R-universe search: snf

Showing 11 of total 11 results (show query)

branchlab

metasnf:Meta Clustering with Similarity Network Fusion

Framework to facilitate patient subtyping with similarity network fusion and meta clustering. The similarity network fusion (SNF) algorithm was introduced by Wang et al. (2014) in <doi:10.1038/nmeth.2810>. SNF is a data integration approach that can transform high-dimensional and diverse data types into a single similarity network suitable for clustering with minimal loss of information from each initial data source. The meta clustering approach was introduced by Caruana et al. (2006) in <doi:10.1109/ICDM.2006.103>. Meta clustering involves generating a wide range of cluster solutions by adjusting clustering hyperparameters, then clustering the solutions themselves into a manageable number of qualitatively similar solutions, and finally characterizing representative solutions to find ones that are best for the user's specific context. This package provides a framework to easily transform multi-modal data into a wide range of similarity network fusion-derived cluster solutions as well as to visualize, characterize, and validate those solutions. Core package functionality includes easy customization of distance metrics, clustering algorithms, and SNF hyperparameters to generate diverse clustering solutions; calculation and plotting of associations between features, between patients, and between cluster solutions; and standard cluster validation approaches including resampled measures of cluster stability, standard metrics of cluster quality, and label propagation to evaluate generalizability in unseen data. Associated vignettes guide the user through using the package to identify patient subtypes while adhering to best practices for unsupervised learning.

Maintained by Prashanth S Velayudhan. Last updated 3 days ago.

bioinformatics clustering metaclustering snf

46.3 match 8 stars 8.21 score 30 scripts

coneill-math

m2r:Interface to 'Macaulay2'

Persistent interface to 'Macaulay2' <http://www.math.uiuc.edu/Macaulay2/> and front-end tools facilitating its use in the 'R' ecosystem. For details see Kahle et. al. (2020) <doi:10.18637/jss.v093.i09>.

Maintained by David Kahle. Last updated 5 years ago.

cpp

6.3 match 5 stars 5.23 score 34 scripts

sharifrahmanie

MBMethPred:Medulloblastoma Subgroups Prediction

Utilizing a combination of machine learning models (Random Forest, Naive Bayes, K-Nearest Neighbor, Support Vector Machines, Extreme Gradient Boosting, and Linear Discriminant Analysis) and a deep Artificial Neural Network model, 'MBMethPred' can predict medulloblastoma subgroups, including wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 from DNA methylation beta values. See Sharif Rahmani E, Lawarde A, Lingasamy P, Moreno SV, Salumets A and Modhukur V (2023), MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches. Front. Genet. 14:1233657. <doi: 10.3389/fgene.2023.1233657> for more details.

Maintained by Edris Sharif Rahmani. Last updated 1 years ago.

6.0 match 3.70 score 1 scripts

cran

SNFtool:Similarity Network Fusion

Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix. The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.

Maintained by Benjamin Brew. Last updated 4 years ago.

3.3 match 19 stars 4.31 score 4 dependents

bioc

CiteFuse:CiteFuse: multi-modal analysis of CITE-seq data

CiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of the analyses.

Maintained by Yingxin Lin. Last updated 5 months ago.

singlecell geneexpression bioinformatics single-cell cpp

1.5 match 27 stars 6.59 score 18 scripts

enricoschumann

SNSFdatasets:Download Datasets from the Swiss National Science Foundation (SNF, FNS, SNSF)

Download and read datasets from the Swiss National Science Foundation (SNF, FNS, SNSF; <https://snf.ch>). The package is lightweight and without dependencies. Downloaded data can optionally be cached, to avoid repeated downloads of the same files. There are also utilities for comparing different versions of datasets, i.e. to report added, removed and changed entries.

Maintained by Enrico Schumann. Last updated 1 years ago.

3.6 match 2.70 score