| Title: | Automated Affymetrix Array Analysis Base Package |
|---|---|
| Description: | Base utility functions are available for the Automated Affymetrix Array Analysis set of packages. |
| Authors: | Willem Talloen [aut], Tine Casneuf [aut], An De Bondt [aut], Steven Osselaer [aut], Hinrich Goehlmann [aut], Willem Ligtenberg [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] |
| Maintainer: | Laure Cougnaud <[email protected]> |
| License: | GPL-3 |
| Version: | 1.53.0 |
| Built: | 2024-07-17 12:08:41 UTC |
| Source: | https://github.com/bioc/a4Base |
Utility function that defines a color palette for use in a4
a4palette(n, alpha = 1, Janssen = FALSE)a4palette(n, alpha = 1, Janssen = FALSE)
n |
Number of color levels the palette should provid |
alpha |
alpha transparency level of the colors |
Janssen |
logical. If |
For n = 1, "blue" is returned; for n = 2
c("red", "blue") is returned; for n = 3
c("red", "green", "blue" is returned; for n = 4
c("red", "green", "blue", "purple") is returned and for n > 2,
the output of rainbow(n) is returned.
a character vector of colors
Steven Osselaer, Tobias Verbeke
rainbow palette in palettes
op <- par(mfrow = c(2, 3)) for (nGroups in 1:6) pie(rep(1, nGroups), a4palette(nGroups)) par(op)op <- par(mfrow = c(2, 3)) for (nGroups in 1:6) pie(rep(1, nGroups), a4palette(nGroups)) par(op)
Compute quantiles on mean expression level for plotGeneDE function. Colors of bars in the plot could then be allocated using buckets defined by those quantiles.
addQuantilesColors(e, ngroups = 3)addQuantilesColors(e, ngroups = 3)
e |
ExpressionSet object to use for computation |
ngroups |
Number of groups to be created |
Number of computed quantiles is equal to (ngroups - 1).
The ExpressionSet object e is returned, with a new column called colorsQuantilesVector in its slot featureData
Eric Lecoutre
if (require(ALL)){ data(ALL, package = "ALL") ALLQ <- addQuantilesColors(ALL) fData(ALLQ) }if (require(ALL)){ data(ALL, package = "ALL") ALLQ <- addQuantilesColors(ALL) fData(ALLQ) }
Create a boxplot for a given gene. The boxplot displays the expression values (y-axis) by groupss (x-axis). The raw data are superimposed as dots, jittered for readability of the plot. Optionally, the dots can be colored by another variable.
boxPlot( probesetId = NULL, geneSymbol = NULL, object, groups, main = NULL, colvec = NULL, colgroups = NULL, probe2gene = TRUE, addLegend = TRUE, legendPos = "topleft", ... )boxPlot( probesetId = NULL, geneSymbol = NULL, object, groups, main = NULL, colvec = NULL, colgroups = NULL, probe2gene = TRUE, addLegend = TRUE, legendPos = "topleft", ... )
probesetId |
The probeset ID. These should be stored in the |
geneSymbol |
The gene symbol. These should be stored in the column |
object |
ExpressionSet object for the experiment |
groups |
String containing the name of the grouping variable. This should be a
the name of a column in the |
main |
Main title on top of the graph |
colvec |
Vector of colors to be used for the groups. If not specified, the default colors of
|
colgroups |
String containing the name of the variable to color the superimposed dots.
This should be a the name of a column in the |
probe2gene |
Boolean indicating whether the probeset should be translated to a gene symbol (used for the default title of the plot) |
addLegend |
Boolean indicating whether a legend for the colors of the dots should be added. |
legendPos |
Specify where the legend should be placed. Typically either |
... |
Possibility to add extra plot options. See |
A plot is drawn to the current device and
probesetId are returned invisibly.
Willem Talloen
# simulated data set esSim <- simulateData() boxPlot(probesetId = 'Gene.1', object = esSim, groups = 'type', addLegend = FALSE) # ALL data set if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) boxPlot(geneSymbol = 'HLA-DPB1', object = ALL, boxwex = 0.3, groups = 'BTtype', colgroups = 'BT', legendPos='topright') }# simulated data set esSim <- simulateData() boxPlot(probesetId = 'Gene.1', object = esSim, groups = 'type', addLegend = FALSE) # ALL data set if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) boxPlot(geneSymbol = 'HLA-DPB1', object = ALL, boxwex = 0.3, groups = 'BTtype', colgroups = 'BT', legendPos='topright') }
Merge two ExpressionSet objects, checking their attributes.
combineTwoExpressionSet(x, y)combineTwoExpressionSet(x, y)
x |
An object of class ExpressionSet |
y |
An object of class ExpressionSet |
exprs and pData are merged. Other data (such as MIAME or annotation) are those of x.
An object of class ExpressionSet
Eric Lecoutre
## Not run: # prepare and combine two ExpressionSet data(data.H2009); data(phenoData.H2009) data(data.SKOV3); data(phenoData.SKOV3) eH2009 <- prepareExpressionSet(exprs = data.H2009, phenoData = phenoData.H2009, changeColumnsNames = TRUE) eSKOV3 <- prepareExpressionSet(exprs = data.SKOV3, phenoData = phenoData.SKOV3, changeColumnsNames = TRUE) newE <- combineTwoExpressionSet(eH2009,eSKOV3) ## End(Not run)## Not run: # prepare and combine two ExpressionSet data(data.H2009); data(phenoData.H2009) data(data.SKOV3); data(phenoData.SKOV3) eH2009 <- prepareExpressionSet(exprs = data.H2009, phenoData = phenoData.H2009, changeColumnsNames = TRUE) eSKOV3 <- prepareExpressionSet(exprs = data.SKOV3, phenoData = phenoData.SKOV3, changeColumnsNames = TRUE) newE <- combineTwoExpressionSet(eH2009,eSKOV3) ## End(Not run)
Compute summary statistics per gene of expression data in a ExpressionSet object.
computeLogRatio( e, reference, within = NULL, across = NULL, nReplicatesVar = 3, ... )computeLogRatio( e, reference, within = NULL, across = NULL, nReplicatesVar = 3, ... )
e |
An object of class ExpressionSe |
reference |
A list with two items: var and level - See details |
within |
Character vector - names of pData columns - See details |
across |
Character vector - names of pData columns - See details |
nReplicatesVar |
Integer - Minimum number of replicates to compute variance |
... |
... |
Summary statistics (mean, variances and difference to reference or control) will be computed on the 'exprs' slot of the ExpressionSet object. The parameters of the computation are specified by the parameters 'reference', 'within' and 'across'.\ The design of the computations is such that the differences and pooled variances are calculated against the sample(s) that was(were) chosen as reference. The reference is specified by the level of a certain variable in the phenoData slot (e.g.: column 'control' and level 'WT' of the phenoData slot or a boolean ('ref') variable with 0 or 1) – the list object of 'var' and 'level' together determine the reference group. \
All groups determined by combining the reference$var and across variables will be
compared to the reference group. Two different approaches to obtain necessary computations:
Prepare a boolean variable that reflects only the reference group and specify all groupings
in the across arguments. E.g.: reference=list(var = 'boolean', level = 1),
across = c('compound','dose')
Add an extra column to the phenoData slot that contains all combinations, with a specific one
for the reference group: for example,
pData(e)['refvar'] <- paste(pData(e)['compound'], pData(e)['dose'],sep='.')
so as to use reference = list(var = 'refvar', level ='comp1.dose1') as argument for reference.
\
Sometimes computations need to be conducted within groups, and are thus nested. For example, when comparing treament values of different cell lines, each will have gene expression values for its own reference. The parameter 'within' allows to define such subgroups, for which computations will be done separately and combined afterwards. Both parameters 'within' and 'across' can be a vector of column names, whose unique combinations will be used for groupings.
Returns an object of class ExpressionSet with pData inherited from the submitted ExpressionSet object, supplemented by the computed statistics in the 'exprs' slot and info thereof in the 'phenoData' slot.
Eric Lecoutre
if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) ALL2 <- ALL[,ALL$BT != 'T1'] # omit subtype T1 as it only contains one sample ALL2$BTtype <- as.factor(substr(ALL2$BT,0,1)) # create a vector with only T and B # Test for differential expression between B and T cells tTestResult <- tTest(ALL, "BTtype", probe2gene = FALSE) topGenes <- rownames(tTestResult)[1:20] # plot the log ratios versus subtype B of the top genes LogRatioALL <- computeLogRatio(ALL2, reference=list(var='BT',level='B')) a <- plotLogRatio(e=LogRatioALL[topGenes,],openFile=FALSE, tooltipvalues=FALSE, device='pdf', colorsColumnsBy=c('BTtype'), main = 'Top 20 genes most differentially between T- and B-cells', orderBy = list(rows = "hclust"), probe2gene = TRUE) }if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) ALL2 <- ALL[,ALL$BT != 'T1'] # omit subtype T1 as it only contains one sample ALL2$BTtype <- as.factor(substr(ALL2$BT,0,1)) # create a vector with only T and B # Test for differential expression between B and T cells tTestResult <- tTest(ALL, "BTtype", probe2gene = FALSE) topGenes <- rownames(tTestResult)[1:20] # plot the log ratios versus subtype B of the top genes LogRatioALL <- computeLogRatio(ALL2, reference=list(var='BT',level='B')) a <- plotLogRatio(e=LogRatioALL[topGenes,],openFile=FALSE, tooltipvalues=FALSE, device='pdf', colorsColumnsBy=c('BTtype'), main = 'Top 20 genes most differentially between T- and B-cells', orderBy = list(rows = "hclust"), probe2gene = TRUE) }
Basically a wrapper for new('ExpressionSet',...), this function gathers gene
expression and phenotype data, after having checked their compatibility.
createExpressionSet( exprs = matrix(nrow = 0, ncol = 0), phenoData = AnnotatedDataFrame(), varMetadata = NULL, dimLabels = c("rowNames", "colNames"), featureData = NULL, experimentData = MIAME(), annotation = character(0), changeColumnsNames = TRUE, ... )createExpressionSet( exprs = matrix(nrow = 0, ncol = 0), phenoData = AnnotatedDataFrame(), varMetadata = NULL, dimLabels = c("rowNames", "colNames"), featureData = NULL, experimentData = MIAME(), annotation = character(0), changeColumnsNames = TRUE, ... )
exprs |
gene expression matrix |
phenoData |
phenotype data associated with exprs columns, as a matrix or data.frame |
varMetadata |
optional metadata on phenotype data |
dimLabels |
see |
featureData |
see |
experimentData |
see |
annotation |
see |
changeColumnsNames |
Change exprs columns names – see details |
... |
|
If changeColumnsNames is TRUE, then the procedure is the following: first one checks if phenoData contains a column named 'colNames'. If so, content will be used to rename exprs colums. On the other case, one uses combinations of phenoData columns to create new names. In any case, old columns names
are stored within a column named 'oldcolnames' in the pData.
