Add text to points on plot

Description

This function allows to add custom text as lables to points on standard xy-plot.

Usage

.addTextToPoints(
 x,
 paramLst = NULL,
 labels = NULL,
 cex = NULL,
 col = NULL,
 adj = "auto",
 callFrom = NULL,
 silent = TRUE,
 debug = FALSE
)

Arguments

Value

This function make a plot and may retiurn an optional matrix of outlyer-data (depending on argument getOutL)

See Also

prcomp (used here for the PCA underneith) , princomp , see the package FactoMineR for multiple plotting options or ways of combining categorical and numeric data

Examples

set.seed(2019); dat1 <- matrix(round(runif(30),2), ncol=2)
plot(dat1) # traditional plot
.addTextToPoints(dat1, labels=letters[1:nrow(dat1)])

Search best corner of plot for placing for legend This function aims to find best corner for plotting a legend.

Description

Search best corner of plot for placing for legend

This function aims to find best corner for plotting a legend.

Usage

.bestLegendLoc(
 dat,
 txtLen,
 txtHi,
 displayPlotSearch = FALSE,
 silent = TRUE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Value

numeric vector with counts of umber of points expected to enter legend-location for each corner (ie legend-localization)

See Also

checkForLegLoc , legend

Examples

dat1 <- matrix(c(1:5,1,1:5,5), ncol=2)
(legLoc <- .bestLegendLoc(dat1, txtLen=0.4, txtHi=28))

Colors based on p-Values

Description

This function helps defining color-gradient based on p-Values. This fuction requires package RColorBrewer being installed

Usage

.colorByPvalue(
 x,
 br = NULL,
 col = NULL,
 asIndex = FALSE,
 silent = FALSE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Value

This function retruns a color-gradient based on p-Values

See Also

(for PCA) plotPCAw )

Examples

.colorByPvalue((1:10)/10)

Estimate size/distance of margin to edge of image (png) in pixels

Description

This function allows estimating size/distance of margin to edge of image (png) in pixel and return numeric vector.

Usage

.determFigMargPix(marg, res, callFrom = NULL, silent = FALSE, debug = FALSE)

Arguments

Value

This function returns a numeric vetctor matrix with (estimated) distance to figure margins in pixels

See Also

convertPlotCoordPix , mouseOverHtmlFile

Examples

.determFigMargPix(c(5,4,4,2),100)

Transform levels into index

Description

This function transforms levels into index. This function has been depreciated, please use wrMisc::levIndex() insted.

Usage

.levIndex(dat, asSortedLevNa = FALSE)

Arguments

Value

This function retruns a color-gradient based on p-Values

See Also

(for PCA) plotPCAw )

Examples

library(wrMisc)

Predict and return pixel location of points of current plot

Description

This function allows predicting the pixel location of points of current plot. Note: may be imprecise in case of x or y with all same values.

Usage

.predPointsPix(
 x,
 y,
 dimPng,
 res,
 marg,
 fromTop = TRUE,
 scExt = 0.04,
 displ = FALSE,
 callFrom = NULL,
 silent = FALSE,
 debug = FALSE
)

Arguments

Value

This function returns a numeric matrix with 2 columns 'xPix' and 'yPix' (with length(x) rows); and optionally a plot (if argument displ=TRUE)

See Also

convertPlotCoordPix , mouseOverHtmlFile

Examples

.predPointsPix(x=c(1,100), y=c(1,100), dimPng=c(700,600), res=200, marg=c(5,4,4,2))

Locate sample index from index or name of pair-wise comparisons

Description

This function helps locating sample index from index or name of pair-wise comparisons

Usage

.sampNoDeMArrayLM(
 MArrayObj,
 useComp,
 groupSep = "-",
 lstMeans = "means",
 lstP = "BH",
 silent = FALSE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Value

This function returns a integer vector of indexes

See Also

(for PCA) plotPCAw )

Examples

aa <- 1:5

Search Column Name This function provides help when seraching column names

Description

Search Column Name

This function provides help when seraching column names

Usage

.serachColName(
 x,
 searchColNa,
 plusLowerCaps = TRUE,
 returnList = TRUE,
 silent = FALSE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Value

integer vector with index of colnames found or list with $foundNa and $remainNa

See Also

convertPlotCoordPix ; use htmlSpecCharConv to convert special characters for proper html display

Examples

mat1 <- matrix(1:6, ncol=3, dimnames=list(NULL, LETTERS[1:3]))
.serachColName(mat1, c("C","F","A"))

MA-plot (differential intensity versus average intensity)

Description

This type of plot for display of relative changes versus (mean) absolute abundance is very common in high-throughput biology, see MA-plot. Basically one compares two independent series of measures (ie gene transcript or protein abundance values) of 2 samples/data-sets or the means of 2 groups of replicates. And the log-fold-change ('Minus'=M) is plotted againts the absolute mean value ('Average'=A). Furthermore, output from statistical testing by moderTest2grp or moderTestXgrp can be directly read to produce MA plots for diagnostic purpose. Please note, that plotting a very high number of points in transparency (eg >10000) may take several seconds.

