| Title: | Correlation Guided Network Integration (CoNI) |
|---|---|
| Description: | Integrates two numerical omics data sets from the same samples using partial correlations. The output can be represented as a network, bipartite graph or a hypergraph structure. The method used in the package refers to Klaus et al (2021) <doi:10.1016/j.molmet.2021.101295>. |
| Authors: | José Manuel Monroy Kuhn [aut, cre], Dominik Lutter [ths], Valentina Klaus [ctb] |
| Maintainer: | José Manuel Monroy Kuhn <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2024-11-20 04:36:48 UTC |
| Source: | https://github.com/jmanuelmk/coni |
This function assigns two color columns (color name and rgb) to an annotation data frame according to a column named 'Class' or 'class'
assign_colorsAnnotation(AnnotationDf, col = "Class")assign_colorsAnnotation(AnnotationDf, col = "Class")
AnnotationDf |
Annotation data frame that contains a factor variable to use to assign colors |
col |
Column with factor variable that will be used to assign colors |
The input data.frame with two extra columns specifying the colors for all vertexes according to their respective vertex-class
Chow gene expression data
Jose Manuel Monroy [email protected]
doi:10.1016/j.molmet.2021.101295
Chow metabolite data
Jose Manuel Monroy [email protected]
doi:10.1016/j.molmet.2021.101295
Compare vertexFeature-vertexFeature-edgeFeature between two treatments, that is, find the shared triplets between two different CoNI runs.
Compare_Triplets( Treat1_path, Treat2_path, OutputName = "Shared_Genes_and_Edges_Treat1vsTreat2.csv" )Compare_Triplets( Treat1_path, Treat2_path, OutputName = "Shared_Genes_and_Edges_Treat1vsTreat2.csv" )
Treat1_path |
TableForNetwork_file1 (file generated by CoNI) with path for Treatment 1 |
Treat2_path |
TableForNetwork_file2 (file generated by CoNI) with path for Treatment 2 |
OutputName |
Output file name with path |
A data.frame with the shared triplets (vertex1 vertex2 edge_feature) between two CoNI runs
#For an example see the vignette#For an example see the vignette
CoNI is the main function of Correlation guided Network Integration (CoNI). Input data should come from two sources (e.g., gene expression and metabolite expression), and it should come from the same samples. It calculates all pairwise correlations of the second data input elements and the partial correlations of these pairwise elements with respect to the elements of the first data input. Both data inputs can be prefiltered to include only those elements that significantly correlate. The first data input can be prefiltered to keep just low variance elements (var<0.5). A Steiger test is used to identify significant changes between the correlation and partial correlation values. Results can be visually represented in a Network.
CoNI( edgeD, vertexD, outputDir = "./CoNIOutput/", saveRaw = TRUE, outputNameRaw = "CoNIOutput", onlySgRes = FALSE, multipleTAdj = TRUE, padjustvertexD = TRUE, correlateDFs = TRUE, filter_highVarianceEdge = TRUE, splitedgeD = TRUE, split_number = 2, delPrevious = FALSE, delIntermediaryFiles = TRUE, iteration_start = 1, numCores = NULL, verbose = TRUE, more_coef = FALSE, edgeDname = "edgeD", vertexDname = "vertexD", saveFiles = TRUE )CoNI( edgeD, vertexD, outputDir = "./CoNIOutput/", saveRaw = TRUE, outputNameRaw = "CoNIOutput", onlySgRes = FALSE, multipleTAdj = TRUE, padjustvertexD = TRUE, correlateDFs = TRUE, filter_highVarianceEdge = TRUE, splitedgeD = TRUE, split_number = 2, delPrevious = FALSE, delIntermediaryFiles = TRUE, iteration_start = 1, numCores = NULL, verbose = TRUE, more_coef = FALSE, edgeDname = "edgeD", vertexDname = "vertexD", saveFiles = TRUE )
edgeD |
Object to use as first data input (e.g., protein expression) |
vertexD |
Object to use as second data input (e.g., metabolite expression) |
outputDir |
Output Directory where results are stored |
saveRaw |
logical. If TRUE the raw output of CoNI is saved in the output directory (outputDir) |
outputNameRaw |
Name for the raw output file if saved |
onlySgRes |
logical. If TRUE CoNI output is filtered and only significant results are kept |
multipleTAdj |
logical. If TRUE it will filter results after adjustment of multiple testing |
padjustvertexD |
logical. If TRUE vertexD is filtered according to the significant adjusted p-value of its pairwise correlations |