An object of class ExpressionSet
Eric Lecoutre
# simulate expression data of 10 features (genes) measured in 4 samples x <- matrix(rnorm(40), ncol = 4) colnames(x) <- paste("sample", 1:4, sep = "_") rownames(x) <- paste("feature", 1:10, sep = "_") # simulate a phenodata with two variables ToBePheno <- data.frame(Gender = rep(c('Male','Female'), 2), Treatment = rep(c('Trt','Control'), each=2)) rownames(ToBePheno) <- paste("sample", 1:4, sep = "_") eset <- createExpressionSet(exprs = x, phenoData = ToBePheno)# simulate expression data of 10 features (genes) measured in 4 samples x <- matrix(rnorm(40), ncol = 4) colnames(x) <- paste("sample", 1:4, sep = "_") rownames(x) <- paste("feature", 1:10, sep = "_") # simulate a phenodata with two variables ToBePheno <- data.frame(Gender = rep(c('Male','Female'), 2), Treatment = rep(c('Trt','Control'), each=2)) rownames(ToBePheno) <- paste("sample", 1:4, sep = "_") eset <- createExpressionSet(exprs = x, phenoData = ToBePheno)
This class adds statistical information to the exprs of the ExpressionSet as well as descriptive information to the pData of the ExpressionSet
assayDataObject of class "AssayData"
phenoDataObject of class "AnnotatedDataFrame"
featureDataObject of class "AnnotatedDataFrame"
experimentDataObject of class "MIAME"
annotationObject of class "character"
.__classVersion__Object of class "Versions"
Objects can be created by calls of the form new("ExpressionSetWithComputation", assayData, phenoData, featureData, experimentData, annotation, exprs, ...).
Class ExpressionSet, directly.
Class eSet, by class "ExpressionSet", distance 2.
Class VersionedBiobase, by class "ExpressionSet", distance 3.
Class Versioned, by class "ExpressionSet", distance 4.
No methods defined with class "ExpressionSetWithComputation" in the signature.
Tobias Verbeke
ExpressionSet, computeLogRatio
Function to filter on intensity and variance as typically used in gene expression studies
filterVarInt( object, IntCutOff = log2(100), IntPropSamples = 0.25, VarCutOff = 0.5 )filterVarInt( object, IntCutOff = log2(100), IntPropSamples = 0.25, VarCutOff = 0.5 )
object |
ExpressionSet object |
IntCutOff |
cut-off value used for the intensity filt |
IntPropSamples |
proportion of samples used by the intensity
filter; by default |
VarCutOff |
cut-off value used for the variance filter |
The intensity filter implies that (by default) the intensity levels must be greater than log2(100) in at least 25 percent of the samples. The variance filter requires that the features have an interquartile range (IQR) greater than 0.5. Note that the IQR is quite insensitive to outliers such that genes with outlying expression values in a few samples are excluded as long as their overall variation is small.
Object of class ExpressionSet containing only the features that pass the variance and intensity filter.
Willem Talloen
Gentleman, R. et al. (2005). Bioinformatics and Computational Biology Solutions using R and BioConductor, New York: Springer. Goehlmann, H. and W. Talloen (2009). Gene Expression Studies Using Affymetrix Microarrays, Chapman \& Hall/CRC, p. 128.
if (require(ALL)){ data(ALL, package = "ALL") fALL <- filterVarInt(ALL) fALL }if (require(ALL)){ data(ALL, package = "ALL") fALL <- filterVarInt(ALL) fALL }
Grid version of heatmap function adapted to expressionSet objects with some specific requirements such as the possibility to display subgroups, define colors, adapt text graphical parameters (sizes...). The function also suggests a size appropriate for a device to generate a complete plot with all elements.
heatmap.expressionSet( eset, col.groups = pData(phenoData(eset))[, "subGroup"], col.orderBy = order(pData(phenoData(eset))[, "subGroup"]), col.groups.sep.width = unit(8, "points"), col.labels = sampleNames(eset), col.labels.sep.width = unit(10, "points"), col.labels.gpar = gpar(cex = 1), col.labels.max.nchar = 20, colors.pergroup = FALSE, colors.groups = NULL, colors.groups.min = rgb(1, 1, 1), colors.max = rgb(1, 0, 0), colors.min = rgb(1, 1, 1), colors.nbreaks = 128, colors.palette = NULL, cell.gpar = gpar(lty = 0), row.groups.sep.height = unit(15, "points"), row.labels.sep.height = unit(10, "points"), row.col.groups.display = ifelse(length(unique(col.groups)) > 1, TRUE, FALSE), row.col.groups.display.height = unit(6, "points"), row.labels.gpar = gpar(cex = 1, col = "black"), row.labels.max.nchar = 45, row.labels = list("SYMBOL", "GENENAME"), row.labels.sep = " - ", row.groups = rep(1, nrow(exprs(eset))), row.order = "none", row.groups.hclust = FALSE, row.groups.hclust.n = 4, distfun = dist, hclustfun = function(d) { hclust(d, method = "ward") }, values.min = 0, values.max = 16, title.gpar = gpar(cex = 1.4), title.main = "This is the title possibly being very long - it will be splited on several lines or even displayed with dots at the end -- see there (does it work? addendum)", title.just = c("right", "top"), title.maxlines = 4, title.cutpoint = 40, subtitle.gpar = gpar(cex = 1), subtitle.main = "This is subtitle", subtitle.maxlines = 4, subtitle.just = title.just, subtitle.cutpoint = 40, margin.top = unit(2, "lines"), margin.left = unit(2, "lines"), margin.right = unit(2, "lines"), margin.bottom = unit(2, "lines"), legend.display = TRUE, legend.range = "full", legend.data.display = ifelse(legend.range == "full", TRUE, FALSE), legend.gpar = gpar(cex = 1), legend.width = unit(250, "points"), legend.height = unit(40, "points"), ... )heatmap.expressionSet( eset, col.groups = pData(phenoData(eset))[, "subGroup"], col.orderBy = order(pData(phenoData(eset))[, "subGroup"]), col.groups.sep.width = unit(8, "points"), col.labels = sampleNames(eset), col.labels.sep.width = unit(10, "points"), col.labels.gpar = gpar(cex = 1), col.labels.max.nchar = 20, colors.pergroup = FALSE, colors.groups = NULL, colors.groups.min = rgb(1, 1, 1), colors.max = rgb(1, 0, 0), colors.min = rgb(1, 1, 1), colors.nbreaks = 128, colors.palette = NULL, cell.gpar = gpar(lty = 0), row.groups.sep.height = unit(15, "points"), row.labels.sep.height = unit(10, "points"), row.col.groups.display = ifelse(length(unique(col.groups)) > 1, TRUE, FALSE), row.col.groups.display.height = unit(6, "points"), row.labels.gpar = gpar(cex = 1, col = "black"), row.labels.max.nchar = 45, row.labels = list("SYMBOL", "GENENAME"), row.labels.sep = " - ", row.groups = rep(1, nrow(exprs(eset))), row.order = "none", row.groups.hclust = FALSE, row.groups.hclust.n = 4, distfun = dist, hclustfun = function(d) { hclust(d, method = "ward") }, values.min = 0, values.max = 16, title.gpar = gpar(cex = 1.4), title.main = "This is the title possibly being very long - it will be splited on several lines or even displayed with dots at the end -- see there (does it work? addendum)", title.just = c("right", "top"), title.maxlines = 4, title.cutpoint = 40, subtitle.gpar = gpar(cex = 1), subtitle.main = "This is subtitle", subtitle.maxlines = 4, subtitle.just = title.just, subtitle.cutpoint = 40, margin.top = unit(2, "lines"), margin.left = unit(2, "lines"), margin.right = unit(2, "lines"), margin.bottom = unit(2, "lines"), legend.display = TRUE, legend.range = "full", legend.data.display = ifelse(legend.range == "full", TRUE, FALSE), legend.gpar = gpar(cex = 1), legend.width = unit(250, "points"), legend.height = unit(40, "points"), ... )
eset |
expressionSet object |
col.groups |
Vector specifying sub-groups for individual. Sub-groups are treated separately and can thus on plot have different colors. |
col.orderBy |
Vector specifying ordering for individual. In case there are sub-groups, individual must first be ordered by sub-groups, but an additional variable gives a way to sort individual within sub-groups. |
col.groups.sep.width |
Object of class unit (grid package). Width used to visually separate sub-groups of individuals. This can be unit(0,"points") for example for no separation. |
col.labels |
Character vector for columns labels (individuals), by default taken from phenoData. |
col.labels.sep.width |
Object of class |
col.labels.gpar |
Object of class |
col.labels.max.nchar |
Integer. Number of maximum characters to be used for labels truncation |
colors.pergroup |
Boolean. If TRUE, separate colors are used to color image matrix. Colors defined for groups are used. |
colors.groups |
Vector. Colors to be used for each group of individual. If NULL (default), colors are taken from column "sampleColor" of expressionSet phenoData. |
colors.groups.min |
Character vector of length 1 corresponding to a valid color. If colors.groups are provided, a shading if done between color.group and this color (default: white). |
colors.max |
Character vector of length 1 corresponding to a valid color. See colors details. |
colors.min |
Character vector of length 1 corresponding to a valid color. See colors details. |
colors.nbreaks |
Integer. Number of cutpoints used to split the color palette/shading. |
colors.palette |
Character vector of valid color names. |
cell.gpar |
Object of class gpar (grid package). Parameters used to format cells, for example to add border (gpar(lty=1)). |
row.groups.sep.height |
Object of class unit (grid package). Height between rows sub-groups. |
row.labels.sep.height |
Object of class unit (grid package). Height between image plot zone and rows labels |
row.col.groups.display |
Boolean. Display or not colored band for subgroups of individuals. |
row.col.groups.display.height |
Object of class unit (grid package). If row.col.groups.display is TRUE then height used for the displayed band. |
row.labels.gpar |
Object of class gpar (grid package). Parameters to be used for labels (cex,...). |
row.labels.max.nchar |
Integer. Number of maximum characters to be used for labels truncation. |
