Title: | multiWGCNA |
---|---|
Description: | An R package for deeping mining gene co-expression networks in multi-trait expression data. Provides functions for analyzing, comparing, and visualizing WGCNA networks across conditions. multiWGCNA was designed to handle the common case where there are multiple biologically meaningful sample traits, such as disease vs wildtype across development or anatomical region. |
Authors: | Dario Tommasini [aut, cre] , Brent Fogel [aut, ctb] |
Maintainer: | Dario Tommasini <[email protected]> |
License: | GPL-3 |
Version: | 1.5.0 |
Built: | 2024-11-05 06:42:43 UTC |
Source: | https://github.com/bioc/multiWGCNA |
Find all the modules from dataset1 that have a best match to a module in dataset2 if that module in dataset2 is also a best match to the module in dataset1
bidirectionalBestMatches(comparisonList, plot = TRUE)
bidirectionalBestMatches(comparisonList, plot = TRUE)
comparisonList |
a list with an elemnt "overlap", which is a data.frame resulting from a call to computeOverlapsFromWGCNA |
plot |
whether to generate a heatmap; default is TRUE |
A ggplot object
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] comparisonList = list() comparisonList$overlaps = computeOverlapsFromWGCNA(astrocyte_networks$EAE, astrocyte_networks$WT) bidirectionalBestMatches(comparisonList)
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] comparisonList = list() comparisonList$overlaps = computeOverlapsFromWGCNA(astrocyte_networks$EAE, astrocyte_networks$WT) bidirectionalBestMatches(comparisonList)
A function that converts a data.frame where row 1 is gene symbols to a numeric matrix where columns are genes and rows are samples for compatibility with most WGCNA functions.
cleanDatExpr(datExpr, checkGenesSamples = FALSE)
cleanDatExpr(datExpr, checkGenesSamples = FALSE)
datExpr |
a data.frame were columns are samples and rows are samples and the gene symbols are in the first row |
checkGenesSamples |
call the WGCNA function checkGenesSamples? |
Returns a datExpr with rows as samples and columns as genes
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_se = eh_query[["EH8223"]] datExpr = data.frame(X = rownames(assays(astrocyte_se)[[1]]), assays(astrocyte_se)[[1]]) cleanDatExpr(datExpr)
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_se = eh_query[["EH8223"]] datExpr = data.frame(X = rownames(assays(astrocyte_se)[[1]]), assays(astrocyte_se)[[1]]) cleanDatExpr(datExpr)
Plots a line graph showing the co-expression of selected genes across samples
coexpressionLineGraph(datExpr, splitBy = 1, fontSize = 2.15, colors = NULL)
coexpressionLineGraph(datExpr, splitBy = 1, fontSize = 2.15, colors = NULL)
datExpr |
a data.frame with genes as rows and samples as columns |
splitBy |
how much to split genes by on line graph |
fontSize |
the font size of the gene labels |
colors |
a vector of colors; default is random colors generated by colors function |
a ggplot object
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] datExpr = GetDatExpr(astrocyte_networks[[1]], genes = topNGenes(astrocyte_networks$EAE, "EAE_015", 20)) coexpressionLineGraph(datExpr) + geom_vline(xintercept = 20.5, linetype='dashed')
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] datExpr = GetDatExpr(astrocyte_networks[[1]], genes = topNGenes(astrocyte_networks$EAE, "EAE_015", 20)) coexpressionLineGraph(datExpr) + geom_vline(xintercept = 20.5, linetype='dashed')
Computes overlap between the modules of two objects of class WGCNA
computeOverlapsFromWGCNA(dataset1, dataset2)
computeOverlapsFromWGCNA(dataset1, dataset2)
dataset1 |
an object of class WGCNA to compare with dataset2 |
dataset2 |
an object of class WGCNA to compare with dataset1 |
Returns a data.frame showing the overlap results for modules from dataset1 with dataset2
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] computeOverlapsFromWGCNA(astrocyte_networks$EAE, astrocyte_networks$WT)
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] computeOverlapsFromWGCNA(astrocyte_networks$EAE, astrocyte_networks$WT)
A high level function that returns all networks possible for a given experimental design
constructNetworks( datExpr, sampleTable, conditions1, conditions2, write = FALSE, alphaLevel = 0.05, plot = FALSE, ... )
constructNetworks( datExpr, sampleTable, conditions1, conditions2, write = FALSE, alphaLevel = 0.05, plot = FALSE, ... )
datExpr |
either a SummarizedExperiment object or data.frame with genes are rows and samples as columns |
sampleTable |
data.frame with sample names in first column and sample traits in the second and third column. First column should be called "Sample" |
conditions1 |
first design conditions, ie healthy/disease |
conditions2 |
second design conditions, ie frontal lobe/temporal lobe |
write |
write results out to files? |
alphaLevel |
significance value passed to findBestTrait function, default is 0.05 |
plot |
plot modules? Default is false |
... |
Arguments to pass to blockwiseModules function |
A list of WGCNA objects, ie level one, two, and three networks.