Usage

MAplotW(
 Mvalue,
 Avalue = NULL,
 useComp = 1,
 filtFin = NULL,
 ProjNa = NULL,
 FCthrs = NULL,
 subTxt = NULL,
 grayIncrem = TRUE,
 col = NULL,
 pch = 16,
 compNa = NULL,
 batchFig = FALSE,
 cexMa = 1.8,
 cexLa = 1.1,
 limM = NULL,
 limA = NULL,
 annotColumn = c("SpecType", "GeneName", "EntryName", "Accession", "Species", "Contam"),
 annColor = NULL,
 cexPt = NULL,
 cexSub = NULL,
 cexTxLab = 0.7,
 namesNBest = NULL,
 NbestCol = 1,
 NaSpecTypeAsContam = TRUE,
 useMar = c(6.2, 4, 4, 2),
 returnData = FALSE,
 callFrom = NULL,
 silent = FALSE,
 debug = FALSE
)

Arguments

Value

This function plots an MA-plot (to the current graphical device); if returnData=TRUE, a data.frame with ($ID, $Mvalue, $Avalue, $FDRvalue, $passFilt) gets returned

See Also

(for PCA) plotPCAw

Examples

library(wrMisc)
set.seed(2005); mat <- matrix(round(runif(600),2), ncol=6)
rownames(mat) <- c(rep(letters[1:25],each=3), letters[2:26])
MAplotW(mat[,2] -mat[,1], A=rowMeans(mat))
## assume 2 groups with 3 samples each
matMeans <- rowGrpMeans(mat, gr=gl(2,3,labels=LETTERS[3:4]))
MAplotW(M=matMeans[,2] -matMeans[,1], A=matMeans) 
## assume 2 groups with 3 samples each and run moderated t-test (from package 'limma')
tRes <- moderTest2grp(mat, gl(2,3))
MAplotW(tRes$Mval, tRes$Amean) 
MAplotW(M=tRes$Mval, A=tRes$means, FCth=1.3) 
MAplotW(tRes)
MAplotW(tRes, limM=c(-2,2), FCth=1.3) 

Volcano-Plot (Statistical Test Outcome versus Relative Change)

Description

This type of plot is very common in high-throughput biology, see Volcano-plot. Basically, this plot allows comparing the outcome of a statistical test to the relative change (ie log fold-change, M-value).

Usage

VolcanoPlotW(
 Mvalue,
 pValue = NULL,
 useComp = 1,
 filtFin = NULL,
 tit = NULL,
 ProjNa = NULL,
 FCthrs = NULL,
 FdrList = NULL,
 FdrThrs = NULL,
 FdrType = NULL,
 subTxt = NULL,
 grayIncrem = TRUE,
 col = NULL,
 pch = 16,
 compNa = NULL,
 batchFig = FALSE,
 cexMa = 1.8,
 cexLa = 1.1,
 limM = NULL,
 limp = NULL,
 annotColumn = c("SpecType", "GeneName", "EntryName", "Accession", "Species", "Contam"),
 annColor = NULL,
 expFCarrow = FALSE,
 cexPt = NULL,
 cexSub = NULL,
 cexTxLab = 0.7,
 namesNBest = NULL,
 NbestCol = 1,
 sortLeg = "descend",
 NaSpecTypeAsContam = TRUE,
 useMar = c(6.2, 4, 4, 2),
 returnData = FALSE,
 callFrom = NULL,
 silent = FALSE,
 debug = FALSE
)

Arguments

Details

In high-throughput biology data are typically already transformed to log2 and thus, the 'M'-values (obtained by subtrating two group means) represent a relative change. Output from statistical testing by moderTest2grp or moderTestXgrp can be directly read to produce Volcano plots for diagnostic reasons. Please note, that plotting a very high number of points (eg >10000) in transparency may take several seconds.

Value

This function simply plots an MA-plot (to the current graphical device), if returnData=TRUE an optional data.frame with (ID, Mvalue, pValue, FDRvalue, passFilt) can be returned

See Also

(for PCA) plotPCAw )

Examples

library(wrMisc)
set.seed(2005); mat <- matrix(round(runif(900),2), ncol=9)
rownames(mat) <- paste0(rep(letters[1:25], each=4), rep(letters[2:26],4))
mat[1:50,4:6] <- mat[1:50,4:6] + rep(c(-1,1)*0.1,25)
mat[3:7,4:9] <- mat[3:7,4:9] + 0.7
mat[11:15,1:6] <- mat[11:15,1:6] - 0.7
## assume 2 groups with 3 samples each
gr3 <- gl(3, 3, labels=c("C","A","B"))
tRes2 <- moderTest2grp(mat[,1:6], gl(2,3), addResults = c("FDR","means"))
# Note: due to the small number of lines only FDR chosen to calculate
VolcanoPlotW(tRes2)
## Add names of points passing custom filters
VolcanoPlotW(tRes2, FCth=1.3, FdrThrs=0.2, namesNBest="passThr")
## assume 3 groups with 3 samples each
tRes <- moderTestXgrp(mat, gr3, addResults = c("FDR","means"))
# Note: due to the small number of lines only FDR chosen to calculate
VolcanoPlotW(tRes)
VolcanoPlotW(tRes, FCth=1.3, FdrThrs=0.2)
VolcanoPlotW(tRes, FCth=1.3, FdrThrs=0.2, useComp=2)