row.labels |
Character vector or list. If vector, direct labels to be used. If list, elements of the list will be taken from featureData and collapsed using row.labels.sep. |
row.labels.sep |
In case labels are taken from featureData (list for row.labels), separator used to paste the provided columns. |
row.groups |
Boolean specifying whether rows are split into sub-groups. |
row.order |
Either a vector of indices to be used to reorder features (rows) or "none" or "hclust" to use clustering. |
row.groups.hclust |
Boolean. If row.order equals "hclust", one can ask to split features into sub-groups based on a cut of the clustering dendogram. |
row.groups.hclust.n |
Integer. If row.order equals "hclust" and row.groups.hclust is TRUE, number of sub-groups. |
distfun |
Function. For row.order equals "hclust", metric function. |
hclustfun |
Function. For row.order equals "hclust", clustering function. |
values.min |
Minimum value for the data range. Values that are inferior are assigned to that value. That ensures a maximal cutpoint for the coloring scale. |
values.max |
Maximum value for the data range. Values that are superior are assigned to that value. That ensures a maximal cutpoint for the coloring scale. |
title.gpar |
Object of class gpar (grid package). Parameters to be used for the main title (cex,...). |
title.main |
Character vector. Main title to be displayed. |
title.just |
Title justification, one of "center","left","right" (first letter of the word can also be used). |
title.maxlines |
Maximum number of lines for the title split. |
title.cutpoint |
Integer. Maximum number of characters a line must have. Title is split into lines according to that cutpoint. |
subtitle.gpar |
Object of class gpar (grid package). Parameters to be used for the subtitle (cex, col,...). |
subtitle.main |
Character vector. Subtitle. The subtitle will be split into lines following same rules as used for main title. |
subtitle.maxlines |
Maximum number of lines for the subtitle split. |
subtitle.just |
Subtitle justification, one of "center","left","right" (first letter of the word can also be used). |
subtitle.cutpoint |
Integer. Maximum number of characters a line must have. Subtitle is split into lines according to that cutpoint. |
margin.top |
Object of class unit (grid package). Top margin. |
margin.left |
Object of class unit (grid package). Left margin. |
margin.right |
Object of class unit (grid package). Right margin. |
margin.bottom |
Object of class unit (grid package). Bottom margin. |
legend.display |
Boolean. Display or not the legend. Legend is positionned in upper right corner. |
legend.range |
Character: "full" (default) or "data". If full, color scale legend ranges from values.min to values.max. If "data", range is c(min(data),max(data)). |
legend.data.display |
Boolean. Display or not color scale legend. |
legend.gpar |
Object of class gpar (grid package). Parameters to be used for color scale legend axis (cex,...). |
legend.width |
Object of class unit (grid package). Width for the color scale legend. |
legend.height |
Object of class unit (grid package). Height for the color scale legend. |
... |
Additional parameters the function may have. Not used currently |
The function suggests a size (width, height) for the graphic returned as a vector. A typical usage will be to call the function a first time to get those values and call it again with an output device
There are several ways to specify colors used for the image zone. The usual way is to have a shading from colors.groups.min to a color per group (typically the same). By default, a shading is indeed proposed between white (for colors.groups.min) and a same color shared by groups (red for colors.groups.max). The number of possible colors in the shading is determined by colors.nbreaks. In case one asks for distinct colors for groups, only a single value for colors.groups.min is allowed. By default, subgroups colors are taken from phenoData ("sampleColor" column), consequence of colors.groups being NULL. Colors for groups are overided by providing a vector of valid colors for this colors.groups argument. An additional and flexible way to determine colors is to provide a complete palette of possible colors, as a character vector of valid colors (argument colors.palette). Note that in this case the argument colors.nbreaks has no effect as the number of possible values is the length of the palette.
Eric Lecoutre <[email protected]>
## Not run: library(RColorBrewer) library(dichromat) library(Biobase) library(grid) pdf.directory=getwd() load(file.path(getwd(),"expressionSetRma.Rda")) #expressionSetRma eset <- expressionSetRma[100:130,pData(phenoData(expressionSetRma))[,"sample"]%in%c(1:10,41:50)] # ARG ##### !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! exprs(eset)[1,5] <- 13.8 exprs(eset)[10,7] <- 0.5 eset2 <- expressionSetRma[200:250,] # ARG eset3 <- expressionSetRma[1000:1009,pData(phenoData(expressionSetRma))[,"sample"]%in%c(1:3,41:46)] # ARG eset4 <- expressionSetRma[100:230,pData(phenoData(expressionSetRma))[,"sample"]%in%c(1:20,31:50)] # ARG eset5 <- expressionSetRma[1:400,] # ARG # eset <- eset2 pdf(file.path(pdf.directory,"eset.pdf")) size <- heatmap.expressionSet(eset,subtitle.main=" ") dev.off() pdf(file.path(pdf.directory,"eset.pdf"),width=size[1],height=size[2]) heatmap.expressionSet(eset,subtitle.main=" ") dev.off() pdf(file.path(pdf.directory,"eset2.pdf")) size <- heatmap.expressionSet( eset2, colors.nbreaks = 20, colors.pergroup=TRUE, legend.range="data", row.col.groups.display=FALSE, cell.gpar=gpar(lwd=0.5), legend.height=unit(50,"points"), title.just=c("center","center"), title.maxlines=2, col.groups.sep.width=unit(0,"points"), row.labels=featureNames(eset), subtitle.main="This is subtitle", row.order="hclust",row.groups.hclust=FALSE, title.gpar=gpar(cex=2), subtitle.gpar=gpar(cex=1.5) ) dev.off() pdf(file.path(pdf.directory,"eset2.pdf"),width=size[1],height=size[2]) size <- heatmap.expressionSet( eset2, colors.nbreaks = 20, colors.pergroup=TRUE, legend.range="data", row.col.groups.display=FALSE, cell.gpar=gpar(lwd=0.5), legend.height=unit(50,"points"), title.just=c("center","center"), title.maxlines=2, col.groups.sep.width=unit(0,"points"), row.labels=featureNames(eset), subtitle.main="This is subtitle", row.order="hclust",row.groups.hclust=FALSE, title.gpar=gpar(cex=2), subtitle.gpar=gpar(cex=1.5) ) dev.off() pdf(file.path(pdf.directory,"eset3.pdf")) size <- heatmap.expressionSet( eset3, row.labels.gpar=gpar(cex=0.4,col=c(rep("red",2),rep("black",49)) ), # col will correctly be a vector only if no group... col.labels.gpar=gpar(cex=0.6), colors.nbreaks = 20, colors.pergroup=TRUE, legend.range="data", row.col.groups.display=FALSE, cell.gpar=gpar(lwd=0.5), legend.height=unit(50,"points"), title.just=c("center","center"), title.maxlines=2, col.groups.sep.width=unit(0,"points"), row.labels=featureNames(eset), subtitle.main="Essai subtitle", row.order="hclust",row.groups.hclust=FALSE, interactive=FALSE ) dev.off() pdf(file.path(pdf.directory,"eset3.pdf"),width=size[1],height=size[2]) size <- heatmap.expressionSet( eset3, row.labels.gpar=gpar(cex=0.4,col=c(rep("red",2),rep("black",49)) ), # col will correctly be a vector only if no group... col.labels.gpar=gpar(cex=0.6), colors.nbreaks = 20, colors.pergroup=TRUE, legend.range="data", row.col.groups.display=FALSE, cell.gpar=gpar(lwd=0.5), legend.height=unit(50,"points"), title.just=c("center","center"), title.maxlines=2, col.groups.sep.width=unit(0,"points"), row.labels=featureNames(eset), subtitle.main="Essai subtitle", row.order="hclust",row.groups.hclust=FALSE, interactive=FALSE ) dev.off() pdf(file.path(pdf.directory,"eset4.pdf")) size <- heatmap.expressionSet( eset4, legend.range="data", colors.palette = dichromat(rich.colors(190)[1:128]), row.col.groups.display=TRUE, title.just=c("left","top"), title.maxlines=2, row.labels=featureNames(eset), subtitle.main="", row.order="hclust",row.groups.hclust=FALSE, ) dev.off() pdf(file.path(pdf.directory,"eset4.pdf"),width=size[1],height=size[2]) size <- heatmap.expressionSet( eset4, legend.range="data", colors.palette = dichromat(rich.colors(190)[1:128]), row.col.groups.display=TRUE, title.just=c("left","top"), title.maxlines=2, row.labels=featureNames(eset), subtitle.main="", row.order="hclust",row.groups.hclust=FALSE, ) dev.off() pdf(file.path(pdf.directory,"eset5.pdf")) size <- heatmap.expressionSet(eset5,row.order="hclust",row.groups.hclust=FALSE) dev.off() pdf(file.path(pdf.directory,"eset5.pdf"),width=size[1],height=size[2]) heatmap.expressionSet(eset5,row.order="hclust",row.groups.hclust=FALSE) dev.off() ## End(Not run)## Not run: library(RColorBrewer) library(dichromat) library(Biobase) library(grid) pdf.directory=getwd() load(file.path(getwd(),"expressionSetRma.Rda")) #expressionSetRma eset <- expressionSetRma[100:130,pData(phenoData(expressionSetRma))[,"sample"]%in%c(1:10,41:50)] # ARG ##### !