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bioc
MsCoreUtils:Core Utils for Mass Spectrometry Data
MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning, baseline estimation), quantitative aggregation functions (median polish, robust summarisation, ...), missing data imputation, data normalisation (quantiles, vsn, ...), misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages.
Maintained by RforMassSpectrometry Package Maintainer. Last updated 11 days ago.
infrastructureproteomicsmassspectrometrymetabolomicsbioconductormass-spectrometryutils
16 stars 10.57 score 41 scripts 71 dependents
columbia-prime
pcpr:Principal Component Pursuit for Environmental Epidemiology
Implementation of the pattern recognition technique Principal Component Pursuit tailored to environmental health data, as described in Gibson et al (2022) <doi:10.1289/EHP10479>.
Maintained by Lawrence G. Chillrud. Last updated 5 days ago.
dimensionality-reductionenvironmental-healthenvironmental-mixturesepidemiologymachine-learningpattern-recognitionpublic-healthstatistical-modeling
4 stars 5.48 score
selbouhaddani-umc
OmicsPLS:Data Integration with Two-Way Orthogonal Partial Least Squares
Performs the O2PLS data integration method for two datasets, yielding joint and data-specific parts for each dataset. The algorithm automatically switches to a memory-efficient approach to fit O2PLS to high dimensional data. It provides a rigorous and a faster alternative cross-validation method to select the number of components, as well as functions to report proportions of explained variation and to construct plots of the results. See the software article by el Bouhaddani et al (2018) <doi:10.1186/s12859-018-2371-3>, and Trygg and Wold (2003) <doi:10.1002/cem.775>. It also performs Sparse Group (Penalized) O2PLS, see Gu et al (2020) <doi:10.1186/s12859-021-03958-3> and cross-validation for the degree of sparsity.
Maintained by Said el Bouhaddani. Last updated 7 hours ago.
3.84 score 57 scripts 1 dependents