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bioc
signeR:Empirical Bayesian approach to mutational signature discovery
The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variation (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided.
Maintained by Renan Valieris. Last updated 5 months ago.
genomicvariationsomaticmutationstatisticalmethodvisualizationbioconductorbioinformaticsopenblascpp
13 stars 7.67 score 22 scriptsbioc
miRSM:Inferring miRNA sponge modules in heterogeneous data
The package aims to identify miRNA sponge or ceRNA modules in heterogeneous data. It provides several functions to study miRNA sponge modules at single-sample and multi-sample levels, including popular methods for inferring gene modules (candidate miRNA sponge or ceRNA modules), and two functions to identify miRNA sponge modules at single-sample and multi-sample levels, as well as several functions to conduct modular analysis of miRNA sponge modules.
Maintained by Junpeng Zhang. Last updated 5 months ago.
geneexpressionbiomedicalinformaticsclusteringgenesetenrichmentmicroarraysoftwaregeneregulationgenetargetcernamirnamirna-spongemirna-targetsmodulesopenjdk
4 stars 5.51 score 5 scripts
zcebeci
odetector:Outlier Detection Using Partitioning Clustering Algorithms
An object is called "outlier" if it remarkably deviates from the other objects in a data set. Outlier detection is the process to find outliers by using the methods that are based on distance measures, clustering and spatial methods (Ben-Gal, 2005 <ISBN 0-387-24435-2>). It is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for outlier removing in data processing. This package provides the implementations of some novel approaches to detect the outliers based on typicality degrees that are obtained with the soft partitioning clustering algorithms such as Fuzzy C-means and its variants.
Maintained by Zeynel Cebeci. Last updated 2 years ago.
anomaly-detectioncluster-analysisclusteringclustering-methodsdatadatapreparationdatapreprocessingexception-handlingfcmfraud-detectionfuzzy-clusteringnovelty-detectionoutlier-detectionoutlier-removaloutlierspartitioningpcmsurprise-exploration
3.70 score 4 scripts
giorgiazaccaria
PUGMM:Parsimonious Ultrametric Gaussian Mixture Models
Finite Gaussian mixture models with parsimonious extended ultrametric covariance structures estimated via a grouped coordinate ascent algorithm, which is equivalent to the Expectation-Maximization algorithm. The thirteen ultrametric covariance structures implemented allow for the inspection of different hierarchical relationships among variables. The estimation of an ultrametric correlation matrix is included as a function. The methodologies are described in Cavicchia, Vichi, Zaccaria (2024) <doi:10.1007/s11222-024-10405-9>, Cavicchia, Vichi, Zaccaria (2022) <doi:10.1007/s11634-021-00488-x> and Cavicchia, Vichi, Zaccaria (2020) <doi:10.1007/s11634-020-00400-z>.
Maintained by Giorgia Zaccaria. Last updated 11 months ago.
2 stars 3.30 score