Reorganized proportional plots into a "further reading" section; added nested-box, and sankey plot visualization of proportions. Introduced plotly.
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BIN-MYSPE.R
307
BIN-MYSPE.R
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# Purpose: A Bioinformatics Course:
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# R code accompanying the BIN-MYSPE 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: 2020-09-18
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# Date: 2020-10-01
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# V 1.2 Reorganized proportional plot section into a "further reading"
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# section, added nested-box, and sankey plot visualization of
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# proportions. Introduced plotly.
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# V 1.1 2020 Workflow changes
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# V 1.0.1 Move ABC-makeMYSPElist.R to ./scripts directory
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# V 1.0 Final code, after rewriting BLAST parser and updating MYSPElist
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@ -28,17 +31,22 @@
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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 47
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#TOC> 2 Suitable MYSPE Species 59
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#TOC> 3 Adopt "MYSPE" 83
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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 49
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#TOC> 2 SUITABLE MYSPE SPECIES 61
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#TOC> 3 ADOPT "MYSPE" 85
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#TOC> 4 FURTHER READING: PLOTTING PROPORTIONS 124
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#TOC> 4.1 Percentages 142
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#TOC> 4.2 Visualizing proportions: Pie chart 161
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#TOC> 4.3 Visualizing proportions: Nested squares 238
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#TOC> 4.4 Visualizing proportions: Sankey diagrams 275
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#TOC>
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#TOC> ==========================================================================
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# = 1 Preparations ========================================================
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# = 1 PREPARATIONS ========================================================
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#
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# Execute the two conditionals below:
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@ -50,12 +58,12 @@ if (! exists("myStudentNumber")) {
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}
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# = 2 Suitable MYSPE Species ==============================================
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# = 2 SUITABLE MYSPE SPECIES ==============================================
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# In this unit we will select one species from a list of genome sequenced fungi
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# and write it into your personalized profile file. This species will be called
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# "MYSPE" (Your Favourite Organism) for other learning units and exercises.
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# "MYSPE" (My Species) for other learning units and exercises.
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# A detailed description of the process of compiling the list of genome
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# sequenced fungi with protein annotations and Mbp1 homologues is in the file
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@ -74,69 +82,258 @@ if (! exists("myStudentNumber")) {
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# implemented in practice.
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# = 3 Adopt "MYSPE" =======================================================
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# = 3 ADOPT "MYSPE" =======================================================
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# Execute:
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( MYSPE <- getMYSPE(myStudentNumber) )
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# If this produced an error, this session has not been properly set up. You
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# may not yet have run init() and edited .myProfile.R , or that file is not
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# in your myScripts/ folder. Fix this, and execute source(".Rprofile") .
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# in your myScripts/ folder. Fix this, and execute:
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#
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# source(".Rprofile") .
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# If this produced NA, your Student Number may not be correct, or you are not
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# in my class-list. Contact me.
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# Otherwise, this should have printed a species name. Your unique species
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# for this course.
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# If this produced NA, your Student Number may not be correct, or you are not in
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# my class-list. Contact me. Otherwise, this should have printed a species name,
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# and the taxonomy ID of its genome-sequenced strain. This is your unique species
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# for this course. Note it in your journal ...
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biCode(MYSPE) # and what is it's "BiCode" ... ?
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biCode(MYSPE) # and also note it's "BiCode" ...
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( myTaxID <- names(MYSPE) ) # and its taxID
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# Task: Note down the species name and its five letter BiCode on your Student
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# Wiki user page. Use this species whenever this or future assignments refer
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# to MYSPE. Whenever you start a session, it will automatically be loaded
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# from myScripts/.myProfile.R and is available as MYSPE .
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# Here is some more information:
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# Task:
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# =====
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# Note down the species name and its five letter BiCode on your Student
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# Wiki user page. Use this species whenever this or future assignments refer
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# to MYSPE. Whenever you start a session, it will automatically be loaded
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# from myScripts/.myProfile.R and is available as MYSPE .
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# Here is some more information about MYSPE, taken from the table of genome-
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# sequenced fungi that is in your ./data folder.
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fungiDat <- read.csv("data/Species.csv")
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iMs <- which(fungiDat$Taxon.ID == myTaxID)
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# number of sequenced fungal genomes:
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nrow(fungiDat)
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( myOr <- fungiDat$Classification[iMs] ) # Taxonomic order
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( myGn <- gsub("\\s.*", "", MYSPE)) # Taxonomic genus
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( mySt <- fungiDat$Name[iMs] ) # Taxonomic strain
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# sequenced genomes of species:
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sel <- MYSPE == gsub("^(\\S+\\s\\S+).*$", "\\1", fungiDat$Name)
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( x <- fungiDat[sel, "Name"] )
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Nspc <- length(x) # save this for later ...