correlateDFs |
logical. If TRUE the elements that significantly correlate of vertexD are correlated with the elements of edgeD. Only the elements that significantly correlate are kept |
filter_highVarianceEdge |
logical. If TRUE features of edgeD with high variance are filtered out |
splitedgeD |
logical. If TRUE edgeD will be split in n subsets for the computation (some instances n+1). Keep as TRUE unless the data input is small |
split_number |
Number of parts to split the elements of edgeD |
delPrevious |
logical. If TRUE previous files of a previous run are deleted |
delIntermediaryFiles |
logical. If TRUE the output file of every iteration is deleted and only a single file with all results is kept |
iteration_start |
Iteration start for CoNI. Useful if run is interrupted as one can restart from the last iteration |
numCores |
Cores assigned for parallelization |
verbose |
logical. If TRUE output in the console is more verbose |
more_coef |
logical. If TRUE it will include the partial correlation of edge and vertex Features |
edgeDname |
File name extension for the edge features that significantly correlate with at least one vertex feature. This file will be read if the function is called again with the same input and with delPrevious=FALSE |
vertexDname |
File name extension for the vertex features that are involved in at least one significant correlation. This file will be read if the function is called again with the same input and with delPrevious=FALSE |
saveFiles |
logical. If FALSE CoNI function will not save any file to disk |
CoNI returns a data.frame with the correlation coefficients of the vertex-pairs, the partial correlation coefficients for every triplet, and the pvalue of the Steiger tests
#Run CoNI #Load gene expression - Toy dataset of two treatments data(GeneExpToy) #Samples in rows and genes in columns GeneExp <- as.data.frame(t(GeneExpToy)) hfd_gene <- GeneExp[1:8,] #high fat diet chow_gene<- GeneExp[9:nrow(GeneExp),] #chow diet #Load metabolite expression - Toy dataset of two treatments data(MetaboExpToy) MetaboExp <- MetaboExpToy hfd_metabo <- MetaboExp[11:18,] #high fat diet chow_metabo <- MetaboExp[1:10,] #chow diet #Match row names both data sets rownames(hfd_metabo)<-rownames(hfd_gene) rownames(chow_metabo)<-rownames(chow_gene) #Run CoNI with tiny example and no significance testing #High fat diet #For big datasets it is recommended to set splitedgeD to TRUE #and split_number should be adjusted accordingly #See vignette for an example #Running CoNI with only a tiny dataset CoNIResultsHFD <- CoNI(hfd_gene,hfd_metabo, numCores = 2, onlySgRes = FALSE, filter_highVarianceEdge=FALSE, padjustvertexD = FALSE, correlateDFs = FALSE, edgeDname="HFD_genes", vertexDname = "HFD_metabolites", saveFiles = FALSE, splitedgeD = FALSE, outputDir = "./")#Run CoNI #Load gene expression - Toy dataset of two treatments data(GeneExpToy) #Samples in rows and genes in columns GeneExp <- as.data.frame(t(GeneExpToy)) hfd_gene <- GeneExp[1:8,] #high fat diet chow_gene<- GeneExp[9:nrow(GeneExp),] #chow diet #Load metabolite expression - Toy dataset of two treatments data(MetaboExpToy) MetaboExp <- MetaboExpToy hfd_metabo <- MetaboExp[11:18,] #high fat diet chow_metabo <- MetaboExp[1:10,] #chow diet #Match row names both data sets rownames(hfd_metabo)<-rownames(hfd_gene) rownames(chow_metabo)<-rownames(chow_gene) #Run CoNI with tiny example and no significance testing #High fat diet #For big datasets it is recommended to set splitedgeD to TRUE #and split_number should be adjusted accordingly #See vignette for an example #Running CoNI with only a tiny dataset CoNIResultsHFD <- CoNI(hfd_gene,hfd_metabo, numCores = 2, onlySgRes = FALSE, filter_highVarianceEdge=FALSE, padjustvertexD = FALSE, correlateDFs = FALSE, edgeDname="HFD_genes", vertexDname = "HFD_metabolites", saveFiles = FALSE, splitedgeD = FALSE, outputDir = "./")
CoNI Results Chow
Jose Manuel Monroy [email protected]
doi:10.1016/j.molmet.2021.101295
CoNI Results HFD
Jose Manuel Monroy [email protected]
doi:10.1016/j.molmet.2021.101295
Toy data HFD results
Jose Manuel Monroy [email protected]
doi:10.1016/j.molmet.2021.101295
This function will create a barplot from the output of Compare_VertexClasses_sharedEdgeFeatures for a specific shared Edge Feature (e.g., a shared gene).
