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Transcriptomic information of endothelial cells.

Description

The data set contains the transcriptomic information of endothelial cellsfrom two steady state tissues (brain and heart). More detailed informationcan be found in the paper by Nolan et al. 2013. The data was normalized and astatistical analysis was performed to determine differentially expressedgenes. DAVID functional annotation tool was used to perform a gene-annotation enrichment analysis of the set of differentially expressed genes(adjusted p-value < 0.05).

Usage

data(EC)

Format

A list containing 5 items

Source

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE47067

Z-score coloured barplot.

Description

Z-score coloured barplot of terms ordered alternatively by z-score or the negative logarithm of the adjusted p-value

Usage

GOBar(data, display, order.by.zscore = T, title, zsc.col)

Arguments

Details

Ifdisplay is used to facet the plot the width of the panels will be proportional to the length of the x scale.

Examples

## Not run: #Load the included datasetdata(EC)#Building the circ objectcirc<-circular_dat(EC$david, EC$genelist)#Creating the bar plotGOBar(circ)#Faceting the plotGOBar(circ, display='multiple')## End(Not run)

Bubble plot.

Description

The function creates a bubble plot of the inputdata. Theinputdata can be created with the help of thecircle_dat function.

Usage

GOBubble(data, display, title, colour, labels, ID = T, table.legend = T,  table.col = T, bg.col = F)

Arguments

Details

The x- axis of the plot represents the z-score. The negative logarithm of the adjusted p-value (corresponding to the significance of theterm) is displayed on the y-axis. The area of the plotted circles is proportional to the number of genes assigned to the term. Each circle is coloured according to its category and labeled alternatively with the ID or term name.If static is set to FALSE the mouse hover effect will be enabled.

Examples

## Not run: #Load the included datasetdata(EC)#Building the circ objectcirc <- circular_dat(EC$david, EC$genelist)#Creating the bubble plot colouring the table entries according to the categoryGOBubble(circ, table.col = T)#Creating the bubble plot displaying the term instead of the ID and without the tableGOBubble(circ, ID = F, table.legend = F)#Faceting the plotGOBubble(circ, display = 'multiple')## End(Not run)

Displays the relationship between genes and terms.

Description

The GOChord function generates a circularly composited overview of selected/specific genes and their assigned processes or terms. More generally, it joins genes and processes via ribbons in an intersection-likegraph. The input can be generated with thechord_dat function.

Usage

GOChord(data, title, space, gene.order, gene.size, gene.space, nlfc = 1,  lfc.col, lfc.min, lfc.max, ribbon.col, border.size, process.label, limit)

Arguments

Details

Thegene.order argument has three possible options: "logFC", "alphabetical", "none", which are quite self- explanatory.

Maybe the most important argument of the function isnlfc.If yourdata does not contain a column of logFC values you have to setnlfc = 0. Differential expression analysis can be performed formultiple conditions and/or batches. Therefore, the data frame might containmore than one logFC value per gene. To adjust to this situation thenlfc argument is used as well. It is a numeric value and it definesthe number of logFC columns of yourdata. The default is "1"assuming that most of the time only one contrast is considered.

To represent the data more useful it might be necessary to reduce the dimension ofdata. This can be achieved withlimit. The firstvalue of the vector defines the threshold for the minimum number of terms agene has to be assigned to in order to be represented in the plot. Most ofthe time it is more meaningful to represent genes with various functions. Avalue of 3 excludes all genes with less than three term assignments. Whereas the second value of the parameter restricts the number of terms according to the number of assigned genes. All terms with a count smaller or equal to the threshold are excluded.

See Also

chord_dat

Examples

## Not run: # Load the included datasetdata(EC)# Generating the binary matrixchord<-chord_dat(circ,EC$genes,EC$process)# Creating the chord plotGOChord(chord)# Excluding process with less than 5 assigned genesGOChord(chord, limit = c(0,5))# Creating the chord plot genes ordered by logFC and a different logFC color scaleGOChord(chord,space=0.02,gene.order='logFC',lfc.col=c('red','black','cyan'))## End(Not run)

Circular visualization of the results of a functional analysis.

Description

The circular plot combines gene expression and gene- annotation enrichment data. A subset of terms is displayed like theGOBar plot in combination with a scatterplot of the gene expression data. The whole plot is drawn on a specific coordinate system to achieve the circular layout.The segments are labeled with the term ID.

Usage

GOCircle(data, title, nsub, rad1, rad2, table.legend = T, zsc.col, lfc.col,  label.size, label.fontface)

Arguments

Details

The outer circle shows a scatter plot for each term of the logFC of the assigned genes. The colours can be changed with the argumentlfc.col.

Thensub argument needs a bit more explanation to be used wisely. First of all, it can be a numeric or a character vector. If it is a character vectorthen it contains the IDs or term descriptions of the displayed processes.Ifnsub is a numeric vector then the number defines how many terms are displayed. It starts with the first row of the input data frame.

See Also

circle_dat,GOBar

Examples

## Not run: # Load the included datasetdata(EC)# Building the circ objectcirc <- circle_dat(EC$david, EC$genelist)# Creating the circular plotGOCircle(circ)# Creating the circular plot with a different colour scale for the logFCGOCircle(circ, lfc.col = c('purple', 'orange'))# Creating the circular plot with a different colour scale for the z-scoreGOCircle(circ, zsc.col = c('yellow', 'black', 'cyan'))# Creating the circular plot with different font styleGOCircle(circ, label.size = 5, label.fontface = 'italic')## End(Not run)

Circular dendrogram.