Add bagplot to existing plot

Description

This function adds a bagplot on an existing (scatter-)plot allowing to highlight the central area of the data. Briefly, a bagplot is a bivariate boxplot, see Bagplot, following the basic idea of a boxplot in two dimensions. Of course, multimodal distributions - if not separated first - may likely lead to mis-interpretation, similarly as it is known for interpreting boxplots. If a group of data consists only of 2 data-points, they will be conected using a straight line. It is recommended using transparent colors to highlight the core part of a group (if only 2 points are available, they will be conected using a straight line), in addition, one could use the option to re-plot all (non-outlyer) points (arguments reCol, rePch and reCex must be used).

Usage

addBagPlot(
 x,
 lev1 = 0.5,
 outCoef = 2,
 bagCol = NULL,
 bagCont = bagCol,
 bagLwd = 1.5,
 nCore = 4,
 outlCol = NULL,
 outlPch = NULL,
 outlCex = 0.6,
 reCol = NULL,
 rePch = NULL,
 reCex = NULL,
 ctrPch = NULL,
 ctrCol = NULL,
 ctrCex = NULL,
 addSubTi = TRUE,
 returnOutL = FALSE,
 silent = TRUE,
 callFrom = NULL,
 debug = FALSE
)

Arguments

Details

The outlyer detection works similar to the one used in boxplot: The distance of a given point is compared to the median distance of all points to their respective group-center plus the 25 - 75 quantile-distance (of all points) times the multiplicative factor of argument outCoef.

Value

This function returns primarily a plot, optionally it may return of matrix with outlyers (if argument returnOutL=TRUE)

See Also

plotPCAw , princomp

Examples

set.seed(2020); dat1 <- matrix(round(rnorm(2000),3),ncol=2); rownames(dat1) <- 1:nrow(dat1)
dat1 <- dat1 + 5*matrix(rep(c(0,1,1,0,0,0,1,1),nrow(dat1)/4), byrow=TRUE, ncol=2)
col1 <- rgb(red=c(153,90,203,255), green=c(143,195,211,125), blue=c(204,186,78,115),
 alpha=90, maxColorValue=255)
## suppose we know the repartition into 4 subgroups which we would like to highlight them
grp1 <- rep(1:4, nrow(dat1)/4)
plot(dat1, col=grey(0.8), xlab="x", ylab="y", las=1, pch=grp1)
for(i in 1:4) addBagPlot(dat1[which(grp1==i),], bagCol=col1[i])
## slightly improved
if(requireNamespace("wrMisc", quietly=TRUE)) {library(wrMisc); 
 col2 <- convColorToTransp(col1, 255)} else col2 <- col1
plot(dat1, col=grey(0.8), xlab="x", ylab="y", las=1, pch=grp1)
for(i in 1:4) addBagPlot(dat1[which(grp1==i),], bagCol=col1[i], outlPch=i,
 outlCol=col2[i], bagLwd=3)

Find best place on plot for placing legend

Description

This function tries to find the best location for placing a legend of a bivariate plot, ie scatter-plot. All 4 corners of the data to plot are inspected for the least occupation by data plotted while displaying the content of sampleGrp. Alternatively, by setting the argument showLegend the user-defined legend will be returned

Usage

checkForLegLoc(
 matr,
 sampleGrp = NULL,
 showLegend = TRUE,
 suplSpace = 4,
 testCorner = 1:4,
 silent = TRUE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Value

This function returns a list with $showL indicating if legend is desired and $loc for the proposition of the best location, $nConflicts gives the counts of conflicts

See Also

legend

Examples

dat1 <- matrix(c(1:5,1,1:5,5), ncol=2)
grp <- c("abc","efghijk")
(legLoc <- checkForLegLoc(dat1, grp)) 
plot(dat1, cex=3)
legend(legLoc$loc, legend=grp, text.col=2:3, pch=1, cex=0.8)

Convert points of plot to coordinates in pixels

Description

This function allows conversion the plotting positions ('x' and 'y' coordinates) of points in a given plot into coordinates in pixels (of the entire plotting region). It was designed to be used as coordinates in an html file for mouse-over interactivity (display of names of points and links). Of course, the size of the plotting region is crucial and may not be changed afterwards (if the plot is not written to file using png etc). In turn the function mouseOverHtmlFile will use the pixel-coordinates, allowing to annotate given points of a plot for mouse-over interactive html.