create_edgeFBarplot( CompTreatTable, edgeF, treat1 = "Treatment1", treat2 = "Treatment2", factorOrder = NULL, col1 = "red", col2 = "blue", EdgeFeatureType = "Edge Feature", xlb = "Vertex-Class Pairs", ylb = "Number of pairs", szaxisTxt = 12, szaxisTitle = 12 )create_edgeFBarplot( CompTreatTable, edgeF, treat1 = "Treatment1", treat2 = "Treatment2", factorOrder = NULL, col1 = "red", col2 = "blue", EdgeFeatureType = "Edge Feature", xlb = "Vertex-Class Pairs", ylb = "Number of pairs", szaxisTxt = 12, szaxisTitle = 12 )
CompTreatTable |
Output of Compare_VertexClasses_sharedEdgeFeatures |
edgeF |
Edge feature present in output of Compare_VertexClasses_sharedEdgeFeatures |
treat1 |
Name of treatment one, default Treatment1. It should match the column names of the output of Compare_VertexClasses_sharedEdgeFeatures |
treat2 |
Name of treatment one, default Treatment2. It should match the column names of the output of Compare_VertexClasses_sharedEdgeFeatures |
factorOrder |
A list specifying the order of the treatments. |
col1 |
Color for Treatment 1 |
col2 |
Color for Treatment 2 |
EdgeFeatureType |
Type of Edge Feature (e.g., Gene) |
xlb |
Name for x-axis |
ylb |
Name for the y-axis |
szaxisTxt |
Size axis text |
szaxisTitle |
Size axis titles |
A ggplot object for a barplot. The barplot shows the vertex-class pairs profile of a single shared edge feature between two treatments
data(VertexClassesSharedGenes_HFDvsChow) create_edgeFBarplot(CompTreatTable = VertexClassesSharedGenes_HFDvsChow, edgeF = "Lilr4b", treat1 = "HFD", treat2 = "Chow", factorOrder = c("HFD","Chow"), EdgeFeatureType = "Gene")data(VertexClassesSharedGenes_HFDvsChow) create_edgeFBarplot(CompTreatTable = VertexClassesSharedGenes_HFDvsChow, edgeF = "Lilr4b", treat1 = "HFD", treat2 = "Chow", factorOrder = c("HFD","Chow"), EdgeFeatureType = "Gene")
This function will create a barplot from the output of Compare_VertexClasses_sharedEdgeFeatures using all shared Edge Features (e.g., genes).
create_GlobalBarplot( CompTreatTable, treat1 = "Treatment1", treat2 = "Treatment2", factorOrder = NULL, col1 = "red", col2 = "blue", maxpairs = 1, xlb = "Vertex-Class Pairs", ylb = "Number of pairs", szggrepel = 3.5, nudgey = 0.5, nudgex = 0.5, szaxisTxt = 12, szaxisTitle = 12 )create_GlobalBarplot( CompTreatTable, treat1 = "Treatment1", treat2 = "Treatment2", factorOrder = NULL, col1 = "red", col2 = "blue", maxpairs = 1, xlb = "Vertex-Class Pairs", ylb = "Number of pairs", szggrepel = 3.5, nudgey = 0.5, nudgex = 0.5, szaxisTxt = 12, szaxisTitle = 12 )
CompTreatTable |
Output of Compare_VertexClasses_sharedEdgeFeatures |
treat1 |
Name of treatment one, default Treatment1. It should match the column names of the output of Compare_VertexClasses_sharedEdgeFeatures |
treat2 |
Name of treatment one, default Treatment2. It should match the column names of the output of Compare_VertexClasses_sharedEdgeFeatures |
factorOrder |
A list specifying the order of the treatments. |
col1 |
Color for Treatment 1 |
col2 |
Color for Treatment 2 |
maxpairs |
If number of class-vertex-pairs > maxpairs, display number pairs on top of bar |
xlb |
Name for x-axis |
ylb |
Name for the y-axis |
szggrepel |
Size ggrepel labels |
nudgey |
Nudge y ggrepel |
nudgex |
Nudge x ggrepel |
szaxisTxt |
Size axis text |
szaxisTitle |
Size axis title |
A ggplot object for a barplot. The barplot shows the vertex-class pairs profile of all shared edge features between treatments
data(VertexClassesSharedGenes_HFDvsChow) create_GlobalBarplot(CompTreatTable = VertexClassesSharedGenes_HFDvsChow, treat1 = "HFD", treat2 = "Chow", factorOrder = c("HFD","Chow"), col1="red", col2 ="blue", maxpairs = 1, szggrepel = 6, szaxisTxt = 15, szaxisTitle = 15, xlb = "Metabolite-pair classes")data(VertexClassesSharedGenes_HFDvsChow) create_GlobalBarplot(CompTreatTable = VertexClassesSharedGenes_HFDvsChow, treat1 = "HFD", treat2 = "Chow", factorOrder = c("HFD","Chow"), col1="red", col2 ="blue", maxpairs = 1, szggrepel = 6, szaxisTxt = 15, szaxisTitle = 15, xlb = "Metabolite-pair classes")
This function will create a stacked barplot from the output of Compare_VertexClasses_sharedEdgeFeatures using all shared Edge Features (e.g., genes) between two treatments.
