DataSlingers/IMPACC: Fast and Interpretable Consensus Clustering via Minipatch Learning
This package provides powerful methodologies for consensus clustering using minipatch learning with random or adaptive schemes. The methods provide interpretability in terms of feature importance and are particularly applicable to sparse, high-dimensional data sets common in bioinformatics.
Getting started
Browse package contents
Package details |
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| Bioconductor views | Clustering DifferentialExpression FeatureExtraction KEGG RNASeq Regression SingleCell Software |
| Maintainer | |
| License | Artistic-2.0 |
| Version | 0.1.0 |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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