| Title: | Tools for manipulating GO and microarrays |
|---|---|
| Description: | A set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations. |
| Authors: | Robert Gentleman [aut], Seth Falcon [ctb], Robert Castelo [ctb], Sonali Kumari [ctb] (Converted vignettes from Sweave to R Markdown / HTML.), Dennis Ndubi [ctb] (Converted GOstatsHyperG vignette from Sweave to R Markdown / HTML.), Bioconductor Package Maintainer [cre] |
| Maintainer: | Bioconductor Package Maintainer <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 2.73.0 |
| Built: | 2024-10-31 01:16:44 UTC |
| Source: | https://github.com/bioc/GOstats |
A set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations.
| Package: | GOstats |
| Version: | 1.7.4 |
| Date: | 23-08-2006 |
| biocViews: | Statistics, Annotation, GO, MultipleComparisons |
| Depends: | graph (>= 1.9.25), GO, annotate, RBGL, xtable, Biobase, genefilter, multtest, Category (>= 1.3.7), methods |
| Imports: | methods, Category |
| Suggests: | hgu95av2.db (>= 1.6.0) |
| License: | Artistic |
Index:
ALL Acute Lymphoblastic Leukemia Data from the Ritz
Laboratory
GOstats-defunct Defunct Functions in GOstats Package
Ndists Distance matrices for the BCR/ABL and NEG
subgroups.
compCorrGraph A function to compute a correlation based graph
from Gene Expression Data
compGdist A function to compute the distance between
pairs of nodes in a graph.
dropECode Drop GO labels for specified Evidence Codes
getEvidence Get the Evidence codes for a set of GO terms.
getGOTerm Functions to Access GO data.
getOntology Get GO terms for a specified ontology
hasGOannote Check for GO annotation
idx2dimnames Index to Dimnames
makeGOGraph Construct a GO Graph
notConn Find genes that are not connected to the
others.
oneGOGraph Construct the GO graph given a set of leaves.
shortestPath Shortest Path Analysis
simLL Functions to compute similarities between GO
graphs and also between Entrez Gene IDs based on
their induced GO graphs.
triadCensus Triad Functions
Further information is available in the following vignettes:
GOstats |
Using GOstats (source, pdf) |
GOusage |
Basic GO Usage (source, pdf) |
GOvis |
Visualizing Data Using GOstats (source, pdf) |
R. Gentleman with contributions from S. Falcon
Maintainer: R. Gentleman <[email protected]>
Given a set of gene expression data (an instance of the
ExpressionSet class) this function computes a graph based on
correlations between the probes.
compCorrGraph(eSet, k = 1, tau = 0.6)compCorrGraph(eSet, k = 1, tau = 0.6)
eSet |
An instance of the |
k |
The power to raise the correlations to. |
tau |
The lower cutoff for absolute correlations. |
Zhou et al. describe a method of computing a graph between probes (genes) based on estimated correlations between probes. This function implements some of their methods.
Pearson correlations between probes are computed and then these are
raised to the power k. Any of the resulting estimates that are
less than tau in absolute value are set to zero.
An instance of the graph class. With edges and edge weights
determined by applying the algorithm described previously.
R. Gentleman
Zhou et al., Transitive functional annotation by shortest-path analysis of gene expression data.
## Create an ExpressionSet to work with set.seed(123) exprMat <- matrix(runif(50 * 5), nrow=50) genData <- new("ExpressionSet", exprs=exprMat) corrG = compCorrGraph(genData)## Create an ExpressionSet to work with set.seed(123) exprMat <- matrix(runif(50 * 5), nrow=50) genData <- new("ExpressionSet", exprs=exprMat) corrG = compCorrGraph(genData)
Given a graph, g, and a set of nodes in the graph,
whNodes, Dijkstra's shortest path algorithm is used to compute
the distance between all pairs of nodes in whNodes.
compGdist(g, whNodes, verbose = FALSE)compGdist(g, whNodes, verbose = FALSE)
g |
An instance of the |
whNodes |
A vector of lables of the nodes in |
verbose |
If |
This function can be quite slow, computation of the pairwise
distances is not especially fast and if whNodes is long then
there are many of them to compute.
A matrix containing the pairwise distances. It might be worth making
this an instance of the dist class at some point.