Usage

convertPlotCoordPix(
 x,
 y,
 useMar = c(6.2, 4, 4, 2),
 plotDim = c(1400, 800),
 plotRes = 100,
 fromTop = TRUE,
 callFrom = NULL,
 silent = FALSE,
 debug = FALSE
)

Arguments

Value

This function returns a matrix with x- and y-coordinates in pixels

See Also

mouseOverHtmlFile

Examples

df1 <- data.frame(id=letters[1:10], x=1:10, y=rep(5,10),mou=paste("point",letters[1:10]),
 link=file.path(tempdir(),paste0(LETTERS[1:10],".html")), stringsAsFactors=FALSE)
## Typically one wants to get pixel-coordinates for plots written to file.
## Here we'll use R's tempdir, later you may want to choose other locations
pngFile <- file.path(tempdir(),"test01.png")
png(pngFile, width=800, height=600, res=72)
## here we'll just plot a set of horiontal points at default parameters ...
plot(df1[,2:3], las=1, main="test01")
dev.off()
## Note: Special characters should be converted for proper display in html during mouse-over
library(wrMisc)
df1$mou <- htmlSpecCharConv(df1$mou)
## Let's add the x- and y-coordiates of the points in pixels to the data.frame
df1 <- cbind(df1,convertPlotCoordPix(x=df1[,2], y=df1[,3], plotD=c(800,600),plotRes=72))
head(df1)
## using mouseOverHtmlFile() one could now make an html document with interactive
## display of names and clockable links to the coordinates we determined here ...

Cumulative (or sorted) frequency plot (takes columns of 'dat' as separate series)

Description

Display data as sorted or cumulative frequency plot. This type of plot represents an alternative to plotting data as histograms. Histograms are very universal and which are very intuitive. However,fine-tuning the bandwith (ie width of the bars) may be very delicate, fine resultion details may often remain hidden. One of the advanges of directly displaying all data-points is that subtile differences may be revealed easier, compared to calssical histograms. Furthermore, the plot prensented her offeres more options to display multiple series of data simultaneaously. Thus, this type of plot may be useful to compare eg results of data normalization. Of course, with very large data-sets (eg > 3000 values) this gain of 'details' will be less important (compared to histograms) and will penalize speed. In such cases the argument thisResol will get useful as it allows to reduce the resultion and introduce binning. Alternatively for very large data-sets one may looking into density-plots or vioplots (eg vioplotW ). The argument CVlimit allows optionally excluding extreme values. If numeric (& > 2 columns), its value will be used exclExtrValues to identify series with column-median > 'CVlimit'. Of course, exclusion of extreme values should be done with great care, important features of the data may get lost.

Usage

cumFrqPlot(
 dat,
 cumSum = FALSE,
 exclCol = NULL,
 colNames = NULL,
 displColNa = TRUE,
 tit = NULL,
 xLim = NULL,
 yLim = NULL,
 xLab = NULL,
 yLab = NULL,
 col = NULL,
 CVlimit = NULL,
 thisResol = NULL,
 supTxtAdj = 0,
 supTxtYOffs = 0,
 useLog = "",
 silent = FALSE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Value

This function plots to the current garphical device

See Also

layout , exclExtrValues for decision of potential outliers; hist , vioplotW

Examples

set.seed(2017); dat0 <- matrix(rnorm(500), ncol=5, dimnames=list(NULL,1:5))
cumFrqPlot(dat0, tit="Sorted values")
cumFrqPlot(dat0, cumSum=TRUE, tit="Sum of sorted values")

Add arrow for expected Fold-Change to VolcanoPlot or MA-plot

Description

This function allows adding an arrow indicating a fold-change to MA- or Volcano-plots. When comparing mutiple concentratios of standards in benchmark-tests it may be useful to indicate the expected ratio in a pair-wise comparison. In case of main input as list or MArrayLM-object (as generated from limma), the colum-names of multiple pairwise comparisons can be used for extracting a numeric content (supposed as concentrations in sample-names) which will be used to determine the expected ratio used for plotting. Optionally the ratio used for plotting can be returned as numeric value.

Usage

foldChangeArrow(
 FC,
 useComp = 1,
 isLin = TRUE,
 asX = TRUE,
 col = 2,
 arr = c(0.005, 0.15),
 lwd = NULL,
 addText = c(line = -0.9, cex = 0.7, txt = "expected", loc = "toright"),
 returnRatio = FALSE,
 silent = FALSE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Details

#' @details The argument addText also allows specifying a fixed position when using addText=c(loc="bottomleft"), also bottomright, topleft, topright, toleft and toright may be used. In this case the elemts side and adjust will be redefined to accomodate the text in the corner specified.

Value

This function plots only an arrow onto current plotting device (and some explicative text), if returnRatio=TRUE also returns numeric value for extracted ratio

See Also

MAplotW , VolcanoPlotW

Examples

plot(rnorm(20,1.5,0.1), 1:20)
foldChangeArrow(FC=1.5) 

Histogram (version W)

Description

This function proposes a few special tweaks to the general hist function : In a number of settings data are treated and plotted as log-data. This function allows feeding directly log2-data and displaying the x-axis (re-translated) in linear scale (see argument isLog).

