swapTwoBranches: Select, swap, or reflect branches in a dendrogram.

In WGCNA: Weighted Correlation Network Analysis

Select, swap, or reflect branches in a dendrogram.

Description

swapTwoBranches takes the a gene tree object and two genes as input, and swaps the branches containing these two genes at the nearest branch point of the dendrogram.

reflectBranch takes the a gene tree object and two genes as input, and reflects the branch containing the first gene at the nearest branch point of the dendrogram.

selectBranch takes the a gene tree object and two genes as input, and outputs indices for all genes in the branch containing the first gene, up to the nearest branch point of the dendrogram.

Usage

swapTwoBranches(hierTOM, g1, g2)
reflectBranch(hierTOM, g1, g2, both = FALSE)
selectBranch(hierTOM, g1, g2)

Arguments

hierTOM	A hierarchical clustering object (or gene tree) that is used to plot the dendrogram. For example, the output object from the function hclust or fastcluster::hclust. Note that elements of hierTOM$order MUST be named (for example, with the corresponding gene name).
g1	Any gene in the branch of interest.
g2	Any gene in a branch directly adjacent to the branch of interest.
both	Logical: should the selection include the branch gene g2?

Value

swapTwoBranches and reflectBranch return a hierarchical clustering object with the hierTOM$order variable properly adjusted, but all other variables identical as the heirTOM input.

selectBranch returns a numeric vector corresponding to all genes in the requested branch.

Author(s)

Jeremy Miller

Examples

## Not run: 
## Example: first simulate some data.
n = 30;
n2 = 2*n;
n.3 = 20;
n.5 = 10;
MEturquoise = sample(1:(2*n),n)
MEblue      = c(MEturquoise[1:(n/2)], sample(1:(2*n),n/2))
MEbrown     = sample(1:n2,n)
MEyellow    = sample(1:n2,n) 
MEgreen     = c(MEyellow[1:n.3], sample(1:n2,n.5))
MEred	    = c(MEbrown [1:n.5], sample(1:n2,n.3))

ME     = data.frame(MEturquoise, MEblue, MEbrown, MEyellow, MEgreen, MEred)
dat1   = simulateDatExpr(ME,8*n ,c(0.16,0.12,0.11,0.10,0.10,0.09,0.15), 
                         signed=TRUE)
TOM1   = TOMsimilarityFromExpr(dat1$datExpr, networkType="signed")
colnames(TOM1) <- rownames(TOM1) <- colnames(dat1$datExpr)
tree1  = fastcluster::hclust(as.dist(1-TOM1),method="average")
colorh = labels2colors(dat1$allLabels)

plotDendroAndColors(tree1,colorh,dendroLabels=FALSE)

## Reassign modules using the selectBranch and chooseOneHubInEachModule functions

datExpr = dat1$datExpr
hubs    = chooseOneHubInEachModule(datExpr, colorh)
colorh2 = rep("grey", length(colorh))
colorh2 [selectBranch(tree1,hubs["blue"],hubs["turquoise"])] = "blue"
colorh2 [selectBranch(tree1,hubs["turquoise"],hubs["blue"])] = "turquoise"
colorh2 [selectBranch(tree1,hubs["green"],hubs["yellow"])]   = "green"
colorh2 [selectBranch(tree1,hubs["yellow"],hubs["green"])]   = "yellow"
colorh2 [selectBranch(tree1,hubs["red"],hubs["brown"])]      = "red"
colorh2 [selectBranch(tree1,hubs["brown"],hubs["red"])]      = "brown"
plotDendroAndColors(tree1,cbind(colorh,colorh2),c("Old","New"),dendroLabels=FALSE)

## Now swap and reflect some branches, then optimize the order of the branches

# Open a suitably sized graphics window

sizeGrWindow(12,9);

# partition the screen for 3 dendrogram + module color plots

layout(matrix(c(1:6), 6, 1), heights = c(0.8, 0.2, 0.8, 0.2, 0.8, 0.2));

plotDendroAndColors(tree1,colorh2,dendroLabels=FALSE,main="Starting Dendrogram", 
                    setLayout = FALSE)

tree1 = swapTwoBranches(tree1,hubs["red"],hubs["turquoise"])
plotDendroAndColors(tree1,colorh2,dendroLabels=FALSE,main="Swap blue/turquoise and red/brown", 
                    setLayout = FALSE)

tree1 = reflectBranch(tree1,hubs["blue"],hubs["green"])
plotDendroAndColors(tree1,colorh2,dendroLabels=FALSE,main="Reflect turquoise/blue", 
                    setLayout = FALSE)


## End(Not run)

WGCNA documentation built on Sept. 18, 2024, 5:08 p.m.