Showing 20 of total 20 results (show query)
bioc
ropls:PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data
Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment).
Maintained by Etienne A. Thevenot. Last updated 5 months ago.
regressionclassificationprincipalcomponenttranscriptomicsproteomicsmetabolomicslipidomicsmassspectrometryimmunooncology
7.56 score 210 scripts 8 dependentsbioc
lipidr:Data Mining and Analysis of Lipidomics Datasets
lipidr an easy-to-use R package implementing a complete workflow for downstream analysis of targeted and untargeted lipidomics data. lipidomics results can be imported into lipidr as a numerical matrix or a Skyline export, allowing integration into current analysis frameworks. Data mining of lipidomics datasets is enabled through integration with Metabolomics Workbench API. lipidr allows data inspection, normalization, univariate and multivariate analysis, displaying informative visualizations. lipidr also implements a novel Lipid Set Enrichment Analysis (LSEA), harnessing molecular information such as lipid class, total chain length and unsaturation.
Maintained by Ahmed Mohamed. Last updated 5 months ago.
lipidomicsmassspectrometrynormalizationqualitycontrolvisualizationbioconductor
30 stars 7.46 score 40 scriptsbioc
multiHiCcompare:Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available
multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner.
Maintained by Mikhail Dozmorov. Last updated 5 months ago.
softwarehicsequencingnormalization
9 stars 7.30 score 37 scripts 2 dependentsbioc
MultiDataSet:Implementation of MultiDataSet and ResultSet
Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages.
Maintained by Xavier Escribà Montagut. Last updated 5 months ago.
6.45 score 28 scripts 10 dependentsbioc
HiCDOC:A/B compartment detection and differential analysis
HiCDOC normalizes intrachromosomal Hi-C matrices, uses unsupervised learning to predict A/B compartments from multiple replicates, and detects significant compartment changes between experiment conditions. It provides a collection of functions assembled into a pipeline to filter and normalize the data, predict the compartments and visualize the results. It accepts several type of data: tabular `.tsv` files, Cooler `.cool` or `.mcool` files, Juicer `.hic` files or HiC-Pro `.matrix` and `.bed` files.
Maintained by Maigné Élise. Last updated 4 months ago.
hicdna3dstructurenormalizationsequencingsoftwareclusteringcpp
4 stars 5.86 score 6 scripts 1 dependentsbioc
CNVRanger:Summarization and expression/phenotype association of CNV ranges
The CNVRanger package implements a comprehensive tool suite for CNV analysis. This includes functionality for summarizing individual CNV calls across a population, assessing overlap with functional genomic regions, and association analysis with gene expression and quantitative phenotypes.
Maintained by Ludwig Geistlinger. Last updated 5 months ago.
copynumbervariationdifferentialexpressiongeneexpressiongenomewideassociationgenomicvariationmicroarrayrnaseqsnpbioconductor-packageu24ca289073
7 stars 5.77 score 12 scriptsbioc
ASICS:Automatic Statistical Identification in Complex Spectra
With a set of pure metabolite reference spectra, ASICS quantifies concentration of metabolites in a complex spectrum. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty. The method and its statistical properties are described in Tardivel et al. (2017) <doi:10.1007/s11306-017-1244-5>.
Maintained by Gaëlle Lefort. Last updated 5 months ago.
softwaredataimportcheminformaticsmetabolomics
5.08 score 30 scriptsbioc
phenomis:Postprocessing and univariate analysis of omics data
The 'phenomis' package provides methods to perform post-processing (i.e. quality control and normalization) as well as univariate statistical analysis of single and multi-omics data sets. These methods include quality control metrics, signal drift and batch effect correction, intensity transformation, univariate hypothesis testing, but also clustering (as well as annotation of metabolomics data). The data are handled in the standard Bioconductor formats (i.e. SummarizedExperiment and MultiAssayExperiment for single and multi-omics datasets, respectively; the alternative ExpressionSet and MultiDataSet formats are also supported for convenience). As a result, all methods can be readily chained as workflows. The pipeline can be further enriched by multivariate analysis and feature selection, by using the 'ropls' and 'biosigner' packages, which support the same formats. Data can be conveniently imported from and exported to text files. Although the methods were initially targeted to metabolomics data, most of the methods can be applied to other types of omics data (e.g., transcriptomics, proteomics).
Maintained by Etienne A. Thevenot. Last updated 5 months ago.
batcheffectclusteringcoveragekeggmassspectrometrymetabolomicsnormalizationproteomicsqualitycontrolsequencingstatisticalmethodtranscriptomics
4.40 score 6 scriptsbioc
rqt:rqt: utilities for gene-level meta-analysis
Despite the recent advances of modern GWAS methods, it still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant. The R- package rqt offers gene-level GWAS meta-analysis. For more information, see: "Gene-set association tests for next-generation sequencing data" by Lee et al (2016), Bioinformatics, 32(17), i611-i619, <doi:10.1093/bioinformatics/btw429>.
Maintained by Ilya Zhbannikov. Last updated 5 months ago.
genomewideassociationregressionsurvivalprincipalcomponentstatisticalmethodsequencing
2 stars 4.30 score 4 scriptsbioc
omicRexposome:Exposome and omic data associatin and integration analysis
omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA).