Description

GOCluster generates a circular dendrogram of thedata clustering using by default euclidean distance and average linkage.The inner ring displays the color coded logFC while the outside one encodes theassigned terms to each gene.

Usage

GOCluster(data, process, metric, clust, clust.by, nlfc, lfc.col, lfc.min,  lfc.max, lfc.space, lfc.width, term.col, term.space, term.width)

Arguments

Details

The inner ring can be split into smaller rings to display multiplylogFC values resulting from various comparisons.

Examples

## Not run: #Load the included datasetdata(EC)#Generating the circ objectcirc<-circular_dat(EC$david, EC$genelist)#Creating the cluster plotGOCluster(circ, EC$process)#Cluster the data according to gene expression and assigning a different color scale for the logFCGOCluster(circ,EC$process,clust.by='logFC',lfc.col=c('darkgoldenrod1','black','cyan1'))## End(Not run)

Displays heatmap of the relationship between genes and terms.

Description

The GOHeat function generates a heatmap of the relationship between genes and terms. Biological processes are displayed in rows andgenes in columns. In addition genes are clustered to highlight groups ofgenes with similar annotated functions. The input can be generated with thechord_dat function.

Usage

GOHeat(data, nlfc, fill.col)

Arguments

Details

The heatmap has in general two modes which depend on thenlfcargument. Ifnlfc = 0, so no logFC values are available, the coloring encodes for the overall number of processes the respective gene isassigned to. In case ofnlfc = 1 the color corresponds to the logFC of the gene.

Examples

## Not run: # Load the included datasetdata(EC)# Generate the circ objectcirc <- circle_dat(EC$david, EC$genelist)# Generate the chord objectchord <- chord_dat(circ, EC$genes, EC$process)# Create the plot with user-defined colorsGOHeat(chord, nlfc = 1, fill.col = c('red', 'yellow', 'green'))## End(Not run)

Venn diagram of differentially expressed genes.

Description

The function compares lists of differentially expressed genes and illustrates possible relations.Additionally it represents the variety of gene expression patterns within the intersection in small pie charts with three segements. Clockwise are shown the number of commonly up- regulated, commonly down- regulated and contra- regulated genes.

Usage

GOVenn(data1, data2, data3, title, label, lfc.col, circle.col, plot = T)

Arguments

Details

Theplot argument can be used to adjust the amount of information that is returned by calling the function. If you are only interested in the actual plot of the venn diagram,plot should be set to TRUE. Sometimes you also want to know the elements of the intersections. In this caseplot should be set to FALSE and the function call will return a list of two items. The first item, that can be accessed by $plot, contains the plotting information. Additionally, a list($table) will be returned containing the elements of the various overlaps.

Examples

## Not run: #Load the included datasetdata(EC)#Generating the circ objectcirc<-circular_dat(EC$david, EC$genelist)#Selecting terms of interestl1<-subset(circ,term=='heart development',c(genes,logFC))l2<-subset(circ,term=='plasma membrane',c(genes,logFC))l3<-subset(circ,term=='tissue morphogenesis',c(genes,logFC))GOVenn(l1,l2,l3, label=c('heart development','plasma membrane','tissue morphogenesis'))## End(Not run)

Creates a binary matrix.

Description

The function creates a matrix which represents the binary relation (1= is related to, 0= is not related to) between selected genes (row) and processes (column). The resulting matrix can be visualized with theGOChord function.

Usage

chord_dat(data, genes, process)

Arguments

Details

If more than one logFC value for each gene is at disposal, only one should be used to create the binary matrix. The other values have to be added manually later.

Value

A binary matrix

See Also

GOChord

Examples

## Not run: # Load the included datasetdata(EC)# Building the circ objectcirc <- circle_dat(EC$david, EC$genelist)# Building the binary matrixchord <- chord_dat(circ, EC$genes, EC$process)## End(Not run)

Creates a plotting object.

Description

The function takes the results from a functional analysis (for example DAVID) and combines it with a list of selected genes and their logFC. The resulting data frame can be used as an input for various plotingfunctions.

Usage

circle_dat(terms, genes)

Arguments

Details

Since most of the gene- annotation enrichment analysis are based on the gene ontology database the package was build with this structure in mind, but is not restricted to it. Gene ontology is structured as an acyclic graph and it provides terms covering different areas. These terms are grouped into three independentcategories: BP (biological process), CC (cellular component) or MF (molecular function).

The "ID" and "term" columns of theterms data frame refer to the ID and term description, whereas the ID is optional.

The "ID" column of thegenes data frame can contain any unique identifier. Nevertheless, the identifier has to be the same as in "genes" fromterms.

Examples

## Not run: #Load the included datasetdata(EC)#Building the circ objectcirc<-circular_dat(EC$david, EC$genelist)## End(Not run)

Eliminates redundant terms.

Description

The function eliminates all terms with a gene overlap >= setthreshold (overlap) The reduced dataset can be used to improve thereadability of plots such asGOBubble andGOBar

Usage

reduce_overlap(data, overlap)

Arguments

Details

The function is currently very slow.

Examples

## Not run: # Load the included datasetdata(EC)# Building the circ objectcirc <- circle_dat(EC$david, EC$genelist)# Eliminate redundant termsreduced_circ <- reduce_overlap(circ)# Plot reduced dataGOBubble(reduced_circ)## End(Not run)