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add cex examples

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hyginn 2020-10-12 09:18:10 +10:00
parent 0c395a1c83
commit 3bee83495f
1 changed files with 107 additions and 25 deletions
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plottingReference.R
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@ -20,7 +20,7 @@
#TOC> ========================================================================== #TOC> ==========================================================================
#TOC> #TOC>
#TOC> Section Title Line #TOC> Section Title Line
#TOC> ------------------------------------------------------------------ #TOC> ------------------------------------------------------------------
#TOC> 01 INITIALIZE 67 #TOC> 01 INITIALIZE 67
@ -49,18 +49,18 @@
#TOC> 08 LEGENDS 1054 #TOC> 08 LEGENDS 1054
#TOC> 08.1 basic legends 1057 #TOC> 08.1 basic legends 1057
#TOC> 08.2 Color bars 1061 #TOC> 08.2 Color bars 1061
#TOC> 09 LAYOUT 1168 #TOC> 09 LAYOUT 1180
#TOC> 10 TEXT 1203 #TOC> 10 TEXT 1215
#TOC> 11 DRAWING ON PLOTS 1224 #TOC> 11 DRAWING ON PLOTS 1243
#TOC> 12 IMAGES 1291 #TOC> 12 IMAGES 1310
#TOC> 13 CONTOUR LINES 1296 #TOC> 13 CONTOUR LINES 1315
#TOC> 14 3D PLOTS 1301 #TOC> 14 3D PLOTS 1320
#TOC> 15 GRAPHS AND NETWORKS 1306 #TOC> 15 GRAPHS AND NETWORKS 1325
#TOC> 16 OTHER GRPAHICS PACKAGES 1311 #TOC> 16 OTHER GRPAHICS PACKAGES 1330
#TOC> 17 INTERACTIVE PLOTS 1335 #TOC> 17 INTERACTIVE PLOTS 1354
#TOC> 17.1 locator() 1339 #TOC> 17.1 locator() 1358
#TOC> 17.2 plotly:: 1342 #TOC> 17.2 plotly:: 1361
#TOC> #TOC>
#TOC> ========================================================================== #TOC> ==========================================================================
@ -352,7 +352,7 @@ hist(SC$mdl$peaks, # gene expression first peaks
col = myPal(81)) # get 81 color values from myPal() col = myPal(81)) # get 81 color values from myPal()
# === 03.4.1 overlaying histograms # === 03.4.1 overlaying histograms
# Histograms can be plotted one-over another to compare them # Histograms can be plotted one-over another to compare them
# Example: when do genes with different GO term annotations first peak? # Example: when do genes with different GO term annotations first peak?
@ -498,7 +498,7 @@ points(t, SC$xpr[169, ], type = "b", cex = 0.7, col = "#EE5500CC")
plot(SC$xpr[169, ], SC$xpr[1124, ]) # these two profiles are anticorrelated plot(SC$xpr[169, ], SC$xpr[1124, ]) # these two profiles are anticorrelated
# = 05 ENCODING INFORMATION: SYMBOL, SIZE, COLOR ========================== # = 05 ENCODING INFORMATION: SYMBOL, SIZE, COLOuR =========================
# We have many options to encode information in scatter plots (and others), in order to be able to visualize and discover patterns and relationships. The most important ones are the type of symbol to use, its size, and its color. # We have many options to encode information in scatter plots (and others), in order to be able to visualize and discover patterns and relationships. The most important ones are the type of symbol to use, its size, and its color.
@ -679,7 +679,7 @@ legend ("bottom",
box.col = "#DDDDDD", box.col = "#DDDDDD",
bg = "#FFFFFF") bg = "#FFFFFF")
# === 05.1.1 Line types # === 05.1.1 Line types
# Basically all plots take arguments lty to define the line type, and lwd # Basically all plots take arguments lty to define the line type, and lwd
# to define line width # to define line width
@ -701,6 +701,68 @@ for (i in 1:10) {
text(6, y, paste("lwd = ", (0.3*i)^2), col="grey60", adj=0, cex=0.75) text(6, y, paste("lwd = ", (0.3*i)^2), col="grey60", adj=0, cex=0.75)
