| Title: | R Onto-Tools suite |
|---|---|
| Description: | Suite of tools for functional analysis. |
| Authors: | Calin Voichita <[email protected]> and Sahar Ansari <[email protected]> and Sorin Draghici <[email protected]> |
| Maintainer: | Sorin Draghici <[email protected]> |
| License: | CC BY-NC-ND 4.0 + file LICENSE |
| Version: | 2.33.0 |
| Built: | 2024-07-03 06:18:14 UTC |
| Source: | https://github.com/bioc/ROntoTools |
Transform a vector of p-values into weights.
alpha1MR(pv, threshold = max(pv))alpha1MR(pv, threshold = max(pv))
pv |
vector of p-values |
threshold |
the threshold value that was used to select DE genes |
Computes a set of weights from p-values using the formula 1-pv/threshold.
Calin Voichita and Sorin Draghici
load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) head(alpha1MR(top$adj.P.Val))load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) head(alpha1MR(top$adj.P.Val))
Transform a vector of p-values into weights.
alphaMLG(pv, threshold = max(pv))alphaMLG(pv, threshold = max(pv))
pv |
vector of p-values |
threshold |
the threshold value that was used to select DE genes |
Computes a set of weights from p-values using the formula -log10(pv/threshold).
Calin Voichita and Sorin Draghici
load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) head(alphaMLG(top$adj.P.Val))load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) head(alphaMLG(top$adj.P.Val))
Combine independent p-values using the Fisher method
compute.fisher(p, eps = 1e-06)compute.fisher(p, eps = 1e-06)
p |
a vector of independent p-values |
eps |
the minimal p-value considered (all p-values smaller will be set to this value) |
the combined p-value
Calin Voichita and Sorin Draghici
Tarca AL., Draghici S., Khatri P., Hassan SS., Kim J., Kim CJ., Kusanovic JP., Romero R.: "A Signaling Pathway Impact Analysis for Microarray Experiments", 2008, Bioinformatics, 2009, 25(1):75-82.
p <- c(.1, .01) compute.fisher(p)p <- c(.1, .01) compute.fisher(p)
Combine independent p-values using the normal inversion method
compute.normalInv(p, eps = 1e-06)compute.normalInv(p, eps = 1e-06)
p |
a vector of independent p-values |
eps |
the minimal p-value considered (all p-values smaller will be set to this value) |
the combined p-value
Calin Voichita and Sorin Draghici
Tarca AL., Draghici S., Romero R.: "A Mmore Specific Method To Combine Perturbation and Over-representation Evidence in Pathway Analysis", PSB 2010 poster.
p <- c(.1, .01) compute.normalInv(p)p <- c(.1, .01) compute.normalInv(p)
Download and parse KEGG pathway data
keggPathwayGraphs(organism = "hsa", targRelTypes = c("GErel", "PCrel", "PPrel"), relPercThresh = 0.9, nodeOnlyGraphs = FALSE, updateCache = FALSE, verbose = TRUE)keggPathwayGraphs(organism = "hsa", targRelTypes = c("GErel", "PCrel", "PPrel"), relPercThresh = 0.9, nodeOnlyGraphs = FALSE, updateCache = FALSE, verbose = TRUE)
organism |
organism code as defined by KEGG |
targRelTypes |
target relation types |
relPercThresh |
percentage of the number of relation types over all possible realtions in the pathway |
nodeOnlyGraphs |
allow graphs with no edges |
updateCache |
re-download KEGG data |
verbose |
show progress of downloading and parsing |
A list of graphNEL objects encoding the pathway information.
Calin Voichita and Sorin Draghici
# The pathway cache provided as part of the pathway contains only the # pathways that passed the default filtering. We recommend, re-downloading # the pathways using the updateCache parameter kpg <- keggPathwayGraphs("hsa") # to update the pathway cache for human run: # kpg <- keggPathwayGraphs("hsa", updateCache = TRUE) # this is time consuming and depends on the available bandwith. head(names(kpg)) kpg[["path:hsa04110"]] head(nodes(kpg[["path:hsa04110"]])) head(edges(kpg[["path:hsa04110"]]))# The pathway cache provided as part of the pathway contains only the # pathways that passed the default filtering. We recommend, re-downloading # the pathways using the updateCache parameter kpg <- keggPathwayGraphs("hsa") # to update the pathway cache for human run: # kpg <- keggPathwayGraphs("hsa", updateCache = TRUE) # this is time consuming and depends on the available bandwith. head(names(kpg)) kpg[["path:hsa04110"]] head(nodes(kpg[["path:hsa04110"]])) head(edges(kpg[["path:hsa04110"]]))
Obtain KEGG pathway titles
keggPathwayNames(organism = "hsa", updateCache = FALSE, verbose = TRUE)keggPathwayNames(organism = "hsa", updateCache = FALSE, verbose = TRUE)
organism |
organism code as defined by KEGG |
updateCache |
re-download KEGG data |
verbose |
show progress of downloading and parsing |
A named vector of pathway titles. The names of the vector are the pathway KEGG IDs.