R. Gentleman
example(compCorrGraph) compGdist(corrG, nodes(corrG)[1:5])example(compCorrGraph) compGdist(corrG, nodes(corrG)[1:5])
This class represents the results of a test for overrepresentation of GO categories among genes in a selected gene set based upon the Hypergeometric distribution.
For details on extracting information from this object, be sure to read the accessor documentation in the Category package: HyperGResult-accessors.
Objects can be created by calls of the form new("GOHyperGResult", ...).
goDag:Object of class "graph" representing
the DAG of GO terms tested.
pvalue.order:Object of class "integer".
The sort order of the computed p-values.
annotation:Object of class "character". The
name of the annotation data package used in the analysis.
geneIds:Object of class "ANY". The
intersection of the gene identifiers given as input and the
computed gene universe.
testName:Object of class "character".
Identifies the testing method used to produce this result instance.
pvalueCutoff:Object of class "numeric".
The cutoff for significance used for some testing methods.
Also used for pretty display in the show method.
conditional:A logical indicating whether the calculation should condition on the GO structure.
testDirection:A string which can be either "over" or "under". This determines whether the test performed detects over or under represented GO terms.
Class "HyperGResultBase", directly.
signature(r = "GOHyperGResult"):
return the graph instance representing the DAG of the
GO terms that were tested.
signature(r = "GOHyperGResult"): Returns a
data.frame summarizing the test result. Optional
arguments pvalue and categorySize allow
specification of maximum p-value and minimum categorySize,
respectively. Optional argument htmlLinks is a logical
value indicating whether to add HTML links (useful in
conjunction with xtables print method with type set to
"html").
signature(r = "GOHyperGResult"): Write an
HTML version of the table produced by the summary method.
The path of a file to write the report to can be specified using
the file argument. The default is file="" which
will cause the report to be printed to the screen. If you wish
to create a single report comprising multiple results you can
set append=TRUE. The default is FALSE (overwrite
preexisting report file). You can specify a string to use as an
identifier for each table by providing a value for the
label argument. Additional named arguments will be
passed to the summary method.
signature(object = "GOHyperGResult"):
Return a string giving a one-line description of the result.
Seth Falcon
The functions or variables listed here are no longer part of GOstats as they are not needed (any more).
combGOGraph() hyperGtable() hyperG2Affy() selectedGenes() GOHyperG() GOKEGGHyperG() getGoGraph()combGOGraph() hyperGtable() hyperG2Affy() selectedGenes() GOHyperG() GOKEGGHyperG() getGoGraph()
combGOGraph was replaced by join.
hyperGtable was replaced by summary.
hyperG2Affy was replaced by probeSetSummary.
GOLeaves was replaced by graph::leaves.
selectedGenes was replaced by geneIdsByCategory.
GOHyperG was replaced by hyperGTest.
GOKEGGHyperG was replaced by hyperGTest.
getGoGraph was replaced by GOGraph.
Given a GOHyperGParams instance
containing a set of unique Entrez Gene Identifiers, a microarray
annotation data package name, and the GO ontology of interest, this
function will compute Hypergeomtric p-values for over or
under-representation of each GO term in the specified ontology among
the GO annotations for the interesting genes. The computations can
be done conditionally based on the structure of the GO graph.
p |
A |
When conditional(p) == TRUE, the hyperGTest function
uses the structure of the GO graph to estimate for each term whether
or not there is evidence beyond that which is provided by the term's
children to call the term in question statistically overrepresented.
The algorithm conditions on all child terms that are themselves
significant at the specified p-value, odds ratio, minimum or
maximum gene set size cutoff. Given a subgraph of
one of the three GO ontologies, or the ontology given in the
OBOHyperGParams instance, the terms with no child categories
are tested first. Next the nodes whose children have already been
tested are tested. If any of a given node's children tested
significant, the appropriate conditioning is performed.
A GOHyperGResult or OBOHyperGResult instance.
