Chow gene expression data

Description

Chow gene expression data

Author(s)

Jose Manuel Monroy nolozz@gmail.com

References

doi: 10.1016/j.molmet.2021.101295

Chow metabolite data

Description

Chow metabolite data

Author(s)

Jose Manuel Monroy nolozz@gmail.com

References

doi: 10.1016/j.molmet.2021.101295

Correlation guided Network Integration

Description

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.

Usage

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
)

Arguments

Value

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

Examples

#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 = "./")

Toy data HFD results

Description

Toy data HFD results

Author(s)

Jose Manuel Monroy nolozz@gmail.com

References

doi: 10.1016/j.molmet.2021.101295

CoNI Results Chow

Description

CoNI Results Chow

Author(s)

Jose Manuel Monroy nolozz@gmail.com

References

doi: 10.1016/j.molmet.2021.101295

CoNI Results HFD

Description

CoNI Results HFD

Author(s)

Jose Manuel Monroy nolozz@gmail.com

References

doi: 10.1016/j.molmet.2021.101295

Compare triplets

Description

Compare vertexFeature-vertexFeature-edgeFeature between two treatments, that is, find the shared triplets between two different CoNI runs.

Usage

Compare_Triplets(
  Treat1_path,
  Treat2_path,
  OutputName = "Shared_Genes_and_Edges_Treat1vsTreat2.csv"
)

Arguments

Value

A data.frame with the shared triplets (vertex1 vertex2 edge_feature) between two CoNI runs

Examples

#For an example see the vignette

Table VertexClass pairs of shared Edge Features

Description

Compare VertexClass pairs of the shared Edge Features of two treatments (e.g., lipid-class-pairs per shared gene)

Usage

Compare_VertexClasses_sharedEdgeFeatures(
  Treat1_path,
  Treat2_path,
  OutputName = "Shared_Genes_and_Edges_Treat1vsTreat2.csv",
  Treat1Name = "Treat1",
  Treat2Name = "Treat2"
)

Arguments

Value

A data.frame with all possible vertex-class pairs and their numbers per edge-feature and treatment.

Examples

#For an example see the vignette

Toy data gene expression

Description

Toy data gene expression

Author(s)

Jose Manuel Monroy nolozz@gmail.com

References

doi: 10.1016/j.molmet.2021.101295

HFD gene expression data

Description

HFD gene expression data

Author(s)

Jose Manuel Monroy nolozz@gmail.com

References

doi: 10.1016/j.molmet.2021.101295

HFD metabolite data

Description

HFD metabolite data

Author(s)

Jose Manuel Monroy nolozz@gmail.com

References

doi: 10.1016/j.molmet.2021.101295

Toy data annotation

Description

Toy data annotation

Author(s)

Jose Manuel Monroy nolozz@gmail.com

References

doi: 10.1016/j.molmet.2021.101295

Toy data metabolite expression

Description

Toy data metabolite expression

Author(s)

Jose Manuel Monroy nolozz@gmail.com

References

doi: 10.1016/j.molmet.2021.101295

Metabolite Annotation

Description

Metabolite Annotation

Author(s)

Jose Manuel Monroy nolozz@gmail.com

References

doi: 10.1016/j.molmet.2021.101295

Network Statistics

Description

This function calculates simple network statistics and returns them as a dataframe

Usage

NetStats(Network)

Arguments

Value

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.

Examples

#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)

Toy data comparison treatments

Description

Toy data comparison Vertex classes shared edge featuresHFD vs Chow

Author(s)

Jose Manuel Monroy nolozz@gmail.com

References

doi: 10.1016/j.molmet.2021.101295

Assing Colors to Class

Description

This function assigns two color columns (color name and rgb) to an annotation data frame according to a column named 'Class' or 'class'

Usage

assign_colorsAnnotation(AnnotationDf, col = "Class")

Arguments

Value

The input data.frame with two extra columns specifying the colors for all vertexes according to their respective vertex-class

Number Vertex features per class for every shared edge feature

Description

Internal use. This function creates a barplot depicting the number of vertex features per class for every edge feature. To use this function one has to split first the file (or not if it is small) with the funciton chunk2

Usage

barplot_VertexsPerEdgeFeature(
  SplitFile,
  title = "Vertex Features per class",
  AnnotationWithColors,
  ggrepelL = TRUE,
  xlb = "Gene",
  szggrepel = 2.5,
  szTitle = 12,
  szaxisTxt = 12,
  szaxisTitle = 12,
  szLegendText = 10,
  szLegendKey = 1
)

Value

ggplot object for a barplot depicting the number of vertex features per class for every edge feature

Check input parameters

Description

Internal use. Function to check if input parameters are of the right class

Usage

checkInputParameters(ParaList)

Value

No return value, called for side effects

Output directory

Description

Internal use. This function checks if the output directory exists and if it does not, it will create it

Usage

check_outputDir(outputDir, verb)

Value

No return value, called for side effects

Check previous files

Description

Internal use. This function checks previous files and deletes according to the User input option Requires some modifications...

