Maintenance. Use AACOLS.
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hyginn
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@ -1,19 +1,14 @@
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# tocID <- "BIN-SEQA-Composition.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-SEQA-Comparison 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 11 - 2019 01
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# Date: 2017-11 - 2020-09
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# 1.2 2020 Maintenance
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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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@ -38,14 +33,14 @@
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#TOC>
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#TOC> Section Title Line
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#TOC> ----------------------------------------------------------
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#TOC> 1 Preparation 47
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#TOC> 2 Aggregate properties 68
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#TOC> 3 Sequence Composition Enrichment 111
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#TOC> 3.1 Barplot, and side-by-side barplot 134
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#TOC> 3.2 Plotting ratios 169
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#TOC> 3.3 Plotting log ratios 185
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#TOC> 3.4 Sort by frequency 200
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#TOC> 3.5 Color by amino acid type 215
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#TOC> 1 Preparation 48
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#TOC> 2 Aggregate properties 69
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#TOC> 3 Sequence Composition Enrichment 113
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#TOC> 3.1 Barplot, and side-by-side barplot 136
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#TOC> 3.2 Plotting ratios 171
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#TOC> 3.3 Plotting log ratios 188
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#TOC> 3.4 Sort by frequency 204
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#TOC> 3.5 Color by amino acid type 221
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#TOC>
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#TOC> ==========================================================================
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@ -94,19 +89,20 @@ s <- unlist(s) # strsplit() returns a list! Why?
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seqinr::s2c(mySeq)
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seqinr::computePI(s2c(mySeq)) # isoelectric point
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seqinr::pmw(s2c(mySeq)) # molecular weight
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seqinr::AAstat(s2c(mySeq)) # This also plots the distribution of
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seqinr::computePI(seqinr::s2c(mySeq)) # isoelectric point
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seqinr::pmw(seqinr::s2c(mySeq)) # molecular weight
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seqinr::AAstat(seqinr::s2c(mySeq)) # This also plots the distribution of
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# values along the sequence
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# A true Labor of Love has gone into the
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# compilation of the "aaindex" data:
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?aaindex
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data(aaindex) # "attach" the dataset - i.e. make it accessible as an
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# R object
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?seqinr::aaindex
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data(aaindex, package = "seqinr") # "attach" the dataset - i.e. make it
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# accessible as an R object
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length(aaindex)
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length(aaindex) # no seqinr:: needed for the dataset since we just
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# "attached" it with data()
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# Here are all the index descriptions
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for (i in 1:length(aaindex)) {
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@ -133,7 +129,7 @@ refData # ... in %
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# tabulate the amino acid counts in mySeq
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(obsData <- table(s2c(mySeq))) # counts
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(obsData <- table(seqinr::s2c(mySeq))) # counts
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(obsData <- 100 * (obsData / sum(obsData))) # frequencies
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@ -180,12 +176,13 @@ legend (x = 1, y = 12,
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barplot(obsData / refData,
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col = "#CCCCCC",
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ylab = "Sequence / Average",
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ylim = c(0, 2.5),
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cex.names = 0.7)
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abline(h = 1, col="#BB0000")
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abline(h = c(1/3, 3), lty = 2, col="#BB000055")
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abline(h = c(1/2, 2), lty = 2, col="#BB000055")
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# ... but ratios are not very good here, since the difference in height on the
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# plot now depends on the order we compare in: ratios of 1/3 and 3 (dotted
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# plot now depends on the order we compare in: ratios of 1/2 and 2 (dotted
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# lines) are exactly the same fold-difference !
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# == 3.3 Plotting log ratios ===============================================
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@ -196,69 +193,53 @@ abline(h = c(1/3, 3), lty = 2, col="#BB000055")
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barplot(log(obsData / refData),
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col = "#CCCCCC",
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ylab = "log(Sequence / Average)",
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ylim = log(c(1/3, 3)),
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cex.names = 0.7)
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abline(h = log(1), col="#BB0000")
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abline(h = log(c(1/3, 3)), lty = 2, col="#BB000055")
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abline(h = log(c(1/2, 2)), lty = 2, col="#BB000055")
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# Note how the three-fold difference lines are now the same distance from the
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# Note how the two-fold difference lines are now the same distance from the
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# line of equal ratio.