Seth Falcon
FIXME
GOHyperGResult-class,
geneGoHyperGeoTest,
geneKeggHyperGeoTest
A function to map from integer offsets in an array to the corresponding values of the row and column names. There is probably a better way but I didn't find it.
idx2dimnames(x, idx)idx2dimnames(x, idx)
x |
a |
idx |
An integer vector of offsets into the matrix (values
between 1 and the |
A list with two components. If it is a LIST, use
rowNames |
The row names corresponding to the integer index. |
colNames |
The column names corresponding to the integer index. |
R. Gentleman
data(Ndists) ltInf = is.finite(Ndists) xx = idx2dimnames(Ndists, ltInf)data(Ndists) ltInf = is.finite(Ndists) xx = idx2dimnames(Ndists, ltInf)
The directed acyclic graph (DAG) based on finding the most specific terms for the supplied Entrez Gene IDs is constructed and returned. The constructuion is per GO ontology (there are three, MF, BP and CC) and once the most specific terms have been identified then all less specific terms are found (these are the parents of the terms) and then their parents and so on, until the root is encountered.
makeGOGraph(x, Ontology = "MF", removeRoot = TRUE, mapfun = NULL, chip = NULL)makeGOGraph(x, Ontology = "MF", removeRoot = TRUE, mapfun = NULL, chip = NULL)
x |
A vector of Entrez Gene IDs. |
Ontology |
Which GO ontology to use (CC, BP, or MF). |
removeRoot |
A logical value indicating whether the GO root node should be removed or not. |
mapfun |
A function taking a character vector of Entrez Gene IDs
as its only argument and returning a list of "GO lists" matching the
structure of the lists in the GO maps of annotation data packages.
The function should behave similarly to
|
chip |
The name of a DB-based annotation data package (the name
will end in ".db"). This package will be used to generate an Entrez
ID to GO ID mapping instead of |
For each supplied Entrez Gene identifier all the GO annotations (in the specified ontology) are found. The mapping is achieved in one of three ways:
If mapfun is provided, it will be used to perform the
needed lookups. In this case, chip will be ignored.
If chip is provided and mapfun=NULL, then the
needed lookups will be done based on the Entrez to GO mappings
encapsulated in the specified annotation data package. This is
the recommended usage.
If mapfun and chip are NULL or missing,
then the function will attempt to load the GO package (the
environment-based package, distinct from GO.db). This package
contains a legacy environment mapping Entrez IDs to GO IDs. If
the GO package is not available, an error will be raised.
Omitting both mapfun and chip is not recommended as
it is not compatible with the DB-based annotation data packages.
The mappings are different for the different ontologies. Typically a GO indentifier is used only in one specific ontology.
The resulting structure is stored in a graph using the graph
package, again from Bioconductor.
An object that inherits from the graph class. The particular
implementation is not specified.
R. Gentleman
The Gene Ontology Consortium
library("hgu95av2.db") set.seed(321) gN <- unique(sample(keys(hgu95av2.db, 'ENTREZID'), 4)) gg1 <- makeGOGraph(gN, "BP", chip="hgu95av2.db")library("hgu95av2.db") set.seed(321) gN <- unique(sample(keys(hgu95av2.db, 'ENTREZID'), 4)) gg1 <- makeGOGraph(gN, "BP", chip="hgu95av2.db")
These are precomputed distance matrices between all transcription factors selected. In the future they will be computed on the fly but currently that takes about 3 hours and so precomputed versions are supplied.
data(Ndists) data(Bdists)data(Ndists) data(Bdists)
These are both distance matrices.
They are based on the ALL data, ALL.
data(Ndists) data(Bdists)data(Ndists) data(Bdists)
A function that takes as input a distance matrix and finds those
entries that are not connected to any others (ie. those with distance
Inf.
notConn(dists)notConn(dists)
dists |
A distance matrix. |
It is a very naive implementation. It presumes that not connected
entries are not connected to any other entries, and this might not be
true. Using the connComp function from the graph
package or the RBGL package might be a better approach.
A vector of the names of the items that are not connected.
R. Gentleman
data(Ndists) notConn(Ndists)data(Ndists) notConn(Ndists)
This class represents the results of a test for overrepresentation of OBO categories among genes in a selected gene set based upon the Hypergeometric distribution.
For details on extracting information from this object, be sure to read the accessor documentation in the Category package: HyperGResult-accessors.
Objects can be created by calls of the form new("OBOHyperGResult", ...).
goDag:Object of class "graph" representing
the DAG of OBO terms tested.
pvalue.order:Object of class "integer".
The sort order of the computed p-values.
annotation:Object of class "character". The
name of the annotation data package used in the analysis.
geneIds:Object of class "ANY". The
intersection of the gene identifiers given as input and the
computed gene universe.
testName:Object of class "character".
Identifies the testing method used to produce this result instance.
pvalueCutoff:Object of class "numeric".
The cutoff for significance used for some testing methods.