Maintained by Xavier Escribà Montagut. Last updated 5 months ago.
immunooncologyworkflowstepmultiplecomparisonvisualizationgeneexpressiondifferentialexpressiondifferentialmethylationgeneregulationepigeneticsproteomicstranscriptomicsstatisticalmethodregression
4.30 score 5 scriptsbioc
biosigner:Signature discovery from omics data
Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics.
Maintained by Etienne A. Thevenot. Last updated 5 months ago.
classificationfeatureextractiontranscriptomicsproteomicsmetabolomicslipidomicsmassspectrometry
4.00 score 10 scripts
johnchaston
MAGNAMWAR:A Pipeline for Meta-Genome Wide Association
Correlates variation within the meta-genome to target species phenotype variations in meta-genome with association studies. Follows the pipeline described in Chaston, J.M. et al. (2014) <doi:10.1128/mBio.01631-14>.
Maintained by John Chaston. Last updated 7 years ago.
3.90 score 16 scriptsbioc
MultiBaC:Multiomic Batch effect Correction
MultiBaC is a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. MultiBaC is the first Batch effect correction algorithm that dealing with batch effect correction in multiomics datasets. MultiBaC is able to remove batch effects across different omics generated within separate batches provided that at least one common omic data type is included in all the batches considered.
Maintained by The package maintainer. Last updated 5 months ago.
softwarestatisticalmethodprincipalcomponentdatarepresentationgeneexpressiontranscriptionbatcheffect
3.30 score 7 scripts
umich-cphds
medScan:Large Scale Single Mediator Hypothesis Testing
A collection of methods for large scale single mediator hypothesis testing. The six included methods for testing the mediation effect are Sobel's test, Max P test, joint significance test under the composite null hypothesis, high dimensional mediation testing, divide-aggregate composite null test, and Sobel's test under the composite null hypothesis. Du et al (2023) <doi:10.1002/gepi.22510>.
Maintained by Michael Kleinsasser. Last updated 1 years ago.
2.70 score 5 scripts
aariq
chemhelper:Helper Functions For Dealing With GCMS and LCMS data from IonAnalytics
Provides helper functions for parsing data exported from IonAnalytics, calculating retention indecies, and other miscelanous helper functions to assist in data wrangling.
Maintained by Eric Scott. Last updated 2 years ago.
analytical-chemistrymultivariate-analysispls
4 stars 2.41 score 13 scripts
cran
metaGE:Meta-Analysis for Detecting Genotype x Environment Associations
Provides functions to perform all steps of genome-wide association meta-analysis for studying Genotype x Environment interactions, from collecting the data to the manhattan plot. The procedure accounts for the potential correlation between studies. In addition to the Fixed and Random models, one can investigate the relationship between QTL effects and some qualitative or quantitative covariate via the test of contrast and the meta-regression, respectively. The methodology is available from: (De Walsche, A., et al. (2025) \doi{10.1371/journal.pgen.1011553}).
Maintained by Annaïg De Walsche. Last updated 1 months ago.
2.30 score
tyler-hansen
HodgesTools:Common Use Tools for Genomic Analysis
Built by Hodges lab members for current and future Hodges lab members. Other individuals are welcome to use as well. Provides useful functions that the lab uses everyday to analyze various genomic datasets. Critically, only general use functions are provided; functions specific to a given technique are reserved for a separate package. As the lab grows, we expect to continue adding functions to the package to build on previous lab members code.
Maintained by Tyler Hansen. Last updated 2 years ago.
1.70 score 1 scripts
rickhelmus
KPIC:Mass Spectrometry-Based Metabolomics Using Pure Ion Chromatograms
KPIC2 is an effective platform for LC-MS based metabolomics using pure ion chromatograms, which is developed for metabolomics studies. KPIC2 can detect pure ions accurately, align PICs across samples, group PICs to annotate isotope and adduct PICs, fill missing peaks and pattern recognition. High-resolution mass spectrometers like TOF and Orbitrap are more suitable.
Maintained by Hongchao Ji. Last updated 2 years ago.
1.70 score 3 scriptscran
treediff:Testing Differences Between Families of Trees
Perform test to detect differences in structure between families of trees. The method is based on cophenetic distances and aggregated Student's tests.
Maintained by Nathalie Vialaneix. Last updated 1 years ago.
1.00 score
nie-xiuquan
EMAS:Epigenome-Wide Mediation Analysis Study
DNA methylation is essential for human, and environment can change the DNA methylation and affect body status. Epigenome-Wide Mediation Analysis Study (EMAS) can find potential mediator CpG sites between exposure (x) and outcome (y) in epigenome-wide. For more information on the methods we used, please see the following references: Tingley, D. (2014) <doi:10.18637/jss.v059.i05>, Turner, S. D. (2018) <doi:10.21105/joss.00731>, Rosseel, D. (2012) <doi:10.18637/jss.v048.i02>.
Maintained by Xiuquan Nie. Last updated 3 years ago.
1.00 score