| Title: | Visualization of Functional Analysis Data |
|---|---|
| Description: | Implementation of multilayered visualizations for enhanced graphical representation of functional analysis data. It combines and integrates omics data derived from expression and functional annotation enrichment analyses. Its plotting functions have been developed with an hierarchical structure in mind: starting from a general overview to identify the most enriched categories (modified bar plot, bubble plot) to a more detailed one displaying different types of relevant information for the molecules in a given set of categories (circle plot, chord plot, cluster plot, Venn diagram, heatmap). |
| Authors: | Wencke Walter [aut, cre], Fatima Sanchez-Cabo [aut] |
| Maintainer: | Wencke Walter <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0.2 |
| Built: | 2025-02-11 06:28:30 UTC |
| Source: | https://github.com/wencke/wencke.github.io |
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.
chord_dat(data, genes, process)chord_dat(data, genes, process)
data |
A data frame with at least two coloumns: GO ID|term and genes.
Each row contains exactly one GO ID|term and one gene. A column containing
logFC values is optional and might be used if |
genes |
A character vector of selected genes OR data frame with coloumns for gene ID and logFC. |
process |
A character vector of selected processes |
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.
A binary matrix
## 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)## 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)
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.
circle_dat(terms, genes)circle_dat(terms, genes)
terms |
A data frame with columns for 'category', 'ID', 'term', adjusted p-value ('adj_pval') and 'genes' |
genes |
A data frame with columns for 'ID', 'logFC' |
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.
## Not run: #Load the included dataset data(EC) #Building the circ object circ<-circular_dat(EC$david, EC$genelist) ## End(Not run)## Not run: #Load the included dataset data(EC) #Building the circ object circ<-circular_dat(EC$david, EC$genelist) ## End(Not run)
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).
data(EC)data(EC)
A list containing 5 items
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE47067
Z-score coloured barplot of terms ordered alternatively by z-score or the negative logarithm of the adjusted p-value
GOBar(data, display, order.by.zscore = T, title, zsc.col, label.size)GOBar(data, display, order.by.zscore = T, title, zsc.col, label.size)
data |
A data frame containing at least the term ID and/or term, the
adjusted p-value and the z-score. A possible input can be generated with
the |
display |
A character vector indicating whether a single plot ('single') or a facet plot with panels for each category should be drawn (default='single') |
order.by.zscore |
Defines the order of the bars. If TRUE the bars are ordered according to the z-scores of the processes. Otherwise the bars are ordered by the negative logarithm of the adjusted p-value |
title |
The title of the plot |
zsc.col |
Character vector to define the colour scale for the z-score of the form c(high, midpoint,low) |
label.size |
Defines the font size of x-axis text labels. |
If display is used to facet the plot the width of the panels
will be proportional to the length of the x scale.
## 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)## 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)
The function creates a bubble plot of the input data. The
input data can be created with the help of the
circle_dat function.
GOBubble(data, display, title, colour, labels, ID = T, table.legend = T, table.col = T, bg.col = F)GOBubble(data, display, title, colour, labels, ID = T, table.legend = T, table.col = T, bg.col = F)
data |
A data frame with coloumns for category, GO ID, term, adjusted p-value, z-score, count(num of genes) |
display |
A character vector. Indicates whether it should be a single plot ('single') or a facet plot with panels for each category (default='single') |
title |
The title (on top) of the plot |
colour |
A character vector which defines the colour of the bubbles for each category |
labels |
Sets a threshold for the displayed labels. The threshold refers to the -log(adjusted p-value) (default=5) |
ID |
If TRUE then labels are IDs else terms |
table.legend |
Defines whether a table of GO ID and GO term should be displayed on the right side of the plot or not (default = TRUE) |
table.col |
If TRUE then the table entries are coloured according to their category, if FALSE then entries are black |
bg.col |
Should only be used in case of a facet plot. If TRUE then the panel backgrounds are coloured according to the displayed category |
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.
## 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)## 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)
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.