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! exprs(eset)[1,5] <- 13.8 exprs(eset)[10,7] <- 0.5 eset2 <- expressionSetRma[200:250,] # ARG eset3 <- expressionSetRma[1000:1009,pData(phenoData(expressionSetRma))[,"sample"]%in%c(1:3,41:46)] # ARG eset4 <- expressionSetRma[100:230,pData(phenoData(expressionSetRma))[,"sample"]%in%c(1:20,31:50)] # ARG eset5 <- expressionSetRma[1:400,] # ARG # eset <- eset2 pdf(file.path(pdf.directory,"eset.pdf")) size <- heatmap.expressionSet(eset,subtitle.main=" ") dev.off() pdf(file.path(pdf.directory,"eset.pdf"),width=size[1],height=size[2]) heatmap.expressionSet(eset,subtitle.main=" ") dev.off() pdf(file.path(pdf.directory,"eset2.pdf")) size <- heatmap.expressionSet( eset2, colors.nbreaks = 20, colors.pergroup=TRUE, legend.range="data", row.col.groups.display=FALSE, cell.gpar=gpar(lwd=0.5), legend.height=unit(50,"points"), title.just=c("center","center"), title.maxlines=2, col.groups.sep.width=unit(0,"points"), row.labels=featureNames(eset), subtitle.main="This is subtitle", row.order="hclust",row.groups.hclust=FALSE, title.gpar=gpar(cex=2), subtitle.gpar=gpar(cex=1.5) ) dev.off() pdf(file.path(pdf.directory,"eset2.pdf"),width=size[1],height=size[2]) size <- heatmap.expressionSet( eset2, colors.nbreaks = 20, colors.pergroup=TRUE, legend.range="data", row.col.groups.display=FALSE, cell.gpar=gpar(lwd=0.5), legend.height=unit(50,"points"), title.just=c("center","center"), title.maxlines=2, col.groups.sep.width=unit(0,"points"), row.labels=featureNames(eset), subtitle.main="This is subtitle", row.order="hclust",row.groups.hclust=FALSE, title.gpar=gpar(cex=2), subtitle.gpar=gpar(cex=1.5) ) dev.off() pdf(file.path(pdf.directory,"eset3.pdf")) size <- heatmap.expressionSet( eset3, row.labels.gpar=gpar(cex=0.4,col=c(rep("red",2),rep("black",49)) ), # col will correctly be a vector only if no group... col.labels.gpar=gpar(cex=0.6), colors.nbreaks = 20, colors.pergroup=TRUE, legend.range="data", row.col.groups.display=FALSE, cell.gpar=gpar(lwd=0.5), legend.height=unit(50,"points"), title.just=c("center","center"), title.maxlines=2, col.groups.sep.width=unit(0,"points"), row.labels=featureNames(eset), subtitle.main="Essai subtitle", row.order="hclust",row.groups.hclust=FALSE, interactive=FALSE ) dev.off() pdf(file.path(pdf.directory,"eset3.pdf"),width=size[1],height=size[2]) size <- heatmap.expressionSet( eset3, row.labels.gpar=gpar(cex=0.4,col=c(rep("red",2),rep("black",49)) ), # col will correctly be a vector only if no group... col.labels.gpar=gpar(cex=0.6), colors.nbreaks = 20, colors.pergroup=TRUE, legend.range="data", row.col.groups.display=FALSE, cell.gpar=gpar(lwd=0.5), legend.height=unit(50,"points"), title.just=c("center","center"), title.maxlines=2, col.groups.sep.width=unit(0,"points"), row.labels=featureNames(eset), subtitle.main="Essai subtitle", row.order="hclust",row.groups.hclust=FALSE, interactive=FALSE ) dev.off() pdf(file.path(pdf.directory,"eset4.pdf")) size <- heatmap.expressionSet( eset4, legend.range="data", colors.palette = dichromat(rich.colors(190)[1:128]), row.col.groups.display=TRUE, title.just=c("left","top"), title.maxlines=2, row.labels=featureNames(eset), subtitle.main="", row.order="hclust",row.groups.hclust=FALSE, ) dev.off() pdf(file.path(pdf.directory,"eset4.pdf"),width=size[1],height=size[2]) size <- heatmap.expressionSet( eset4, legend.range="data", colors.palette = dichromat(rich.colors(190)[1:128]), row.col.groups.display=TRUE, title.just=c("left","top"), title.maxlines=2, row.labels=featureNames(eset), subtitle.main="", row.order="hclust",row.groups.hclust=FALSE, ) dev.off() pdf(file.path(pdf.directory,"eset5.pdf")) size <- heatmap.expressionSet(eset5,row.order="hclust",row.groups.hclust=FALSE) dev.off() pdf(file.path(pdf.directory,"eset5.pdf"),width=size[1],height=size[2]) heatmap.expressionSet(eset5,row.order="hclust",row.groups.hclust=FALSE) dev.off() ## End(Not run)
This function displays the distribution of the p values using a histogram; the horizontal line represents a uniform distribution based on the p value distribution between 0.5 and 1. This represents the hypothetical p value distribution arising just by chance. This uniform distribution is used to estimate the proportion of differentially expressed genes.
histPvalue(object, ...) ## S4 method for signature 'limma' histPvalue(object, ...) ## S4 method for signature 'MArrayLM' histPvalue(object, coef, ...) ## S4 method for signature 'numeric' histPvalue(object, ...)histPvalue(object, ...) ## S4 method for signature 'limma' histPvalue(object, ...) ## S4 method for signature 'MArrayLM' histPvalue(object, coef, ...) ## S4 method for signature 'numeric' histPvalue(object, ...)
object |
either a numeric vector of p-values,
or an object of class |
... |
further arguments passed to the method |
coef |
index of the coefficient for which the p values should be plotted; only applies to the MArrayLM method |
The histogram is displayed on the current device.
Willem Talloen and Tobias Verbeke
Goehlmann, H. and W. Talloen (2009). Gene Expression Studies Using Affymetrix Microarrays, Chapman \& Hall/CRC, p. 253.
if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) tTestResult <- tTest(ALL, "BTtype") histPvalue(tTestResult[,"p"], addLegend = TRUE) propDEgenesRes <- propDEgenes(tTestResult[,"p"]) }if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) tTestResult <- tTest(ALL, "BTtype") histPvalue(tTestResult[,"p"], addLegend = TRUE) propDEgenesRes <- propDEgenes(tTestResult[,"p"]) }
Workhorse function for the histPvalue function. This function displays the distribution of the p values using a histogram; the horizontal line represents a uniform distribution based on the p value distribution between 0.5 and 1. This represents the hypothetical p value distribution arising just by chance. This uniform distribution is used to estimate the proportion of differentially expressed genes.
histpvalueplotter( pValue, addLegend = FALSE, xlab = NULL, ylab = NULL, main = NULL, ... )histpvalueplotter( pValue, addLegend = FALSE, xlab = NULL, ylab = NULL, main = NULL, ... )
pValue |
numeric vector of p values |
addLegend |
logical; should a legend be added (TRUE) or not (FALSE; default |
xlab |
label for the x axis; defaults to NULL (no label) |
ylab |
label for the y axis; defaults to NULL (no label |
main |
main title for the plot; if NULL (default) no main title is displayed |
... |
further arguments for the |
no returned value, a plot is drawn to the current device.
Willem Talloen and Tobias Verbeke
histPvalue, propdegenescalculation
if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) tTestResult <- tTest(ALL, "BTtype") histPvalue(tTestResult[,"p"], addLegend = TRUE, xlab = "Adjusted P Value") histPvalue(tTestResult[,"p"], addLegend = TRUE, main = "Histogram of Adjusted P Values") propDEgenesRes <- propDEgenes(tTestResult[,"p"]) }if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) tTestResult <- tTest(ALL, "BTtype") histPvalue(tTestResult[,"p"], addLegend = TRUE, xlab = "Adjusted P Value") histPvalue(tTestResult[,"p"], addLegend = TRUE, main = "Histogram of Adjusted P Values") propDEgenesRes <- propDEgenes(tTestResult[,"p"]) }
Multiple regression using the Lasso algorithm as implemented in the glmnet package. This is a theoretically nice approach to see which combination of genes predict best a continuous response. Empirical evidence that this actually works with high-dimensional data is however scarce.
lassoReg(object, covariate)lassoReg(object, covariate)
object |
object containing the expression measurements; currently the only method supported is one for ExpressionSet objects |
covariate |
character string indicating the column containing the continuous covariate. |
object of class glmnet
Willem Talloen
Goehlmann, H. and W. Talloen (2009). Gene Expression Studies Using Affymetrix Microarrays, Chapman \& Hall/CRC, pp. 211.
if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) resultLasso <- lassoReg(object = ALL[1:100,], covariate = "age") plot(resultLasso, label = TRUE, main = "Lasso coefficients in relation to degree of penalization.") featResultLasso <- topTable(resultLasso, n = 15) }if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) resultLasso <- lassoReg(object = ALL[1:100,], covariate = "age") plot(resultLasso, label = TRUE, main = "Lasso coefficients in relation to degree of penalization.") featResultLasso <- topTable(resultLasso, n = 15) }
Wrapper for the limma function for the comparison of two groups (two factor levels)
limmaReg(object, covariable, probe2gene = TRUE)limmaReg(object, covariable, probe2gene = TRUE)
object |
object of class ExpressionSet |
covariable |
string indicating the variable defining the continuous covariate |
probe2gene |
logical; if |
Wrapper for the limma function for the comparison of two groups (two factor levels)
limmaTwoLevels(object, group, probe2gene = TRUE)limmaTwoLevels(object, group, probe2gene = TRUE)
object |
object of class ExpressionSet |
group |
string indicating the variable defining the two groups to be compared |
probe2gene |
logical; if |
S4 object of class 'limma' with the following two components:
MArrayLM |
S4 object of class MArrayLM as returned by the limma function of the limma package |
geneSymbols |
character vector of gene symbols; this slot is only
populated if |
A 'topTable' method is defined for 'limma' objects.
Tobias Verbeke and Willem Talloen
Logistic regression for predicting the probability to belong to a certain class in binary classification problems.
logReg( object, groups, probesetId = NULL, geneSymbol = NULL, main = NULL, probe2gene = TRUE, ... )logReg( object, groups, probesetId = NULL, geneSymbol = NULL, main = NULL, probe2gene = TRUE, ... )
object |
ExpressionSet object for the experiment |
groups |
String containing the name of the grouping variable. This should be a
the name of a column in the |
probesetId |
The probeset ID. These should be stored in the |
geneSymbol |
The gene symbol. These should be stored in the column |
main |
Main title on top of the gra |
probe2gene |
Boolean indicating whether the probeset should be translated to a gene symbol (used for the default title of the plot) |
... |
Possibility to add extra plot options. See |
It will always estimate probability scores to belong to the second level of the factor variable. If a probability score to other level is preferred, then you need to change the order of the levels of the factor.