Usage

histW(
 dat,
 fileName = "histW",
 output = "screen",
 nBars = 8,
 breaks = NULL,
 tit = NULL,
 subTi = NULL,
 xLab = NULL,
 yLab = NULL,
 xLim = NULL,
 las = NULL,
 xcex = 0.7,
 imgxSize = 900,
 useCol = NULL,
 useBord = NULL,
 isLog = TRUE,
 cexSubTi = NULL,
 cropHist = TRUE,
 parDefault = TRUE,
 silent = FALSE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Details

The default settings allow making (very) small histograms ('low resolution'), which may be used as a rough overview of bandwidth and distribution of values in dat.

Similar to hist , by changing the parameters nBars and/or breaks very 'high resolution' histograms can be produced. By default it displays n (the nuber of values used underneith) per set of data (on the top of the figure). Note that the argument for (costom) title main is now called tit.

Value

This function produces a histogram type graphic (to the current graphical device), it may also plot to file if the arguments fileName designs a valid (path&) file for the output

See Also

hist

Examples

set.seed(2016); dat1 <- round(c(rnorm(200,6,0.5),rlnorm(300,2,0.5),rnorm(100,17)),2)
dat1 <- dat1[which(dat1 <50 & dat1 > 0.2)]
histW(dat1, br="FD", isLog=FALSE)
histW(log2(dat1), br="FD", isLog=TRUE)
## quick overview of distributions 
layout(partitionPlot(4))
for(i in 1:4) histW(iris[,i], isLog=FALSE, tit=colnames(iris)[i])

Display Numeric Content Of Matrix As Image

Description

To get a quick overview of the spatial distribution of smaller data-sets it may be useful to display numeric values as colored boxes. Such an output may also be referred to as heatmap (note that the term 'heatmap' is frequently associated with graphical display of hierarchcal clustering results). The function image provides the basic support to do so (ie heatmap without rearranging rows and columns by clustering). To do this more conveniently, the function imageW offers additional options for displaying row- and column-names or displaying NA-values as custom-color.

Usage

imageW(
 data,
 latticeVersion = FALSE,
 transp = TRUE,
 NAcol = "grey95",
 tit = NULL,
 rowNa = NULL,
 colNa = NULL,
 xLab = NULL,
 yLab = NULL,
 xLabVal = NA,
 yLabVal = NA,
 las = 2,
 col = NULL,
 nColor = 9,
 balanceCol = TRUE,
 gridCol = "grey75",
 gridLty = 1,
 centColShift = 0,
 cexDispl = NULL,
 panel.background.col = "white",
 supLat = list(),
 rotXlab = 0,
 rotYlab = 0,
 cexTit = 1.6,
 cexAxs = NULL,
 cexXlab = 0.7,
 cexYlab = 0.9,
 showValues = FALSE,
 Xtck = 0,
 Ytck = 0,
 silent = FALSE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Details

This function allows two modes of operation : 1) plotting using standard R -graphics or 2) using the framework of grid- and lattice-graphics (since version 1.2.6). The latter version allows integrating a legend for the color-scale and adding grid-lines, rotation of axis-labels or removing tick-marks. Please note that sometimes the center-color segment may not end up directly with the center of the scale, in this case you may adjust using the argument centColShift=-1

Value

This function plots in image (to the current graphical device) similar to as image does

See Also

image , for the lattice version levelplot , heatmaps including hierarchical clustering heatmap or heatmap.2 from package gplots

Examples

imageW(iris[1:40,1:4], transp=FALSE, tit="Iris (head)")
imageW(iris[1:20,1:4], latticeVersion=TRUE, col=c("blue","red"), 
 rotXlab=45, yLab="Observation no", tit="Iris (head)")
ma=matrix(round(runif(20),3) ,nrow=4, dimnames=list(letters[1:4],LETTERS[1:5]))
imageW(ma, cexYlab=2, transp=FALSE)
imageW(ma, cexYlab=2, transp=TRUE)
## lattice version
pl1 <- imageW(ma, latticeVersion=TRUE, trans=TRUE, yLab="my y label (Caps)")
plot(pl1) 

Add histogram to existing plot

Description

Add histogram at pleace of legend using colors from 'colorRamp'.

Usage

legendHist(
 x,
 colRamp = NULL,
 location = "bottomright",
 legTit = NULL,
 cex = 0.7,
 srt = 67,
 offS = NULL,
 border = TRUE,
 silent = FALSE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Value

This function produces a histogram on the current plottig device

Examples

dat <- rnorm(90); plot(dat)
legendHist(dat, col=1:5)

Create mouse-over interactive html-pages (with links)

Description

This function allows generating html pages with interactive mouse-over to display information for the points of the plot and www-links when clicking based on embedded png file. Basically, an html page will be generated which contains a call to display to an image file specified in pngFileNa and in the body below pixel-coordinated will be given for disply of information at mouse-over and embedded links.

