Overview

The goal of diceR is to provide a systematic framework for generating diverse cluster ensembles in R. There are a lot of nuances in cluster analysis to consider. We provide a process and a suite of functions and tools to implement a systematic framework for cluster discovery, guiding the user through the generation of a diverse clustering solutions from data, ensemble formation, algorithm selection and the arrival at a final consensus solution. We have additionally developed visual and analytical validation tools to help with the assessment of the final result. We implemented a wrapper function dice() that allows the user to easily obtain results and assess them. Thus, the package is accessible to both end user with limited statistical knowledge. Full access to the package is available for informaticians and statisticians and the functions are easily expanded. More details can be found in our companion paper published at BMC Bioinformatics.

Installation

You can install diceR from CRAN with:

Or get the latest development version from GitHub:

# install.packages("devtools")
devtools::install_github("AlineTalhouk/diceR")

Example

The following example shows how to use the main function of the package, dice(). A data matrix hgsc contains a subset of gene expression measurements of High Grade Serous Carcinoma Ovarian cancer patients from the Cancer Genome Atlas publicly available datasets. Samples as rows, features as columns. The function below runs the package through the dice() function. We specify (a range of) nk clusters over reps subsamples of the data containing 80% of the full samples. We also specify the clustering algorithms to be used and the ensemble functions used to aggregated them in cons.funs.

library(diceR)
data(hgsc)
obj <- dice(hgsc, nk = 4, reps = 5, algorithms = c("hc", "diana"),
            cons.funs = c("kmodes", "majority"))

The first few cluster assignments are shown below:

knitr::kable(head(obj$clusters))
kmodes majority
TCGA.04.1331_PRO.C5 2 2
TCGA.04.1332_MES.C1 2 2
TCGA.04.1336_DIF.C4 4 2
TCGA.04.1337_MES.C1 2 2
TCGA.04.1338_MES.C1 2 2
TCGA.04.1341_PRO.C5 2 2

You can also compare the base algorithms with the cons.funs using internal evaluation indices:

knitr::kable(obj$indices$ii$`4`)
Algorithms calinski_harabasz dunn pbm tau gamma c_index davies_bouldin mcclain_rao sd_dis ray_turi g_plus silhouette s_dbw Compactness Connectivity
HC_Euclidean HC_Euclidean 3.104106 0.2608547 59.73711 0.1994677 0.6349401 0.2844073 1.839182 0.8009149 0.1306062 1.4765665 0.0180141 NaN NaN 24.83225 41.62183
DIANA_Euclidean DIANA_Euclidean 53.647400 0.3348103 33.87817 0.4296601 0.6314448 0.1589442 2.824201 0.8051915 0.2119281 3.2978986 0.0853202 0.0692233 NaN 21.93396 241.66310
kmodes kmodes 55.336853 0.3396909 50.73168 0.4553051 0.6600223 0.1455632 2.010999 0.7974362 0.1180643 1.1528004 0.0808925 0.1250821 NaN 21.90504 204.58333
majority majority 19.373248 0.3544371 85.05173 0.3694313 0.6529652 0.2102487 1.622799 0.8019453 0.1108674 0.9200511 0.0555431 0.1884934 NaN 23.85408 64.04921

Pipeline

This figure is a visual schematic of the pipeline that dice() implements.

Ensemble Clustering pipeline.

Please visit the overview page for more detail.