Also used for pretty display in the show method.
conditional:A logical indicating whether the calculation should condition on the OBO structure.
testDirection:A string which can be either "over" or "under". This determines whether the test performed detects over or under represented OBO terms.
Class "HyperGResultBase", directly.
signature(r = "OBOHyperGResult"):
return the graph instance representing the DAG of the
OBO terms that were tested.
signature(r = "OBOHyperGResult"): Returns a
data.frame summarizing the test result. Optional
arguments pvalue and categorySize allow
specification of maximum p-value and minimum categorySize,
respectively. Optional argument htmlLinks is a logical
value indicating whether to add HTML links (useful in
conjunction with xtables print method with type set to
"html").
signature(r = "OBOHyperGResult"): Write an
HTML version of the table produced by the summary method.
The path of a file to write the report to can be specified using
the file argument. The default is file="" which
will cause the report to be printed to the screen. If you wish
to create a single report comprising multiple results you can
set append=TRUE. The default is FALSE (overwrite
preexisting report file). You can specify a string to use as an
identifier for each table by providing a value for the
label argument. Additional named arguments will be
passed to the summary method.
signature(object = "OBOHyperGResult"):
Return a string giving a one-line description of the result.
Robert Castelo
Given one or more GO identifiers (which indicate the leaves in the graph) and a set of mappings to the less specific sets of nodes this function will construct the graph that includes that node and all children down to the root node for the ontology.
oneGOGraph(x, dataenv) GOGraph(x, dataenv)oneGOGraph(x, dataenv) GOGraph(x, dataenv)
x |
A character vector of GO identifiers. |
dataenv |
An environment for finding the parents of that term. |
For any set of GO identifiers (from a common ontology) we define the induced GO graph to be that graph, based on the DAG structure (child - parent) of the GO ontology of terms, which takes the most specific set of GO terms that apply (for that ontology) and then joins these to all less specific terms. These functions help construct such graphs.
The induced GO graph (or NULL) for the given GO identifier.
R. Gentleman
library("GO.db") g1 <- oneGOGraph("GO:0003680", GOMFPARENTS) g2 <- oneGOGraph("GO:0003701", GOMFPARENTS) g3 <- join(g1, g2) g4 <- GOGraph(c("GO:0003680", "GO:0003701"), GOMFPARENTS) if( require("Rgraphviz") && interactive() ) plot(g3)library("GO.db") g1 <- oneGOGraph("GO:0003680", GOMFPARENTS) g2 <- oneGOGraph("GO:0003701", GOMFPARENTS) g3 <- join(g1, g2) g4 <- GOGraph(c("GO:0003680", "GO:0003701"), GOMFPARENTS) if( require("Rgraphviz") && interactive() ) plot(g3)
Given the result of a hyperGTest run (an instance of
GOHyperGResult), this function lists all Probe Set IDs
associated with the selected Entrez IDs annotated at each
significant GO term in the test result.
probeSetSummary(result, pvalue, categorySize, sigProbesets, ids = "ENTREZID")probeSetSummary(result, pvalue, categorySize, sigProbesets, ids = "ENTREZID")
result |
A |
pvalue |
Optional p-value cutoff. Only results for GO terms
with a p-value less than the specified value will be returned.
If omitted, |
categorySize |
Optional minimum size (number of annotations)
for the GO terms. Only results for GO terms with
|
sigProbesets |
Optional vector of probeset IDs. See details for more information. |
ids |
Character. The type of IDs used in creating the
|
Usually the goal of doing a Fisher's exact test on a set of significant probesets is to find pathways or cellular activities that are being perturbed in an experiment. After doing the test, one usually gets a list of significant GO terms, and the next logical step might be to determine which probesets contributed to the significance of a certain term.
Because the input for the Fisher's exact test consists of a vector of unique Entrez Gene IDs, and there may be multiple probesets that interrogate a particular transcript, the ouput for this function lists all of the probesets that map to each Entrez Gene ID, along with an indicator that shows which of the probesets were used as input.
The rationale for this is that one might not be able to assume a given probeset actually interrogates the intended transcript, so it might be useful to be able to check to see what other similar probesets are doing.
Because one of the first steps before running hyperGTest is to
subset the input vectors of geneIds and universeGeneIds, any
information about probeset IDs that interrogate the same gene
transcript is lost. In order to recover this information, one can pass
a vector of probeset IDs that were considered significant. This vector
will then be used to indicate which of the probesets that map to a
given GO term were significant in the original analysis.