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) autism_se = eh_query[["EH8219"]] set.seed(1) autism_se = autism_se[sample(rownames(autism_se), 500),] sampleTable = colData(autism_se) conditions1 = unique(sampleTable[,2]) conditions2 = unique(sampleTable[,3]) autism_networks = constructNetworks(autism_se, sampleTable, conditions1[[1]], conditions2[[1]], networkType = "signed", TOMType = "unsigned", power = 10, minModuleSize = 100, maxBlockSize = 25000, reassignThreshold = 0, minKMEtoStay = 0, mergeCutHeight = 0, numericLabels = TRUE, pamRespectsDendro = FALSE, deepSplit = 4, verbose = 3) autism_networks[["combined"]]
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) autism_se = eh_query[["EH8219"]] set.seed(1) autism_se = autism_se[sample(rownames(autism_se), 500),] sampleTable = colData(autism_se) conditions1 = unique(sampleTable[,2]) conditions2 = unique(sampleTable[,3]) autism_networks = constructNetworks(autism_se, sampleTable, conditions1[[1]], conditions2[[1]], networkType = "signed", TOMType = "unsigned", power = 10, minModuleSize = 100, maxBlockSize = 25000, reassignThreshold = 0, minKMEtoStay = 0, mergeCutHeight = 0, numericLabels = TRUE, pamRespectsDendro = FALSE, deepSplit = 4, verbose = 3) autism_networks[["combined"]]
Performs a differential co-expression ananlysis given an expression data.frame and a conditions vector
diffCoexpression( datExpr, conditions, geneList = NULL, plot = FALSE, method = c("pearson", "spearman"), removeFreeNodes = TRUE, labelSize = 0.5, labelDist = 0, shape = "circle", degreeForSize = FALSE, label = FALSE, onlyPositive = FALSE, z.threshold = NULL, FDR.threshold = 0.05, nodeSize = 3 )
diffCoexpression( datExpr, conditions, geneList = NULL, plot = FALSE, method = c("pearson", "spearman"), removeFreeNodes = TRUE, labelSize = 0.5, labelDist = 0, shape = "circle", degreeForSize = FALSE, label = FALSE, onlyPositive = FALSE, z.threshold = NULL, FDR.threshold = 0.05, nodeSize = 3 )
datExpr |
a data.frame containing expression values |
conditions |
a vector containing conditions for the samples |
geneList |
vector of genes, will use all genes if NULL (default) |
plot |
plot a network? |
method |
either "pearson" or "spearman" |
removeFreeNodes |
remove free nodes from network? |
labelSize |
label size |
labelDist |
distance from labels to nodes |
shape |
shape of nodes |
degreeForSize |
should node size correspond to degree? |
label |
label nodes? |
onlyPositive |
only draw positive correlations? |
z.threshold |
z-score threshold |
FDR.threshold |
FDR threshold |
nodeSize |
size of node |
A list including a matrix of z-scores, a matrix of raw p-values, a matrix of adjusted p-values, and a summary data.frame
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_se = eh_query[["EH8223"]] datExpr = assays(astrocyte_se)[[1]] diffCoexpression(datExpr, c(rep(1,20), rep(2,16)), geneList = c("Gfap", "Vim", "Aspg", "Serpina3n", "Cp", "Osmr", "Cd44", "Cxcl10", "Hspb1", "Timp1", "S1pr3", "Steap4", "Lcn2"))
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_se = eh_query[["EH8223"]] datExpr = assays(astrocyte_se)[[1]] diffCoexpression(datExpr, c(rep(1,20), rep(2,16)), geneList = c("Gfap", "Vim", "Aspg", "Serpina3n", "Cp", "Osmr", "Cd44", "Cxcl10", "Hspb1", "Timp1", "S1pr3", "Steap4", "Lcn2"))
