WGCNA: outputting multiple hubgenes from a module

Question

[RNAseqer]

I know that WGCNA has the function chooseTopHubInEachModule() to pick the gene with the single highest connectivity within a module, but does it have a function to output several hubgenes from a module? Im looking through the manual and it certainly seems like it should be a straightforward task but I'm having some trouble executing it, so I thought I'd just ask if anyone had done this before.

Answer 1

[Kevin Blighe]

Hi,

I would honestly just hack the function code and create my own function, if I were you. The code is simple:

WGCNA::chooseTopHubInEachModule

function (datExpr, colorh, omitColors = "grey", power = 2, type = "signed", 
    ...) 
{
    isIndex = FALSE
    modules = names(table(colorh))
    if (!is.na(omitColors)[1])) 
        modules = modules[!is.element(modules, omitColors)]
    if (is.null(colnames(datExpr))) {
        colnames(datExpr) = 1:dim(datExpr)[2]
        isIndex = TRUE
    }
    hubs = rep(NA, length(modules))
    names(hubs) = modules
    for (m in modules) {
        adj = adjacency(datExpr[, colorh == m], power = power, 
            type = type, ...)
        hub = which.max(rowSums(adj))
        hubs[m] = colnames(adj)[hub]
    }
    if (isIndex) {
        hubs = as.numeric(hubs)
        names(hubs) = modules
    }
    return(hubs)
}

The line that you'll probably want to change is:

hub = which.max(rowSums(adj))

Note that there are many hub-selection metrics.

Kevin

Answer 2

[tiancaigg]

    # the grey module is omitted
topHubs <- function (datExpr, colorh, omitColors = "grey", power = 2, type = "signed", 
    ...) 
{
    # modified from chooseTopHubInEachModule, but return the table of all genes connectivity
    isIndex = FALSE
    modules = names(table(colorh))
    if (!is.na(omitColors)[1]) 
        modules = modules[!is.element(modules, omitColors)]
    if (is.null(colnames(datExpr))) {
        colnames(datExpr) = 1:dim(datExpr)[2]
        isIndex = TRUE
    }

    connectivity_table <- data.frame(matrix(ncol = 3)) %>% setNames(c('gene', 'connectivity_rowSums_adj', 'module'))
    hubs = rep(NA, length(modules))
    names(hubs) = modules
    for (m in modules) {
        adj = adjacency(datExpr[, colorh == m], power = power, 
            type = type, ...)

        hub = which.max(rowSums(adj))

        hubs[m] = colnames(adj)[hub]

        sorted_genes <- rowSums(adj) %>% sort(decreasing = T) %>% as.data.frame()  %>%  
                tibble::rownames_to_column() %>% setNames(c('gene', 'connectivity_rowSums_adj')) %>% mutate(module = m)
        connectivity_table <- connectivity_table %>% rbind(sorted_genes)



    }
    if (isIndex) {
        hubs = as.numeric(hubs)
        names(hubs) = modules
    }
    return(connectivity_table %>% na.omit)
}

hope this help. It needs dplyr. you can

connectivity_table= topHubs(dataExpr, colorh=  mergedColors, power=power, type=type)
connectivity_table %>% group_by(module) %>% top_n( 3, wt = connectivity_rowSums_adj)

which return top 3 hubs of each module.