A list of data.frame. Each element of the list
corresponds to one of the GO terms (the term is provides as the name
of the element). Each data.frame has three columns:
the Entrez Gene ID (EntrezID), the probe set ID
(ProbeSetID), and a 0/1 indicator of whether the probe set ID
was provided as part of the initial input (selected)
Note that this 0/1 indicator will only be correct if the 'geneId'
vector used to construct the GOHyperGParams object was a named
vector (where the names are probeset IDs), or if a vector of
'sigProbesets' was passed to this function.
S. Falcon and J. MacDonald
## Fake up some data library("hgu95av2.db") library("annotate") prbs <- ls(hgu95av2GO)[1:300] ## Only those with GO ids hasGO <- lengths(lapply(mget(prbs, hgu95av2GO), names)) != 0 prbs <- prbs[hasGO] prbs <- getEG(prbs, "hgu95av2") ## remove duplicates, but keep named vector prbs <- prbs[!duplicated(prbs)] ## do the same for universe univ <- ls(hgu95av2GO)[1:5000] hasUnivGO <- lengths(lapply(mget(univ, hgu95av2GO), names)) != 0 univ <- univ[hasUnivGO] univ <- unique(getEG(univ, "hgu95av2")) p <- new("GOHyperGParams", geneIds=prbs, universeGeneIds=univ, ontology="BP", annotation="hgu95av2", conditional=TRUE) ## this part takes time... if(interactive()){ hyp <- hyperGTest(p) ps <- probeSetSummary(hyp, 0.05, 10) }## Fake up some data library("hgu95av2.db") library("annotate") prbs <- ls(hgu95av2GO)[1:300] ## Only those with GO ids hasGO <- lengths(lapply(mget(prbs, hgu95av2GO), names)) != 0 prbs <- prbs[hasGO] prbs <- getEG(prbs, "hgu95av2") ## remove duplicates, but keep named vector prbs <- prbs[!duplicated(prbs)] ## do the same for universe univ <- ls(hgu95av2GO)[1:5000] hasUnivGO <- lengths(lapply(mget(univ, hgu95av2GO), names)) != 0 univ <- univ[hasUnivGO] univ <- unique(getEG(univ, "hgu95av2")) p <- new("GOHyperGParams", geneIds=prbs, universeGeneIds=univ, ontology="BP", annotation="hgu95av2", conditional=TRUE) ## this part takes time... if(interactive()){ hyp <- hyperGTest(p) ps <- probeSetSummary(hyp, 0.05, 10) }
The shortest path analysis was proposed by Zhou et. al. The basic computation is to find the shortest path in a supplied graph between two Entrez Gene IDs. Zhou et al claim that other genes annotated along that path are likely to have the same GO annotation as the two end points.
shortestPath(g, GOnode, mapfun=NULL, chip=NULL)shortestPath(g, GOnode, mapfun=NULL, chip=NULL)
g |
An instance of the |
GOnode |
A length one character vector specifying the GO node of interest. |
mapfun |
A function taking a character vector of GO IDs as its
only argument and returning a list of character vectors of Enterz
Gene IDs annotated at each corresponding GO ID. The function should
behave similarly to |
chip |
The name of a DB-based annotation data package (the name
will end in ".db"). This package will be used to generate an Entrez
ID to GO ID mapping instead of |
The algorithm implemented here is quite simple. All Entrez Gene
identifiers that are annotated at the GO node of interest are
obtained. Those that are found as nodes in the graph are retained and
used for the computation. For every pair of nodes at the GO term the
shortest path between them is computed using sp.between from
the RBGL package.
There is a presumption that the graph is undirected. This
restriction could probably be lifted if there was some reason for it -
a patch would be gratefully accepted.
The mapping of GO node to Entrez ID is achieved in one of three ways:
If mapfun is provided, it will be used to perform the
needed lookups. In this case, chip will be ignored.
If chip is provided and mapfun=NULL, then the
needed lookups will be done based on the GO to Entrez mappings
encapsulated in the specified annotation data package. This is
the recommended usage.
If mapfun and chip are NULL or missing,
then the function will attempt to load the GO package (the
environment-based package, distinct from GO.db). This package
contains a legacy environment mapping GO IDs to Entrez IDs. If
the GO package is not available, an error will be raised.
Omitting both mapfun and chip is not recommended as
it is not compatible with the DB-based annotation data packages.