create_stackedGlobalBarplot_perTreatment( CompTreatTable, treat = NULL, xlb = "Vertex-Class Pairs", ylb = "Number of pairs", max_pairsLegend = 2, mx.overlaps = Inf, szggrepel = 6, force = 0.1, szTitle = 12, szaxisTxt = 12, szaxisTitle = 12, ylim = NULL )create_stackedGlobalBarplot_perTreatment( CompTreatTable, treat = NULL, xlb = "Vertex-Class Pairs", ylb = "Number of pairs", max_pairsLegend = 2, mx.overlaps = Inf, szggrepel = 6, force = 0.1, szTitle = 12, szaxisTxt = 12, szaxisTitle = 12, ylim = NULL )
CompTreatTable |
Output of Compare_VertexClasses_sharedEdgeFeatures |
treat |
Name of treatment to display. It should match the column name in the output of Compare_VertexClasses_sharedEdgeFeatures |
xlb |
Name for x-axis |
ylb |
Name for y-axis |
max_pairsLegend |
If number of Edge Features >= max_pairsLegend, display number of Edge Features as label with ggrepel |
mx.overlaps |
Max number of overlaps ggrepel |
szggrepel |
Size ggrepel labels |
force |
Repelling force for ggrepel labels |
szTitle |
Size Title |
szaxisTxt |
Size axis text |
szaxisTitle |
Size axis titles |
ylim |
Optional y-limits of the plot |
A ggplot object to create a stacked barplot. The stacked barplot shows the vertex-class pairs profile of all shared edge features but restricted to a single treatment. Every bar consists of multiple edge features (stacked) that are represented with different colors
data(VertexClassesSharedGenes_HFDvsChow) create_stackedGlobalBarplot_perTreatment(CompTreatTable = VertexClassesSharedGenes_HFDvsChow, treat = "HFD", max_pairsLegend = 9, xlb = "Metabolite-class-pairs")data(VertexClassesSharedGenes_HFDvsChow) create_stackedGlobalBarplot_perTreatment(CompTreatTable = VertexClassesSharedGenes_HFDvsChow, treat = "HFD", max_pairsLegend = 9, xlb = "Metabolite-class-pairs")
This function creates a simple bipartite graph, it shows the driver and linker features as nodes.
createBipartiteGraph(TableNetwork, colorVertexTable, incidenceMatrix = FALSE)createBipartiteGraph(TableNetwork, colorVertexTable, incidenceMatrix = FALSE)
TableNetwork |
TableForNetwork_file (file generated by CoNI) with path |
colorVertexTable |
Table specifying the colors for the vertex features. The first column should contain the names matching the features of the vertex Data and another column should specify the colors (column name: Colors). |
incidenceMatrix |
logical. If TRUE it returns a hypergraph incidence matrix instead of a bipartite graph |
An igraph object for a bipartite graph or a hypergraph incidence matrix to represent ResultsCoNI. Basic network statistics are included in the bipartite graph. See generate_network function for details or consult the igraph package
#See vignette for an example#See vignette for an example
This function applies for a selected subnetwork a binomial test using the frequency of appearance of an edge feature and the total number of edge features. The probability corresponds to 1/n_df, where n_df corresponds to the total number of edge features in the network. The selected subnetwork corresponds to the second level neighborhood of a specific node. The test is applied to all possible second level neighborhoods in the network.
find_localControllingFeatures(ResultsCoNI, network, padjust = TRUE)find_localControllingFeatures(ResultsCoNI, network, padjust = TRUE)
ResultsCoNI |
The output of CoNI (after p-adjustment) |
network |
Network created with the function generate_network |
padjust |
logical. Filter output based on adjusted p values |
Returns a data.frame with the results of the binomial tests. Significant results correspond to local controlling features
#Load color nodes table data(MetColorTable) #Assign colors according to "Class" column MetColorTable<-assign_colorsAnnotation(MetColorTable) #Load CoNI results data(CoNIResultsHFDToy) #Generate Network #Note: Colors not visible when ploting in Igraph HFDNetwork<-generate_network(ResultsCoNI = CoNIResultsHFDToy, colorVertexNetwork = TRUE, colorVertexTable = MetColorTable, Class = MetColorTable, outputDir = "./", outputFileName = "HFD", saveFiles = FALSE) #Note: For this tiny example nothing is significant LCG_BinomialTestTableHFD<- find_localControllingFeatures(ResultsCoNI = CoNIResultsHFDToy, network = HFDNetwork ) LCGenes_HFD<-as.character(unique(LCG_BinomialTestTableHFD$edgeFeatures))#Load color nodes table data(MetColorTable) #Assign colors according to "Class" column MetColorTable<-assign_colorsAnnotation(MetColorTable) #Load CoNI results data(CoNIResultsHFDToy) #Generate Network #Note: Colors not visible when ploting in Igraph HFDNetwork<-generate_network(ResultsCoNI = CoNIResultsHFDToy, colorVertexNetwork = TRUE, colorVertexTable = MetColorTable, Class = MetColorTable, outputDir = "./", outputFileName = "HFD", saveFiles = FALSE) #Note: For this tiny example nothing is significant LCG_BinomialTestTableHFD<- find_localControllingFeatures(ResultsCoNI = CoNIResultsHFDToy, network = HFDNetwork ) LCGenes_HFD<-as.character(unique(LCG_BinomialTestTableHFD$edgeFeatures))
Toy data gene expression
Jose Manuel Monroy [email protected]
doi:10.1016/j.molmet.2021.101295
This function creates a network using as input the output of CoNI and a table specifying the colors for the nodes.
