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.
You can install diceR from CRAN with:
install.packages("diceR")Or get the latest development version from GitHub:
# install.packages("devtools")
devtools::install_github("AlineTalhouk/diceR")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 |
This figure is a visual schematic of the pipeline that
dice() implements.
Please visit the overview page for more detail.