The return values is a list with the following components:
shortestpaths |
A list of the ouput from |
nodesUsed |
A vector of the Entrez Gene IDs that were both found
at the GO term of interest and were nodes in the supplied graph,
|
nodesNotUsed |
A vector of Entrez Gene IDs that were annotated at
the GO term, but were not found in the graph |
R. Gentleman
Transitive functional annotation by shortest-path analysis of gene expression data, by X. Zhou and M-C J. Kao and W. H. Wong, PNAS, 2002
library("hgu95av2.db") library("RBGL") set.seed(321) uniqun <- function(x) unique(unlist(x)) goid <- "GO:0005778" egIds <- uniqun(mget(uniqun(hgu95av2GO2PROBE[[goid]]), hgu95av2ENTREZID)) v1 <- randomGraph(egIds, 1:10, .3, weights=FALSE) ## Since v1 is random, it might be disconnected and we need a ## connected graph to guarantee the existence of a path. c1 <- connComp(v1) largestComp <- c1[[which.max(sapply(c1, length))]] v2 <- subGraph(largestComp, v1) a1 <- shortestPath(v2, goid, chip="hgu95av2.db")library("hgu95av2.db") library("RBGL") set.seed(321) uniqun <- function(x) unique(unlist(x)) goid <- "GO:0005778" egIds <- uniqun(mget(uniqun(hgu95av2GO2PROBE[[goid]]), hgu95av2ENTREZID)) v1 <- randomGraph(egIds, 1:10, .3, weights=FALSE) ## Since v1 is random, it might be disconnected and we need a ## connected graph to guarantee the existence of a path. c1 <- connComp(v1) largestComp <- c1[[which.max(sapply(c1, length))]] v2 <- subGraph(largestComp, v1) a1 <- shortestPath(v2, goid, chip="hgu95av2.db")
Both simUI and simLP compute a similarity measure
between two GO graphs. For simLL, first the induced GO graph
for each of its arguments is found and then these are passed to one
of simUI or simLP.
simLL(ll1, ll2, Ontology = "MF", measure = "LP", dropCodes = NULL, mapfun = NULL, chip = NULL) simUI(g1, g2) simLP(g1, g2)simLL(ll1, ll2, Ontology = "MF", measure = "LP", dropCodes = NULL, mapfun = NULL, chip = NULL) simUI(g1, g2) simLP(g1, g2)
ll1 |
A Entrez Gene ID as a character vector. |
ll2 |
A Entrez Gene ID as a character vector. |
Ontology |
Which ontology to use ("MF", "BP", "CC"). |
measure |
Which measure to use ("LP", "UI"). |
dropCodes |
A set of evidence codes to be ignored in constructing the induced GO graphs. |
mapfun |
A function taking a character vector of Entrez Gene IDs
as its only argument and returning a list of "GO lists" matching the
structure of the lists in the GO maps of annotation data packages.
The function should behave similarly to |
chip |
The name of a DB-based annotation data package (the name
will end in ".db"). This package will be used to generate an Entrez
ID to GO ID mapping instead of |
g1 |
An instance of the |
g2 |
An instance of the |
For each of ll1 and ll2 the set of most specific GO
terms within the ontology specified (Ontology) that are not
based on any excluded evidence code (dropCodes) are found. The
mapping is achieved in one of three ways:
If mapfun is provided, it will be used to perform the
needed lookups. In this case, chip will be ignored.
If chip is provided and mapfun=NULL, then the
needed lookups will be done based on the Entrez to GO mappings
encapsulated in the specified annotation data package. This is
the recommended usage.
If mapfun and chip are NULL or missing,
then the function will attempt to load the GO package (the
environment-based package, distinct from GO.db). This package
contains a legacy environment mapping Entrez IDs to GO IDs. If
the GO package is not available, an error will be raised.
Omitting both mapfun and chip is not recommended as
it is not compatible with the DB-based annotation data packages.
Next, the induced GO graphs are computed.
Finally these graphs are passed to one of simUI, (union
intersection), or simLP (longest path). For simUI the
distance is the size of the intersection of the node sets divided by
the size of the union of the node sets. Large values indicate more
similarity. These similarities are between 0 and 1.
For simLP the length of the longest path in the intersection
graph of the two supplied graph. Again, large values indicate more
similarity. Similarities are between 0 and the maximum leaf depth of
the graph for the specified ontology.
