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bioc
MetaboCoreUtils:Core Utils for Metabolomics Data
MetaboCoreUtils defines metabolomics-related core functionality provided as low-level functions to allow a data structure-independent usage across various R packages. This includes functions to calculate between ion (adduct) and compound mass-to-charge ratios and masses or functions to work with chemical formulas. The package provides also a set of adduct definitions and information on some commercially available internal standard mixes commonly used in MS experiments.
Maintained by Johannes Rainer. Last updated 5 months ago.
infrastructuremetabolomicsmassspectrometrymass-spectrometry
9 stars 9.40 score 58 scripts 36 dependentsbioc
autonomics:Unified Statistical Modeling of Omics Data
This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). It provides a fast enrichment analysis implementation. And an intuitive contrastogram visualisation to summarize contrast effects in complex designs.
Maintained by Aditya Bhagwat. Last updated 2 months ago.
softwaredataimportpreprocessingdimensionreductionprincipalcomponentregressiondifferentialexpressiongenesetenrichmenttranscriptomicstranscriptiongeneexpressionrnaseqmicroarrayproteomicsmetabolomicsmassspectrometry
5.89 score 5 scripts
boxiang-wang
ARTtransfer:Adaptive and Robust Pipeline for Transfer Learning
Adaptive and Robust Transfer Learning (ART) is a flexible framework for transfer learning that integrates information from auxiliary data sources to improve model performance on primary tasks. It is designed to be robust against negative transfer by including the non-transfer model in the candidate pool, ensuring stable performance even when auxiliary datasets are less informative. See the paper, Wang, Wu, and Ye (2023) <doi:10.1002/sta4.582>.
Maintained by Boxiang Wang. Last updated 2 months ago.
4.40 score 1 scripts
techtonique
crossvalidation:Generic cross-validation functions
Generic functions for cross-validation of Statistical/Machine Learning models
Maintained by T. Moudiki. Last updated 7 months ago.
2 stars 3.64 score 11 scripts