Runs (and plots) the differential module expression analysis
diffModuleExpression( WGCNAobject, geneList, design, plotTitle = NULL, mode = c("PC1", "Zscore"), testColumn = 2, refColumn = 3, test = c("ANOVA", "PERMANOVA"), plot = TRUE )
diffModuleExpression( WGCNAobject, geneList, design, plotTitle = NULL, mode = c("PC1", "Zscore"), testColumn = 2, refColumn = 3, test = c("ANOVA", "PERMANOVA"), plot = TRUE )
WGCNAobject |
WGCNA object |
geneList |
vector of genes in WGCNAobject |
design |
the sampleTable |
plotTitle |
title for the plot |
mode |
either PC1 or Zscore, default is PC1 |
testColumn |
the column of the sampleTable to be resolved |
refColumn |
the column of the sampleTable to be used as biological variation |
test |
statistical test to perform, either "ANOVA" or "PERMANOVA" |
plot |
generate a plot? |
a data.frame with the resulting p-values
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_se = eh_query[["EH8223"]] sampleTable = colData(astrocyte_se) astrocyte_networks = eh_query[["EH8222"]] diffModuleExpression(astrocyte_networks[["combined"]], topNGenes(astrocyte_networks$combined, "combined_013"), sampleTable, test = "ANOVA", plotTitle = "combined_013", plot = TRUE)
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_se = eh_query[["EH8223"]] sampleTable = colData(astrocyte_se) astrocyte_networks = eh_query[["EH8222"]] diffModuleExpression(astrocyte_networks[["combined"]], topNGenes(astrocyte_networks$combined, "combined_013"), sampleTable, test = "ANOVA", plotTitle = "combined_013", plot = TRUE)
Draw a network where nodes are modules and edges represent significant gene overlap. Modules are sorted by levels 1, 2, and 3.
drawMultiWGCNAnetwork( WGCNAlist, comparisonList, moduleOfInterest, design, overlapCutoff = 0, padjCutoff = 1, removeOutliers = TRUE, alpha = 1e-50, layout = NULL, hjust = 0.4, vjust = 0.3, width = 0.5, colors = NULL )
drawMultiWGCNAnetwork( WGCNAlist, comparisonList, moduleOfInterest, design, overlapCutoff = 0, padjCutoff = 1, removeOutliers = TRUE, alpha = 1e-50, layout = NULL, hjust = 0.4, vjust = 0.3, width = 0.5, colors = NULL )
WGCNAlist |
list of WGCNA objects |
comparisonList |
the list of overlap comparisons ie from iterate(myNetworks, overlapComparisons, ...) |
moduleOfInterest |
module of interest, ie "combined_001" |
design |
the sampleTable design matrix |
overlapCutoff |
cutoff to remove module correspondences with less than this number of genes |
padjCutoff |
cutoff to remove module correspondences above this significance value |
removeOutliers |
remove outlier modules? |
alpha |
alpha level of significance |
layout |
layout of network to be passed to plot function of igraph object, defaults to multiWGCNA custom layout |
hjust |
horizontal justification of labels |
vjust |
vertical justification of labels |
width |
width of labels |
colors |
colors to use for modules, should be the same length as the number of WGCNA objects in the WGCNAlist. Defaults to random colors for each condition. |
an igraph plot
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_se = eh_query[["EH8223"]] sampleTable = colData(astrocyte_se) astrocyte_networks = eh_query[["EH8222"]] results = list() results$overlaps = iterate(astrocyte_networks, overlapComparisons, plot=FALSE) drawMultiWGCNAnetwork(astrocyte_networks, results$overlaps, "combined_013", sampleTable)