Usage

mouseOverHtmlFile(
 myCoor,
 pngFileNa,
 HtmFileNa = NULL,
 mouseOverTxt = NULL,
 displSi = c(800, 600),
 colNa = NULL,
 tit = "",
 myHtmTit = "",
 myComment = NULL,
 textAtStart = NULL,
 textAtEnd = NULL,
 pxDiam = 5,
 addLinks = NULL,
 linkExt = NULL,
 htmlExt = "htm",
 callFrom = NULL,
 silent = FALSE,
 debug = FALSE
)

Arguments

Details

Basically theer are two options for defining the path to the image embedded : 1) Absolute path : I turn you can moove the html to different locations, as long as it still can see the png-file the image can be displayed. However, this may not be any more the case when the html file is sent to another person. If the png-file is accessible as url, it should be easily visible. 2) Relative path : The simplest case would be to give only the file-name with no path at all, thus the png-file is supposed to be in the same directory as the html-file. This option is very 'transportable'. Basically the same applies to the clickable links which may be provided. In high-throughput biology one typically points here to data-bases accessible over the internet where urls to specific pages. With UniProt such links can easily be constructed when using protein identifiers as rownames.

Value

plot

See Also

convertPlotCoordPix ; use htmlSpecCharConv to convert special characters for proper html display

Examples

## Note, this example writes files to R's tempdir,
## Otherwise, if you simply work in the current directory without spcifying paths you'll 
## get an html with relatove paths, which simply needs the png file in the same path 
df1 <- data.frame(id=letters[1:10], x=1:10, y=rep(5,10), mou=paste("point",letters[1:10]),
 link=file.path(tempdir(),paste0(LETTERS[1:10],".html")), stringsAsFactors=FALSE) 
## here we'll use R's tempdir, later you may want to choose other locations
pngFile <- file.path(tempdir(),"test01.png")
png(pngFile,width=800, height=600,res=72)
## here we'll just plot a set of horiontal points ...
plot(df1[,2:3],las=1,main="test01")
dev.off()
## Note : Special characters should be converted for display in html pages during mouse-over
library(wrMisc)
df1$mou <- htmlSpecCharConv(df1$mou)
## Let's add the x- and y-coordiates of the points in pixels to the data.frame
df1 <- cbind(df1,convertPlotCoordPix(x=df1[,2],y=df1[,3],plotD=c(800,600),plotRes=72))
head(df1)
## Now make the html-page allowing to display mouse-over to the png made before
htmFile <- file.path(tempdir(),"test01.html")
mouseOverHtmlFile(df1,pngFile,HtmFileNa=htmFile,pxDiam=15,
 textAtStart="Points in the figure are interactive to mouse-over ...",
 textAtEnd="and/or may contain links")
## We still need to make some toy links
for(i in 1:nrow(df1)) cat(paste0("point no ",i," : ",df1[i,1]," x=",df1[i,2]," y=",
 df1[i,3]), file=df1$link[i]) 
## Now we are ready to open the html file using any browser
## Not run: 
browseURL(htmFile)
## End(Not run)

Make matrix for layout to partition plotting area

Description

This function proposes a matrix for use with layout to arrange given number of plots to be placed on a page/plotting area. In certain instances the proposed layout may accomodate slightly more plots, eg nFig=5 can not be arranged in 2 or 3 columns without an empty last spot. Portrait (vertival) or lanscape (horizontal) layout proportions can be chosen. The user can also impose a given number of columns.

Usage

partitionPlot(
 nFig,
 returnMatr = TRUE,
 horiz = TRUE,
 figNcol = NULL,
 byrow = TRUE,
 silent = TRUE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Value

This function returns a matrix for use with layout or (if returnMatr=FALSE numeric vector with number of segements in x- an y-axis)

See Also

layout

Examples

partitionPlot(5); partitionPlot(14,horiz=TRUE)

Separate and plot data by 2 groups

Description

Plot series of data as membership of 2 different grouping vectors (eg by grp=patient and grp2=age-group).

Usage

plotBy2Groups(
 dat,
 grp,
 grp2 = NULL,
 col = NULL,
 pch = NULL,
 tit = NULL,
 cex = 2,
 lwd = 0.5,
 lty = 2,
 yLab = NULL,
 cexLab = NULL,
 sepLines = FALSE,
 silent = FALSE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Value

This function returns a list with $annot, $abund for initial/raw abundance values and $quant with final normalized quantitations, or returns data.frame with annot and quant if separateAnnot=FALSE

See Also

read.table , normalizeThis )

Examples

set.seed(2020); rand1 <- round(runif(12),2) +rep(1:3,each=4)
plotBy2Groups(rand1, gl(2,6,labels=LETTERS[5:6]), gl(4,3,labels=letters[1:4]))
 

Plot linear regression and confidence interval of regression

Description

This function provides help to display a series of bivariate points given in 'dat' (multiple data formats possible), to model a linear regression and plot the results. Furthermore, a confidence interval to the regression may be added to the plot, regression parameters get be displayed.