} }
# == 05.1 cex ("character expansion" size) =============================
# The size of characters can be controlled with the cex parameter. cex takes a
# vector of numbers, mapped to the vecors of plotted elements. The usual R
# conventions of vector recycling apply, so if cex s only a single number, it is
# applied to all plotted elements. Here is an example plotting expression
# amplitudes in a comparison of pre-replication vs. replication phase
# expression.
tPre <- c("t.0", "t.5", "t.10") # Pre replication-phase time points
tMid <- c("t.25", "t.30", "t.35") # Midway between replication and duplication
x <- rowMeans(SC$xpr[ , tPre]) # row means average out fluctuations
y <- rowMeans(SC$xpr[ , tMid])
plot(x, y)
myAmps <- SC$mdl$A # fetch the correlations of the fitted
# low correlations are not well modeled
# with a periodic function
hist(myAmps, breaks = 40) # examine the distribution
hist(log(myAmps), breaks = 40) # sometimes log() is be better suited
# to map to symbol size
# useful cex sizes are approximately between 0.5 and 5
# Example:
N <- 10
cexMin <- 0.5
cexMax <- 5.0
myCex <- seq(cexMax, cexMin, length.out=N) # Vector of cex values of length N
# We plot these points from largest to smallest, so we don't overplot
x1 <- runif(N)
y1 <- runif(N)
plot(x1, y1, xlim = 0:1, ylim = 0:1, xlab = "", ylab = "", pch = 16,
cex = myCex, col= colorRampPalette(c("#F4F0F8", "#3355DD"))(N))
# Note that the smallest cex at 0.5 is _really_ small.
points(x1[N], y1[N], cex = 3.0, col = "#FF000077") # Here it is!
# To rescale a vector into the desired interval (lo, up) we write a little
# function. First we normalize the vector into the interval (0, 1), then we
# multiply it by up-lo, and add lo.
#
reScale <- function(x, lo = 0, up = 1) {
return((((x-min(x)) / (max(x)-min(x))) * (up-lo)) + lo )
}
# with this, our coding amplitudes as plot character size becomes quite easy:
plot(x, y, pch=16,
cex = reScale(myAmps, lo=0.5, up=5), # <<<---- That's all we need
col="#4499BB22")
abline(a = 0, b = 1, col = "#DD333344")
abline(h = 0, col = "#33333344")
abline(v = 0, col = "#33333344")
# ToDo: add titles, scale legend, and interpretation
# = 06 COLOUR ============================================================= # = 06 COLOUR =============================================================
# Colour is immensely important for data visualization, and colours in R can be # Colour is immensely important for data visualization, and colours in R can be
@ -808,7 +870,7 @@ N <- 40
barplot(rep(1, N), col = col2hex(myCols,pal=TRUE,N=N) ) barplot(rep(1, N), col = col2hex(myCols,pal=TRUE,N=N) )
# === 06.3.1 Inbuilt palettes # === 06.3.1 Inbuilt palettes
# In R, a palette is a function (!) that takes a number as its argument and # In R, a palette is a function (!) that takes a number as its argument and
# returns that number of colors, those colors can then be used to color points # returns that number of colors, those colors can then be used to color points
@ -884,7 +946,7 @@ RColorBrewer::display.brewer.all(colorblindFriendly = TRUE)
plotPal(RColorBrewer::brewer.pal(11, "PuOr"), N = 11) plotPal(RColorBrewer::brewer.pal(11, "PuOr"), N = 11)
# === 06.3.2 Custom palettes # === 06.3.2 Custom palettes
# #
# Bespoke palettes are easily created with colorRampPalette(). The function # Bespoke palettes are easily created with colorRampPalette(). The function
# returns a palette, i.e. a function (not a vector of colors). You assign the # returns a palette, i.e. a function (not a vector of colors). You assign the
@ -934,7 +996,7 @@ plotPal(myOutliers)
# also an option to extract palettes from user-supplied images. # also an option to extract palettes from user-supplied images.
# === 06.3.3 Transparency: The Alpha Channel # === 06.3.3 Transparency: The Alpha Channel
# R colours are actually specified as quartets: the fourth value # R colours are actually specified as quartets: the fourth value
# the "Alpha channel" defines the transparency. Setting this to # the "Alpha channel" defines the transparency. Setting this to
@ -942,7 +1004,7 @@ plotPal(myOutliers)
# crowded plots, or for creating overlays. # crowded plots, or for creating overlays.
x <- SC$xpr[, "t.10"] # towards the begiining of the cycle x <- SC$xpr[, "t.10"] # towards the beginning of the cycle
y <- SC$xpr[, "t.50"] # towards the end of the cycle y <- SC$xpr[, "t.50"] # towards the end of the cycle
# compare: # compare:
@ -979,7 +1041,6 @@ abline(v = 0, col = "#FFFFFF", lwd = 0.5)
# ToDo: expand to "real" density plots (cf. ) # ToDo: expand to "real" density plots (cf. )
# == 06.4 abline(), lines() and segments() ================================ # == 06.4 abline(), lines() and segments() ================================
# lines() draws an arbitrary line from the supplied points # lines() draws an arbitrary line from the supplied points
@ -1065,8 +1126,8 @@ grid()
# Example: plotting expression levels in two cell-cycle phases against each other, and applying a divergent color spectrum that displays the model correlations: genes with low correlations have expression profiles that can not be well approximated with a periodic function. # Example: plotting expression levels in two cell-cycle phases against each other, and applying a divergent color spectrum that displays the model correlations: genes with low correlations have expression profiles that can not be well approximated with a periodic function.