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# == 3.4 Sort by frequency =================================================
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barplot(sort(log(obsData / refData), decreasing = TRUE),
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ylim = c(-3.5,2),
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ylim = log(c(1/3, 3)),
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col = "#CCCCCC",
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ylab = "log(Sequence / Average)",
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cex.names = 0.7)
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abline(h = log(1), col="#BB0000")
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abline(h = log(c(1/3, 3)), lty = 2, col="#BB000055")
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abline(h = log(c(1/2, 2)), lty = 2, col="#BB000055")
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arrows(4, 1.8, 0, 1.8, length = 0.07)
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text(5.5, 1.8, "Enriched", cex = 0.7)
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arrows(20, 1.8, 24, 1.8, length = 0.07)
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text(19.5, 1.8, "Depleted", pos = 2, cex = 0.7)
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yTxt <- log(0.9)
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arrows(4, yTxt, 0, yTxt, length = 0.07)
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text(5.5, yTxt, "Enriched", cex = 0.7)
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yTxt <- log(1.1)
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arrows(20, yTxt, 24, yTxt, length = 0.07)
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text(19.5, yTxt, "Depleted", pos = 2, cex = 0.7)
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# == 3.5 Color by amino acid type ==========================================
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# color the bars by type.
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# define colors
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AAcol <- character()
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AAcol["A"] <- "#AABBAA"
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AAcol["C"] <- "#FFEE77"
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AAcol["D"] <- "#DD6600"
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AAcol["E"] <- "#DD3300"
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AAcol["F"] <- "#767D38"
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AAcol["G"] <- "#BBBBCC"
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AAcol["H"] <- "#A2A1FD"
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AAcol["I"] <- "#70B6C6"
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AAcol["K"] <- "#4563BB"
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AAcol["L"] <- "#80C6B6"
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AAcol["M"] <- "#AFCC34"
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AAcol["N"] <- "#BB88CC"
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AAcol["P"] <- "#7292B7"
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AAcol["Q"] <- "#8866BB"
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AAcol["R"] <- "#74A0FF"
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AAcol["S"] <- "#9999CC"
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AAcol["T"] <- "#99AADD"
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AAcol["V"] <- "#9DB500"
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AAcol["W"] <- "#76AD48"
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AAcol["Y"] <- "#44CA97"
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# Color the bars by amino acid type. Use AACOLS , defined in the .utilities.R
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# script, or define your own.
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barplot(rep(1, 20), names.arg = names(AAcol), col = AAcol, cex.names = 0.5)
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barplot(rep(1, 20), names.arg = names(AACOLS), col = AACOLS, cex.names = 0.5)
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lR <- sort(log(obsData / refData), decreasing = TRUE)
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barplot(lR,
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ylim = c(-3.5,2),
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col = AAcol[names(lR)],
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ylim = log(c(1/3, 3)),
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col = AACOLS[names(lR)],
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ylab = "log(Sequence / Average)",
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cex.names = 0.7)
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abline(h = log(1), col="#00000055")
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abline(h = log(c(1/3, 3)), lty = 2, col="#00000033")
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abline(h = log(c(1/2, 2)), lty = 2, col="#00000033")
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arrows(4, 1.8, 0, 1.8, length = 0.07)
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text(5.5, 1.8, "Enriched", cex = 0.7)
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arrows(20, 1.8, 24, 1.8, length = 0.07)
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text(19.5, 1.8, "Depleted", pos = 2, cex = 0.7)
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yTxt <- log(0.9)
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arrows(4, yTxt, 0, yTxt, length = 0.07)
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text(5.5, yTxt, "Enriched", cex = 0.7)
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yTxt <- log(1.1)
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arrows(20, yTxt, 24, yTxt, length = 0.07)
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text(19.5, yTxt, "Depleted", pos = 2, cex = 0.7)
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# Task:
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