R-universe search: lipids

bioc

LOBSTAHS:Lipid and Oxylipin Biomarker Screening through Adduct Hierarchy Sequences

LOBSTAHS is a multifunction package for screening, annotation, and putative identification of mass spectral features in large, HPLC-MS lipid datasets. In silico data for a wide range of lipids, oxidized lipids, and oxylipins can be generated from user-supplied structural criteria with a database generation function. LOBSTAHS then applies these databases to assign putative compound identities to features in any high-mass accuracy dataset that has been processed using xcms and CAMERA. Users can then apply a series of orthogonal screening criteria based on adduct ion formation patterns, chromatographic retention time, and other properties, to evaluate and assign confidence scores to this list of preliminary assignments. During the screening routine, LOBSTAHS rejects assignments that do not meet the specified criteria, identifies potential isomers and isobars, and assigns a variety of annotation codes to assist the user in evaluating the accuracy of each assignment.

Maintained by Henry Holm. Last updated 5 months ago.

immunooncology massspectrometry metabolomics lipidomics dataimport adduct algae bioconductor hplc-esi-ms lipid mass-spectrometry oxidative-stress-biomarkers oxidized-lipids oxylipins plankton

29.5 match 8 stars 6.56 score 9 scripts

bioc

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.

lipidomics massspectrometry normalization qualitycontrol visualization bioconductor

17.7 match 29 stars 7.44 score 40 scripts

maialba3

LipidMS:Lipid Annotation for LC-MS/MS DDA or DIA Data

Lipid annotation in untargeted LC-MS lipidomics based on fragmentation rules. Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) <doi:10.1021/acs.analchem.8b03409>.

Maintained by M Isabel Alcoriza-Balaguer. Last updated 7 months ago.

cpp

18.1 match 2 stars 5.33 score 12 scripts 1 dependents

pmartr

pmartR:Panomics Marketplace - Quality Control and Statistical Analysis for Panomics Data

Provides functionality for quality control processing and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data, as well as RNA-seq based count data and nuclear magnetic resonance (NMR) data. This includes data transformation, specification of groups that are to be compared against each other, filtering of features and/or samples, data normalization, data summarization (correlation, PCA), and statistical comparisons between defined groups. Implements methods described in: Webb-Robertson et al. (2014) <doi:10.1074/mcp.M113.030932>. Webb-Robertson et al. (2011) <doi:10.1002/pmic.201100078>. Matzke et al. (2011) <doi:10.1093/bioinformatics/btr479>. Matzke et al. (2013) <doi:10.1002/pmic.201200269>. Polpitiya et al. (2008) <doi:10.1093/bioinformatics/btn217>. Webb-Robertson et al. (2010) <doi:10.1021/pr1005247>.

Maintained by Lisa Bramer. Last updated 3 days ago.

data-summarization lipids mass-spectrometry metabolites metabolomics-data peptides proteins rna-seq-analysis openblas cpp

10.0 match 40 stars 7.69 score 144 scripts

bioc

rgoslin:Lipid Shorthand Name Parsing and Normalization

The R implementation for the Grammar of Succint Lipid Nomenclature parses different short hand notation dialects for lipid names. It normalizes them to a standard name. It further provides calculated monoisotopic masses and sum formulas for each successfully parsed lipid name and supplements it with LIPID MAPS Category and Class information. Also, the structural level and further structural details about the head group, fatty acyls and functional groups are returned, where applicable.

Maintained by Nils Hoffmann. Last updated 5 months ago.

software lipidomics metabolomics preprocessing normalization massspectrometry cpp

13.1 match 5 stars 5.64 score 22 scripts

usepa

httk:High-Throughput Toxicokinetics

Pre-made models that can be rapidly tailored to various chemicals and species using chemical-specific in vitro data and physiological information. These tools allow incorporation of chemical toxicokinetics ("TK") and in vitro-in vivo extrapolation ("IVIVE") into bioinformatics, as described by Pearce et al. (2017) (<doi:10.18637/jss.v079.i04>). Chemical-specific in vitro data characterizing toxicokinetics have been obtained from relatively high-throughput experiments. The chemical-independent ("generic") physiologically-based ("PBTK") and empirical (for example, one compartment) "TK" models included here can be parameterized with in vitro data or in silico predictions which are provided for thousands of chemicals, multiple exposure routes, and various species. High throughput toxicokinetics ("HTTK") is the combination of in vitro data and generic models. We establish the expected accuracy of HTTK for chemicals without in vivo data through statistical evaluation of HTTK predictions for chemicals where in vivo data do exist. The models are systems of ordinary differential equations that are developed in MCSim and solved using compiled (C-based) code for speed. A Monte Carlo sampler is included for simulating human biological variability (Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>) and propagating parameter uncertainty (Wambaugh et al., 2019 <doi:10.1093/toxsci/kfz205>). Empirically calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017 <doi:10.1007/s10928-017-9548-7>). These functions and data provide a set of tools for using IVIVE to convert concentrations from high-throughput screening experiments (for example, Tox21, ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK") (Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).