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_se = eh_query[["EH8223"]] sampleTable = colData(astrocyte_se) astrocyte_networks = eh_query[["EH8222"]] results = list() results$overlaps = iterate(astrocyte_networks, overlapComparisons, plot=FALSE) drawMultiWGCNAnetwork(astrocyte_networks, results$overlaps, "combined_013", sampleTable)
Returns the expression data frame a WGCNA object as a data.frame
GetDatExpr(object, genes = NULL)
GetDatExpr(object, genes = NULL)
object |
An object of class WGCNA |
genes |
a list of genes to subset to; default is NULL |
a data.frame
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] datExpr = GetDatExpr(astrocyte_networks[[1]], genes = topNGenes(astrocyte_networks$EAE, "EAE_015", 20)) coexpressionLineGraph(datExpr) + geom_vline(xintercept = 20.5, linetype='dashed')
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] datExpr = GetDatExpr(astrocyte_networks[[1]], genes = topNGenes(astrocyte_networks$EAE, "EAE_015", 20)) coexpressionLineGraph(datExpr) + geom_vline(xintercept = 20.5, linetype='dashed')
Performs a network preservation analysis
getPreservation(reference, test, nPermutations = 100, write = FALSE)
getPreservation(reference, test, nPermutations = 100, write = FALSE)
reference |
reference network of class WGCNA |
test |
test network of class WGCNA |
nPermutations |
number of permutations to perform; at least 50 permutations |
write |
write to file? |
a data.frame summarizing results of preservation analysis
Dario Tommasini
A high level function that iterates functions across a list of WGCNA objects
iterate(WGCNAlist, FUN, ...)
iterate(WGCNAlist, FUN, ...)
WGCNAlist |
a vector of objects of type WGCNAobject |
FUN |
function to iterate, either overlapComparisons or preservationComparisons |
... |
argmuents to be passed on to overlapComparisons or preservationComparisons |
a comparison list from overlapComparisons or preservationComparisons
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] results = list() iterate(astrocyte_networks, overlapComparisons, plot=FALSE)
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] results = list() iterate(astrocyte_networks, overlapComparisons, plot=FALSE)
Generates a WGCNA-compatible trait table from a sampleTable dataframe
makeTraitTable(inputTable, column, detectNumbers = FALSE)
makeTraitTable(inputTable, column, detectNumbers = FALSE)
inputTable |
the sampleTable data.frame |
column |
the column from the sampleTable to use as traits |
detectNumbers |
whether to consider traits with numbers as numerical rather than categorical variables |
a data.frame with integer values denoting the categorical sample traits
sampleTable = data.frame(Sample = c(paste0("EAE", 1:10), paste0("WT", 1:10)), Disease = c(rep("EAE", 10), rep("WT", 10)), Region = c(rep(c("Cbl", "Sc"), 5))) makeTraitTable(sampleTable, 2)
sampleTable = data.frame(Sample = c(paste0("EAE", 1:10), paste0("WT", 1:10)), Disease = c(rep("EAE", 10), rep("WT", 10)), Region = c(rep(c("Cbl", "Sc"), 5))) makeTraitTable(sampleTable, 2)
A plotting function that returns a heatmap and barplot for a module
moduleComparisonPlot(overlapDf, dataset1, dataset2)
moduleComparisonPlot(overlapDf, dataset1, dataset2)
overlapDf |
a data.frame resulting from a call to computeOverlapsFromWGCNA |
dataset1 |
