The MiRNA-QC-and-Diagnosis (Micro RNA Quality Control and Diagnosis) is an R package to carry out training and classification analyses on datasets containing multiplets of MiRNA expression. This package contains a set of functions that implement the analysis algorithm first proposed in
L. Ricci, V. Del Vescovo, C. Cantaloni, M. Grasso, M. Barbareschi and M. A. Denti, Statistical analysis of a Bayesian classifier based on the expression of miRNAs, BMC Bioinformatics 16:287, 2015. DOI: 10.1186/s12859-015-0715-9
The software package is described in the following work:
M. Castelluzzo, A. Perinelli, S. Detassis, M. A. Denti and L. Ricci, MiRNA-QC-and-Diagnosis: An R package for diagnosis based on MiRNA expression, SoftwareX 12:100569, 2020. DOI: 10.1016/j.softx.2020.100569
Please cite both these references in works that use the present package. Bibliography entries can be also displayed within R by typing
citation("MiRNAQCD")
# or, to get BibTeX items,
toBibtex(citation("MiRNAQCD"))This package is free software. It is distributed under the terms of the GNU General Public License (GPL), version 3.0 - see the LICENSE.txt file for details.
This package requires the R environment, which is free software released under the terms of GPL (see https://www.r-project.org/ for further details).
This package requires the packages stats, utils, tools, pROC and ggplot2 (the latter two for plotting purposes). The package devtools is necessary if the package is installed from source.
- Michele Castelluzzo (1) michele.castelluzzo@unitn.it
- Alessio Perinelli (1) alessio.perinelli@unitn.it
- Simone Detassis (2) simone.detassis@unitn.it
- Michela Denti (2) michela.denti@unitn.it
- Leonardo Ricci (1,3) leonardo.ricci@unitn.it
(1) Department of Physics, University of Trento, 38123 Trento, Italy. (2) Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123 Trento, Italy. (3) CIMeC, Center for Mind/Brain Sciences, University of Trento, 38068 Rovereto, Italy.
If the package turns out to be useful for your research, please cite our two papers [1, 2]:
[1] L. Ricci, V. Del Vescovo, C. Cantaloni, M. Grasso, M. Barbareschi and M. A. Denti, Statistical analysis of a Bayesian classifier based on the expression of miRNAs, BMC Bioinformatics 16:287, 2015. doi: 10.1186/s12859-015-0715-9
[2] M. Castelluzzo, A. Perinelli, S. Detassis, M. A. Denti and L. Ricci, MiRNA-QC-and-Diagnosis: An R package for diagnosis based on MiRNA expression, SoftwareX 12:100569, 2020. DOI: 10.1016/j.softx.2020.100569
The package is available on CRAN at https://CRAN.R-project.org/package=MiRNAQCD. The current package version on CRAN is MiRNAQCD 1.1.3.
The GitHub repository stores the development version of the package, which typically is a few steps ahead of the CRAN release. The current package version on GitHub is MiRNAQCD 1.1.3.
The package consists in a set of functions for the R environment. All source code is under /R/. The package setup file (*.tar.gz), as well as details on how to install it, can be found within /setup/. See the user manual for details on the package functionalities and for setup information. Function documentation can be accessed from within R by typing
help(functionName)The user manual is found in /inst/doc/manual.pdf within the package directory tree or, once the package is installed, in /path-to-library/MiRNAQCD/doc/manual.pdf, where path-to-library can be shown within R by means of the .libPaths() command.
Example code and datasets can be found in /examples/ within the package directory tree or, once the package is installed, in /path-to-library/MiRNAQCD/extdata/, where path-to-library can be shown within R by means of the .libPaths() command.
The script example_synthetic_dataset.R therein contains a detailed example pipeline concerning synthetic data. The scripts example_real_dataset_1.R, example_real_dataset_2.R provide example pipelines for two real, publicly available datasets. A copy of each dataset is stored in the same folder. Example pipelines are also discussed in the user manual /docs/manual.pdf.
A few other works relying on the method implemented by the package:
M. Grasso, P. Piscopo, G. Talarico, L. Ricci, A. Crestini, G. Tosto, M. Gasparini, G. Bruno, M. A. Denti, A. Confaloni, Plasma microRNA profiling distinguishes patients with frontotemporal dementia from healthy subjects, Neurobiology of Aging 84:240.e1 2019. doi: 10.1016/j.neurobiolaging.2019.01.024
S. Detassis, V. del Vescovo, M. Grasso, S. Masella, C. Cantaloni, L. Cima, A. Cavazza, P. Graziano, G. Rossi, M. Barbareschi, L. Ricci and M. A. Denti, miR375-3p Distinguishes Low-Grade Neuroendocrine From Non-neuroendocrine Lung Tumors in FFPE Samples, Frontiers in Molecular Biosciences 7:86, 2020. doi: 10.3389/fmolb.2020.00086