makeConsensus,matrix-method {clusterExperiment} | R Documentation

## Find sets of samples that stay together across clusterings

### Description

Find sets of samples that stay together across clusterings in order to define a new clustering vector.

### Usage

    ## S4 method for signature 'matrix'
    makeConsensus(x, proportion,
      clusterFunction = "hierarchical01", propUnassigned = 0.5,
      minSize = 5, ...)

    ## S4 method for signature 'ClusterExperiment'
    makeConsensus(x, whichClusters,
      eraseOld = FALSE, clusterLabel = "makeConsensus", ...)

### Arguments

`x` | a matrix or `ClusterExperiment` object.
`proportion` | The proportion of times that two sets of samples should be together in order to be grouped into a cluster (if <1, passed to mainClustering via alpha = 1 - proportion)
`clusterFunction` | the clustering to use (passed to `mainClustering`); currently must be of type '01'.
`propUnassigned` | samples with greater than this proportion of assignments equal to '-1' are assigned a '-1' cluster value as a last step (only if proportion < 1)
`minSize` | minimum size required for a set of samples to be considered in a cluster because of shared clustering, passed to `mainClustering`
`...` | arguments to be passed on to the method for signature `matrix,missing`.
`whichClusters` | argument that can be either numeric or character vector indicating the clusterings to be used. See details of `getClusterIndex`.
`eraseOld` | logical. Only relevant if input `x` is of class `ClusterExperiment`. If TRUE, will erase existing workflow results (clusterMany as well as mergeClusters and makeConsensus). If FALSE, existing workflow results will have "`_i`" added to the clusterTypes value, where `i` is one more than the largest such existing workflow clusterTypes.
`clusterLabel` | a string used to describe the type of clustering. By default it is equal to "makeConsensus", to indicate that this clustering is the result of a call to makeConsensus. However, a more informative label can be set (see vignette).

### Details

This function was previously called `combineMany` (versions <= 2.0.0). `combineMany` is still available, but is considered defunct and users should update their code accordingly.

The function tries to find a consensus cluster across many different clusterings of the same samples. It does so by creating a `nSamples` x `nSamples` matrix of the percentage of co-occurance of each sample and then calling mainClustering to cluster the co-occurance matrix. The function assumes that '-1' labels indicate clusters that are not assigned to a cluster. Co-occurance with the unassigned cluster is treated differently than other clusters. The percent co-occurance is taken only with respect to those clusterings where both samples were assigned. Then samples with more than `propUnassigned` values that are '-1' across all of the clusterings are assigned a '-1' regardless of their cluster assignment.

The method calls `mainClustering` on the proportion matrix with `clusterFunction` as the 01 clustering algorithm, `alpha=1-proportion`, `minSize=minSize`, and `evalClusterMethod=c("average")`. See help of `mainClustering` for more details.

### Value

If x is a matrix, a list with values

- `clustering` vector of cluster assignments, with "-1" implying unassigned

- `percentageShared` a nSample x nSample matrix of the percent co-occurance across clusters used to find the final clusters. Percentage is out of those not '-1'

- `noUnassignedCorrection` a vector of cluster assignments before samples were converted to '-1' because had >`propUnassigned` '-1' values (i.e. the direct output of the `mainClustering` output.)

If x is a `ClusterExperiment`, a `ClusterExperiment` object, with an added clustering of clusterTypes equal to `makeConsensus` and the `percentageShared` matrix stored in the `coClustering` slot.

### Examples

    data(simData)

    cl <- clusterMany(simData,nReducedDims=c(5,10,50),  reduceMethod="PCA",
    clusterFunction="pam", ks=2:4, findBestK=c(FALSE), removeSil=TRUE,
    subsample=FALSE)

    #make names shorter for plotting
    clMat <- clusterMatrix(cl)
    colnames(clMat) <- gsub("TRUE", "T", colnames(clMat))
    colnames(clMat) <- gsub("FALSE", "F", colnames(clMat))
    colnames(clMat) <- gsub("k=NA,", "", colnames(clMat))

    #require 100% agreement -- very strict
    clCommon100 <- makeConsensus(clMat, proportion=1, minSize=10)

    #require 70% agreement based on clustering of overlap
    clCommon70 <- makeConsensus(clMat, proportion=0.7, minSize=10)

    oldpar <- par()
    par(mar=c(1.1, 12.1, 1.1, 1.1))
    plotClusters(cbind("70%Similarity"=clCommon70$clustering, clMat,
    "100%Similarity"=clCommon100$clustering), axisLine=-2)

    #method for ClusterExperiment object
    clCommon <- makeConsensus(cl, whichClusters="workflow", proportion=0.7,
    minSize=10)
    plotClusters(clCommon)
    par(oldpar)

[Package clusterExperiment version 2.6.1 Index]