generate_network( ResultsCoNI, colorVertexNetwork = TRUE, colorVertexTable, outputDir = "./", outputFileName = "ResultsCoNI", Class = NULL, saveFiles = TRUE )generate_network( ResultsCoNI, colorVertexNetwork = TRUE, colorVertexTable, outputDir = "./", outputFileName = "ResultsCoNI", Class = NULL, saveFiles = TRUE )
ResultsCoNI |
The input of the function are the results of CoNI. |
colorVertexNetwork |
logical. If TRUE, the table colorVertexTable has to be provided to specify vertex colors |
colorVertexTable |
Table specifying the colors for the nodes (vertex features). The first column should contain the names matching the features of the vertex Data and the colors or other data can be specified in the rest of the columns |
outputDir |
Output directory where the network is saved as well as the file that was used to generate the network. |
outputFileName |
The name of the file used to create the network. |
Class |
Optional data frame with at least two columns, first column contains all vertex features and another column the vertex feature class (column named "Class"). Necessary for treatment comparisons based on class |
saveFiles |
logical. If FALSE TableForNetwork_'outputFileName'.csv and Network_'outputFileName'.graphml are not saved to disk |
Returns an igraph object (network) constructed from ResultsCoNI. The network includes the following network statistics
"degree"The number of the vertex adjacent edges
"hub_score"The principal eigenvector of A*t(A), where A is the adjacency matrix of the graph
"transitivity"Probability that the adjacent vertices of a vertex are connected
"closeness"Steps required to access every other vertex from a given vertex
"betweenness"(roughly) The number of geodesics (shortest paths) going through a vertex or an edge
"eigen_centrality"Takes a graph (graph) and returns the eigenvector centralities of positions v within it
"centralized_betweenness"The vertice-level centrality score according to the betweenness of vertices
"centralized_closeness"The vertice-level centrality score according to the closeness of vertices
"centralized_degree"The vertice-level centrality score according to the degrees of vertices
For more details see igraph package
#Generate Network #Load color nodes table data(MetColorTable) #Assign colors according to "Class" column MetColorTable<-assign_colorsAnnotation(MetColorTable) #Load CoNI results data(CoNIResultsHFDToy) #Generate Network HFDNetwork<-generate_network(ResultsCoNI = CoNIResultsHFDToy, colorVertexNetwork = TRUE, colorVertexTable = MetColorTable, outputDir = "./", outputFileName = "HFD", saveFiles = FALSE)#Generate Network #Load color nodes table data(MetColorTable) #Assign colors according to "Class" column MetColorTable<-assign_colorsAnnotation(MetColorTable) #Load CoNI results data(CoNIResultsHFDToy) #Generate Network HFDNetwork<-generate_network(ResultsCoNI = CoNIResultsHFDToy, colorVertexNetwork = TRUE, colorVertexTable = MetColorTable, outputDir = "./", outputFileName = "HFD", saveFiles = FALSE)
This function will create a stacked barplot from the output of Compare_VertexClasses_sharedEdgeFeatures using all shared Edge Features (e.g., genes) between two treatments. Results of both Treatments are side by side for better comparison.
