Transcriptomic information of endothelial cells.

Description

The data set contains the transcriptomic information of endothelial cells from two steady state tissues (brain and heart). More detailed information can be found in the paper by Nolan et al. 2013. The data was normalized and a statistical analysis was performed to determine differentially expressed genes. 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

If display 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 dataset
data(EC)

#Building the circ object
circ<-circular_dat(EC$david, EC$genelist)

#Creating the bar plot
GOBar(circ)

#Faceting the plot
GOBar(circ, display='multiple')

## End(Not run)

Bubble plot.

Description

The function creates a bubble plot of the input data. The input data can be created with the help of the circle_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 the term) 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 dataset
data(EC)

#Building the circ object
circ <- circular_dat(EC$david, EC$genelist)

#Creating the bubble plot colouring the table entries according to the category
GOBubble(circ, table.col = T)

#Creating the bubble plot displaying the term instead of the ID and without the table
GOBubble(circ, ID = F, table.legend = F)

#Faceting the plot
GOBubble(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-like graph. The input can be generated with the chord_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

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

Maybe the most important argument of the function is nlfc.If your data does not contain a column of logFC values you have to set nlfc = 0. Differential expression analysis can be performed for multiple conditions and/or batches. Therefore, the data frame might contain more than one logFC value per gene. To adjust to this situation the nlfc argument is used as well. It is a numeric value and it defines the number of logFC columns of your data. 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 of data. This can be achieved with limit. The first value of the vector defines the threshold for the minimum number of terms a gene has to be assigned to in order to be represented in the plot. Most of the time it is more meaningful to represent genes with various functions. A value 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 dataset
data(EC)

# Generating the binary matrix
chord<-chord_dat(circ,EC$genes,EC$process)

# Creating the chord plot
GOChord(chord)

# Excluding process with less than 5 assigned genes
GOChord(chord, limit = c(0,5))

# Creating the chord plot genes ordered by logFC and a different logFC color scale
GOChord(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 the GOBar 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 argument lfc.col.

The nsub 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 vector then it contains the IDs or term descriptions of the displayed processes.If nsub 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 dataset
data(EC)

# Building the circ object
circ <- circle_dat(EC$david, EC$genelist)

# Creating the circular plot
GOCircle(circ)

# Creating the circular plot with a different colour scale for the logFC
GOCircle(circ, lfc.col = c('purple', 'orange'))

# Creating the circular plot with a different colour scale for the z-score
GOCircle(circ, zsc.col = c('yellow', 'black', 'cyan'))

# Creating the circular plot with different font style
GOCircle(circ, label.size = 5, label.fontface = 'italic')

## End(Not run)

Circular dendrogram.

Description

GOCluster generates a circular dendrogram of the data clustering using by default euclidean distance and average linkage.The inner ring displays the color coded logFC while the outside one encodes the assigned 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 multiply logFC values resulting from various comparisons.

Examples

## Not run: 
#Load the included dataset
data(EC)

#Generating the circ object
circ<-circular_dat(EC$david, EC$genelist)

#Creating the cluster plot
GOCluster(circ, EC$process)

#Cluster the data according to gene expression and assigning a different color scale for the logFC
GOCluster(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 and genes in columns. In addition genes are clustered to highlight groups of genes with similar annotated functions. The input can be generated with the chord_dat function.

Usage

GOHeat(data, nlfc, fill.col)

Arguments

Details

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

Examples

## Not run: 
# Load the included dataset
data(EC)

# Generate the circ object
circ <- circle_dat(EC$david, EC$genelist)

# Generate the chord object
chord <- chord_dat(circ, EC$genes, EC$process)

# Create the plot with user-defined colors
GOHeat(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

The plot 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 case plot 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 dataset
data(EC)

#Generating the circ object
circ<-circular_dat(EC$david, EC$genelist)

#Selecting terms of interest
l1<-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 the GOChord 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 dataset
data(EC)

# Building the circ object
circ <- circle_dat(EC$david, EC$genelist)

# Building the binary matrix
chord <- 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 ploting functions.

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 independent categories: BP (biological process), CC (cellular component) or MF (molecular function).

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

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

Examples

## Not run: 
#Load the included dataset
data(EC)

#Building the circ object
circ<-circular_dat(EC$david, EC$genelist)

## End(Not run)

Eliminates redundant terms.

Description

The function eliminates all terms with a gene overlap >= set threshold (overlap) The reduced dataset can be used to improve the readability of plots such as GOBubble and GOBar

Usage

reduce_overlap(data, overlap)

Arguments

Details

The function is currently very slow.

Examples

## Not run: 
# Load the included dataset
data(EC)

# Building the circ object
circ <- circle_dat(EC$david, EC$genelist)

# Eliminate redundant terms
reduced_circ <- reduce_overlap(circ)

# Plot reduced data
GOBubble(reduced_circ)


## End(Not run)