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# That's all.
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# sequenced genomes of genus:
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sel <- gsub("\\s.*", "", MYSPE) == gsub("\\s.*", "", fungiDat$Name)
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( x <- fungiDat[sel, "Name"] )
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Ngen <- length(x) - Nspc
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# order:
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( x <- unique(fungiDat[sel, "Classification"]) )
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Nord <- sum(fungiDat$Classification == x) - Ngen - Nspc
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Nfng <- nrow(fungiDat) - Nord - Ngen - Nspc
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# = 4 FURTHER READING: PLOTTING PROPORTIONS ===============================
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# proportions
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# The material below is an exploration of data-preparation and plotting
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# techniques; you can treat this as additional practice and further reading and
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# I expect that some of the code and plotting examples may be useful in a
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# different context.
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# A frequent task is to visualize the proportion of elements with given
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# categories in a sample. For example, we might ask what the proportion of the
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# different orders of fungi is the order of MYSPE? Let's first collect the
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# numbers.
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( nFungi <- nrow(fungiDat) ) # sequenced fungi
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( nOrder <- sum(grepl(myOr, fungiDat$Classification)) ) # same order as MYSPE
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( nGenus <- sum(grepl(myGn, fungiDat$Name)) ) # same genus as MYSPE
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( nSpecies <- sum(grepl(MYSPE, fungiDat$Name)) ) # same species as MYSPE
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# == 4.1 Percentages =======================================================
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# The zeroth-order approach to visualization is simply to print percentages:
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cat(sprintf("\n%s comprise %5.2f%% of fungi.",
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myOr,
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(nOrder * 100) / nFungi))
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# ... or, adding the actual numbers:
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cat(sprintf("\n%s comprise %5.2f%% of fungi (%d of %d).",
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myOr,
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(nOrder * 100) / nFungi,
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nOrder,
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nFungi))
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# But that's hard to visualize for most of us, and anyway, we don't know how
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# that relates to other orders.
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# == 4.2 Visualizing proportions: Pie chart ================================
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# Often, we will use a pie chart instead. Pie charts are rather informal types of plots, not well suited for analysis. But easy to do:
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# Define four colors to identify the four categories
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pCol <- c("#ed394e", "#ff9582", "#ffd5c4", "#f2f2f0")
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oPar <- par(mar = c(0.1, 0.1, 2.5, 0.1))
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pie(c(Nspc, Ngen, Nord, Nfng),
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labels = "",
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oPar <- par(mar = c(0.1, 0.1, 2.5, 0.1)) # set margins to ~ 0
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# and remember the
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# previous setting
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pie(c(nSpecies, # subtract numbers since these
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nGenus - nSpecies, # categories are mutually contained
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nOrder - nGenus - nSpecies, # in each other
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nFungi - nOrder - nGenus -nSpecies),
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labels = "",
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radius = 0.9,
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main = "MYSPE in genome-sequenced fungi",
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lty = 0, # turn borders for wedges off
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col = pCol,
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clockwise = TRUE,
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init.angle = 90)
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title(main=MYSPE, line=0, cex.main=0.7) # add a title to the plot
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legend(x = 0.95, y = 0.8, # place at legend here
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legend = c("Species", "Genus", "Order", "Fungi"),
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y.intersp = 2, # line spacing for labels
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cex = 0.8, # character size for labels
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bty = "n", # "no" box around the legend
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pt.cex = 2, # size of colour boxes
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pch = 15, # a filled square
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col = pCol)
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par(oPar) # reset graphics state
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# Unless MYSPE is one of the frequently sequenced species, there will only be a
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# very thin wedge visible. Pie charts are not well suited to visualize small
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# proportions.