an object of class WGCNA to compare with dataset2 |
dataset2 |
an object of class WGCNA to compare with dataset1 |
Returns a ggplot object (flowplot and heatmap) showing the module correspondence between two objects of class WGCNA
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] overlapDf = computeOverlapsFromWGCNA(astrocyte_networks$EAE, astrocyte_networks$WT) moduleComparisonPlot(overlapDf, astrocyte_networks$EAE, astrocyte_networks$WT)
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] overlapDf = computeOverlapsFromWGCNA(astrocyte_networks$EAE, astrocyte_networks$WT) moduleComparisonPlot(overlapDf, astrocyte_networks$EAE, astrocyte_networks$WT)
A plotting function that returns a heatmap and barplot for a module
moduleExpressionPlot( WGCNAobject, geneList, mode = c("PC1", "averageZscore"), legend = FALSE, title = NULL, clusterGenes = FALSE )
moduleExpressionPlot( WGCNAobject, geneList, mode = c("PC1", "averageZscore"), legend = FALSE, title = NULL, clusterGenes = FALSE )
WGCNAobject |
an object of class WGCNAobject |
geneList |
a vector of gene names to be extracted from WGCNAobject |
mode |
use first principal component or averageZscore? |
legend |
plot legend? |
title |
title of the plot |
clusterGenes |
cluster heatmap genes by hierarchical clustering? |
a patchworked ggplot object
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] moduleExpressionPlot(astrocyte_networks[["combined"]], geneList = topNGenes(astrocyte_networks$combined, "combined_013"))
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] moduleExpressionPlot(astrocyte_networks[["combined"]], geneList = topNGenes(astrocyte_networks$combined, "combined_013"))
Returns a heatmap where color corresponds to FDR-adjusted overlap (hypergeometric test) and the label corresponds to the number of overlapping genes
moduleToModuleHeatmap( comparisonDf, dataset1 = NULL, dataset2 = NULL, trait1 = NULL, trait2 = NULL, list1 = NULL, list2 = NULL, filterByTrait = FALSE, alphaLevel = 0.05 )
moduleToModuleHeatmap( comparisonDf, dataset1 = NULL, dataset2 = NULL, trait1 = NULL, trait2 = NULL, list1 = NULL, list2 = NULL, filterByTrait = FALSE, alphaLevel = 0.05 )
comparisonDf |
the data.frame output of computeOverlapFromWGCNA |
dataset1 |
optional; WGCNA object for dataset 1 |
dataset2 |
optional; WGCNA object for dataset 2 |
trait1 |
optional; subset to modules correlated to this trait for dataset 1 |
trait2 |
optional; subset to modules correlated to this trait for dataset 2 |
list1 |
subset to this list of modules for dataset 1 |
list2 |
subset to this list of modules for dataset 2 |
filterByTrait |
only plot for modules that correlate with some trait? |
alphaLevel |
the alpha level of significance for module-trait correlation, defaults to 0.05 |
A ggplot object
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] overlapDf = computeOverlapsFromWGCNA(astrocyte_networks$EAE, astrocyte_networks$WT) moduleToModuleHeatmap(overlapDf)
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] overlapDf = computeOverlapsFromWGCNA(astrocyte_networks$EAE, astrocyte_networks$WT) moduleToModuleHeatmap(overlapDf)
Returns the name of a WGCNAobject.
name(WGCNAobject)
name(WGCNAobject)
WGCNAobject |
an object of class WGCNA |
Returns the name of the WGCNA object, ie "EAE" for astrocyte_networks$EAE.