Usage

check_previous(del, iteration, outDir, verb)

Value

No return value, called for side effects

Split function

Description

Internal use. Function to split the EdgeFeatures in smaller groups

Usage

chunk2(x, n)

Value

A list of character vectors, each vector contains n edge features

Compare sample names

Description

Internal use. This function compares the sample names between the two datasets provided. If names do not match CoNI stops and outputs an error message.

Usage

compare_sampleNames(df1, df2)

Value

No return value, called for side effects

Number lipid features per class

Description

Internal use. This function counts for every edge feature the number of vertex features (e.g. lipids) per class. It disregards the number of vertex pairs...

Usage

countClassPerEdgeFeature(ResTable, treatment = "Chow")

Value

A data.frame with the number of vertex features per class and edge feature

Bipartite Network

Description

This function creates a simple bipartite graph, it shows the driver and linker features as nodes.

Usage

createBipartiteGraph(TableNetwork, colorVertexTable, incidenceMatrix = FALSE)

Arguments

Value

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

Examples

#See vignette for an example

Bipartite Table

Description

Internal use. This function creates a table that is used to create a simple bipartite network

Usage

createBipartiteTable(CoNINetworkTable)

Value

A matrix containing two columns, one for vertexes and one for edge features. The matching happens if they are adjacent to one another

Vertex-class pairs profile of shared features

Description

This function will create a barplot from the output of Compare_VertexClasses_sharedEdgeFeatures using all shared Edge Features (e.g., genes).

Usage

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
)

Arguments

Value

A ggplot object for a barplot. The barplot shows the vertex-class pairs profile of all shared edge features between treatments

Examples

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")

Vertex-class pairs profile of one shared edge feature

Description

This function will create a barplot from the output of Compare_VertexClasses_sharedEdgeFeatures for a specific shared Edge Feature (e.g., a shared gene).

Usage

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
)

Arguments

Value

A ggplot object for a barplot. The barplot shows the vertex-class pairs profile of a single shared edge feature between two treatments

Examples

data(VertexClassesSharedGenes_HFDvsChow)
create_edgeFBarplot(CompTreatTable = VertexClassesSharedGenes_HFDvsChow,
                    edgeF = "Lilr4b",
                    treat1 = "HFD",
                    treat2 = "Chow",
                    factorOrder = c("HFD","Chow"),
                    EdgeFeatureType = "Gene")

Stacked Global Barplot (One treatment)

Description

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.

Usage

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
)

Arguments

Value

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

Examples

data(VertexClassesSharedGenes_HFDvsChow)
create_stackedGlobalBarplot_perTreatment(CompTreatTable = VertexClassesSharedGenes_HFDvsChow,
                                         treat = "HFD",
                                         max_pairsLegend = 9,
                                         xlb = "Metabolite-class-pairs")

Delete intermediary files

Description

Internal use. This function deletes intermediary files.

Usage

delIntFiles(del, outDir)

Value

No return value, called for side effects

Check if files exist

Description

Internal use. This function tests if the input files exist, if they do not it will output an error and end CoNI

Usage

do_objectsExist(gene_exp, norm_metabo_dat, verb)

Value

No return value, called for side effects

Find local controlling features

Description

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.

Usage

find_localControllingFeatures(ResultsCoNI, network, padjust = TRUE)

Arguments

Value

Returns a data.frame with the results of the binomial tests. Significant results correspond to local controlling features

Examples

#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))

Flatten

Description

Internal use. This function gets the upper part of the matrix after calculating the correlation coefficients between all pairs of elements cormat : matrix of the correlation coefficients pmat : matrix of the correlation p-values

Usage

flattenCorrMatrix(cormat, pmat)

Value

A data.frame with all pairwise correlations of the input elements and their respective p-values

Create network

Description

This function creates a network using as input the output of CoNI and a table specifying the colors for the nodes.

Usage

generate_network(
  ResultsCoNI,
  colorVertexNetwork = TRUE,
  colorVertexTable,
  outputDir = "./",
  outputFileName = "ResultsCoNI",
  Class = NULL,
  saveFiles = TRUE
)

Arguments

Value

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

Examples

#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)

Vertex Class profile per edge feature (one treatment)

Description

This function creates a barplot or barplots showing the number of vertex features per class for every shared edge feature between two treatments

Usage

getVertexsPerEdgeFeature(
  CompTreatTable,
  Annotation,
  chunks = 5,
  treat = NULL,
  small = FALSE,
  ggrep = TRUE,
  xlb = "Gene",
  onlyTable = FALSE,
  szTitle = 12,
  szaxisTxt = 12,
  szaxisTitle = 12,
  ...
)

Arguments

Value

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)

Examples

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")

Vertex-Class profile per edge feature Side-by-Side (two treatments)

Description

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.