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# It is a little more useful if we have non-nested proportions - like the
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# number of species in the same order overall:
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myTbl <- sort(table(fungiDat$Classification), decreasing = TRUE)
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head(myTbl)
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# pie() does a reasonable job out of the box to interpret table() data:
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pie(myTbl)
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# ... we can improve this quickly with a bit of tweaking:
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N <- length(myTbl)
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sel <- myOrder == names(myTbl) # TRUE for the MYSPE order, FALSE elsewhere
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myCol <- rep(pCol[4], N) # N elements of pCol[1]
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myCol[sel] <- pCol[1] # replace this one color
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myLbl <- rep("", N) # N labels of ""
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myLbl[sel] <- myOr # replace this one label with the MYSPE order
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oPar <- par(mar = c(0.1, 0.1, 2.5, 0.1)) # set margins to ~ 0
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pie(myTbl,
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labels = myLbl,
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radius = 0.9,
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main = "MYSPE in genome-sequenced fungi",
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lty = 0, # no borders for wedges
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col = pCol,
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main = "MYSPE order",
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border = "#DDDDDD",
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col = myCol,
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clockwise = TRUE,
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init.angle = 90)
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title(main = MYSPE, line = 0, cex.main = 0.7)
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legend(x = 0.95, y = 0.8, # position
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legend = c("Species", "Genus", "Order", "Fungi"),
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y.intersp = 2, # line spacing for labels
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cex = 0.8, # character size for labels
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bty = "n", # "no" box around the legend
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pt.cex = 2, # size of colour boxes
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pch = 15,
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col = pCol)
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par(oPar)
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par(oPar) # reset graphics state
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# But the overall problem remains.
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# == 4.3 Visualizing proportions: Nested squares ===========================
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# A simple alternative is to draw such proportions as nested squares:
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x <- sqrt(nFungi)
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# set margins to ~ 0 and type to square
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oPar <- par(mar = c(0.1, 0.1, 0.1, 0.1), pty = "s")
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# empty, square plot
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plot(c(0, x), c(0, x), xlim = c(0, x), ylim = c(0, x),
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type="n", axes=FALSE, xlab="", ylab="")
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# basic square for all genomes
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rect(0, 0, x, x, col = pCol[4])
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# grid
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u <- 0:floor(x)
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N <- length(u)
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segments(rep(0, N), u, rep(x, N), u, col = "#0000FF18")
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segments(u, rep(0, N), u, rep(x, N), col = "#0000FF18")
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# each square on this grid is one genome
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# colored squares
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rect(0, 0, sqrt(nOrder), sqrt(nOrder), col = pCol[3])
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rect(0, 0, sqrt(nGenus), sqrt(nGenus), col = pCol[2])
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rect(0, 0, sqrt(nSpecies), sqrt(nSpecies), col = pCol[1])
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# labels
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text(x/2, x/2, "Fungi")
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text(x * 0.08, x * 0.11, myOr, pos = 4, cex = 0.9)
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text(x * 0.08, x * 0.06, myGn, pos = 4, cex = 0.8)
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text(x * 0.08, x * 0.02, MYSPE, pos = 4, cex = 0.7)
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par(oPar) # reset graphics state
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# == 4.4 Visualizing proportions: Sankey diagrams ==========================
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# Sankey diagrams are an excellent way to visualize complicated nested
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# proportions and their changes (see here for example:
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# https://www.r-graph-gallery.com/sankey-diagram.html). Here is a very simple
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# example with the MYSPE proportions, as an illustration of the plotting
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# principle.
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if (! requireNamespace("plotly")) {
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install.packages("plotly")
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}
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# Package information:
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# library(help = plotly) # basic information
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# browseVignettes("plotly") # available vignettes
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# data(package = "plotly") # available datasets
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# Here, we use the plotly package that wraps a very well developed javascript library with many options for interactive plots.
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myNodes <- list(label = c("Fungi (1014)", # 0 <- node ID
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"Ophiostomatales (6)", # 1
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"Other...", # 2
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"Sporothrix (4)", # 3
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"Other...", # 4
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"Sporothrix schenckii (2)", # 5
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"Other..." # 6
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),
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x = c(0.1, 0.4, 0.4, 0.7, 0.7, 1.0, 1.0),
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y = c(0.3, 0.1, 0.7, 0.2, 0.7, 0.3, 0.7),
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color = c("#f2f2f0", #
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"#ffd5c4",
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"#CCCCCC",
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"#ff9582",
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"#CCCCCC",
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"#ed394e",
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"#CCCCCC"
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),
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pad = 15,
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thickness = 20,
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line = list(color = "black",
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width = 0.5))
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myLinks <- list(source = c(0, 0, 1, 1, 3, 3), # i.e. there is a link of
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target = c(1, 2, 3, 4, 5, 6), # weight 6 between node 0
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value = c(6, 18, 4, 2, 2, 2)) # and node 1
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# Setting up the actual plot ...
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fig <- plotly::plot_ly(type = "sankey",
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arrangement = "snap",
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orientation = "h",
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node = myNodes,
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link = myLinks)
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# Adding and adjusting a few layout parameters
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fig <- plotly::layout(fig,
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title = "Fungi Genomes - Classification",
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font = list(size = 10))
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fig # plot the diagram
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# Note that the plot appears in the Viewer window, not the Plot window, and that
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# it is interactive: you can hover over nodes and links, and drag the nodes
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# around.
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# [END]
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