getstackedGlobalBarplot_and_Grid( CompTreatTable, Treat1, Treat2, ggrep = TRUE, max_pairsLegend = 1, force = 0.1, mx.overlaps = Inf, szggrepel = 6, xlb = "Vertex-Class Pairs", ... )getstackedGlobalBarplot_and_Grid( CompTreatTable, Treat1, Treat2, ggrep = TRUE, max_pairsLegend = 1, force = 0.1, mx.overlaps = Inf, szggrepel = 6, xlb = "Vertex-Class Pairs", ... )
CompTreatTable |
Output of Compare_VertexClasses_sharedEdgeFeatures |
Treat1 |
Name treatment 1 as in table CompTreatTable |
Treat2 |
Name treatment 2 as in table CompTreatTable |
ggrep |
logical. If TRUE includes ggrepel labels for every bar |
max_pairsLegend |
If number of Edge Features >= max_pairsLegend, display number of Edge Features as ggrepel label |
force |
Repelling force for ggrepel labels |
mx.overlaps |
Max number of overlaps ggrepel |
szggrepel |
Size ggrepel labels |
xlb |
Name for x-axis |
... |
Other parameters for inner functions, mainly ggplot2 visual parameters |
A gtable containing stacked barplots. The barplots show the vertex-class pairs profile of all shared edge features between two treatments (one bar plot per treatment). Every bar consists of multiple edge features that are depicted with different colors
data(VertexClassesSharedGenes_HFDvsChow) VCSGs<-VertexClassesSharedGenes_HFDvsChow HFD_vs_Chow_stackedBarplot<-getstackedGlobalBarplot_and_Grid(VCSGs, Treat1 = "HFD", Treat2 = "Chow", xlb = "Metabolite-class-pairs", max_pairsLegend=9) plot(HFD_vs_Chow_stackedBarplot)data(VertexClassesSharedGenes_HFDvsChow) VCSGs<-VertexClassesSharedGenes_HFDvsChow HFD_vs_Chow_stackedBarplot<-getstackedGlobalBarplot_and_Grid(VCSGs, Treat1 = "HFD", Treat2 = "Chow", xlb = "Metabolite-class-pairs", max_pairsLegend=9) plot(HFD_vs_Chow_stackedBarplot)
This function creates a barplot or barplots showing the number of vertex features per class for every shared edge feature between two treatments
getVertexsPerEdgeFeature( CompTreatTable, Annotation, chunks = 5, treat = NULL, small = FALSE, ggrep = TRUE, xlb = "Gene", onlyTable = FALSE, szTitle = 12, szaxisTxt = 12, szaxisTitle = 12, ... )getVertexsPerEdgeFeature( CompTreatTable, Annotation, chunks = 5, treat = NULL, small = FALSE, ggrep = TRUE, xlb = "Gene", onlyTable = FALSE, szTitle = 12, szaxisTxt = 12, szaxisTitle = 12, ... )
CompTreatTable |
Output of Compare_VertexClasses_sharedEdgeFeatures |
Annotation |
Data frame that includes the rgb colors for every class. The column 'class' (or 'Class') has to be present and also the column 'ColorRgb' |
chunks |
To avoid a non readable dense plot the results can be spitted in multiple plots |
treat |
Specify the treatment for which the plot will be created. It should be one of the two treatments in the output of Compare_VertexClasses_sharedEdgeFeatures |
small |
logical. If only a few edge features are in the input set as TRUE. A single plot will be created |
ggrep |
logical. If TRUE includes ggrepel labels for every bar |
xlb |
x-axis label |
onlyTable |
logical. If TRUE a table is returned instead of a plot |
szTitle |
Size title |
szaxisTxt |
Size axis text |
szaxisTitle |
Size axis title |
... |
Other parameters for inner functions, mainly ggplot2 visual parameters |
A list of ggplot objects to create different barplots. The barplots show the number of vertex features per class for every shared edge feature between two treatments. The barplots restrict to one of the compared treatments. An alternative output is a data.frame with the number of vertex features per class and edge feature (onlyTable=TRUE)
data(VertexClassesSharedGenes_HFDvsChow) data(MetColorTable) #Note: No differences in example as all the Output of CoNI was kept getVertexsPerEdgeFeature(CompTreatTable = VertexClassesSharedGenes_HFDvsChow, Annotation = MetColorTable, chunks = 2, treat = "HFD")data(VertexClassesSharedGenes_HFDvsChow) data(MetColorTable) #Note: No differences in example as all the Output of CoNI was kept getVertexsPerEdgeFeature(CompTreatTable = VertexClassesSharedGenes_HFDvsChow, Annotation = MetColorTable, chunks = 2, treat = "HFD")
This function creates a grid of barplots. The barplot of one side depicts the number of class vertex features per edge feature for treatment 1 and the other side the same barplot for treatment 2. Results of both Treatments are side by side for better comparison.