A data.frame object with three columns and rownames
rownames |
The 'sampleNames' of the expressionSet object |
x |
The expression values for the specified gene for all samples |
y |
The labels of the samples |
fit |
The fitted probability score to belong to one of the two classes. |
Willem Talloen
## Not run: if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) logRegRes <- logReg(geneSymbol = "HLA-DPB1", object = ALL, groups = "BTtype") # scoresplot probabilitiesPlot(proportions = logRegRes$fit, classVar = logRegRes$y, sampleNames = rownames(logRegRes), main = 'Probability of being a T-cell type ALL') # barplot probabilitiesPlot(proportions = logRegRes$fit, classVar = logRegRes$y, barPlot=TRUE, sampleNames = rownames(logRegRes), main = 'Probability of being a T-cell type ALL') } ## End(Not run)## Not run: if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) logRegRes <- logReg(geneSymbol = "HLA-DPB1", object = ALL, groups = "BTtype") # scoresplot probabilitiesPlot(proportions = logRegRes$fit, classVar = logRegRes$y, sampleNames = rownames(logRegRes), main = 'Probability of being a T-cell type ALL') # barplot probabilitiesPlot(proportions = logRegRes$fit, classVar = logRegRes$y, barPlot=TRUE, sampleNames = rownames(logRegRes), main = 'Probability of being a T-cell type ALL') } ## End(Not run)
Simulated data set used to demonstrate nlcv and accompanying plot functions to study classification problems
nlcvTTnlcvTT
The object is of class "nlcv", an object as produced
by the nlcv function.
data simulated using:
nlcvTT <- nlcv(selBcrAblOrNeg, classVar = 'mol.biol',
classdist = "unbalanced", nRuns = 10, fsMethod = "t.test",
verbose = TRUE)
## Not run: data(nlcvTT) if (require(nlcv)) # on R-Forge scoresPlot(nlcvTT, tech = 'svm', nfeat = 25) ## End(Not run)## Not run: data(nlcvTT) if (require(nlcv)) # on R-Forge scoresPlot(nlcvTT, tech = 'svm', nfeat = 25) ## End(Not run)
Pick One or More OA Colors
oaColors(color = NULL, alpha = 1)oaColors(color = NULL, alpha = 1)
color |
a character vector of color names; possible values are "red", "orange", "yellow", "green", "cyan", "blue", "pink", "limegreen", "purple", "black", "white", "grey" or "gray" |
alpha |
transparency level for the color(s) |
character vector of colors
Tobias Verbeke
Generate a Palette of OA Colors
oaPalette(numColors = NULL, alpha = 1)oaPalette(numColors = NULL, alpha = 1)
numColors |
number of colors to be contained in the palette |
alpha |
transparency level of the colors |
vector of colors
Jason Waddell
Create a profile plot for a given gene. A profile plot displays the expression values (y-axis)
by samples (x-axis), sorted by group. This is a useful working graph as samples can be
directly identified. For presentation purposes, a boxPlot can also be considered. with jittered for readability of the plot.
plot1gene( probesetId = NULL, geneSymbol = NULL, object, groups, main = NULL, colvec = NULL, colgroups = NULL, probe2gene = TRUE, sampleIDs = TRUE, addLegend = TRUE, legendPos = "topleft", cex = 1.5, ... )plot1gene( probesetId = NULL, geneSymbol = NULL, object, groups, main = NULL, colvec = NULL, colgroups = NULL, probe2gene = TRUE, sampleIDs = TRUE, addLegend = TRUE, legendPos = "topleft", cex = 1.5, ... )
probesetId |
The probeset ID. These should be stored in the |
geneSymbol |
The gene symbol. These should be stored in the column |
object |
ExpressionSet object for the experiment |
groups |
String containing the name of the grouping variable. This should be a
name of a column in the |
main |
Main title on top of the graph |
colvec |
Vector of colors to be used for the groups. If not specified, the default colors of
|
colgroups |
String containing the name of the variable to color the superimposed dots.
This should be a the name of a column in the |
probe2gene |
Boolean indicating whether the probeset should be translated to a gene symbol (used for the default title of the plot) |
sampleIDs |
A boolean or a string to determine the labels on the x-axis. Setting it to FALSE
results in no labels (interesting when the labels are unreadable due to large sample sizes).
Setting it to a string will put the values of that particular |
addLegend |
Boolean indicating whether a legend for the colors of the dots should be added. |
legendPos |
Specify where the legend should be placed. Typically either |
cex |
character expansion used for the plot symbols; defaults to 1.5 |
... |
Further arguments, e.g. to add extra plot options. See |
If a geneSymbol is given that has more than one probeSet,
the plots for only the first probeSet is displayed.
A character vector of corresponding probeset IDs is returned invisibly,
so that one can check the profiles of the other related probeset IDs with
an extra plot1gene statement
If a probesetId is given, one single profile plot for the probeset is
displayed.
S. Osselaer, W. Talloen, T. Verbeke
plotCombination2genes, boxPlot
if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) # one variable (specified by groups) plot1gene(geneSymbol = 'HLA-DPB1', object = ALL, groups = "BT", addLegend = TRUE, legendPos = 'topright') # two variables (specified by groups and colGroups) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) plot1gene(probeset = '1636_g_at', object = ALL, groups = 'BT', colgroups = 'mol.biol', legendPos='topright', sampleIDs = 'BT') }if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) # one variable (specified by groups) plot1gene(geneSymbol = 'HLA-DPB1', object = ALL, groups = "BT", addLegend = TRUE, legendPos = 'topright') # two variables (specified by groups and colGroups) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) plot1gene(probeset = '1636_g_at', object = ALL, groups = 'BT', colgroups = 'mol.biol', legendPos='topright', sampleIDs = 'BT') }
Plots the correlation in gene expression between two samples. Each dot represents a gene, and the dots have a density-dependent coloring. Genes with exceptional behavior can be highlighted by showing their gene symbol.
plotComb2Samples( object, x, y, trsholdX = NULL, trsholdY = NULL, probe2gene = TRUE, ... )plotComb2Samples( object, x, y, trsholdX = NULL, trsholdY = NULL, probe2gene = TRUE, ... )
object |
ExpressionSet object for the experiment |
x |
String containing the name of the first sample. This should be a
the name of a column in the |
y |
String containing the name of the second sample. See |
trsholdX |
Vector of two values specifying the X-axis thresholds within which genes should be highlighted by their gene symbol. |
trsholdY |
Vector of two values specifying the Y-axis thresholds within which genes should be highlighted by their gene symbol. |
probe2gene |
Boolean indicating whether the probeset should be translated to a gene symbol (used for the default title of the plot) |
... |
Possibility to add extra plot options. See |
No returned value, a plot is drawn to the current device.
W. Talloen
if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) plotComb2Samples(ALL,"84004", "01003", trsholdX = c(10,12), trsholdY = c(4,6), xlab = "a B-cell", ylab = "a T-cell") }if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) plotComb2Samples(ALL,"84004", "01003", trsholdX = c(10,12), trsholdY = c(4,6), xlab = "a B-cell", ylab = "a T-cell") }
Plot a Combination of Two Genes
plotCombination2genes( probesetId1 = NULL, probesetId2 = NULL, geneSymbol1 = NULL, geneSymbol2 = NULL, object, groups, addLegend = TRUE, legendPos = "topleft", probe2gene = TRUE, colvec = NULL, ... )plotCombination2genes( probesetId1 = NULL, probesetId2 = NULL, geneSymbol1 = NULL, geneSymbol2 = NULL, object, groups, addLegend = TRUE, legendPos = "topleft", probe2gene = TRUE, colvec = NULL, ... )
probesetId1 |
First probeset id, plotted in the x-axis |
probesetId2 |
Second probeset id, plotted in the y-axis |
geneSymbol1 |
First gene symbol, plotted in the x-axis |
geneSymbol2 |
Second gene symbol, plotted in the y-axi |
object |
ExpressionSet object for the experiment |
groups |
string containing the name of the grouping variable |
addLegend |
Logical value to indicate whether a legend needs to be draw |
legendPos |
Position on the graph where to put the legend |
probe2gene |
should the probeset be translated to a gene symbol (used for the default title of the plot |
colvec |
a character vector of colors. If not specified it will be
automatically generated by |
... |
This allows to specify typical arguments in the |
If a gene id is given, the plots for only the first probeset is displayed and a character vector of corresponding probeset IDs is returned invisibly. It is a list containing
probeset1 |
Probeset ids measuring 'gene1' |
probeset1 |
Probeset ids measuring 'gene1' |
If a probeset id is given, one single profile plot for the probeset is displayed.
W. Talloen, T. Verbeke
if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) aa <- plotCombination2genes(geneSymbol1 = 'HLA-DPB1', geneSymbol2 = 'CD3D', object = ALL, groups = "BT", addLegend = TRUE, legendPos = 'topright') aa }if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) aa <- plotCombination2genes(geneSymbol1 = 'HLA-DPB1', geneSymbol2 = 'CD3D', object = ALL, groups = "BT", addLegend = TRUE, legendPos = 'topright') aa }
Plots the correlation in gene expression between more than 2 samples
plotCombMultSamples(exprsMatrix, ...)plotCombMultSamples(exprsMatrix, ...)
exprsMatrix |
ExpressionSet object to plot. For larger datasets, this will typically be a subset of the data. |
... |
Further arguments, e.g. to add extra plot options. See |
no returned value, a plots is drawn in the current device
Willem Talloen
if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) plotCombMultSamples(exprs(ALL)[,c("84004", "11002", "01003")]) }if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) plotCombMultSamples(exprs(ALL)[,c("84004", "11002", "01003")]) }
Plot ratios of expression values observed in a treatment versus those of a reference. First the ratios and variances are computated on the gene expression data.