tRep <- c("t.15", "t.20", "t.25", "t.30") # Replication-phase time points tRep <- c("t.15", "t.20", "t.25") # Replication-phase time points
tDup <- c("t.40", "t.45", "t.50", "t.55") # Duplication-phase time points tDup <- c("t.40", "t.45", "t.50") # Duplication-phase time points
x <- rowMeans(SC$xpr[ , tRep]) x <- rowMeans(SC$xpr[ , tRep])
y <- rowMeans(SC$xpr[ , tDup]) y <- rowMeans(SC$xpr[ , tDup])
plot(x, y) plot(x, y)
@ -1090,6 +1151,8 @@ hist(myCors,
N <- 10 # We'll map into N intervals N <- 10 # We'll map into N intervals
#$$$
# The usual way to scale a vector x to the [0,1] interval # The usual way to scale a vector x to the [0,1] interval
# is: x-min / max-min # is: x-min / max-min
idx <- (myCors - min(myCors)) / (max(myCors) - min(myCors)) idx <- (myCors - min(myCors)) / (max(myCors) - min(myCors))
@ -1119,7 +1182,9 @@ basePlot <- function() {
main = m, main = m,
xlab = "mean Expression in Replication phase", xlab = "mean Expression in Replication phase",
ylab = "mean Expression in Duplication phase", ylab = "mean Expression in Duplication phase",
sub = "Colour by Correlation to Fitted Periodic Function", sub = paste("Coloured by Correlation of",
"Expression Profile (xpr) to",
"Fitted Periodic Function (mdl)."),
cex.main = 0.9, cex.main = 0.9,
cex.lab = 0.7, cex.lab = 0.7,
cex.sub = 0.7, cex.sub = 0.7,
@ -1144,6 +1209,7 @@ corLabels <- sprintf("%4.2f", # craft the labels to plot
length.out = length(corCols))) length.out = length(corCols)))
oPar <- par(mar=c(5,4,4,7)) # more space on the margin oPar <- par(mar=c(5,4,4,7)) # more space on the margin
par("xpd" = FALSE)
basePlot() basePlot()
# we need to figure out where to put the legend. par("usr") gives us the coordinates of the last plot window. We'll space the legend between 1.05 and 1.1 times the plot width, and centred along the y-axis with a height of 0.8 times the plot height. # we need to figure out where to put the legend. par("usr") gives us the coordinates of the last plot window. We'll space the legend between 1.05 and 1.1 times the plot width, and centred along the y-axis with a height of 0.8 times the plot height.
@ -1158,12 +1224,21 @@ yt <- yb + (0.8 * dy) # top
plotrix::color.legend(xl, yb, xr, yt, plotrix::color.legend(xl, yb, xr, yt,
corLabels, corLabels,
corCols, corCols,
align = "rb", # labels to the right of the bar align = "rb", # labels to the right of the bar
cex = 0.8, cex = 0.8,
gradient = "y") # vertical gradient gradient = "y") # vertical gradient
# Almost perfect. Except we need a title for the color bar.
# in this case the title is "r:" ... but the "r" should be in italics.
par("xpd" = TRUE) # enable plotting into the margin
text(xr + (0.01 * dx), yt + 0.015 * dy,
expression(italic(r)["xpr,mdl"]),
adj = c(0, 0),
cex = 0.9)
par(oPar) par(oPar)
# ToDo - this is a common plot prototype - cast it into a function
# = 09 LAYOUT ============================================================= # = 09 LAYOUT =============================================================
@ -1203,6 +1278,7 @@ plot(x,y) # confirm reset
# = 10 TEXT =============================================================== # = 10 TEXT ===============================================================
# ToDo: sprintf()
# Plotting arbitrary text # Plotting arbitrary text
# use the text() function to plot characters and strings to coordinates # use the text() function to plot characters and strings to coordinates
@ -1218,6 +1294,12 @@ text(x3-0.4, y3, extra, col="slateblue", cex=0.75, vfont=c("serif", "plain"))
# ToDo: writing into the margin ... # ToDo: writing into the margin ...
# par("mar")
# par("xpd")
# par("srt")
#
# ToDo: plotmath() # demo(plotmath)
# ToDo: expression()