Maintained by John Wambaugh. Last updated 1 months ago.

comptox ord

3.4 match 27 stars 10.22 score 307 scripts 1 dependents

integrated-inferences

CausalQueries:Make, Update, and Query Binary Causal Models

Users can declare causal models over binary nodes, update beliefs about causal types given data, and calculate arbitrary queries. Updating is implemented in 'stan'. See Humphreys and Jacobs, 2023, Integrated Inferences (<DOI: 10.1017/9781316718636>) and Pearl, 2009 Causality (<DOI:10.1017/CBO9780511803161>).

Maintained by Till Tietz. Last updated 22 days ago.

bayes causal dags mixedmethods stan cpp

3.6 match 27 stars 9.03 score 54 scripts

cran

lipidomeR:Integrative Visualizations of the Lipidome

Create lipidome-wide heatmaps of statistics with the 'lipidomeR'. The 'lipidomeR' provides a streamlined pipeline for the systematic interpretation of the lipidome through publication-ready visualizations of regression models fitted on lipidomics data. With 'lipidomeR', associations between covariates and the lipidome can be interpreted systematically and intuitively through heatmaps, where lipids are categorized by the lipid class and are presented on two-dimensional maps organized by the lipid size and level of saturation. This way, the 'lipidomeR' helps you gain an immediate understanding of the multivariate patterns in the lipidome already at first glance. You can create lipidome-wide heatmaps of statistical associations, changes, differences, variation, or other lipid-specific values. The heatmaps are provided with publication-ready quality and the results behind the visualizations are based on rigorous statistical models.

Maintained by Tommi Suvitaival. Last updated 5 years ago.

17.5 match 1.70 score 1 scripts

martin3141

spant:MR Spectroscopy Analysis Tools

Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>.

Maintained by Martin Wilson. Last updated 30 days ago.

brain mri mrs mrshub spectroscopy fortran

3.4 match 24 stars 8.55 score 81 scripts

bioc

gatom:Finding an Active Metabolic Module in Atom Transition Network

This package implements a metabolic network analysis pipeline to identify an active metabolic module based on high throughput data. The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest. The package further provides functions for module post-processing, annotation and visualization.

Maintained by Alexey Sergushichev. Last updated 5 months ago.

geneexpression differentialexpression pathways network

4.5 match 6 stars 5.26 score 8 scripts

mrcieu

MVMR:MVMR

An R package for performing multivariable Mendelian randomization analyses.

Maintained by Wes Spiller. Last updated 2 months ago.

3.2 match 45 stars 6.65 score 166 scripts 1 dependents

cstewartgh

QFASA:Quantitative Fatty Acid Signature Analysis

Accurate estimates of the diets of predators are required in many areas of ecology, but for many species current methods are imprecise, limited to the last meal, and often biased. The diversity of fatty acids and their patterns in organisms, coupled with the narrow limitations on their biosynthesis, properties of digestion in monogastric animals, and the prevalence of large storage reservoirs of lipid in many predators, led to the development of quantitative fatty acid signature analysis (QFASA) to study predator diets.

Maintained by Connie Stewart. Last updated 7 months ago.

4.4 match 1 stars 4.83 score 17 scripts

mrcieu

mrbayes:Bayesian Summary Data Models for Mendelian Randomization Studies

Bayesian estimation of inverse variance weighted (IVW), Burgess et al. (2013) <doi:10.1002/gepi.21758>, and MR-Egger, Bowden et al. (2015) <doi:10.1093/ije/dyv080>, summary data models for Mendelian randomization analyses.

Maintained by Tom Palmer. Last updated 11 days ago.

cpp

3.2 match 4 stars 5.45 score 2 scripts

newystats

sld:Estimation and Use of the Quantile-Based Skew Logistic Distribution

The skew logistic distribution is a quantile-defined generalisation of the logistic distribution (van Staden and King 2015). Provides random numbers, quantiles, probabilities, densities and density quantiles for the distribution. It provides Quantile-Quantile plots and method of L-Moments estimation (including asymptotic standard errors) for the distribution.

Maintained by Robert King. Last updated 3 years ago.

5.0 match 3.29 score 13 scripts 1 dependents

bioc

signifinder:Collection and implementation of public transcriptional cancer signatures

signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows to compute a variety of signatures, based on gene expression values, and return single-sample scores. Currently, signifinder contains more than 60 distinct signatures collected from the literature, relating to multiple tumors and multiple cancer processes.