Calin Voichita and Sorin Draghici
kpn <- keggPathwayNames("hsa") # to update the pathway cache for human run: # kpn <- keggPathwayNames("hsa", updateCache = TRUE) # this is time consuming and depends on the available bandwidth. head(kpn)kpn <- keggPathwayNames("hsa") # to update the pathway cache for human run: # kpn <- keggPathwayNames("hsa", updateCache = TRUE) # this is time consuming and depends on the available bandwidth. head(kpn)
A generic function that returns the node weights of a graph. If index is specified, only the weights of the specified nodes are returned. The user can control which node attribute is interpreted as the weight.
nodeWeights(object, index, ..., attr = "weight", default = 1) ## S4 method for signature 'graph,character' nodeWeights(object, index, attr, default) ## S4 method for signature 'graph,numeric' nodeWeights(object, index, attr, default) ## S4 method for signature 'graph,missing' nodeWeights(object, index, attr, default)nodeWeights(object, index, ..., attr = "weight", default = 1) ## S4 method for signature 'graph,character' nodeWeights(object, index, attr, default) ## S4 method for signature 'graph,numeric' nodeWeights(object, index, attr, default) ## S4 method for signature 'graph,missing' nodeWeights(object, index, attr, default)
object |
A graph, any object that inherits the |
index |
If supplied, a character or numeric vector of node names or indices. |
... |
Unused. |
attr |
The name of the node attribute to use as a weight. You can view the list of defined node attributes and their default values using nodeDataDefaults. |
default |
The value to use if |
The weights of all nodes identified by the index are returned. If index is not supplied, the weights of all nodes are returned.
By default, nodeWeights looks for an node attribute with name "weight" and, if found, uses these values to construct the node weight vector.
You can make use of attributes stored under a different name by providing a value for the attr argument.
For example, if object is a graph instance with an node attribute named "WTS", then the call nodeWeights(object, attr="WTS") will attempt to use those values.
If the graph instance does not have an node attribute with name given by the value of the attr argument, default will be used as the weight for all nodes.
Note that if there is an attribute named by attr, then its default value will be used for nodes not specifically customized.
See nodeData and nodeDataDefaults for more information.
A named vector with the node weights. The names of the vector are the names of the specified index, or all nodes if index was not provided.
Calin Voichita and Sorin Draghici
library(graph) V <- LETTERS[1:4] g <- graphNEL(nodes = V, edgemode = "directed") nodeWeights(g) nodeWeights(g, "B") nodeWeights(g, attr = "WT", default = 3)library(graph) V <- LETTERS[1:4] g <- graphNEL(nodes = V, edgemode = "directed") nodeWeights(g) nodeWeights(g, "B") nodeWeights(g, attr = "WT", default = 3)
Primary dis-regulation: Pathway analysis approach based on the unexplained dis-regulation of genes
pDis(x, graphs, ref = NULL, nboot = 2000, verbose = TRUE, cluster = NULL, seed = NULL)pDis(x, graphs, ref = NULL, nboot = 2000, verbose = TRUE, cluster = NULL, seed = NULL)
x |
named vector of log fold changes for the differentially expressed genes; |
graphs |
list of pathway graphs as objects of type |
ref |
the reference vector for all genes in the analysis; if the reference is not provided or it is identical to |
nboot |
number of bootstrap iterations |
verbose |
print progress output |
cluster |
a cluster object created by makeCluster for parallel computations |
seed |
an integer value passed to set.seed() during the boostrap permutations |
See details in the cited articles.
An object of class pDisRes-class.