getVertexsPerEdgeFeature_and_Grid( CompTreatTable, Treat1, Treat2, Annotation, chunks = 3, ggrep = TRUE, xlb = "Edge Feature", onlyT = FALSE, small = FALSE, ... )getVertexsPerEdgeFeature_and_Grid( CompTreatTable, Treat1, Treat2, Annotation, chunks = 3, ggrep = TRUE, xlb = "Edge Feature", onlyT = FALSE, small = FALSE, ... )
CompTreatTable |
Output of Compare_VertexClasses_sharedEdgeFeatures |
Treat1 |
Name treatment 1 as in table CompTreatTable |
Treat2 |
Name treatment 2 as in table CompTreatTable |
Annotation |
Data frame that includes the rgb colors for every class. The column 'class' (or 'Class') has to be present and also the column 'ColorRgb' |
chunks |
To avoid a non readable dense plot the results can be spitted in multiple plots |
ggrep |
logical. If TRUE includes ggrepel labels for every bar |
xlb |
Change the x-axis label |
onlyT |
logical. If TRUE a table is returned instead of a grid of plots |
small |
logical. If only a few edge features are in the input set as TRUE. A single plot will be created |
... |
Other parameters for inner functions, mainly ggplot2 visual parameters |
A gtable containing side-by-side barplots, one for each treatment, showing the number of vertex features per class for every shared edge feature
data(VertexClassesSharedGenes_HFDvsChow) VCSGs<-VertexClassesSharedGenes_HFDvsChow data(MetColorTable) HFD_vs_Chow_LCP_Gene<-getVertexsPerEdgeFeature_and_Grid(VCSGs, "HFD","Chow", Annotation=MetColorTable, ggrep=FALSE, small = FALSE, chunks = 3, szLegendKey=0.2) plot(HFD_vs_Chow_LCP_Gene)data(VertexClassesSharedGenes_HFDvsChow) VCSGs<-VertexClassesSharedGenes_HFDvsChow data(MetColorTable) HFD_vs_Chow_LCP_Gene<-getVertexsPerEdgeFeature_and_Grid(VCSGs, "HFD","Chow", Annotation=MetColorTable, ggrep=FALSE, small = FALSE, chunks = 3, szLegendKey=0.2) plot(HFD_vs_Chow_LCP_Gene)
HFD gene expression data
Jose Manuel Monroy [email protected]
doi:10.1016/j.molmet.2021.101295
HFD metabolite data
Jose Manuel Monroy [email protected]
doi:10.1016/j.molmet.2021.101295
Toy data metabolite expression
Jose Manuel Monroy [email protected]
doi:10.1016/j.molmet.2021.101295
Metabolite Annotation
Jose Manuel Monroy [email protected]
doi:10.1016/j.molmet.2021.101295
Toy data annotation
Jose Manuel Monroy [email protected]
doi:10.1016/j.molmet.2021.101295
This function calculates simple network statistics and returns them as a dataframe
NetStats(Network)NetStats(Network)
Network |
An Igraph network |
Returns a data.frame with nine rows with the following network statistics:
"net_avg_pathL"Shortest paths between vertices
"net_edge_density"Graph density, ratio of the number of edges and the number of possible edges
"net_transitivity"Probability that the adjacent vertices of a vertex are connected
"net_diameter"Length of the longest geodesic
"net_nodes_first_path_diameter"The nodes along the first found path with the length of diameter
"net_eigenvalue"The eigenvalue corresponding to the centrality scores.
"net_centralized_betweenessIdx"The graph level centrality index after centralizing the graph according to the betweenness of vertices
"net_centralized_closenessIdx"The graph level centrality index after centralizing the graph according to the closeness of vertices
"net_centralized_degreeIdx"The graph level centrality index after centralizing the graph according to the degrees of vertices
For more information on the statistics consult the igraph package.
#Load color nodes table data(MetColorTable) #Assign colors according to "Class" column MetColorTable<-assign_colorsAnnotation(MetColorTable) #Load CoNI results data(CoNIResultsHFDToy) #Generate Network HFDNetwork<-generate_network(ResultsCoNI = CoNIResultsHFDToy, colorVertexNetwork = TRUE, colorVertexTable = MetColorTable, outputDir = "./", outputFileName = "HFD", saveFiles = FALSE) NetStats(HFDNetwork)#Load color nodes table data(MetColorTable) #Assign colors according to "Class" column MetColorTable<-assign_colorsAnnotation(MetColorTable) #Load CoNI results data(CoNIResultsHFDToy) #Generate Network HFDNetwork<-generate_network(ResultsCoNI = CoNIResultsHFDToy, colorVertexNetwork = TRUE, colorVertexTable = MetColorTable, outputDir = "./", outputFileName = "HFD", saveFiles = FALSE) NetStats(HFDNetwork)
This function fits two linear models on standardize data and plots the results. It generates a scatter plot with two regression lines, where the slopes correspond to the correlation and partial correlation coefficients (blue for cor and red for pcor)
