Showing 200 of total 812 results (show query)

covid19datahub

COVID19:COVID-19 Data Hub

Unified datasets for a better understanding of COVID-19.

Maintained by Emanuele Guidotti. Last updated 1 months ago.

2019-ncovcoronaviruscovid-19covid-datacovid19-data

252 stars 11.08 score 265 scripts

john-d-fox

Rcmdr:R Commander

A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.

Maintained by John Fox. Last updated 5 months ago.

4 stars 9.48 score 636 scripts 38 dependents

pik-piam

remind2:The REMIND R package (2nd generation)

Contains the REMIND-specific routines for data and model output manipulation.

Maintained by Renato Rodrigues. Last updated 1 days ago.

8.87 score 161 scripts 5 dependents

pik-piam

mip:Comparison of multi-model runs

Package contains generic functions to produce comparison plots of multi-model runs.

Maintained by David Klein. Last updated 4 days ago.

1 stars 8.07 score 70 scripts 21 dependents

pik-piam

magpie4:MAgPIE outputs R package for MAgPIE version 4.x

Common output routines for extracting results from the MAgPIE framework (versions 4.x).

Maintained by Benjamin Leon Bodirsky. Last updated 1 days ago.

2 stars 7.89 score 254 scripts 9 dependents

mrc-ide

naomi:Naomi Model for Subnational HIV Estimates

This package implements the Naomi model for subnational HIV estimates.

Maintained by Jeff Eaton. Last updated 19 days ago.

cpp

9 stars 7.74 score 54 scripts 2 dependents

eltebioinformatics

mulea:Enrichment Analysis Using Multiple Ontologies and False Discovery Rate

Background - Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. Results - mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. Conclusions - mulea is distributed as a CRAN R package. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

Maintained by Tamas Stirling. Last updated 4 months ago.

annotationdifferentialexpressiongeneexpressiongenesetenrichmentgographandnetworkmultiplecomparisonpathwaysreactomesoftwaretranscriptionvisualizationenrichmentenrichment-analysisfunctional-enrichment-analysisgene-set-enrichmentontologiestranscriptomicscpp

28 stars 7.36 score 34 scripts

pik-piam

piamInterfaces:Project specific interfaces to REMIND / MAgPIE

Project specific interfaces to REMIND / MAgPIE.

Maintained by Falk Benke. Last updated 5 days ago.

6.64 score 38 scripts 7 dependents

huanglabumn

oncoPredict:Drug Response Modeling and Biomarker Discovery

Allows for building drug response models using screening data between bulk RNA-Seq and a drug response metric and two additional tools for biomarker discovery that have been developed by the Huang Laboratory at University of Minnesota. There are 3 main functions within this package. (1) calcPhenotype is used to build drug response models on RNA-Seq data and impute them on any other RNA-Seq dataset given to the model. (2) GLDS is used to calculate the general level of drug sensitivity, which can improve biomarker discovery. (3) IDWAS can take the results from calcPhenotype and link the imputed response back to available genomic (mutation and CNV alterations) to identify biomarkers. Each of these functions comes from a paper from the Huang research laboratory. Below gives the relevant paper for each function. calcPhenotype - Geeleher et al, Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. GLDS - Geeleher et al, Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models. IDWAS - Geeleher et al, Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.

Maintained by Robert Gruener. Last updated 12 months ago.

svapreprocesscorestringrbiomartgenefilterorg.hs.eg.dbgenomicfeaturestxdb.hsapiens.ucsc.hg19.knowngenetcgabiolinksbiocgenericsgenomicrangesirangess4vectors

18 stars 6.47 score 41 scripts

pik-piam

mrwater:madrat based MAgPIE water Input Data Library

Provides functions for MAgPIE cellular input data generation and stand-alone water calculations.

Maintained by Felicitas Beier. Last updated 5 months ago.

6.45 score 4 scripts 3 dependents

pik-piam

mrremind:MadRat REMIND Input Data Package

The mrremind packages contains data preprocessing for the REMIND model.

Maintained by Lavinia Baumstark. Last updated 1 days ago.