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)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)
data |
The matrix represents the binary relation (1= is related to, 0= is not related to) between a set of genes (rows) and processes (columns); a column for the logFC of the genes is optional |
title |
The title (on top) of the plot |
space |
The space between the chord segments of the plot |
gene.order |
A character vector defining the order of the displayed gene labels |
gene.size |
The size of the gene labels |
gene.space |
The space between the gene labels and the segement of the logFC |
nlfc |
Defines the number of logFC columns (default=1) |
lfc.col |
The fill color for the logFC specified in the following form: c(color for low values, color for the mid point, color for the high values) |
lfc.min |
Specifies the minimium value of the logFC scale (default = -3) |
lfc.max |
Specifies the maximum value of the logFC scale (default = 3) |
ribbon.col |
The background color of the ribbons |
border.size |
Defines the size of the ribbon borders |
process.label |
The size of the legend entries |
limit |
A vector with two cutoff values (default= c(0,0)). The first value defines the minimum number of terms a gene has to be assigned to. The second the minimum number of genes assigned to a selected term. |
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.
## 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)## 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)
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.
GOCircle(data, title, nsub, rad1, rad2, table.legend = T, zsc.col, lfc.col, label.size, label.fontface)GOCircle(data, title, nsub, rad1, rad2, table.legend = T, zsc.col, lfc.col, label.size, label.fontface)
data |
A special data frame which should be the result of
|
title |
The title of the plot |
nsub |
A numeric or character vector. If it's numeric then the number
defines how many processes are displayed (starting from the first row of
|
rad1 |
The radius of the inner circle (default=2) |
rad2 |
The radius of the outer circle (default=3) |
table.legend |
Shall a table be displayd or not? (default=TRUE) |
zsc.col |
Character vector to define the colour scale for the z-score of the form c(high, midpoint,low) |
lfc.col |
A character vector specifying the colour for up- and down-regulated genes |
label.size |
Size of the segment labels (default=5) |
label.fontface |
Font style of the segment labels (default='bold') |
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.
## 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)## 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)
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.
GOCluster(chord, process, metric, clust, clust.by, nlfc, lfc.col, lfc.min, lfc.max, lfc.space, lfc.width, term.col, term.space, term.width)GOCluster(chord, process, metric, clust, clust.by, nlfc, lfc.col, lfc.min, lfc.max, lfc.space, lfc.width, term.col, term.space, term.width)
process |
A character vector of selected processes (ID or term description) |
metric |
A character vector specifying the distance measure to be used
(default='euclidean'), see |
clust |
A character vector specifying the agglomeration method to be
used (default='average'), see |
clust.by |
A character vector specifying if the clustering should be done for gene expression pattern or functional categories. By default the clustering is done based on the functional categories. |
nlfc |
If TRUE |
lfc.col |
Character vector to define the color scale for the logFC of the form c(high, midpoint,low) |
lfc.min |
Specifies the minimium value of the logFC scale (default = -3) |
lfc.max |
Specifies the maximum value of the logFC scale (default = 3) |
lfc.space |
The space between the leafs of the dendrogram and the ring for the logFC |
lfc.width |
The width of the logFC ring |
term.col |
A character vector specifying the colors of the term bands |
term.space |
The space between the logFC ring and the term ring |
term.width |
The width of the term ring |
data |
A data frame which should be the result of
|
The inner ring can be split into smaller rings to display multiply logFC values resulting from various comparisons.
## 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)## 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)
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.
GOHeat(data, nlfc, fill.col)GOHeat(data, nlfc, fill.col)
data |
The matrix represents the binary relation (1= is related to, 0= is not related to) between a set of genes (rows) and processes (columns) |
nlfc |
Defines the number of logFC columns (default = 0) |
fill.col |
Defines the color scale break points |
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.
## 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)## 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)
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.
GOVenn(data1, data2, data3, title, label, lfc.col, circle.col, plot = T)GOVenn(data1, data2, data3, title, label, lfc.col, circle.col, plot = T)
data1 |
A data frame consisting of two columns: ID, logFC |
data2 |
A data frame consisting of two columns: ID, logFC |
data3 |
A data frame consisting of two columns: ID, logFC |
title |
The title of the plot |
label |
A character vector to define the legend keys |
lfc.col |
A character vector determining the background colors of the pie segments representing up- and down- regulated genes |
circle.col |
A character vector to assign clockwise colors for the circles |
plot |
If TRUE only the venn diagram is plotted. Otherwise the function returns a list with two items: the actual plot and a list containing the overlap entries (default= TRUE) |
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.
## 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)## 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)
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
reduce_overlap(data, overlap)reduce_overlap(data, overlap)
data |
A data frame created with |
overlap |
Skalar indicating the threshold for gene overlap (default = 0.75). |
The function is currently very slow.
## 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)## 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)