A list with:
sim |
The numeric similarity measure. |
measure |
Which measure was used. |
g1 |
The graph induced by |
g2 |
The graph induced by |
If one of the supplied Gene IDs does not have any GO terms associated
with it, in the selected ontology and with the selected evidence codes
then NA is returned.
R. Gentleman
library("hgu95av2.db") eg1 = c("9184", "3547") bb = simLL(eg1[1], eg1[2], "BP", chip="hgu95av2.db")library("hgu95av2.db") eg1 = c("9184", "3547") bb = simLL(eg1[1], eg1[2], "BP", chip="hgu95av2.db")
These functions extract and plot graph instances representing the
relationships among GO terms tested using hyperGTest.
termGraphs(r, id = NULL, pvalue = NULL, use.terms = TRUE) inducedTermGraph(r, id, children = TRUE, parents = TRUE) plotGOTermGraph(g, r = NULL, add.counts = TRUE, max.nchar = 20, node.colors=c(sig="lightgray", not="white"), node.shape="plaintext", ...)termGraphs(r, id = NULL, pvalue = NULL, use.terms = TRUE) inducedTermGraph(r, id, children = TRUE, parents = TRUE) plotGOTermGraph(g, r = NULL, add.counts = TRUE, max.nchar = 20, node.colors=c(sig="lightgray", not="white"), node.shape="plaintext", ...)
r |
A |
id |
A character vector of category IDs that specifies which terms should be included in the graph. |
pvalue |
Numeric p-value cutoff to use for selecting category
terms to include. Will be ignored if |
use.terms |
Logical value indicating whether a |
children |
A logical value indicating whether to include direct
child terms of the terms specified by |
parents |
A logical value indicating whether to include direct
parent terms of the terms specified by |
g |
A |
add.counts |
A logical value indicating whether category size counts should be added to the node labels when plotting. |
max.nchar |
The maximum character length for node labels in the plot. |
node.colors |
A named character vector of length two with
compoents |
node.shape |
This argument controls the shape of the plotted nodes and must take on a value allowed by Rgraphviz. |
... |
For |
returns a list of graph objects each
representing one of the connected components of the subgraph of
the GO ontology induced by selecting the specified GO IDs (if
id is present) or by selecting the GO IDs that have a
p-value less that pvalue. If use.terms is
TRUE the GO IDs will be translated into GO Term names and
attached to the nodes as node attributes (see nodeData).
Edges in the graphs go from child (more specific) to parent (less
specific).
returns a graph object representing
the GO graph induced by the terms specified by id. The
children and parent arguments control whether direct
children and/or direct parents of the terms specified by id
are added to the graph (at least one of the two must be
TRUE).
Create a plot using Rgraphviz of a
graph object as returned by either termGraphs or
inducedTermGraph. If a GOHyperGResult object is
provided, then the nodes will be colored according to significance
(based on the result object's pvalueCutoff) and counts will
be added to show the size of the categories.
Seth Falcon
These functions provide some tools for finding triads in an undirected
graph. A triad is a clique of size 3. The function triadCensus
returns a list of all triads.
triadCensus(graph) isTriad(x, y, z, elz, ely) reduce2Degreek(graph, k) enumPairs(iVec)triadCensus(graph) isTriad(x, y, z, elz, ely) reduce2Degreek(graph, k) enumPairs(iVec)
graph |
An instance of the |
k |
An integer indicating the minimum degree wanted. |
x |
A node |
y |
A node |
z |
A node |
elz |
The edgelist for |
ely |
The edgelist for |
iVec |
A vector of unique values |
enumPairs takes a vector as input and returns a list of length
choose(length(iVec),2)/2 containing all unordered pairs of
elements.
isTriad takes three nodes as arguments. It is already known
that x has edges to both y and z and we want to
determine whether these are reciprocated. This is determined by
examining elz for both x and y and then examining
ely for both x and z.
reduce2Degreek is a function that takes an undirected graph as
input and removes all nodes of degree less than k. This process
is iterated until there are no nodes left (an error is thrown) or all
nodes remaining have degree at least k. The resultant subgraph
is returned. It is used here because to be in a triad all nodes must
have degree 2 or more.
triadCensus makes use of the helper functions described above
and finds all triads in the graph.
A list where each element is a triple indicating the members of the triad. Order is not important and all triads are reported in alphabetic order.
See the graph package, RBGL and Rgraphviz for more details and alternatives.
R. Gentleman
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random,##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random,