Maintained by Stefania Pirrotta. Last updated 2 months ago.

geneexpression genetarget immunooncology biomedicalinformatics rnaseq microarray reportwriting visualization singlecell spatial genesignaling

2.0 match 7 stars 6.40 score 15 scripts

mvogel78

childsds:Data and Methods Around Reference Values in Pediatrics

Calculation of standard deviation scores and percentiles adduced from different standards (WHO, UK, Germany, Italy, China, etc). Also, references for laboratory values in children and adults are available, e.g., serum lipids, iron-related blood parameters, IGF, liver enzymes. See package documentation for full list.

Maintained by Mandy Vogel. Last updated 2 months ago.

4.3 match 2.83 score 51 scripts

bioc

MetMashR:Metabolite Mashing with R

A package to merge, filter sort, organise and otherwise mash together metabolite annotation tables. Metabolite annotations can be imported from multiple sources (software) and combined using workflow steps based on S4 class templates derived from the `struct` package. Other modular workflow steps such as filtering, merging, splitting, normalisation and rest-api queries are included.

Maintained by Gavin Rhys Lloyd. Last updated 5 months ago.

workflowstep metabolomics kegg

2.0 match 2 stars 5.81 score 5 scripts

sb452

MendelianRandomization:Mendelian Randomization Package

Encodes several methods for performing Mendelian randomization analyses with summarized data. Summarized data on genetic associations with the exposure and with the outcome can be obtained from large consortia. These data can be used for obtaining causal estimates using instrumental variable methods.

Maintained by Stephen Burgess. Last updated 2 years ago.

openblas cpp

1.7 match 1 stars 6.83 score 940 scripts 1 dependents

mrcieu

RMVMR:RMVMR

An R package for performing radial multivariable Mendelian randomization analyses.

Maintained by Wes Spiller. Last updated 5 months ago.

3.2 match 12 stars 3.37 score 13 scripts

yufree

enviGCMS:GC/LC-MS Data Analysis for Environmental Science

Gas/Liquid Chromatography-Mass Spectrometer(GC/LC-MS) Data Analysis for Environmental Science. This package covered topics such molecular isotope ratio, matrix effects and Short-Chain Chlorinated Paraffins analysis etc. in environmental analysis.

Maintained by Miao YU. Last updated 2 months ago.

environment mass-spectrometry metabolomics

1.7 match 17 stars 6.49 score 30 scripts 1 dependents

zhanyq

nlrr:Non-Linear Relative Risk Estimation and Plotting

Estimate the non-linear odds ratio and plot it against a continuous exposure.

Maintained by Yiqiang Zhan. Last updated 9 years ago.

4.5 match 1.70 score 2 scripts

scar

sohungry:Southern Ocean Diet and Energetics Data

Provides access to data from the SCAR Southern Ocean Diet and Energetics Database.

Maintained by Ben Raymond. Last updated 1 years ago.

1.3 match 5 stars 3.40 score 8 scripts

mingzhuotina

lipidmapsR:Lipid Maps Rest Service

Lipid Maps Rest service. Researchers can access the Lipid Maps Rest service programmatically and conveniently integrate it into the current workflow or packages.

Maintained by Mingzhuo Tian. Last updated 3 years ago.

4.0 match 1.00 score

sritchie73

ukbnmr:Removal of Unwanted Technical Variation from UK Biobank NMR Metabolomics Biomarker Data

A suite of utilities for working with the UK Biobank <https://www.ukbiobank.ac.uk/> Nuclear Magnetic Resonance spectroscopy (NMR) metabolomics data <https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=220>. Includes functions for extracting biomarkers from decoded UK Biobank field data, removing unwanted technical variation from biomarker concentrations, computing an extended set of lipid, fatty acid, and cholesterol fractions, and for re-deriving composite biomarkers and ratios after adjusting data for unwanted biological variation. For further details on methods see Ritchie SC et al. Sci Data (2023) <doi:10.1038/s41597-023-01949-y>.

Maintained by Scott C Ritchie. Last updated 4 months ago.

0.5 match 33 stars 5.82 score 6 scripts

mingshi1

LipidomicsR:Elegant Tools for Processing and Visualization of Lipidomics Data

An elegant tool for processing and visualizing lipidomics data generated by mass spectrometry. 'LipidomicsR' simplifies channel and replicate handling while providing thorough lipid species annotation. Its visualization capabilities encompass principal components analysis plots, heatmaps, volcano plots, and radar plots, enabling concise data summarization and quality assessment. Additionally, it can generate bar plots and line plots to visualize the abundance of each lipid species.

Maintained by Hengyu Zhu. Last updated 11 months ago.

