Runs consensus clustering across subsamples of the data, clustering algorithms, and cluster sizes.

consensus_cluster( data, nk = 2:4, p.item = 0.8, reps = 1000, algorithms = NULL, nmf.method = c("brunet", "lee"), hc.method = "average", xdim = NULL, ydim = NULL, rlen = 200, alpha = c(0.05, 0.01), minPts = 5, distance = "euclidean", prep.data = c("none", "full", "sampled"), scale = TRUE, type = c("conventional", "robust", "tsne"), min.var = 1, progress = TRUE, seed.nmf = 123456, seed.data = 1, file.name = NULL, time.saved = FALSE )

data | data matrix with rows as samples and columns as variables |
---|---|

nk | number of clusters (k) requested; can specify a single integer or a range of integers to compute multiple k |

p.item | proportion of items to be used in subsampling within an algorithm |

reps | number of subsamples |

algorithms | vector of clustering algorithms for performing consensus clustering. Must be any number of the following: "nmf", "hc", "diana", "km", "pam", "ap", "sc", "gmm", "block", "som", "cmeans", "hdbscan". A custom clustering algorithm can be used. |

nmf.method | specify NMF-based algorithms to run. By default the
"brunet" and "lee" algorithms are called. See |

hc.method | agglomeration method for hierarchical clustering. The
the "average" method is used by default. See |

xdim | x dimension of the SOM grid |

ydim | y dimension of the SOM grid |

rlen | the number of times the complete data set will be presented to the SOM network. |

alpha | SOM learning rate, a vector of two numbers indicating the amount
of change. Default is to decline linearly from 0.05 to 0.01 over |

minPts | minimum size of clusters for HDBSCAN. Default is 5. |

distance | a vector of distance functions. Defaults to "euclidean".
Other options are given in |

prep.data | Prepare the data on the "full" dataset, the "sampled" dataset, or "none" (default). |

scale | logical; should the data be centered and scaled? |

type | if we use "conventional" measures (default), then the mean and standard deviation are used for centering and scaling, respectively. If "robust" measures are specified, the median and median absolute deviation (MAD) are used. Alternatively, we can apply "tsne" for dimension reduction. |

min.var | minimum variability measure threshold used to filter the
feature space for only highly variable features. Only features with a
minimum variability measure across all samples greater than |

progress | logical; should a progress bar be displayed? |

seed.nmf | random seed to use for NMF-based algorithms |

seed.data | seed to use to ensure each algorithm operates on the same set of subsamples |

file.name | if not |

time.saved | logical; if |

An array of dimension `nrow(x)`

by `reps`

by `length(algorithms)`

by
`length(nk)`

. Each cube of the array represents a different k. Each slice
of a cube is a matrix showing consensus clustering results for algorithms.
The matrices have a row for each sample, and a column for each subsample.
Each entry represents a class membership.

When "hdbscan" is part of `algorithms`

, we do not include its clustering
array in the consensus result. Instead, we report two summary statistics as
attributes: the proportion of outliers and the number of clusters.

See examples for how to use custom algorithms and distance functions. The default clustering algorithms provided are:

"nmf": Nonnegative Matrix Factorization (using Kullback-Leibler Divergence or Euclidean distance; See Note for specifications.)

"hc": Hierarchical Clustering

"diana": DIvisive ANAlysis Clustering

"km": K-Means Clustering

"pam": Partition Around Medoids

"ap": Affinity Propagation

"sc": Spectral Clustering using Radial-Basis kernel function

"gmm": Gaussian Mixture Model using Bayesian Information Criterion on EM algorithm

"block": Biclustering using a latent block model

"som": Self-Organizing Map (SOM) with Hierarchical Clustering

"cmeans": Fuzzy C-Means Clustering

"hdbscan": Hierarchical Density-based Spatial Clustering of Applications with Noise (HDBSCAN)

The progress bar increments on every unit of `reps`

.

The `nmf.method`

options are "brunet" (Kullback-Leibler Divergence) and
"lee" (Euclidean distance). When "hdbscan" is chosen as an algorithm to
use, its results are excluded from the rest of the consensus clusters. This
is because there is no guarantee that the cluster assignment will have
every sample clustered; more often than not there will be noise points or
outliers. In addition, the number of distinct clusters may not even be
equal to `nk`

.

Derek Chiu, Aline Talhouk

data(hgsc) dat <- hgsc[1:100, 1:50] # Custom distance function manh <- function(x) { stats::dist(x, method = "manhattan") } # Custom clustering algorithm agnes <- function(d, k) { return(as.integer(stats::cutree(cluster::agnes(d, diss = TRUE), k))) } assign("agnes", agnes, 1) cc <- consensus_cluster(dat, reps = 6, algorithms = c("pam", "agnes"), distance = c("euclidean", "manh"), progress = FALSE) str(cc)#> int [1:100, 1:6, 1:4, 1:3] 1 1 NA NA NA 1 1 NA 2 NA ... #> - attr(*, "dimnames")=List of 4 #> ..$ : chr [1:100] "TCGA.04.1331_PRO.C5" "TCGA.04.1332_MES.C1" "TCGA.04.1336_DIF.C4" "TCGA.04.1337_MES.C1" ... #> ..$ : chr [1:6] "R1" "R2" "R3" "R4" ... #> ..$ : chr [1:4] "PAM_Euclidean" "PAM_Manh" "AGNES_Euclidean" "AGNES_Manh" #> ..$ : chr [1:3] "2" "3" "4"