Calin Voichita, Sahar Ansari and Sorin Draghici
Voichita C., Donato M., Draghici S.: "Incorporating gene significance in the impact analysis of signaling pathways", IEEE Machine Learning and Applications (ICMLA), 2012 11th International Conference on, Vol. 1, p.126-131, 2012 Ansari, S., Voichita, C., Donato, M., Tagett, R., & Draghici, S. A Novel Pathway Analysis Approach Based on the Unexplained Disregulation of Genes.
Summary,
keggPathwayGraphs, setNodeWeights, setEdgeWeights
# load a multiple sclerosis study (public data available in Array Express # ID: E-GEOD-21942) # This file contains the top table, produced by the limma package with # added gene information. All the probe sets with no gene associate to them, # have been removed. Only the most significant probe set for each gene has been # kept (the table is already ordered by p-value) # The table contains the expression fold change and signficance of each # probe set in peripheral blood mononuclear cells (PBMC) from 12 MS patients # and 15 controls. load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) head(top) # select differentially expressed genes at 1% and save their fold change in a # vector fc and their p-values in a vector pv fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] pv <- top$P.Value[top$adj.P.Val <= .01] names(pv) <- top$entrez[top$adj.P.Val <= .01] # alternativly use all the genes for the analysis # NOT RUN: # fc <- top$logFC # names(fc) <- top$entrez # pv <- top$P.Value # names(pv) <- top$entrez # get the reference ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") # set the beta information (see the citated documents for meaning of beta) kpg <- setEdgeWeights(kpg) # inlcude the significance information in the analysis (see Voichita:2012 # for more information) # set the alpha information based on the pv with one of the predefined methods kpg <- setNodeWeights(kpg, weights = alphaMLG(pv), defaultWeight = 1) # perform the pathway analysis # in order to obtain accurate results the number of boostraps, nboot, should # be increase to a number like 2000 pDisRes <- pDis(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) # obtain summary of results head(Summary(pDisRes))# load a multiple sclerosis study (public data available in Array Express # ID: E-GEOD-21942) # This file contains the top table, produced by the limma package with # added gene information. All the probe sets with no gene associate to them, # have been removed. Only the most significant probe set for each gene has been # kept (the table is already ordered by p-value) # The table contains the expression fold change and signficance of each # probe set in peripheral blood mononuclear cells (PBMC) from 12 MS patients # and 15 controls. load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) head(top) # select differentially expressed genes at 1% and save their fold change in a # vector fc and their p-values in a vector pv fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] pv <- top$P.Value[top$adj.P.Val <= .01] names(pv) <- top$entrez[top$adj.P.Val <= .01] # alternativly use all the genes for the analysis # NOT RUN: # fc <- top$logFC # names(fc) <- top$entrez # pv <- top$P.Value # names(pv) <- top$entrez # get the reference ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") # set the beta information (see the citated documents for meaning of beta) kpg <- setEdgeWeights(kpg) # inlcude the significance information in the analysis (see Voichita:2012 # for more information) # set the alpha information based on the pv with one of the predefined methods kpg <- setNodeWeights(kpg, weights = alphaMLG(pv), defaultWeight = 1) # perform the pathway analysis # in order to obtain accurate results the number of boostraps, nboot, should # be increase to a number like 2000 pDisRes <- pDis(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) # obtain summary of results head(Summary(pDisRes))
Class that encodes the result of pDis analysis for a single pathway
map:an object of type graph (e.g., graphNEL).
input:named vector of fold changes for genes on this pathway. The names of the genes are the orignal IDS used in the analysis
ref:vector of reference IDs on this pathway
boot:an object of class boot encoding the bootstrap information.
pDis:the gene primary dis-regulation for all genes on the pathway, as computed by primary dis-regulation.
asGS:pathway was considered as gene set
Calin Voichita, Sahar Ansari and Sorin Draghici
This class is used to encode the results of the pathway analysis performed by the function pDis.
The slots input and ref record global information related to the whole analysis,
while the pathways slot records the specific results as pDisPathway-class for each one of the pathways used in the analysis.
pathways:A list of pDisPathway-class objects.
input:named vector of fold changes used for the analysis. The names of the vector are the IDs originaly used.
ref:character vector containing the IDs used as reference in the analysis.
cutOffFree:boolean value indicating if a cut-of-free analysis has been performed.