plotLogRatio( e, reference, within = NULL, across = NULL, nReplicatesVar = 3, filename = "Rplots", device = "svg", orderBy = list(rows = "hclust", cols = NULL), colorsColumns = NULL, colorsColumnsBy = NULL, colorsColumnsByPalette = c("#1B9E77", "#D95F02", "#7570B3", "#E7298A", "#66A61E", "#E6AB02", "#A6761D", "#666666"), colorsUseMeanQuantiles = FALSE, colorsMeanQuantilesPalette = c("orange", "red", "darkred"), colorsBarsMatrix = NULL, colorsGenesNames = c("black"), main = paste("log2 ratio's"), shortvarnames = NULL, longvarnames = NULL, gene.length = 50, gene.fontsize = 6, main.fontsize = 9, columnhead.fontsize = 8, mx = 1.5, exp.width = 1.8, exp.height = 0.2, log2l.show = TRUE, log4l.show = FALSE, quantiles.show = FALSE, quantiles.compute = c(0.9), error.show = TRUE, view.psid = FALSE, errorLabel = "Error bars show the pooled standard deviation", closeX11 = FALSE, openFile = FALSE, tooltipvalues = FALSE, probe2gene = TRUE, ... )plotLogRatio( e, reference, within = NULL, across = NULL, nReplicatesVar = 3, filename = "Rplots", device = "svg", orderBy = list(rows = "hclust", cols = NULL), colorsColumns = NULL, colorsColumnsBy = NULL, colorsColumnsByPalette = c("#1B9E77", "#D95F02", "#7570B3", "#E7298A", "#66A61E", "#E6AB02", "#A6761D", "#666666"), colorsUseMeanQuantiles = FALSE, colorsMeanQuantilesPalette = c("orange", "red", "darkred"), colorsBarsMatrix = NULL, colorsGenesNames = c("black"), main = paste("log2 ratio's"), shortvarnames = NULL, longvarnames = NULL, gene.length = 50, gene.fontsize = 6, main.fontsize = 9, columnhead.fontsize = 8, mx = 1.5, exp.width = 1.8, exp.height = 0.2, log2l.show = TRUE, log4l.show = FALSE, quantiles.show = FALSE, quantiles.compute = c(0.9), error.show = TRUE, view.psid = FALSE, errorLabel = "Error bars show the pooled standard deviation", closeX11 = FALSE, openFile = FALSE, tooltipvalues = FALSE, probe2gene = TRUE, ... )
e |
ExpressionSet object to use |
reference |
A list with two items: var and level - See details |
within |
Character vector - names of pData columns - See details |
across |
Character vector - names of pData columns - See details |
nReplicatesVar |
Integer - Minimum number of replicates to compute variance |
filename |
Name of the filename to use. No need to specify extension which will be added according to device. |
device |
One of 'pdf', 'X11', 'png', 'svg'. For svg device, one X11 device is also opened. |
orderBy |
See details |
colorsColumns |
A vector of colors to be used for plotting columns; default value is NULL which ends up with red – see Colors section |
colorsColumnsBy |
A vector of pData columns which combinations specify different colors to be used – see Colors section |
colorsColumnsByPalette |
If colorsColumns is NULL, vector of colors to be used for coloring columns potentially splitted by colorsColumnsBy |
colorsUseMeanQuantiles |
Boolean to indicate if the quantile groups computed on averages over all treatments should be used for coloring – see Colors section |
colorsMeanQuantilesPalette |
if colorsUseMeanQuantiles is TRUE, these colors will be used for the different groups – see Colors section |
colorsBarsMatrix |
Matrix of colors to be used for each individual bar; colors are provided for genes in data order and thus are possibly reordered according to orderBy – see Colors sectio |
colorsGenesNames |
Vector of colors to be used for gene names; will be recycled if necessary; colors are provided for genes in data order and thus are possibly reordered according to orderBy |
main |
Main title |
shortvarnames |
ector or pData column to be used to display in graph columns. If NULL, those names will be used from the coded names added to pData during computations (list of columns values pasted with a dot). Warning: shortvarnames must be defined in the order columns are present in the ExpressionSet object so that they will be reordered if one asks to order columns. |
longvarnames |
pData column to be used in SVG tooltip title. If NULL, shortvarnames will be used. Same warning than shortvarnames about ordering |
gene.length |
Maximum number of characters that will be printed of the gene names |
gene.fontsize |
Font size for the gene names , default = |
main.fontsize |
Font size for the main, default = 9 |
columnhead.fontsize |
Font size for the column headers, default = 8 |
mx |
Expansion factor for the width of the bars that represent the expression ratios |
exp.width |
Expansion factor for global graph width, and the space between the plotted colum |
exp.height |
Expansion factor for global graph height, and the space between the plotted row |
log2l.show |
A logical value. If 'TRUE', the line for log2 values on each column (when max(data) > 2) is draw |
log4l.show |
A logical value. If 'TRUE', the line for log4 values on each column (when max(data) > 4) is drawn |
quantiles.show |
A logical value. If 'TRUE', a line is drawn for quantiles computed separately on each column |
quantiles.compute |
A logical value. If 'TRUE', the vector quantiles will be computed and displayed
provided that |
error.show |
A logical value. If 'TRUE', errors bars are displayed on the graph (only for those columns for which they are available |
view.psid |
A logical value. If 'TRUE', the genes psid is displayed on the gene name |
errorLabel |
A character vector describing the error bars, printed at the bottom of the figu |
closeX11 |
If |
openFile |
A logical value. If 'TRUE', the produced output file is opened |
tooltipvalues |
If device is SVG, one can choose to display each bar separately, with data values as tooltips. Note however that each bar will be considered as a distinct object instead of a column, which will takes much more time to create the graph and produces a much bigger SVG file |
probe2gene |
Boolean indicating whether the probeset should be translated to a gene symbol (used for the default title of the plot |
... |
|
The ExpressionSet object with the computated variables is returned.
orderBy: A list with two components, rows and cols, each one possibly being NULL (no ordering
on the specific dimension). Ordering on cols can be done according to (a) pData column(s)
(for example: c('cellline','compound','dose'. Ordering on rows can be done using of the
following values:
NULLno reordering on rows
numeric vectoruse the vector values to sort rows
alphause genes names alphabetice order
effecttry to assess global gene expression level by taking sum(abs(values)) on specified exprs columns)
hclustuse the ordering returned by hclust invoked on specified exprs columns
The management of colors is very flexible but is a little bit tricky, as a variety of parameters are available to the user. Basically, combinations of arguments allow to set colors for columns headers (text), columns as a whole (different colors for the different columns) or for each of the inividual horizontal bars. By default, everything is red. There are four main different arguments that can be used and that are applied in a consecutive order. Each one may override a previous argument value. Below is a list of arguments and their consecutive actions:
colorsColumns The first way to assign colors is to provide a vector of colors that will
be used for each column (headers and its horizontal bars). This vector is recycled so that providing one unique
value will color all columns, whereas providing a vector of length 2 will alternate columnns colors.
colorsColumnsByTo be used when the experiment involves groupings for pData, for example dose,
cellline or treatment. In order to see the effects of such variables, one can color columns using
combinations of those. The argument is a vector of pData columns such as c('cellline','dose').
Unique combinations will be computed and a color will be assigned for each group of columns.
The vector that is provided with the argument colorsColumnsByPalette is used to assign colors.
If the argument colorColumnsBy is not NULL then it overrides the previous argument colorsColumns.
colorsUseMeanQuantiles A logical value. The default plotGeneDE displays for each gene the expression value difference
between treatment and reference, but does not reveal any information about the expression levels in these conditions.
Parameter colorsUseMeanQuantiles allows to color the horizontal bars according to expression level that
is derived from quantiles computed on averages of the complete ExpressionSet object.
As it involves the expression data of all probesets, computations must be done
before subsetting the ExpressionSet object and the plotGeneDEting. The function addQuantilesColors
computes quantiles and corresponding mean expression level intervals. If colorsUseMeanQuantiles 'TRUE',
previous coloring parameters are overriden. The parameter colorsMeanQuantilesPalette is used to assign
colors for average-quantiles-groups. Note that columns headers are still given by previous arguments.
colorsBarsMatrixThe most flexible way to assign colors as the matrix will be used to color each bar
of the plot individually. A check is done to ensure that the number of rows and columns are not less than the number of
probesets and columns. If not NULL, this parameter overrides the previous ones.
Hinrich Goehlmann and Eric Lecoutre
computeLogRatio,addQuantilesColors
if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) ALL2 <- ALL[,ALL$BT != 'T1'] # omit subtype T1 as it only contains one sample ALL2$BTtype <- as.factor(substr(ALL2$BT,0,1)) # create a vector with only T and B # Test for differential expression between B and T cells tTestResult <- tTest(ALL, "BTtype", probe2gene = FALSE) topGenes <- rownames(tTestResult)[1:20] # plot the log ratios versus subtype B of the top genes LogRatioALL <- computeLogRatio(ALL2, reference=list(var='BT',level='B')) a <- plotLogRatio(e=LogRatioALL[topGenes,],openFile=FALSE, tooltipvalues=FALSE, device='pdf', colorsColumnsBy=c('BTtype'), main = 'Top 20 genes most differentially between T- and B-cells', orderBy = list(rows = "hclust"), probe2gene = TRUE) ## Not run: a <- plotLogRatio(e=LogRatioALL[topGenes,],openFile=TRUE, tooltipvalues=FALSE, device='pdf', colorsColumnsBy=c('BTtype'), main = 'Top 20 genes most differentially between T- and B-cells', orderBy = list(rows = "hclust", cols = "sex"), probe2gene = TRUE) ## End(Not run) }if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) ALL2 <- ALL[,ALL$BT != 'T1'] # omit subtype T1 as it only contains one sample ALL2$BTtype <- as.factor(substr(ALL2$BT,0,1)) # create a vector with only T and B # Test for differential expression between B and T cells tTestResult <- tTest(ALL, "BTtype", probe2gene = FALSE) topGenes <- rownames(tTestResult)[1:20] # plot the log ratios versus subtype B of the top genes LogRatioALL <- computeLogRatio(ALL2, reference=list(var='BT',level='B')) a <- plotLogRatio(e=LogRatioALL[topGenes,],openFile=FALSE, tooltipvalues=FALSE, device='pdf', colorsColumnsBy=c('BTtype'), main = 'Top 20 genes most differentially between T- and B-cells', orderBy = list(rows = "hclust"), probe2gene = TRUE) ## Not run: a <- plotLogRatio(e=LogRatioALL[topGenes,],openFile=TRUE, tooltipvalues=FALSE, device='pdf', colorsColumnsBy=c('BTtype'), main = 'Top 20 genes most differentially between T- and B-cells', orderBy = list(rows = "hclust", cols = "sex"), probe2gene = TRUE) ## End(Not run) }
Function to plot the probabilities to belong to a certain class in binary classification problems. These probabilities are often calculated using a logistic regression model. The class membership of the samples is displayed using a colored strip (with legend below the plot).
probabilitiesPlot( proportions, classVar, sampleNames, plot = TRUE, barPlot = FALSE, layout = TRUE, main = NULL, sub = NULL, ... )probabilitiesPlot( proportions, classVar, sampleNames, plot = TRUE, barPlot = FALSE, layout = TRUE, main = NULL, sub = NULL, ... )
proportions |
A vector containing the calculated probabilities to belong to a certain class in binary classification problems. These probabilities are often calculated using a logistic regression model. |
classVar |
A vector containing the class where the sample belongs t |
sampleNames |
A vector with the names of the samp |
plot |
logical. If |
barPlot |
Should a barplot be drawn ( |
layout |
boolean indicating whether |
main |
Main title for the scores plot; if not supplied, 'Scores Plot' is used as a defaul |
sub |
Subtitle for the scores plot; if not supplied, the classification technique and the chosen number of features are displayed |
... |
Additional graphical parameters to pass to the plot functio |
no returned value, a plot is drawn in the current device.