Usage

getVertexsPerEdgeFeature_and_Grid(
  CompTreatTable,
  Treat1,
  Treat2,
  Annotation,
  chunks = 3,
  ggrep = TRUE,
  xlb = "Edge Feature",
  onlyT = FALSE,
  small = FALSE,
  ...
)

Arguments

Value

A gtable containing side-by-side barplots, one for each treatment, showing the number of vertex features per class for every shared edge feature

Examples

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)

Low variance features

Description

Internal use. Get low variance features.

Usage

get_lowvarFeatures(df)

Value

A data.frame where low variance features were removed

Get class rgb color

Description

Internal use. This function gets the class rgb color of a specific (lipid) class

Usage

getcolor(ClassM, tableColor)

Value

A character object, that corresponds to a color in hexadecimal format

Stacked Global Barplot Side-by-side (two treatments)

Description

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.

Usage

getstackedGlobalBarplot_and_Grid(
  CompTreatTable,
  Treat1,
  Treat2,
  ggrep = TRUE,
  max_pairsLegend = 1,
  force = 0.1,
  mx.overlaps = Inf,
  szggrepel = 6,
  xlb = "Vertex-Class Pairs",
  ...
)

Arguments

Value

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

Examples

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)

Get vertexes for edge feature

Description

Get vertexes for edge feature

Usage

getvertexes_edgeFeature(edgeFeature, CoNIResults)

Value

A character vector with the vertexes connected to a given edge feature

Labels to colors

Description

Internal use. This function is modified version of labels2colors of WGCNA and the internet

Usage

labels2colors_2(
  labels,
  zeroIsGrey = TRUE,
  colorSeq = NULL,
  naColor = "grey",
  commonColorCode = TRUE
)

Value

A character vector with colors

Merge Files.

Description

Internal use. This function reads the output split files generated by CoNI and generates a single result object. It is slow. Probably there are other faster alternatives.

Usage

merge_outpuSplitFiles(outputDir)

Value

A single data.table with the results of CoNI

Get colors

Description

Internal use. This function gets the rgb colors of every (lipid) class and names them according to the class

Usage

obtain_groupcolors(Annotation)

Value

A character vector with the rgb colors named after the vertex-class (e.g. lipid class)

Correlation vs Partial correlation

Description

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)

Usage

plotPcorvsCor(
  ResultsCoNI,
  edgeFeature,
  vertexD,
  edgeD,
  vertexFeatures = NULL,
  outputDir = "./",
  fname,
  label_edgeFeature = "Edge Feature",
  plot_to_screen = TRUE,
  height = 10,
  width = 8,
  saveFiles = FALSE
)

Arguments

Value

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

Examples

#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)

Pairwise correlations

Description

Internal use. This function calculates the pairwise correlations of a matrix (it uses Hmisc::rcorr function) and gets the significant correlations

Usage

sig_correlation2(input_edgeD, padj = TRUE, method = "BH", verb)

Value

A data.frame with the significant correlations of a correlation matrix

Significant correlations 2 Df

Description

Internal use. This function input are two data frames (e.g. metabolites and genes). It calculates the correlation matrix and creates a table with only significant pairs. No correction for multiple testing is done

Usage

sig_correlation2Dfs(metabolite_data, gene_expression)

Value

A data.frame with vertex-edge significant correlation coefficients and their p-values

Split dataset

Description

Internal use. This function wills split the 'big' omics data into smaller data frames to improve computations, to avoid running out of memory.

Usage

split_df(AbundantDF, numberSplitDF_2 = 2)

Value

A list of data.frames

Table local controlling edge features and vertex pairs

Description

This function creates a table of the local controlling edge features

Usage

tableLCFs_VFs(CoNIResults, LCFs)

Arguments

Value

A data.frane of local controlling edge features and their respective vertex pairs, and unique vertexes.

Examples

#Load CoNI results
data(CoNIResultsHFDToy)
#Note: arbitrary list of genes, not Local controlling features
tableLCFs_VFs(CoNIResultsHFDToy, c("Lilr4b","Rps12"))

Linker Features by magnitude of effect

Description

This function outputs the linker features with the strongest effect on the correlation of the vertex features

Usage

top_n_LF_byMagnitude(ResultsCoNI, topn = 10)

Arguments

Value

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.

Examples

data(CoNIResultsHFDToy)
Top10HFD<-top_n_LF_byMagnitude(CoNIResults_HFD,topn = 10)

Write table

Description

Internal use. This function tries to write a table with fread, if it fails it returns "NA"

Usage

writeTable(results_write, num_cores, outputDir, iteration)

Value

Returns NA if it fails to write file to memory, otherwise no return value