Calin Voichita, Sahar Ansari and Sorin Draghici
Pathway-Express: Pathway analysis of signaling pathways
pe(x, graphs, ref = NULL, nboot = 2000, verbose = TRUE, cluster = NULL, seed = NULL)pe(x, graphs, ref = NULL, nboot = 2000, verbose = TRUE, cluster = NULL, seed = NULL)
x |
named vector of log fold changes for the differentially expressed genes; |
graphs |
list of pathway graphs as objects of type |
ref |
the reference vector for all genes in the analysis; if the reference is not provided or it is identical to |
nboot |
number of bootstrap iterations |
verbose |
print progress output |
cluster |
a cluster object created by makeCluster for parallel computations |
seed |
an integer value passed to set.seed() during the boostrap permutations |
See details in the cited articles.
An object of class peRes-class.
Calin Voichita and Sorin Draghici
Voichita C., Donato M., Draghici S.: "Incorporating gene significance in the impact analysis of signaling pathways", IEEE Machine Learning and Applications (ICMLA), 2012 11th International Conference on, Vol. 1, p.126-131, 2012
Tarca AL., Draghici S., Khatri P., Hassan SS., Kim J., Kim CJ., Kusanovic JP., Romero R.: "A Signaling Pathway Impact Analysis for Microarray Experiments", 2008, Bioinformatics, 2009, 25(1):75-82.
Khatri P., Draghici S., Tarca AL., Hassan SS., Romero R.: "A system biology approach for the steady-state analysis of gene signaling networks". Progress in Pattern Recognition, Image Analysis and Applications, Lecture Notes in Computer Science. 4756:32-41, November 2007.
Draghici S., Khatri P., Tarca A.L., Amin K., Done A., Voichita C., Georgescu C., Romero R.: "A systems biology approach for pathway level analysis". Genome Research, 17, 2007.
Summary, plot,peRes,missing-method,
keggPathwayGraphs, setNodeWeights, setEdgeWeights
# load a multiple sclerosis study (public data available in Array Express # ID: E-GEOD-21942) # This file contains the top table, produced by the limma package with # added gene information. All the probe sets with no gene associate to them, # have been removed. Only the most significant probe set for each gene has been # kept (the table is already ordered by p-value) # The table contains the expression fold change and signficance of each # probe set in peripheral blood mononuclear cells (PBMC) from 12 MS patients # and 15 controls. load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) head(top) # select differentially expressed genes at 1% and save their fold change in a # vector fc and their p-values in a vector pv fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] pv <- top$P.Value[top$adj.P.Val <= .01] names(pv) <- top$entrez[top$adj.P.Val <= .01] # alternativly use all the genes for the analysis # NOT RUN: # fc <- top$logFC # names(fc) <- top$entrez # pv <- top$P.Value # names(pv) <- top$entrez # get the reference ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") # set the beta information (see the citated documents for meaning of beta) kpg <- setEdgeWeights(kpg) # inlcude the significance information in the analysis (see Voichita:2012 # for more information) # set the alpha information based on the pv with one of the predefined methods kpg <- setNodeWeights(kpg, weights = alphaMLG(pv), defaultWeight = 1) # perform the pathway analysis # in order to obtain accurate results the number of boostraps, nboot, should # be increase to a number like 2000 peRes <- pe(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) # obtain summary of results head(Summary(peRes))# load a multiple sclerosis study (public data available in Array Express # ID: E-GEOD-21942) # This file contains the top table, produced by the limma package with # added gene information. All the probe sets with no gene associate to them, # have been removed. Only the most significant probe set for each gene has been # kept (the table is already ordered by p-value) # The table contains the expression fold change and signficance of each # probe set in peripheral blood mononuclear cells (PBMC) from 12 MS patients # and 15 controls. load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) head(top) # select differentially expressed genes at 1% and save their fold change in a # vector fc and their p-values in a vector pv fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] pv <- top$P.Value[top$adj.P.Val <= .01] names(pv) <- top$entrez[top$adj.P.Val <= .01] # alternativly use all the genes for the analysis # NOT RUN: # fc <- top$logFC # names(fc) <- top$entrez # pv <- top$P.Value # names(pv) <- top$entrez # get the reference ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") # set the beta information (see the citated documents for meaning of beta) kpg <- setEdgeWeights(kpg) # inlcude the significance information in the analysis (see Voichita:2012 # for more information) # set the alpha information based on the pv with one of the predefined methods kpg <- setNodeWeights(kpg, weights = alphaMLG(pv), defaultWeight = 1) # perform the pathway analysis # in order to obtain accurate results the number of boostraps, nboot, should # be increase to a number like 2000 peRes <- pe(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) # obtain summary of results head(Summary(peRes))
pePathway-class objectExtract edge render information from a pePathway-class object