Usage

plotLinReg(
 dat,
 indepVarLst = NULL,
 dependVar = NULL,
 cusTxt = NULL,
 regrLty = 1,
 regrLwd = 1,
 regrCol = 1,
 confInt = 0.95,
 confCol = NULL,
 xLab = NULL,
 yLab = NULL,
 xLim = NULL,
 yLim = NULL,
 tit = NULL,
 nSignif = 3,
 col = 1,
 pch = 1,
 silent = FALSE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Value

This functions simply plots (to the current graphical devce); an invisible list containing $data, $linRegr, $confInterval (if calculated) may be returned, too

See Also

exclExtrValues for decision of potential outliers; hist , vioplotW

Examples

set.seed(2020); dat1 <- rep(1:6,each=2) +runif(12,0,1)
plotLinReg(dat1, gl(6,2))
## extract elements out of list :
li2 <- list(aa=gl(5,2), bb=dat1[1:10])
plotLinReg(li2, indepVarLst="aa", dependVar="bb")

PCA plot with bag-plot to highlight groups

Description

This function allows to plot principal components analysis (PCA), with options to show center and potential outliers for each of the groups (columns of data). The main points of this implementation consist in offering bagplots to highlight groups of columns/samples and support to (object-oriented) output from limma and wrProteo.

Usage

plotPCAw(
 dat,
 sampleGrp,
 tit = NULL,
 useSymb = c(21:25, 9:12, 3:4),
 center = TRUE,
 scale. = TRUE,
 colBase = NULL,
 useSymb2 = NULL,
 cexTxt = 1,
 cexSub = 0.6,
 displBagPl = TRUE,
 outCoef = 2,
 getOutL = FALSE,
 showLegend = TRUE,
 nGrpForMedian = 6,
 pointLabelPar = NULL,
 rowTyName = "genes",
 rotatePC = NULL,
 suplFig = TRUE,
 callFrom = NULL,
 silent = TRUE,
 debug = FALSE
)

Arguments

Details

One motivation for this implementation of plotting PCA was to provide a convenient way for doing so with of MArrayLM-objects or lists as created by limma and wrProteo.

Another motivation for this implementation come from integrating the idea of bag-plots to better visualize different groups of points (if they can be organized so beforehand as distinct groups) : The main body of data is shown as 'bag-plots' (a bivariate boxplot, see Bagplot) with different transparent colors to highlight the core part of different groups (if they contain more than 2 values per group). Furthermore, group centers are shown as average or median (see 'nGrpForMedian') with stars & index-number (if <25 groups).

Layout is automatically set to 2 or 4 subplots (if plotting more than 2 principal components makes sense).

Note : This function uses prcomp for calculating Eigenvectors and principal components, with default center=TRUE and scale.=FALSE (different to princomp(). which standardizes by default). This way the user has to option to intervene on arguments center and scale.. However, this should be done with care.

Note: NA-values cannot (by definition) be processed by (any) PCA - all lines with any non-finite values/content (eg NA) will be omitted !

Note : Package RColorBrewer may be used if available.

For more options with PCA (and related methods) you may also see also the package FactoMineR which provides a very wide spectrum of possibiities, in particular for combined numeric and categorical data.

Value

This function make a plot and may retiurn an optional matrix of outlyer-data (depending on argument getOutL)

See Also

prcomp (used here for the PCA underneith) , princomp , see the package FactoMineR for multiple plotting options or ways of combining categorical and numeric data

Examples

set.seed(2019); dat1 <- matrix(round(c(rnorm(1000), runif(1000,-0.9,0.9)),2),
 ncol=20, byrow=TRUE) + matrix(rep(rep(1:5,6:2), each=100), ncol=20)
biplot(prcomp(dat1)) # traditional plot
(grp = factor(rep(LETTERS[5:1],6:2)))
plotPCAw(dat1, grp)

x-y plot with 2 legends

Description

This is a modified version of plot for 2-dimensional data, allowing to choose symbols and colors of points according to two additional columns of dat.

Usage

plotW2Leg(
 dat,
 useCol = c("logp", "slope", "medAbund", "startFr"),
 tit = NULL,
 subTi = NULL,
 subCex = 0.9,
 pch = 21:25,
 xlim = NULL,
 ylim = NULL,
 xlab = NULL,
 ylab = NULL,
 ablines = NULL,
 legendloc = "topright",
 txtLegend = NULL,
 histLoc = "bottomleft",
 legHiTi = NULL,
 silent = TRUE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Value

This function returns graphical output only

See Also

(standard plots) plot from the package base

Examples

x1 <- cbind(x=c(2,1:7), y=8:1 +runif(8), grade=rep(1:4,2))
plotW2Leg(x1,useCol=c("x","y","y","grade"))

Plot profiles according to CLustering

Description

This function was made for visualuzing the result of clustering of a numeric vector or clustering along multiple columns of a matrix. The data will be plotted like a reglar scatter-plot, but some extra space is added to separate clusters and dashed lines highlight cluster-borders. If no mean/representative value is spacified, a geometric mean will be calculated along all columns of dat. In case dat has multiple columns, a legend and a representative (default geometric mean) dashed grey line will be displayed.