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] name(astrocyte_networks$EAE)
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] name(astrocyte_networks$EAE)
Compares modules between two objects of type WGCNAobjects within a WGCNAobject list given the indices. Recommended to be used in conjunction with the iterate function.
overlapComparisons( comparisonList, WGCNAlist, first, second, element, plot = TRUE, write = FALSE )
overlapComparisons( comparisonList, WGCNAlist, first, second, element, plot = TRUE, write = FALSE )
comparisonList |
a list passed by the iterate function |
WGCNAlist |
list of objects of class WGCNA |
first |
index of first WGCNA object |
second |
index of second WGCNA object |
element |
element position in the comparison list (passed by iterate function) |
plot |
generate plots? |
write |
write results to file? |
A list, in which the first element is a data.frame showing the overlap results and the second element is a data.frame showing the best matching modules between the two WGCNA objects.
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] results = list() results$overlaps = iterate(astrocyte_networks, overlapComparisons, plot=FALSE)
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] results = list() results$overlaps = iterate(astrocyte_networks, overlapComparisons, plot=FALSE)
Test association between module expression to traits using ANOVA
performANOVA(datExpr, design, testCondition, refCondition, alphaLevel = 0.05)
performANOVA(datExpr, design, testCondition, refCondition, alphaLevel = 0.05)
datExpr |
expression data.frame |
design |
the sampleTable |
testCondition |
test column in sampleTable |
refCondition |
reference column in sampleTable |
alphaLevel |
the significance level |
a data.frame with p-values for each association
The results of running the PreservationPermutationTest in the astrocyte vignette. This is provided since this function is quite slow. Please see the astrocyte vignette for more details.
data(permutationTestResults)
data(permutationTestResults)
A list of data.frames containing preservation results for each permutation
A plotting function that draws a scatterplot of preservation scores between two WGCNA objects
preservationComparisonPlot( preservationList, dataset1, dataset2, alphaLevel = 0.05, outliers = FALSE )
preservationComparisonPlot( preservationList, dataset1, dataset2, alphaLevel = 0.05, outliers = FALSE )
preservationList |
a list resulting from a call to preservationComparisons |
dataset1 |
an object of class WGCNAobject to compare with dataset2 |
dataset2 |
an object of class WGCNAobject to compare with dataset1 |
alphaLevel |
alpha level of significance, default is 0.05 |
outliers |
leave outlier modules? By default these are removed |
a ggplot object
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] results = list() results$preservation=iterate(astrocyte_networks[c("EAE", "WT")], preservationComparisons, write=FALSE, plot=FALSE, nPermutations=2) preservationComparisonPlot(results$preservation$EAE_vs_WT, astrocyte_networks$EAE, astrocyte_networks$WT)
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] results = list() results$preservation=iterate(astrocyte_networks[c("EAE", "WT")], preservationComparisons, write=FALSE, plot=FALSE, nPermutations=2) preservationComparisonPlot(results$preservation$EAE_vs_WT, astrocyte_networks$EAE, astrocyte_networks$WT)
A high level function that performs a perservation comparison between two WGCNAobjects in a WGCNAlist, usually supplied by iterate function
preservationComparisons( comparisonList, WGCNAlist, first, second, element, plot = FALSE, write = FALSE, alphaLevel = 0.05, nPermutations = 100 )
preservationComparisons( comparisonList, WGCNAlist, first, second, element, plot = FALSE, write = FALSE, alphaLevel = 0.05, nPermutations = 100 )
comparisonList |
a list passed by the iterate function |
WGCNAlist |
list of objects of type WGCNAobject |
first |
index of first WGCNAobject |
second |
index of second WGCNAobject |
element |
element position in the comparison list (passed by iterate function) |
plot |
generate plots? |
write |
write results to file? |
alphaLevel |
alpha level of significance for module-trait correlation |
nPermutations |
number of permutations, defaults to 100 |
a list of preservation comparisons results across levels 1, 2, 3
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] results = list() iterate(astrocyte_networks[c("EAE", "WT")], preservationComparisons, write=FALSE, plot=FALSE, nPermutations=2)
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] results = list() iterate(astrocyte_networks[c("EAE", "WT")], preservationComparisons, write=FALSE, plot=FALSE, nPermutations=2)
Performs a permutation test to determine if a null distribution of expected preservation scores for modules in this dataset if the labels were randomly assigned. Please look at the astrocyte vignette for more info.