peEdgeRenderInfo(x, pos.col = "black", pos.lty = "solid", pos.ah = "vee", neg.col = "black", neg.lty = "dashed", neg.ah = "tee", zero.col = "lightgray", zero.lty = "dotted", zero.ah = "none")peEdgeRenderInfo(x, pos.col = "black", pos.lty = "solid", pos.ah = "vee", neg.col = "black", neg.lty = "dashed", neg.ah = "tee", zero.col = "lightgray", zero.lty = "dotted", zero.ah = "none")
x |
an object of class |
pos.col |
color of the edges with possitive weight |
pos.lty |
line type of the edges with possitive weight |
pos.ah |
arrow head of the edges with possitive weight |
neg.col |
color of the edges with negative weight |
neg.lty |
line type of the edges with negative weight |
neg.ah |
arrow head of the edges with negative weight |
zero.col |
color of the edges with zero weight |
zero.lty |
color of the edges with zero weight |
zero.ah |
color of the edges with zero weight |
a named list as expected by edgeRenderInfo
Calin Voichita and Sorin Draghici
# load experiment load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setEdgeWeights(kpg) kpg <- setNodeWeights(kpg, defaultWeight = 1) # perform the pathway analysis peRes <- pe(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) p <- peRes@pathways[[50]] g <- layoutGraph(p@map, layoutType = "dot") graphRenderInfo(g) <- list(fixedsize = FALSE) edgeRenderInfo(g) <- peEdgeRenderInfo(p) nodeRenderInfo(g) <- peNodeRenderInfo(p) # notice the different type of edges in the graph (solid/dashed/dotted) renderGraph(g)# load experiment load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setEdgeWeights(kpg) kpg <- setNodeWeights(kpg, defaultWeight = 1) # perform the pathway analysis peRes <- pe(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) p <- peRes@pathways[[50]] g <- layoutGraph(p@map, layoutType = "dot") graphRenderInfo(g) <- list(fixedsize = FALSE) edgeRenderInfo(g) <- peEdgeRenderInfo(p) nodeRenderInfo(g) <- peNodeRenderInfo(p) # notice the different type of edges in the graph (solid/dashed/dotted) renderGraph(g)
pePathway-class objectExtract node render information from a pePathway-class object
peNodeRenderInfo(x, y = "Pert", input.shape = "box", default.shape = "ellipse", pos.col = "red", neg.col = "blue", zero.col = "white")peNodeRenderInfo(x, y = "Pert", input.shape = "box", default.shape = "ellipse", pos.col = "red", neg.col = "blue", zero.col = "white")
x |
an object of class |
y |
a string representing the factor to be represented ( |
input.shape |
shape of nodes that have measured expression change |
default.shape |
shape of all other nodes |
pos.col |
color of nodes with a positive |
neg.col |
color of nodes with a negative |
zero.col |
color of nodes with the |
a named list as expected by nodeRenderInfo
Calin Voichita and Sorin Draghici
# load experiment load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setEdgeWeights(kpg) kpg <- setNodeWeights(kpg, defaultWeight = 1) # perform the pathway analysis peRes <- pe(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) p <- peRes@pathways[[50]] g <- layoutGraph(p@map, layoutType = "dot") graphRenderInfo(g) <- list(fixedsize = FALSE) edgeRenderInfo(g) <- peEdgeRenderInfo(p) nodeRenderInfo(g) <- peNodeRenderInfo(p) # notice the different type of nodes in the graph (box/circle) # the color of each node represents the perturbation (red = positive, blue = negative) # the shade represents the stregth of the perturbation renderGraph(g) nodeRenderInfo(g) <- peNodeRenderInfo(p, "Acc") # now, the color of each node represents the accumulation (red = positive, blue = negative) # notice that square nodes with no parents have no accumulation renderGraph(g)# load experiment load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setEdgeWeights(kpg) kpg <- setNodeWeights(kpg, defaultWeight = 1) # perform the pathway analysis peRes <- pe(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) p <- peRes@pathways[[50]] g <- layoutGraph(p@map, layoutType = "dot") graphRenderInfo(g) <- list(fixedsize = FALSE) edgeRenderInfo(g) <- peEdgeRenderInfo(p) nodeRenderInfo(g) <- peNodeRenderInfo(p) # notice the different type of nodes in the graph (box/circle) # the color of each node represents the perturbation (red = positive, blue = negative) # the shade represents the stregth of the perturbation renderGraph(g) nodeRenderInfo(g) <- peNodeRenderInfo(p, "Acc") # now, the color of each node represents the accumulation (red = positive, blue = negative) # notice that square nodes with no parents have no accumulation renderGraph(g)
Class that encodes the result of Pathway-Express for a single pathway
map:an object of type graph (e.g., graphNEL).
input:named vector of fold changes for genes on this pathway. The names of the genes are the orignal IDS used in the analysis
ref:vector of reference IDs on this pathway
boot:an object of class boot encoding the bootstrap information.
