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wanchanglin

mt:Metabolomics Data Analysis Toolbox

Functions for metabolomics data analysis: data preprocessing, orthogonal signal correction, PCA analysis, PCA-DA analysis, PLS-DA analysis, classification, feature selection, correlation analysis, data visualisation and re-sampling strategies.

Maintained by Wanchang Lin. Last updated 1 years ago.

3 stars 4.57 score 50 scripts

bioc

clst:Classification by local similarity threshold

Package for modified nearest-neighbor classification based on calculation of a similarity threshold distinguishing within-group from between-group comparisons.

Maintained by Noah Hoffman. Last updated 5 months ago.

classification

3.78 score 10 scripts 1 dependents

bbuchsbaum

multivarious:Extensible Data Structures for Multivariate Analysis

Provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks.

Maintained by Bradley Buchsbaum. Last updated 3 months ago.

3.53 score 17 scripts

cran

Allspice:RNA-Seq Profile Classifier

We developed a lightweight machine learning tool for RNA profiling of acute lymphoblastic leukemia (ALL), however, it can be used for any problem where multiple classes need to be identified from multi-dimensional data. The methodology is described in Makinen V-P, Rehn J, Breen J, Yeung D, White DL (2022) Multi-cohort transcriptomic subtyping of B-cell acute lymphoblastic leukemia, International Journal of Molecular Sciences 23:4574, <doi:10.3390/ijms23094574>. The classifier contains optimized mean profiles of the classes (centroids) as observed in the training data, and new samples are matched to these centroids using the shortest Euclidean distance. Centroids derived from a dataset of 1,598 ALL patients are included, but users can train the models with their own data as well. The output includes both numerical and visual presentations of the classification results. Samples with mixed features from multiple classes or atypical values are also identified.

Maintained by Ville-Petteri Makinen. Last updated 2 years ago.

2.00 score

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