PreservationPermutationTest( referenceDatExpr, design, constructNetworksIn, testPreservationIn, nPermutations = 100, nPresPermutations = 100, ... )
PreservationPermutationTest( referenceDatExpr, design, constructNetworksIn, testPreservationIn, nPermutations = 100, nPresPermutations = 100, ... )
referenceDatExpr |
the combined datExpr |
design |
the sampleTable |
constructNetworksIn |
the condition to use for network construction, e.g. for the astrocyte data, this is "EAE" |
testPreservationIn |
the condition to use for testing preservation, e.g. for the astrocyte data, this was "WT" |
nPermutations |
the number of permutations to perform for permutation test |
nPresPermutations |
the number of permutations to perform in modulePreservation function |
... |
arguments to pass to blockwiseModules function for network construction (should be the same as used for constructing the original network) |
A list of data.frames with preservation results for each permutation
Dario Tommasini
## Not run: library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] astrocyte_se = eh_query[["EH8223"]] sampleTable = colData(astrocyte_se) results = list() results$permutation.test = PreservationPermutationTest(astrocyte_networks$combined@datExpr[sample(17000,3000),], sampleTable, constructNetworksIn = "EAE", # Construct networks using EAE samples testPreservationIn = "WT", # Test preservation of disease samples in WT samples nPermutations = 10, # Number of permutations for permutation test nPresPermutations = 10, # Number of permutations for modulePreservation function networkType = "signed", TOMType = "unsigned", power = 12, minModuleSize = 100, maxBlockSize = 25000, reassignThreshold = 0, minKMEtoStay = 0, mergeCutHeight = 0, numericLabels = TRUE, pamRespectsDendro = FALSE, deepSplit = 4, verbose = 3 ) ## End(Not run)
## Not run: library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] astrocyte_se = eh_query[["EH8223"]] sampleTable = colData(astrocyte_se) results = list() results$permutation.test = PreservationPermutationTest(astrocyte_networks$combined@datExpr[sample(17000,3000),], sampleTable, constructNetworksIn = "EAE", # Construct networks using EAE samples testPreservationIn = "WT", # Test preservation of disease samples in WT samples nPermutations = 10, # Number of permutations for permutation test nPresPermutations = 10, # Number of permutations for modulePreservation function networkType = "signed", TOMType = "unsigned", power = 12, minModuleSize = 100, maxBlockSize = 25000, reassignThreshold = 0, minKMEtoStay = 0, mergeCutHeight = 0, numericLabels = TRUE, pamRespectsDendro = FALSE, deepSplit = 4, verbose = 3 ) ## End(Not run)
Extracts the preservation score distribution from the results of PreservationPermutationTest.
PreservationScoreDistribution(preservationData, moduleOfInterestSize)
PreservationScoreDistribution(preservationData, moduleOfInterestSize)
preservationData |
the results from PreservationPermutationTest |
moduleOfInterestSize |
the number of genes in your module of interest |
A data.frame with Z-summary preservation scores of the module from each permutation and the corresponding module size
Dario Tommasini
# Remove outlier modules permutationTestResultsFiltered = lapply(permutationTestResults, function(x) x[!x$is.outlier.module,]) # Find preservation score distribution for a given module size scores.summary = PreservationScoreDistribution(permutationTestResultsFiltered, moduleOfInterestSize = 303)
# Remove outlier modules permutationTestResultsFiltered = lapply(permutationTestResults, function(x) x[!x$is.outlier.module,]) # Find preservation score distribution for a given module size scores.summary = PreservationScoreDistribution(permutationTestResultsFiltered, moduleOfInterestSize = 303)
A wrapper to run diffModuleExpression on all the modules in a network
runDME( WGCNAobject, design, alphaLevel = 0.05, testCondition = NULL, refCondition = NULL, p.adjust = "fdr", plot = FALSE, test = c("ANOVA", "PERMANOVA"), write = FALSE, out = NULL )