plotPcorvsCor( ResultsCoNI, edgeFeature, vertexD, edgeD, vertexFeatures = NULL, outputDir = "./", fname, label_edgeFeature = "Edge Feature", plot_to_screen = TRUE, height = 10, width = 8, saveFiles = FALSE )plotPcorvsCor( ResultsCoNI, edgeFeature, vertexD, edgeD, vertexFeatures = NULL, outputDir = "./", fname, label_edgeFeature = "Edge Feature", plot_to_screen = TRUE, height = 10, width = 8, saveFiles = FALSE )
ResultsCoNI |
The significant results generated by CoNI |
edgeFeature |
The edge feature to explore e.g. Fabp2 (for a gene) |
vertexD |
Vertex data that was given as input to CoNI |
edgeD |
Edge data that was given as input to CoNI |
vertexFeatures |
The vertex features to include as a list. If not specified all metabolites available in combination with the edgeFeature will be used |
outputDir |
Output directory with path |
fname |
File name to save the plots |
label_edgeFeature |
Name for plot title e.g. Gene or Protein |
plot_to_screen |
logical. If TRUE plots will be outputted to the plotting screen |
height |
height of the plotting area for the saved file |
width |
width of the plotting are for the saved file |
saveFiles |
logical. If FALSE plot is not saved to disk |
Returns a ggplot object for a scatter plot with two regression lines. The blue line is the regression of the vertex features, and the red line is the regression of the resulting residuals after regressing each vertex feature with the edge feature. The slope of the blue line corresponds to the pearson correlation coefficient and the slope of the red line to the partial correlation coefficient
#Load gene expression - Toy dataset of two treatments data(GeneExpToy) #Samples in rows and genes in columns GeneExp <- as.data.frame(t(GeneExpToy)) hfd_gene <- GeneExp[1:8,] #high fat diet chow_gene<- GeneExp[9:nrow(GeneExp),] #chow diet #Load metabolite expression - Toy dataset of two treatments data(MetaboExpToy) MetaboExp <- MetaboExpToy hfd_metabo <- MetaboExp[11:18,] #high fat diet chow_metabo <- MetaboExp[1:10,] #chow diet #Match row names both data sets rownames(hfd_metabo)<-rownames(hfd_gene) rownames(chow_metabo)<-rownames(chow_gene) #Load CoNI results data(CoNIResultsHFDToy) plotPcorvsCor(ResultsCoNI = CoNIResultsHFDToy, edgeFeature = "Arfrp1", vertexFeatures = c("PC.ae.C40.2", "SM..OH..C22.1"), vertexD = hfd_metabo, edgeD = hfd_gene, label_edgeFeature = "Gene", plot_to_screen = TRUE, height = 10, saveFiles = FALSE)#Load gene expression - Toy dataset of two treatments data(GeneExpToy) #Samples in rows and genes in columns GeneExp <- as.data.frame(t(GeneExpToy)) hfd_gene <- GeneExp[1:8,] #high fat diet chow_gene<- GeneExp[9:nrow(GeneExp),] #chow diet #Load metabolite expression - Toy dataset of two treatments data(MetaboExpToy) MetaboExp <- MetaboExpToy hfd_metabo <- MetaboExp[11:18,] #high fat diet chow_metabo <- MetaboExp[1:10,] #chow diet #Match row names both data sets rownames(hfd_metabo)<-rownames(hfd_gene) rownames(chow_metabo)<-rownames(chow_gene) #Load CoNI results data(CoNIResultsHFDToy) plotPcorvsCor(ResultsCoNI = CoNIResultsHFDToy, edgeFeature = "Arfrp1", vertexFeatures = c("PC.ae.C40.2", "SM..OH..C22.1"), vertexD = hfd_metabo, edgeD = hfd_gene, label_edgeFeature = "Gene", plot_to_screen = TRUE, height = 10, saveFiles = FALSE)
This function creates a table of the local controlling edge features
tableLCFs_VFs(CoNIResults, LCFs)tableLCFs_VFs(CoNIResults, LCFs)
CoNIResults |
The output of CoNI (after p-adjustment) |
LCFs |
Local controlling edge features as a vector |
A data.frane of local controlling edge features and their respective vertex pairs, and unique vertexes.
#Load CoNI results data(CoNIResultsHFDToy) #Note: arbitrary list of genes, not Local controlling features tableLCFs_VFs(CoNIResultsHFDToy, c("Lilr4b","Rps12"))#Load CoNI results data(CoNIResultsHFDToy) #Note: arbitrary list of genes, not Local controlling features tableLCFs_VFs(CoNIResultsHFDToy, c("Lilr4b","Rps12"))
This function outputs the linker features with the strongest effect on the correlation of the vertex features
top_n_LF_byMagnitude(ResultsCoNI, topn = 10)top_n_LF_byMagnitude(ResultsCoNI, topn = 10)
ResultsCoNI |
The output of CoNI |
topn |
Top n number of features to output |
Returns a data.frame, a filtered version of ResultsCoNI, showing the top n features with the strongest effect, that is, the highest difference between the partial correlation and correlation coefficient.
data(CoNIResultsHFDToy) Top10HFD<-top_n_LF_byMagnitude(CoNIResults_HFD,topn = 10)data(CoNIResultsHFDToy) Top10HFD<-top_n_LF_byMagnitude(CoNIResults_HFD,topn = 10)