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BIN-MYSPE.R
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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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# ==============================================================================
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#
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# S T O P :
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# =========
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# Version: 1.3
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#
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# 2021
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# UPDATE WARNING!
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# ---------------
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#
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# This file has not yet been updated for coursework. You may inspect it, but
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# do NOT use it for actual coursework as long as this warning is here. Parts
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# of the code and data will change, and if you use this outdated code it will
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# break your setup and workflow.
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#
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# ==============================================================================
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#
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#
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# Version: 1.2
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#
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# Date: 2020-10-01
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# Date: 2017-09 - 2021-09
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# V 1.3 2021 update of MYSPE mechanics; fix a bug no one had complained about
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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 1.0 Final code, after rewriting BLAST parser and updating MYSPElist
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# V 0.1 First code copied from BCH441_A03_makeMYSPElist.R
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#
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# TODO:
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# TODO: Sample solution for sankey plot function.
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#
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#
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# == HOW TO WORK WITH LEARNING UNIT FILES ======================================
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@ -51,14 +36,14 @@
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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> 1 PREPARATIONS 51
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#TOC> 2 SUITABLE MYSPE SPECIES 63
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#TOC> 3 ADOPT "MYSPE" 87
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#TOC> 4 FURTHER READING: PLOTTING PROPORTIONS 126
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#TOC> 4.1 Percentages 144
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#TOC> 4.2 Visualizing proportions: Pie chart 163
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#TOC> 4.3 Visualizing proportions: Nested squares 241
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#TOC> 4.4 Visualizing proportions: Sankey diagrams 278
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#TOC>
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#TOC> ==========================================================================
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@ -112,8 +97,8 @@ if (! exists("myStudentNumber")) {
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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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# and the taxonomy ID of its genome-sequenced strain. This is your unique
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# speciesfor this course. Note it in your journal ...
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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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@ -177,7 +162,8 @@ cat(sprintf("\n%s comprise %5.2f%% of fungi (%d of %d).",
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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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# Often, we will use a pie chart instead. Pie charts are rather informal types
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# 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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@ -189,7 +175,7 @@ oPar <- par(mar = c(0.1, 0.1, 2.5, 0.1)) # set margins to ~ 0
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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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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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@ -227,7 +213,7 @@ 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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sel <- myOr == 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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@ -305,7 +291,14 @@ if (! requireNamespace("plotly")) {
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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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# Here, we use the plotly package that wraps a very well developed javascript
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# library with many options for interactive plots. I am producing this plot
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# hard-coded for the sample organism "Sporothrix schenkii"; you would need
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# to change the code to adapt it to your own MYSPE - or even build a function
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# for this. Do try this if you have a bit of coding experience, sankey diagrams
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# are a good way to show hierarchical data relations - and if you get this
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# working for your own organism you can be proud that you have understood
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# how preparing the data works.
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myNodes <- list(label = c("Fungi (1014)", # 0 <- node ID
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