Usage

profileAsClu(
 dat,
 clu,
 meanD = NULL,
 tit = NULL,
 col = NULL,
 pch = NULL,
 xlab = NULL,
 ylab = NULL,
 meCol = "grey",
 meLty = 1,
 meLwd = 1,
 cex = NULL,
 cexTit = NULL,
 legLoc = "bottomleft",
 silent = TRUE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Value

This functin returns a plot only

Examples

set.seed(2020); dat1 <- runif(12)/2 + rep(6:8, each=4)
dat1Cl <- stats::kmeans(dat1, 3)$cluster
dat1Cl <- 5- dat1Cl # bring cluster-numbers in ascending form
dat1Cl[which(dat1Cl >3)] <- 1 # bring cluster-numbers in ascending form
profileAsClu(dat1, clu=dat1Cl)

Staggered Chart for Ploting Counts to Multiple Leveles of the Threshold used

Description

The basic idea of this plot is to show how counts data change while shifting a threshold-criterium. At each given threshold the counts are plotted like a staggered bar-chart (or staggered histogram) but without vertical lines to illustrated the almost continuous change from preceedig or following threshold-value. Initially this plot was designed for showing the absolute count-data used when constructing roc-curves (eg using the function summarizeForROC of package wrProteo ). The main input should furnish the panel of threshold as one column and the coresponding counts data as min 2 columns. The threshold coumns gets specified using the argument threColumn, the counts-data may either be specified using argument countsCol or be searched using grep using column-names containing the text given in argument varCountNa with may be combined with a fixed preceeding part given as argument fixedCountPat.

Usage

staggerdCountsPlot(
 roc,
 threColumn = 1,
 countsCol = NULL,
 fixedCountPat = "n.pos.",
 varCountNa = NULL,
 sortAscending = TRUE,
 vertLine = NULL,
 col = NULL,
 tit = NULL,
 logScale = FALSE,
 las.alph = 2,
 displMaxSpec = TRUE,
 silent = FALSE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Details

Investigate count data prepared for plotting ROC curves : cumulative counts plot by species (along different statistical test thresholds). Note : Package wrProteo may be used to prepare input (matrix of ROC data).

Value

This function produces only a plot

See Also

ecdf , for preparing input to ROC: function summarizeForROC in package wrProteo

Examples

set.seed(2019); test1 <- cbind(a=sample.int(n=7,size=50,repl=TRUE),
 b=sample.int(n=11,size=50,repl=TRUE),c=sample.int(n=18,size=50,repl=TRUE))
test1 <- cbind(alph=seq(0,1,length.out=50),a=cumsum(test1[,1]),b=cumsum(test1[,2]),
 c=cumsum(test1[,3]))
staggerdCountsPlot(test1,countsCol=c("a","b","c"))
## example below requires the package wrProteo

Violin-Plots Version W

Description

This function allows generating Violin plots) using a variety of input formats and offers additional options for colors. Main input may be multiple vectors, a matrix or list of multiple data-elements (entries may be of variable length), individual colors for different sets of data or color-gradients can be specified, and the display of n per set of data was integtated (based on an inspiration from the discussion 'Removing-NAs-from-dataframe-for-use-in-Vioplot' on the forum Nabble). It is also possible to plot pairwise half-violins for easier pairwise-comparisons (using halfViolin="pairwise"). Many arguments are kept similar to vioplot (here, the package vioplot is not required/used).

Usage

vioplotW(
 x,
 ...,
 finiteOnly = TRUE,
 removeEmpty = FALSE,
 halfViolin = FALSE,
 boxCol = "def",
 hh = NULL,
 xlim = NULL,
 ylim = NULL,
 nameSer = NULL,
 cexNameSer = NULL,
 horizontal = FALSE,
 col = "rainbow",
 border = "black",
 xlab = NULL,
 ylab = NULL,
 cexLab = NULL,
 cexAxis = NULL,
 lty = 1,
 pointCol = NULL,
 cexPt = NULL,
 tit = NULL,
 las = 1,
 lwd = 1,
 rectCol = "black",
 at = 0,
 add = FALSE,
 wex = NULL,
 silent = FALSE,
 debug = FALSE,
 callFrom = NULL
)

Arguments

Details

The (relative) width of the density-profiles ('Violins') may be manually adjusted using the parameter wex which applies to all profiles drawn.

Note : Arguments have to be given with full names, lazy evaluation of arguments will not work properly with this function (since '...' is used to capture additional data-sets). Note : vioplot offers better options for plotting formulas

Value

This function plots a figure (to the current graphical device)

See Also

the package vioplot, sm is used for the density estimation

Examples

set.seed(2013)
dat6 <- matrix(round(rnorm(300) +3, 1), ncol=6,
	 dimnames=list(paste0("li",1:50), letters[19:24]))
vioplotW(dat6)
## variable number of elements (each n is displayed)
dat6b <- apply(dat6, 2, function(x) x[which(x < 5)])
dat6b[[4]] <- dat6b[[4]][dat6b[[4]] < 4]
vioplotW(dat6b, col="Spectral")
vioplotW(dat6b, col="Spectral" ,halfViolin="pairwise", horizontal=TRUE)
vioplotW(dat6b, col="Spectral", halfViolin="pairwise", horizontal=FALSE)