0.8 match 3.30 score 1 scripts

sufyansuleman

InsuSensCalc:Insulin Sensitivity Indices Calculator

It facilitates the calculation of 40 different insulin sensitivity indices based on fasting, oral glucose tolerance test (OGTT), lipid (adipose), and tracer (palmitate and glycerol rate) and dxa (fat mass) measurement values. It enables easy and accurate assessment of insulin sensitivity, critical for understanding and managing metabolic disorders like diabetes and obesity. Indices calculated are described in Gastaldelli (2022). <doi:10.1002/oby.23503> and Lorenzo (2010). <doi:10.1210/jc.2010-1144>.

Maintained by Sufyan Suleman. Last updated 12 months ago.

0.5 match 1 stars 4.00 score 3 scripts

arunabhacodes

MPGE:A Two-Step Approach to Testing Overall Effect of Gene-Environment Interaction for Multiple Phenotypes

Interaction between a genetic variant (e.g., a single nucleotide polymorphism) and an environmental variable (e.g., physical activity) can have a shared effect on multiple phenotypes (e.g., blood lipids). We implement a two-step method to test for an overall interaction effect on multiple phenotypes. In first step, the method tests for an overall marginal genetic association between the genetic variant and the multivariate phenotype. The genetic variants which show an evidence of marginal overall genetic effect in the first step are prioritized while testing for an overall gene-environment interaction effect in the second step. Methodology is available from: A Majumdar, KS Burch, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) <doi:10.1101/2020.07.06.190256>.

Maintained by Arunabha Majumdar. Last updated 4 years ago.

0.5 match 1 stars 3.70 score 1 scripts

sb452

MRZero:Diet Mendelian Randomization

Encodes several methods for performing Mendelian randomization analyses with summarized data. Similar to the 'MendelianRandomization' package, but with fewer bells and whistles, and less frequent updates. As described in Yavorska (2017) <doi:10.1093/ije/dyx034> and Broadbent (2020) <doi:10.12688/wellcomeopenres.16374.2>.

Maintained by Stephen Burgess. Last updated 11 months ago.

1.7 match 1.00 score

bromaghin

qfasar:Quantitative Fatty Acid Signature Analysis in R

An implementation of Quantitative Fatty Acid Signature Analysis (QFASA) in R. QFASA is a method of estimating the diet composition of predators. The fundamental unit of information in QFASA is a fatty acid signature (signature), which is a vector of proportions describing the composition of fatty acids within lipids. Signature data from at least one predator and from samples of all potential prey types are required. Calibration coefficients, which adjust for the differential metabolism of individual fatty acids by predators, are also required. Given those data inputs, a predator signature is modeled as a mixture of prey signatures and its diet estimate is obtained as the mixture that minimizes a measure of distance between the observed and modeled signatures. A variety of estimation options and simulation capabilities are implemented. Please refer to the vignette for additional details and references.

Maintained by Jeffrey F. Bromaghin. Last updated 5 years ago.

0.5 match 2.90 score 40 scripts

cran

flippant:Dithionite Scramblase Assay Analysis

The lipid scrambling activity of protein extracts and purified scramblases is often determined using a fluorescence-based assay involving many manual steps. flippant offers an integrated solution for the analysis and publication-grade graphical presentation of dithionite scramblase assays, as well as a platform for review, dissemination and extension of the strategies it employs. The package's name derives from a play on the fact that lipid scrambling is also sometimes referred to as 'flipping'. The package is originally published as Cotton, R.J., Ploier, B., Goren, M.A., Menon, A.K., and Graumann, J. (2017). "flippant–An R package for the automated analysis of fluorescence-based scramblase assays." BMC Bioinformatics 18, 146. <DOI:10.1186/s12859-017-1542-y>.

Maintained by Johannes Graumann. Last updated 11 months ago.

0.8 match 1.00 score

cran

FishProxCompAnalyzer:Proximate Composition Analysis of Fish and Feed Ingredients

The proximate composition analysis is the quantification of main components that constitutes nutritional profile of any food and food products including fish, shellfish, fish feed and their ingredients. Understanding this composition is essential for evaluating their nutritional value and for making informed dietary choices. The primary components typically analyzed include; moisture/ water in foods, crude protein, crude fat/ lipid, total ash, fiber and carbohydrates AOAC(2005,ISBN:0-935584-77-3). In case of fish, shellfish and its products, the proximate composition consists of four primary constituents - water, protein, fat, and ash (mostly minerals). Fish exhibit significant variation in their chemical makeup based on age, sex, environment, and season, both within the same species and between individual fish. There is minimal fluctuation in the content of ash and protein. The lipid concentration varies remarkably and is inversely correlated with the water content. In case of fish, carbohydrates are present in minor quantity so that are quantified by subtracting total of other components from 100 to get percentage of carbohydrates.

Maintained by Tanuj Misra. Last updated 7 months ago.

0.8 match 1.00 score