Pert:the gene perturbation factors for all genes on the pathway, as computed by Pathway-Express.
Acc:the gene accumulations for all genes on the pathway, as computed by Pathway-Express.
asGS:pathway was considered as gene set
Calin Voichita and Sorin Draghici
This class is used to encode the results of the pathway analysis performed by the function pe.
The slots input and ref record global information related to the whole analysis,
while the pathways slot records the specific results as pePathway-class for each one of the pathways used in the analysis.
pathways:A list of pePathway-class objects.
input:named vector of fold changes used for the analysis. The names of the vector are the IDs originaly used.
ref:character vector containing the IDs used as reference in the analysis.
cutOffFree:boolean value indicating if a cut-of-free analysis has been performed.
Calin Voichita and Sorin Draghici
Display graphical representation of pathway level statistic like:
i) two way comparison between the measured expression change and one of the
factors computed by Pathway-Express (pe) or ii) the boostrap
statistics of the same factors.
## S4 method for signature 'pePathway,missing' plot(x, y, ..., type = "two.way", eps = 1e-06) ## S4 method for signature 'pePathway,character' plot(x, y, main = "", ..., type = "two.way", eps = 1e-06)## S4 method for signature 'pePathway,missing' plot(x, y, ..., type = "two.way", eps = 1e-06) ## S4 method for signature 'pePathway,character' plot(x, y, main = "", ..., type = "two.way", eps = 1e-06)
x |
an object of type |
y |
if provided, the factor to be ploted (either |
... |
Arguments to be passed to methods, such as |
type |
type of plot (either |
eps |
any value smaller than this will be ploted as 0 |
main |
title |
Calin Voichita and Sorin Draghici
pe, plot,peRes,missing-method, peNodeRenderInfo, peEdgeRenderInfo
# load experiment load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setEdgeWeights(kpg) kpg <- setNodeWeights(kpg, defaultWeight = 1) # perform the pathway analysis (for more accurate results use nboot = 2000) peRes <- pe(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) plot(peRes@pathways[[50]]) plot(peRes@pathways[[50]], "Pert", main = "Perturbation factor") plot(peRes@pathways[[50]], type = "boot") plot(peRes@pathways[[50]], "Pert", type = "boot", main = "Perturbation factor")# load experiment load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setEdgeWeights(kpg) kpg <- setNodeWeights(kpg, defaultWeight = 1) # perform the pathway analysis (for more accurate results use nboot = 2000) peRes <- pe(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) plot(peRes@pathways[[50]]) plot(peRes@pathways[[50]], "Pert", main = "Perturbation factor") plot(peRes@pathways[[50]], type = "boot") plot(peRes@pathways[[50]], "Pert", type = "boot", main = "Perturbation factor")
Display a two-way plot using two of the p-values from the Pathway-Express analysis.
