order to analyse the tree, it is helpful to root it first and reorder its
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# clades. Contrary to documentation, Rproml() returns an unrooted tree.
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@ -168,7 +182,7 @@ plot(apsTree, cex=0.7, root.edge=TRUE)
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nodelabels(text="MRCA", node=12, cex=0.5, adj=0.8, bg="#ff8866")
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# = 1.1 Rotating Clades
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# == 2.2 Rotating Clades ===================================================
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# To interpret the tree, it is useful to rotate the clades so that they appear
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# in the order expected from the cladogram of species.
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@ -202,11 +216,104 @@ plot(rotateConstr(apsTree, apsTree$tip.label[nOrg:1]),
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add.scale.bar(length=0.5)
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layout(matrix(1), widths=1.0, heights=1.0)
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# Study the two trees and consider their similarities and differences. What do
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# you expect? What do you find? Note that this is not a "mixed" gene tree yet,
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# since it contains only a single gene for the species we considered. All of the
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# branch points in this tree are speciation events.
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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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# gene tree yet, since it contains only a single gene for the species
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# we considered. All of the branch points in this tree are speciation
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# events. Thus the gene tree should have the same topology as the
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# species tree. Does it? Are the differences important? How many
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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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# tree distances.
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# == 2.3 Computing tree distances ==========================================
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# Many superb phylogeny tools are contributed by the phangorn package.
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if (!require(phangorn, quietly=TRUE)) {
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install.packages("phangorn")
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library(phangorn)
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}
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# Package information:
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# library(help = phangorn) # basic information
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# browseVignettes("phangorn") # available vignettes
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# data(package = "phangorn") # available datasets
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# To compare two trees, they must have the same tip labels. We delete "MBP1_" or
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# "KILA_" from the existing tip labels in a copy of our APSES domain tree.
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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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treedist(fungiTree, apsTree2, check.labels = TRUE)
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# Numbers. What do they mean? How much more similar is our apsTree to the
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# (presumably) ground truth of fungiTree than a random tree would be?
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# The ape package (which was loaded with RPhylip) provides the function rtree()
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# to compute random trees.
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rtree(n = length(apsTree2$tip.label), # number of tips
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rooted = TRUE, # we rooted the tree above,
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# and fungiTree is rooted anyway
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tip.label = apsTree2$tip.label, # use the apsTree2 labels
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br = NULL) # don't generate branch lengths since
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# fungiTree has none, so we can't
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# compare them anyway.
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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:
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set.seed(112358)
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N <- 10000 # takes about 15 seconds
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myTreeDistances <- matrix(numeric(N * 2), ncol = 2)
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colnames(myTreeDistances) <- c("symm", "path")
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for (i in 1:N) {
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xTree <- 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, ] <- treedist(fungiTree, xTree)
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}
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table(myTreeDistances[, "symm"])
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(symmObs <- 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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hist(myTreeDistances[, "path"],
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col = "aliceblue",
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main = "Distances of random Trees to fungiTree")
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(pathObs <- treedist(fungiTree, apsTree2)[2])
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abline(v = pathObs, col = "chartreuse")
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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 path diff. of observed tree is %1.4f\n",
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(sum(myTreeDistances[ , "path"] <= symmObs) + 1) / (N + 1)))
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# Indeed, our apsTree is _very_ much more similar to the species tree than
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# we would expect by random chance.
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# What do we gain from that analysis? Analyzing the tree we get from a single
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# gene of orthologous sequences is a positive control in our computational
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# experiment. If these genes are indeed orthologues, a correct tree-building
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# program ought to give us a tree that exactly matches the species tree.
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# Evaluating how far off we are from the known correct result gives us a way to
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# validate our workflow and our algorithm. If we can't get that right, we can't
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# expect to get "real" data right either. Employing such positive controls in
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# every computational experiment is essential for research. Not doing so is
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# Cargo Cult Bioinformatics.
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# [END]
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#
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#
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# TODO:
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#
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# Add MrBayes
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# https://cran.r-project.org/web/packages/phangorn/vignettes/IntertwiningTreesAndNetworks.html
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#
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# == DO NOT SIMPLY source() THIS FILE! =======================================
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#
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# ==============================================================================
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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 Calculating Trees 43
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#TOC> 1.1 PROMLPATH ... 64
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#TOC> 1.1.1 ... on the Mac 69
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#TOC> 1.1.2 ... on Windows 80
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#TOC> 1.1.3 ... on Linux 94
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#TOC> 1.1.4 Confirming PROMLPATH 99
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#TOC> 1.2 Building a maxiiimum likelihood tree 108
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#TOC>
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#TOC> ==========================================================================
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# = 1 Calculating Trees
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# = 1 Calculating Trees ===================================================
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# Follow the instructions found at phylip's home on the Web to install. If you
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# This will install RPhylip, as well as its dependency, the package "ape".
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# == 1.1 PROMLPATH ... =====================================================
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# The next part may be tricky. You will need to figure out where
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# on your computer Phylip has been installed and define the path
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# to the proml program that calculates a maximum-likelihood tree.
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# === 1.1.1 ... on the Mac
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# On the Mac, the standard installation places a phylip folder
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# in the /Applications directory. That folder contains all the
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# individual phylip programs as <name>.app files. These are not
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# directly to that subdirectory to find the program it needs:
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# PROMLPATH <- "/Applications/phylip-3.695/exe/proml.app/Contents/MacOS"
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# === 1.1.2 ... on Windows
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# On Windows you need to know where the rograms have been installed, and you
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# need to specify a path that is correct for the Windows OS. Find the folder
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# that is named "exe", and right-click to inspect its properties. The path
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# I have heard that your path must not contain spaces, and it is prudent to
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# avoid other special characters as well.
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# === 1.1.3 ... on Linux
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# If you are running Linux I trust you know what to do. It's probably
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# something like
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# PROMLPATH <- "/usr/local/phylip-3.695/bin"
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# === 1.1.4 Confirming PROMLPATH
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# Confirm that the settings are right.
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PROMLPATH # returns the path
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list.dirs(PROMLPATH) # returns the directories in that path
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@ -87,6 +106,7 @@ list.files(PROMLPATH) # lists the files [1] "proml" "proml.command"
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# If "proml" is NOT among the files that the last command returns, you
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# can't continue. Ask on the mailing list for advice.
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# == 1.2 Building a maxiiimum likelihood tree ==============================
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# Now read the mfa file you have saved in the BIB-PHYLO-Data_preparation unit,
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# as a "proseq" object with the read.protein() function of the RPhylip package:
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@ -94,11 +114,16 @@ apsIn <- read.protein("APSESphyloSet.mfa")
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# ... and you are ready to build a tree.
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# There are many fast options in PHYLIP - we will use the most _accurate_ one
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# that it has: proml, a maximum-likelihood tree building program for protein
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# data.
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# Building maximum-likelihood trees can eat as much computer time
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# as you can throw at it. Calculating a tree of 48 APSES domains
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# with default parameters of Rproml() runs for more than half a day
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# on my computer. But we have only twelve sequences here, so the
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# process will take us about 5 to 10 minutes.
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# process will take us about 5 to 10 minutes. Run this, and anjoy a good cup
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# of coffee while you are waiting.
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apsTree <- Rproml(apsIn, path=PROMLPATH)
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