Willem Talloen and Tobias Verbeke
## Not run: if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) logRegRes <- logReg(geneSymbol = "HLA-DPB1", object = ALL, groups = "BTtype") # scoresplot probabilitiesPlot(proportions = logRegRes$fit, classVar = logRegRes$y, sampleNames = rownames(logRegRes), main = 'Probability of being a T-cell type ALL') # barplot probabilitiesPlot(proportions = logRegRes$fit, classVar = logRegRes$y, barPlot=TRUE, sampleNames = rownames(logRegRes), main = 'Probability of being a T-cell type ALL') } ## End(Not run)## Not run: if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) logRegRes <- logReg(geneSymbol = "HLA-DPB1", object = ALL, groups = "BTtype") # scoresplot probabilitiesPlot(proportions = logRegRes$fit, classVar = logRegRes$y, sampleNames = rownames(logRegRes), main = 'Probability of being a T-cell type ALL') # barplot probabilitiesPlot(proportions = logRegRes$fit, classVar = logRegRes$y, barPlot=TRUE, sampleNames = rownames(logRegRes), main = 'Probability of being a T-cell type ALL') } ## End(Not run)
Auxiliary function for (currently) spectralMap allowing the conversion of Affy probeset IDs to gene symbols
probe2gene(probesetIds, chipPkg)probe2gene(probesetIds, chipPkg)
probesetIds |
Affymetrix probeset IDs |
chipPkg |
string indicating the annotation package for the chip |
Vector containing the respective gene symbols
Tobias Verbeke
spectralMap, lassoClass, ...
if (require(ALL)){ data(ALL, package = "ALL") chip <- annotation(ALL) chipAnnotationPkg <- paste(chip, "db", sep = ".") res <- probe2gene(featureNames(ALL), chipAnnotationPkg) head(res) }if (require(ALL)){ data(ALL, package = "ALL") chip <- annotation(ALL) chipAnnotationPkg <- paste(chip, "db", sep = ".") res <- probe2gene(featureNames(ALL), chipAnnotationPkg) head(res) }
Plot expression profiles of multiple genes or probesets Plot expression profiles of multiple genes or probesets. Each line depicts a gene, and the color legend can be used to identify the gene.
profilesPlot( object, probesetIds, sampleIDs = TRUE, addLegend = TRUE, legendPos = "topleft", colvec = NULL, orderGroups = NULL, ... )profilesPlot( object, probesetIds, sampleIDs = TRUE, addLegend = TRUE, legendPos = "topleft", colvec = NULL, orderGroups = NULL, ... )
object |
ExpressionSet object for the experiment |
probesetIds |
The probeset ID. These should be stored in the |
sampleIDs |
A boolean or a string to determine the labels on the x-axis. Setting it to FALSE
results in no labels (interesting when the labels are unreadable due to large sample sizes).
Setting it to a string will put the values of that particular |
addLegend |
Boolean indicating whether a legend for the colors of the dots should be added. |
legendPos |
Specify where the legend should be placed. Typically either |
colvec |
Vector of colors to be used for the groups. If not specified, the default colors of
|
orderGroups |
String containing the name of the grouping variable to order the samples
in the x-axis accordingly. This should be a name of a column in the |
... |
Possibility to add extra plot options. See |
No returned value, a plot is drawn in the current device.
W. Talloen
if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) myGeneSymbol <- c("LCK") # a gene probesetPos <- which(myGeneSymbol == featureData(ALL)$SYMBOL) myProbesetIds <- featureNames(ALL)[probesetPos] profilesPlot(object = ALL, probesetIds = myProbesetIds, orderGroups = "BT", sampleIDs = "BT") }if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) myGeneSymbol <- c("LCK") # a gene probesetPos <- which(myGeneSymbol == featureData(ALL)$SYMBOL) myProbesetIds <- featureNames(ALL)[probesetPos] profilesPlot(object = ALL, probesetIds = myProbesetIds, orderGroups = "BT", sampleIDs = "BT") }
Generic function to compute the proportion of differentially expressed genes that are present
propDEgenes(object, ...)propDEgenes(object, ...)
object |
object of class |
... |
further arguments for the method (currently none implemented) |
numeric of length one giving the proportion of differentially expressed genes
Willem Talloen and Tobias Verbeke
Generic function to compute the proportion of differentially expressed genes that are present
## S4 method for signature 'limma' propDEgenes(object, ...) ## S4 method for signature 'numeric' propDEgenes(object, ...)## S4 method for signature 'limma' propDEgenes(object, ...) ## S4 method for signature 'numeric' propDEgenes(object, ...)
object |
object of class |
... |
further arguments for the method (currently none implemented) |
numeric of length one giving the proportion of differentially expressed genes
limma
propDEgenes method for a limma object
numeric
propDEgenes method for a numeric vector, i.e. a vector of P Values
Willem Talloen and Tobias Verbeke
Estimation of proportion of differentially expressed genes. This estimation is based on a histogram of the p-values. More specifically, based on the horizontal line representing a uniform distribution based on the p value distribution between 0.5 and 1. This represents the hypothetical p value distribution arising just by chance. All genes with small p-values above this line reflect the expected number of differentially expressed genes not by chance.
propdegenescalculation(pValue)propdegenescalculation(pValue)
pValue |
a vector of p-values |
proportion of differential genes
Willem Talloen and Tobias Verbeke
if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) tTestResult <- tTest(ALL, "BTtype") histPvalue(tTestResult[,"p"], addLegend = TRUE) propDEgenesRes <- propDEgenes(tTestResult[,"p"]) }if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) tTestResult <- tTest(ALL, "BTtype") histPvalue(tTestResult[,"p"], addLegend = TRUE) propDEgenesRes <- propDEgenes(tTestResult[,"p"]) }
Given a vector, returns the replicates in order
replicates(x)replicates(x)
x |
character or numeric vector |
numeric vector
Henrique Dallazuanna
R-help mailing list
x <- c('a','b','a','a','b','a','c','c','c') data.frame(val=x,rep=replicates(x))x <- c('a','b','a','a','b','a','c','c','c') data.frame(val=x,rep=replicates(x))
Generic function to draw a spectral map, according to JnJ Standards
spectralMap(object, groups, ...)spectralMap(object, groups, ...)
object |
object of class ExpressionSet |
groups |
string indicating the name of the column in the phenoData that defines the groups |
... |
further arguments to be passed to the methods |
Object of class plot.mpm, i.e. the S3 output object of the plot.mpm
function of the mpm package
Coloring of groups on the spectralMap uses the a4 palette as produced
by a4palette
Tobias Verbeke
Wouters, L., Goehlmann, H., Bijnens, L., Kass, S.U., Molenberghs, G., Lewi, P.J. (2003). Graphical exploration of gene expression data: a comparative study of three multivariate methods. Biometrics 59, 1131-1140. Goehlmann, H. and W. Talloen (2009). Gene Expression Studies Using Affymetrix Microarrays, Chapman \& Hall/CRC, pp. 148 - 153.
if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) spectralMap(object = ALL, groups = "BT", legendPos = 'bottomright') spectralMap(object = ALL, groups = "BT", plot.mpm.args = list(label.tol = 10, rot = c(-1, 1), sub = "", lab.size = 0.65, dim = c(1,2), sampleNames = FALSE, zoom = c(1,5), col.size = 2, do.smoothScatter = TRUE)) spectralMap(object = ALL, groups = "BT", plot.mpm.args = list(label.tol = 10, rot = c(-1, 1), sub = "", lab.size = 0.65, dim = c(1,2), sampleNames = as.character(pData(ALL)$BT), zoom = c(1,5), col.size = 2, do.smoothScatter = TRUE)) }if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) spectralMap(object = ALL, groups = "BT", legendPos = 'bottomright') spectralMap(object = ALL, groups = "BT", plot.mpm.args = list(label.tol = 10, rot = c(-1, 1), sub = "", lab.size = 0.65, dim = c(1,2), sampleNames = FALSE, zoom = c(1,5), col.size = 2, do.smoothScatter = TRUE)) spectralMap(object = ALL, groups = "BT", plot.mpm.args = list(label.tol = 10, rot = c(-1, 1), sub = "", lab.size = 0.65, dim = c(1,2), sampleNames = as.character(pData(ALL)$BT), zoom = c(1,5), col.size = 2, do.smoothScatter = TRUE)) }
Methods for spectralMap
## S4 method for signature 'ExpressionSet,character' spectralMap( object, groups, makeLognormal = TRUE, mpm.args = list(row.weight = "mean", col.weight = "constant", logtrans = TRUE), plot.mpm.args = list(zoom = c(1, 2), label.tol = 10, rot = c(-1, 1), sub = "", lab.size = 0.85, col.group = pData(object)[, groups], colors = c("wheat", "darkgrey", a4palette(nlevels(pData(object)[, groups]))), col.size = 2, do.smoothScatter = TRUE), probe2gene = TRUE, addLegend = TRUE, legendPos = "topleft", ... )## S4 method for signature 'ExpressionSet,character' spectralMap( object, groups, makeLognormal = TRUE, mpm.args = list(row.weight = "mean", col.weight = "constant", logtrans = TRUE), plot.mpm.args = list(zoom = c(1, 2), label.tol = 10, rot = c(-1, 1), sub = "", lab.size = 0.85, col.group = pData(object)[, groups], colors = c("wheat", "darkgrey", a4palette(nlevels(pData(object)[, groups]))), col.size = 2, do.smoothScatter = TRUE), probe2gene = TRUE, addLegend = TRUE, legendPos = "topleft", ... )
object |
object of class ExpressionSet |
groups |
string indicating the name of the column in the phenoData that defines the groups |
makeLognormal |
boolean indicating whether one wants to exponentiate the
data to make them lognormally shaped ( |
mpm.args |
list of arguments that can be passed to the |
plot.mpm.args |
list of arguments that can be passed to the
|
probe2gene |
boolean indicating whether one wants to display the gene symbols
for the labeled points ( |
addLegend |
Boolean indicating whether a legend for the colors of the dots should be added. |
legendPos |
Specify where the legend should be placed. Typically either |
... |
further arguments to be passed to the methods, currently not used. |
the plot is returned invisibly
Tobias Verbeke
Methods for topTable. topTable extracts the top n most important features for a given classification or regression procedure
## S4 method for signature 'limma' topTable( fit, n = 10, coef = 2, genelist = fit$genes, eb = fit[c("t", "p.value", "lods")], adjust.method = "BH", sort.by = "B", resort.by = NULL, p.value = 1, lfc = 0 ) ## S4 method for signature 'MArrayLM' topTable( fit, n, coef = 2, genelist = fit$genes, eb = fit[c("t", "p.value", "lods")], adjust.method = "BH", sort.by = "B", resort.by = NULL, p.value = 1, lfc = 0 ) ## S4 method for signature 'tTest' topTable(fit, n) ## S4 method for signature 'fTest' topTable(fit, n)## S4 method for signature 'limma' topTable( fit, n = 10, coef = 2, genelist = fit$genes, eb = fit[c("t", "p.value", "lods")], adjust.method = "BH", sort.by = "B", resort.by = NULL, p.value = 1, lfc = 0 ) ## S4 method for signature 'MArrayLM' topTable( fit, n, coef = 2, genelist = fit$genes, eb = fit[c("t", "p.value", "lods")], adjust.method = "BH", sort.by = "B", resort.by = NULL, p.value = 1, lfc = 0 ) ## S4 method for signature 'tTest' topTable(fit, n) ## S4 method for signature 'fTest' topTable(fit, n)
fit |
object resulting from a classification or regression procedure |
n |
number of features that one wants to extract from a table that ranks all features according to their importance in the classification or regression model; defaults to 10 for limma objects |
coef |
column number or column name specifying which coefficient or contrast of the linear model is of interest. For |
genelist |
data frame or character vector containing gene information.