## S4 method for signature 'peRes,missing' plot(x, y, ..., comb.pv.func = compute.fisher, adjust.method = "fdr", threshold = 0.05, eps = 1e-06) ## S4 method for signature 'peRes,character' plot(x, y, ..., comb.pv.func = compute.fisher, adjust.method = "fdr", threshold = 0.05, eps = 1e-06)## S4 method for signature 'peRes,missing' plot(x, y, ..., comb.pv.func = compute.fisher, adjust.method = "fdr", threshold = 0.05, eps = 1e-06) ## S4 method for signature 'peRes,character' plot(x, y, ..., comb.pv.func = compute.fisher, adjust.method = "fdr", threshold = 0.05, eps = 1e-06)
x |
an object of type |
y |
vector of two p-values names to be combined using |
... |
Arguments to be passed to methods, such as |
comb.pv.func |
the function to combine the p-values - takes as input a vector of p-values
and returns the combined p-value (default: |
adjust.method |
the name of the method to adjust the p-value (see |
threshold |
corrected p-value threshold |
eps |
any value smaller than this will be considered as |
Calin Voichita and Sorin Draghici
pe, summary.peRes, plot,pePathway,missing-method
# load experiment load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setEdgeWeights(kpg) kpg <- setNodeWeights(kpg, defaultWeight = 1) # perform the pathway analysis (for more accurate results use nboot = 2000) peRes <- pe(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) plot(peRes) plot(peRes, c("pPert","pORA"), comb.pv.func = compute.normalInv, threshold = .01)# load experiment load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setEdgeWeights(kpg) kpg <- setNodeWeights(kpg, defaultWeight = 1) # perform the pathway analysis (for more accurate results use nboot = 2000) peRes <- pe(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) plot(peRes) plot(peRes, c("pPert","pORA"), comb.pv.func = compute.normalInv, threshold = .01)
setEdgeWeights
setEdgeWeights(graphList, edgeTypeAttr = "subtype", edgeWeightByType = list(activation = 1, inhibition = -1, expression = 1, repression = -1), defaultWeight = 0, combineWeights = sum, nodeOnlyGraphs = FALSE)setEdgeWeights(graphList, edgeTypeAttr = "subtype", edgeWeightByType = list(activation = 1, inhibition = -1, expression = 1, repression = -1), defaultWeight = 0, combineWeights = sum, nodeOnlyGraphs = FALSE)
graphList |
a list of |
edgeTypeAttr |
edge attribute to be considered as the edge type. If the edge has multiple types, the edge type attribute is considered as a comma separeted list of types |
edgeWeightByType |
named list of weigths, where the names of the list are the
edge type (values of the attribute defined by |
defaultWeight |
default value for an edge with a type not defined in |
combineWeights |
for the edges with multiple types, the function to be applied on the vector of weights |
nodeOnlyGraphs |
boolean value marking if graphs with no edges should be returned or not; note that graphs with all edge weights equal to 0 are considered node only graphs |
The graphList with the edge weights set.
Calin Voichita and Sorin Draghici
# load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setEdgeWeights(kpg) edgeWeights(kpg[["path:hsa04110"]])# load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setEdgeWeights(kpg) edgeWeights(kpg[["path:hsa04110"]])
Set node weights
setNodeWeights(graphList, weights = NULL, defaultWeight = 1)setNodeWeights(graphList, weights = NULL, defaultWeight = 1)
graphList |
a list of |
weights |
named vector or matrix; if vector, the node is going to have the same weight in all graphs it appears; if matrix, the rows represent nodes and columns represent graphs and the node will have different weights in each pathway |
defaultWeight |
the default weight for all nodes not set by the parameter |
The graphList with the node weights set.
Calin Voichita and Sorin Draghici
# load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setNodeWeights(kpg) nodeWeights(kpg[["path:hsa04110"]])# load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setNodeWeights(kpg) nodeWeights(kpg[["path:hsa04110"]])
Summarize the results of a Pathway-Express analysis
## S4 method for signature 'pDisRes' Summary(x, ..., na.rm = FALSE)## S4 method for signature 'pDisRes' Summary(x, ..., na.rm = FALSE)
x |
Primary dis-regulation analysis result object obtained using |
... |
see |
na.rm |
ignored |
Summarize the results of a Pathway-Express analysis
## S4 method for signature 'peRes' Summary(x, ..., na.rm = FALSE)## S4 method for signature 'peRes' Summary(x, ..., na.rm = FALSE)
x |
Pathway-Express analysis result object obtained using |
... |
see |
na.rm |
ignored |
Summarize the results of a primary dis-regulation (pDis) analysis
summary.pDisRes(object, ..., pathNames = NULL, totalpDis = TRUE, normalize = TRUE, ppDis = TRUE, pORA = TRUE, comb.pv = c("ppDis", "pORA"), comb.pv.func = compute.fisher, order.by = "pComb", adjust.method = "fdr")summary.pDisRes(object, ..., pathNames = NULL, totalpDis = TRUE, normalize = TRUE, ppDis = TRUE, pORA = TRUE, comb.pv = c("ppDis", "pORA"), comb.pv.func = compute.fisher, order.by = "pComb", adjust.method = "fdr")