runDME( WGCNAobject, design, alphaLevel = 0.05, testCondition = NULL, refCondition = NULL, p.adjust = "fdr", plot = FALSE, test = c("ANOVA", "PERMANOVA"), write = FALSE, out = NULL )
WGCNAobject |
object of class WGCNA with the modules to run DME on |
design |
the sampleTable |
alphaLevel |
level of significance |
testCondition |
the column of the sampleTable to be resolved |
refCondition |
the column of the sampleTable to be used as biological variation |
p.adjust |
adjust for multiple comparisons, argument to pass to p.adjust function |
plot |
generate a plot? |
test |
statistical test to perform, either "ANOVA" or "PERMANOVA" |
write |
write results to a file? |
out |
file name for DME plots, only used if write is TRUE |
a data.frame summarizing the results of the analysis
Dario Tommasini
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_se = eh_query[["EH8223"]] sampleTable = colData(astrocyte_se) astrocyte_networks = eh_query[["EH8222"]] runDME(astrocyte_networks[["combined"]], design = sampleTable, p.adjust = "fdr", refCondition = "Region", testCondition = "Disease")
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_se = eh_query[["EH8223"]] sampleTable = colData(astrocyte_se) astrocyte_networks = eh_query[["EH8222"]] runDME(astrocyte_networks[["combined"]], design = sampleTable, p.adjust = "fdr", refCondition = "Region", testCondition = "Disease")
Prints (or writes) a summary of the results from a results list object
summarizeResults( myNetworks, results, alphaLevel = 0.05, write = FALSE, outputFile = "results.txt" )
summarizeResults( myNetworks, results, alphaLevel = 0.05, write = FALSE, outputFile = "results.txt" )
myNetworks |
a list of WGCNAobjects |
results |
results list |
alphaLevel |
alpha level of significance |
write |
write to file? |
outputFile |
name of output file, defaults to results.txt |
prints a summary of results from the multiWGCNA analysis
Plots a sankey flow diagram showing the movement of genes from one WGCNA to another WGCNA. Uses the ggalluvial framework.
TOMFlowPlot( WGCNAlist, networks, toms, genes_to_label, alpha = 0.1, color = "black", width = 0.05 )
TOMFlowPlot( WGCNAlist, networks, toms, genes_to_label, alpha = 0.1, color = "black", width = 0.05 )
WGCNAlist |
list of WGCNA objects |
networks |
list of network names of length 2 |
toms |
a list of TOM distance objects of length 2 |
genes_to_label |
genes to label across two networks |
alpha |
alpha of flows |
color |
color of flows |
width |
width of the strata |
a ggplot object
Dario Tommasini
Returns the top N number of genes of a module. All genes returned if no number is specified. Genes are in order of intramodular connectivity.
topNGenes(WGCNAobject, module, nGenes = NULL)
topNGenes(WGCNAobject, module, nGenes = NULL)
WGCNAobject |
an object of class WGCNA |
module |
the name of the module in WGCNAobject |
nGenes |
an integer from 1 to module size; returns all genes if left NULL |
a character vector of the genes/probes in the module
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] topNGenes(astrocyte_networks$EAE, "EAE_015", nGenes = 10)
library(ExperimentHub) eh = ExperimentHub() eh_query = query(eh, c("multiWGCNAdata")) astrocyte_networks = eh_query[["EH8222"]] topNGenes(astrocyte_networks$EAE, "EAE_015", nGenes = 10)
The WGCNA class is the main class used in multiWGCNA to store results from a weighted gene co-expression nework analysis. These include the original unaltered expression data used as input, connectivity metrics, module assignment, input sample conditions, trait
NA
datExpr
The expression data, connectivity data, and module assignment
conditions
A data.frame with integer conditions for WGCNA
trait
A data.frame showing pearson correlation values to traits
moduleEigengenes
A data.frame of module eigengenes for each module across samples
outlierModules
A vector of modules classified by our algorithm as being driven by sample outliers