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CombiROC is a totally new music in multi-markers analysis: an R package for efficient and easy combinatorial selection of biomarkers and sensitivity/specificity-driven prioritization of features.

This is the development version of CombiROC package (combiroc), code in this repo is work in progress and it is uploaded here “as-is” with no warranties implied. Improvements and new features will be added on a regular basis, please check on this github page for new features and releases.

The CombiROC approach was first released as a Shiny Application with limited features. This version is still available at combiroc.eu, but it has limited features as well as low computational power and is not further maintained. If you need to cite the method or the web-app please refer to Mazzara et al. Scientific Reports 2017 and Bombaci & Rossi, Methods Mol Biol 2019.

For full capabilities, new and improved features and customized analyses we suggest to install the combiroc package, either the release from CRAN or the development version from this repo. If you are using the combiroc package in your research, please cite also our bioRxiv preprint: Ferrari et al. Combinatorial selection of biomarkers to optimize gene signatures in diagnostics and single cell applications. bioRxiv 2022.01.17.476603; doi: https://doi.org/10.1101/2022.01.17.476603

Combiroc bioRxiv preprint Supplementary material

The bioRxiv preprint’s Supplementary Material 1 and 2 can be accessed here:


# You can install combiroc pulling it from CRAN:

Development version

# To install the most recent development version from this repository install "remotes" first:
# remotes is a lightweight replacement of install functions from devtools
# if you already have devtools, you can also use devtools::install_github() 

# Then install the development version of CombiROC:
                        dependencies = TRUE, build_vignettes = TRUE)

Full Documentation - Tutorial

Full documentation is in the package’s vignette. You can also find the rendered version of the vignette in the combiroc-package website created with pkgdown.

Quick start example


# load the preformatted demo dataset
# (you can load a dataset of yours using load_data() function: see full docs)
data <- demo_data

# shape it in long format (prone to plotting)
data_long <- combiroc_long(data)

# study the distribution of you markers' signal
# arguments values to be adjusted according to  data
distr <- markers_distribution(data_long, case_class = 'A', 
                              y_lim = 0.0015, x_lim = 3000, 
                              signalthr_prediction = TRUE, 
                              min_SE = 40, min_SP = 80, 
                              boxplot_lim = 2000)

# explore the distr object: boxplot of signals

# explore the distr object: densities of classes with signal threshold (signalthr)

# explore the distr object: ROC and its coordinates
head(distr$Coord, n=10)

# combinatorial analysis
tab <- combi(data, signalthr = 407, combithr = 1)

# SE & SP computation
mks <- se_sp(data, tab)

# ranked combinations
rmks <- ranked_combs(data, mks, case_class = 'A', min_SE = 40, min_SP = 80)

# check ranked combinations

# results report for specific markers/combinations
reports <-roc_reports(data, markers_table = tab, case_class = 'A',
                      single_markers =c('Marker1'), 
                      selected_combinations = c(11,15))

# results outputs

Issues - Bugs

If you find a bug, or to share ideas for improvement, feel free to start an issue. We do have a roadmap but we also listen!


  • Package authors and maintainers: Ivan Ferrari & Riccardo L. Rossi
  • Original code of Shiny App: Saveria Mazzara
  • Initial idea & conception: Mauro Bombaci


We were so happy to finally had the chance to develop the combiroc package that we felt very “rock”: this is why the combiroc hexagon sticker logo is a homage to Eddie Van Halen who left us in 2020, and the “Frankenstrat”, his iconic guitar.