object |
pDis analysis result object obtained using |
... |
ignored |
pathNames |
named vector of pathway names; the names of the vector are the IDs of the pathways |
totalpDis |
boolean value indicating if the total primary dis-regulation should be computed |
normalize |
boolean value indicating if normalization with regards to the boostrap simulations should be performed on totalpDis |
ppDis |
boolean value indicating if the significance of the total primary dis-regulation in regards to the bootstrap permutations should be computed |
pORA |
boolean value indicating if the over-represtation p-value should be computed |
comb.pv |
vector of the p-value names to be combine (any of the above p-values) |
comb.pv.func |
the function to combine the p-values; takes as input a vector of p-values and returns the combined p-value |
order.by |
the name of the p-value that is used to order the results |
adjust.method |
the name of the method to adjust the p-value (see p.adjust) |
# load experiment load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setEdgeWeights(kpg) kpg <- setNodeWeights(kpg, defaultWeight = 1) # perform the pathway analysis pDisRes <- pDis(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) # obtain summary of results head(summary(pDisRes)) kpn <- keggPathwayNames("hsa") head(summary(pDisRes)) head(summary(pDisRes, pathNames = kpn, totalpDis = FALSE, pORA = FALSE, comb.pv = NULL, order.by = "pDis"))# load experiment load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setEdgeWeights(kpg) kpg <- setNodeWeights(kpg, defaultWeight = 1) # perform the pathway analysis pDisRes <- pDis(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) # obtain summary of results head(summary(pDisRes)) kpn <- keggPathwayNames("hsa") head(summary(pDisRes)) head(summary(pDisRes, pathNames = kpn, totalpDis = FALSE, pORA = FALSE, comb.pv = NULL, order.by = "pDis"))
Summarize the results of a Pathway-Express analysis
summary.peRes(object, ..., pathNames = NULL, totalAcc = TRUE, totalPert = TRUE, normalize = TRUE, pPert = TRUE, pAcc = TRUE, pORA = TRUE, comb.pv = c("pPert", "pORA"), comb.pv.func = compute.fisher, order.by = "pComb", adjust.method = "fdr")summary.peRes(object, ..., pathNames = NULL, totalAcc = TRUE, totalPert = TRUE, normalize = TRUE, pPert = TRUE, pAcc = TRUE, pORA = TRUE, comb.pv = c("pPert", "pORA"), comb.pv.func = compute.fisher, order.by = "pComb", adjust.method = "fdr")
object |
Pathways-Express result object obtained using |
... |
ignored |
pathNames |
named vector of pathway names; the names of the vector are the IDs of the pathways |
totalAcc |
boolean value indicating if the total accumulation should be computed |
totalPert |
boolean value indicating if the total perturbation should be computed |
normalize |
boolean value indicating if normalization with regards to the boostrap simulations should be performed on totalAcc and totalPert |
pPert |
boolean value indicating if the significance of the total perturbation in regards to the bootstrap permutations should be computed |
pAcc |
boolean value indicating if the significance of the total accumulation in regards to the bootstrap permutations should be computed |
pORA |
boolean value indicating if the over-represtation p-value should be computed |
comb.pv |
vector of the p-value names to be combine (any of the above p-values) |
comb.pv.func |
the function to combine the p-values; takes as input a vector of p-values and returns the combined p-value |
order.by |
the name of the p-value that is used to order the results |
adjust.method |
the name of the method to adjust the p-value (see p.adjust) |
# load experiment load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setEdgeWeights(kpg) kpg <- setNodeWeights(kpg, defaultWeight = 1) # perform the pathway analysis peRes <- pe(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) # obtain summary of results head(summary(peRes)) kpn <- keggPathwayNames("hsa") head(summary(peRes)) head(summary(peRes, pathNames = kpn, totalAcc = FALSE, totalPert = FALSE, pAcc = FALSE, pORA = FALSE, comb.pv = NULL, order.by = "pPert"))# load experiment load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools")) fc <- top$logFC[top$adj.P.Val <= .01] names(fc) <- top$entrez[top$adj.P.Val <= .01] ref <- top$entrez # load the set of pathways kpg <- keggPathwayGraphs("hsa") kpg <- setEdgeWeights(kpg) kpg <- setNodeWeights(kpg, defaultWeight = 1) # perform the pathway analysis peRes <- pe(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE) # obtain summary of results head(summary(peRes)) kpn <- keggPathwayNames("hsa") head(summary(peRes)) head(summary(peRes, pathNames = kpn, totalAcc = FALSE, totalPert = FALSE, pAcc = FALSE, pORA = FALSE, comb.pv = NULL, order.by = "pPert"))