For |
eb |
subset of |
adjust.method |
method used to adjust the p-values for multiple testing. Options, in increasing conservatism, include |
sort.by |
character string specifying which statistic to rank the genes by.
Possible values for |
resort.by |
character string specifying statistic to sort the selected genes by in the output data.frame. Possibilities are the same as for |
p.value |
cutoff value for adjusted p-values. Only genes with lower p-values are listed. |
lfc |
minimum absolute log2-fold-change required.
|
glmnet
glmnet objects are produced by lassoClass or lassoReg
limma
limma objects are produced by limma2Groups
MarrayLM
MarrayLM objects are produced by lmFit of the limma package
pamClass
pamClass objects are produced by pamClass
rfClass
rfClass objects are produced by rfClass
tTest
tTest objects are produced by tTest
fTest
fTest objects are produced by fTest
topTable-methods for: glmnet, lognet and elnet
Use a (modified) t test to compare two groups
tTest(object, groups, probe2gene = TRUE)tTest(object, groups, probe2gene = TRUE)
object |
ExpressionSet object |
groups |
string indicating the name of the variable of the phenoData containing the group information |
probe2gene |
logical; if |
For multiple testing the mt.rawp2adjp function of package
multtest is used.
Object of class "tTest", a data frame with the following columns
gSymbol |
Gene Symbol |
p |
p-value of the difference between the groups |
logRatio |
Log ratio of the expression between the groups |
pBH |
p-value of the difference between the groups, with Benjamini-Hochberg multiplicity correction |
tStat |
Student t-statistic of the different between groups |
Willem Talloen, Tobias Verbeke
rowttests in rowFtests
if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) tTestRes <- tTest(object = ALL, groups = "BTtype", probe2gene = TRUE) volcanoPlot(tTestRes) }if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) tTestRes <- tTest(object = ALL, groups = "BTtype", probe2gene = TRUE) volcanoPlot(tTestRes) }
Generic function to draw a volcano plot. A volcano plot is a graph that allows to simultaneously assess the P values (statistical significance) and log ratios (biological difference) of differential expression for the given genes.
volcanoPlot(x, y, pointLabels, ...)volcanoPlot(x, y, pointLabels, ...)
x |
either an object of class 'tTest', of class 'limma' or a numeric vector of log ratios, i.e. the log of the fold change values; the names of the logRatio vector will be used to display the names of the most interesting gene |
y |
should not be given if an object of class 'tTest' or 'limma' is passed as argument 'x'; if 'x' is a numeric vector of log ratios, 'y' should be given and should be a numeric vector of P-values indicating the statistical significance |
pointLabels |
Labels for points on the volcano plot that are interesting taking into account both the x and y dimensions; typically this is a vector of gene symbols; most methods can access the gene symbols directly from the object passed as 'x' argument; the argument allows for custom labels if needed |
... |
further arguments to specific methods |
The volcano plot is drawn to the current device.
Tobias Verbeke, based on code by Willem Talloen
Goehlmann, H. and W. Talloen (2009). Gene Expression Studies Using Affymetrix Microarrays, Chapman \& Hall/CRC, pp. 148 - 153.
See volcanoplotter
if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) tTestRes <- tTest(object = ALL, groups = "BTtype", probe2gene = TRUE) volcanoPlot(tTestRes) }if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) tTestRes <- tTest(object = ALL, groups = "BTtype", probe2gene = TRUE) volcanoPlot(tTestRes) }
This function draws a volcano plot, a graph that allows to simultaneously assess the statistical and biological significance of differential expression for the given genes.
## S4 method for signature 'tTest,missing,missing' volcanoPlot( x, y, pointLabels, topPValues = 10, topLogRatios = 10, smoothScatter = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, newpage = TRUE, additionalPointsToLabel = NULL, additionalLabelColor = "red" ) ## S4 method for signature 'tTest,missing,character' volcanoPlot( x, y, pointLabels, topPValues = 10, topLogRatios = 10, smoothScatter = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, newpage = TRUE, additionalPointsToLabel = NULL, additionalLabelColor = "red" ) ## S4 method for signature 'limma,missing,missing' volcanoPlot( x, y, pointLabels, topPValues = 10, topLogRatios = 10, smoothScatter = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, newpage = TRUE, additionalPointsToLabel = NULL, additionalLabelColor = "red" ) ## S4 method for signature 'limma,missing,character' volcanoPlot( x, y, pointLabels, topPValues = 10, topLogRatios = 10, smoothScatter = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, newpage = TRUE, additionalPointsToLabel = NULL, additionalLabelColor = "red" ) ## S4 method for signature 'numeric,numeric,character' volcanoPlot( x, y, pointLabels, topPValues = 10, topLogRatios = 10, smoothScatter = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, newpage = TRUE, additionalPointsToLabel = NULL, additionalLabelColor = "red" ) ## S4 method for signature 'numeric,numeric,missing' volcanoPlot( x, y, pointLabels, topPValues = 10, topLogRatios = 10, smoothScatter = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, newpage = TRUE, additionalPointsToLabel = NULL, additionalLabelColor = "red" )## S4 method for signature 'tTest,missing,missing' volcanoPlot( x, y, pointLabels, topPValues = 10, topLogRatios = 10, smoothScatter = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, newpage = TRUE, additionalPointsToLabel = NULL, additionalLabelColor = "red" ) ## S4 method for signature 'tTest,missing,character' volcanoPlot( x, y, pointLabels, topPValues = 10, topLogRatios = 10, smoothScatter = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, newpage = TRUE, additionalPointsToLabel = NULL, additionalLabelColor = "red" ) ## S4 method for signature 'limma,missing,missing' volcanoPlot( x, y, pointLabels, topPValues = 10, topLogRatios = 10, smoothScatter = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, newpage = TRUE, additionalPointsToLabel = NULL, additionalLabelColor = "red" ) ## S4 method for signature 'limma,missing,character' volcanoPlot( x, y, pointLabels, topPValues = 10, topLogRatios = 10, smoothScatter = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, newpage = TRUE, additionalPointsToLabel = NULL, additionalLabelColor = "red" ) ## S4 method for signature 'numeric,numeric,character' volcanoPlot( x, y, pointLabels, topPValues = 10, topLogRatios = 10, smoothScatter = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, newpage = TRUE, additionalPointsToLabel = NULL, additionalLabelColor = "red" ) ## S4 method for signature 'numeric,numeric,missing' volcanoPlot( x, y, pointLabels, topPValues = 10, topLogRatios = 10, smoothScatter = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, newpage = TRUE, additionalPointsToLabel = NULL, additionalLabelColor = "red" )
x |
either an object of class 'tTest', of class 'limma' or a numeric vector of log ratios, i.e. the log of the fold change values; the names of the logRatio vector will be used to display the names of the most interesting gene |
y |
should not be given if an object of class 'tTest' or 'limma' is passed as argument 'x'; if 'x' is a numeric vector of log ratios, 'y' should be given and should be a numeric vector of P-values indicating the statistical significance |
pointLabels |
Labels for points on the volcano plot that are interesting taking into account both the x and y dimensions; typically this is a vector of gene symbols; most methods can access the gene symbols directly from the object passed as 'x' argument; the argument allows for custom labels if needed |
topPValues |
top n points that will be included in the points to label based on their low P Values |
topLogRatios |
top n points that will be included in the points to label based on their high absolute values of the log ratio |
smoothScatter |
use color saturation to indicate dots that are in densely
populated regions of the graph; defaults to |
xlab |
label for the x axis (string) |
ylab |
label for the y axis (string) |
main |
main title for the graph (string) |
sub |
subtitle for the graph (string) |
newpage |
should the graph be drawn to a new grid page? Defaults to
|
additionalPointsToLabel |
Entrez IDs of genes of interest, that will be highlighted on the plot; the color of highlighting is determined by the 'additionalLabelColor' argument. |
additionalLabelColor |
Color used to highlight the 'additionalPointsToLabel'; defaults to "red" |
The set of genes for which labels are displayed is the union of the set of
genes that have lowest P-values (topPValues) and the set of genes
that display the highest absolute values for the log ratios (topLogRatios).
The volcano plot is drawn to the current device.
Tobias Verbeke, based on code by Willem Talloen
Workhorse function for the different volcanoPlot methods. A volcano plot is a graph that allows to simultaneously assess the P values (statistical significance) and log ratios (biological difference) of differential expression for the given genes.
volcanoplotter( logRatio, pValue, pointLabels, topPValues = 10, topLogRatios = 10, logTransformP = TRUE, smoothScatter = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, newpage = TRUE, additionalPointsToLabel = NULL, additionalLabelColor = "red" )volcanoplotter( logRatio, pValue, pointLabels, topPValues = 10, topLogRatios = 10, logTransformP = TRUE, smoothScatter = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, newpage = TRUE, additionalPointsToLabel = NULL, additionalLabelColor = "red" )
logRatio |
numeric vector of log ratios |
pValue |
numeric vector of P values |
pointLabels |
Labels for points on the volcano plot that are interesting taking into account both the x and y dimensions; typically this is a vector of gene symbols; most methods can access the gene symbols directly from the object passed as 'x' argument; the argument allows for custom labels if needed |
topPValues |
top n points that will be included in the points to label based on their low P Values |
topLogRatios |
top n points that will be included in the points to label based on their high absolute values of the log ratio |
logTransformP |
if |
smoothScatter |
use color saturation to indicate dots that are in densely
populated regions of the graph; defaults to |
xlab |
label for the x axis (string) |
ylab |
label for the y axis (string) |
main |
main title for the graph (string) |
sub |
subtitle for the graph (string) |
newpage |
should the graph be drawn to a new grid page? Defaults to
|
additionalPointsToLabel |
Entrez IDs of genes of interest, that will be highlighted on the plot; the color of highlighting is determined by the 'additionalLabelColor' argument. |
additionalLabelColor |
Color used to highlight the 'additionalPointsToLabel'; defaults to "red" |
a volcanoplot is drawn to the current device
Tobias Verbeke