4 stars 6.25 score 15 scripts 1 dependents

eikeluedeling

chillR:Statistical Methods for Phenology Analysis in Temperate Fruit Trees

The phenology of plants (i.e. the timing of their annual life phases) depends on climatic cues. For temperate trees and many other plants, spring phases, such as leaf emergence and flowering, have been found to result from the effects of both cool (chilling) conditions and heat. Fruit tree scientists (pomologists) have developed some metrics to quantify chilling and heat (e.g. see Luedeling (2012) <doi:10.1016/j.scienta.2012.07.011>). 'chillR' contains functions for processing temperature records into chilling (Chilling Hours, Utah Chill Units and Chill Portions) and heat units (Growing Degree Hours). Regarding chilling metrics, Chill Portions are often considered the most promising, but they are difficult to calculate. This package makes it easy. 'chillR' also contains procedures for conducting a PLS analysis relating phenological dates (e.g. bloom dates) to either mean temperatures or mean chill and heat accumulation rates, based on long-term weather and phenology records (Luedeling and Gassner (2012) <doi:10.1016/j.agrformet.2011.10.020>). As of version 0.65, it also includes functions for generating weather scenarios with a weather generator, for conducting climate change analyses for temperature-based climatic metrics and for plotting results from such analyses. Since version 0.70, 'chillR' contains a function for interpolating hourly temperature records.

Maintained by Eike Luedeling. Last updated 5 months ago.

cpp

3 stars 6.13 score 346 scripts 1 dependents

pik-piam

luplot:Landuse Plot Library

Some useful functions to plot data such as a map plot function for MAgPIE objects.

Maintained by Benjamin Bodirsky. Last updated 2 months ago.

6.09 score 124 scripts 11 dependents

yonicd

shinyHeatmaply:Deploy 'heatmaply' using 'shiny'

Access functionality of the 'heatmaply' package through 'Shiny UI'.

Maintained by Jonathan Sidi. Last updated 5 years ago.

47 stars 5.95 score 42 scripts 1 dependents

bioc

SCOPE:A normalization and copy number estimation method for single-cell DNA sequencing

Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we propose SCOPE, a normalization and copy number estimation method for scDNA-seq data. The distinguishing features of SCOPE include: (i) utilization of cell-specific Gini coefficients for quality controls and for identification of normal/diploid cells, which are further used as negative control samples in a Poisson latent factor model for normalization; (ii) modeling of GC content bias using an expectation-maximization algorithm embedded in the Poisson generalized linear models, which accounts for the different copy number states along the genome; (iii) a cross-sample iterative segmentation procedure to identify breakpoints that are shared across cells from the same genetic background.

Maintained by Rujin Wang. Last updated 5 months ago.

singlecellnormalizationcopynumbervariationsequencingwholegenomecoveragealignmentqualitycontroldataimportdnaseq

5.92 score 84 scripts

mrc-ide

hintr:R API for calling naomi district level HIV model

R API for calling naomi district level HIV model.

Maintained by Robert Ashton. Last updated 18 days ago.

2 stars 5.80 score 2 scripts 1 dependents

dpc10ster

RJafroc:Artificial Intelligence Systems and Observer Performance

Analyzing the performance of artificial intelligence (AI) systems/algorithms characterized by a 'search-and-report' strategy. Historically observer performance has dealt with measuring radiologists' performances in search tasks, e.g., searching for lesions in medical images and reporting them, but the implicit location information has been ignored. The implemented methods apply to analyzing the absolute and relative performances of AI systems, comparing AI performance to a group of human readers or optimizing the reporting threshold of an AI system. In addition to performing historical receiver operating receiver operating characteristic (ROC) analysis (localization information ignored), the software also performs free-response receiver operating characteristic (FROC) analysis, where lesion localization information is used. A book using the software has been published: Chakraborty DP: Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples, Taylor-Francis LLC; 2017: <https://www.routledge.com/Observer-Performance-Methods-for-Diagnostic-Imaging-Foundations-Modeling/Chakraborty/p/book/9781482214840>. Online updates to this book, which use the software, are at <https://dpc10ster.github.io/RJafrocQuickStart/>, <https://dpc10ster.github.io/RJafrocRocBook/> and at <https://dpc10ster.github.io/RJafrocFrocBook/>. Supported data collection paradigms are the ROC, FROC and the location ROC (LROC). ROC data consists of single ratings per images, where a rating is the perceived confidence level that the image is that of a diseased patient. An ROC curve is a plot of true positive fraction vs. false positive fraction. FROC data consists of a variable number (zero or more) of mark-rating pairs per image, where a mark is the location of a reported suspicious region and the rating is the confidence level that it is a real lesion. LROC data consists of a rating and a location of the most suspicious region, for every image. Four models of observer performance, and curve-fitting software, are implemented: the binormal model (BM), the contaminated binormal model (CBM), the correlated contaminated binormal model (CORCBM), and the radiological search model (RSM). Unlike the binormal model, CBM, CORCBM and RSM predict 'proper' ROC curves that do not inappropriately cross the chance diagonal. Additionally, RSM parameters are related to search performance (not measured in conventional ROC analysis) and classification performance. Search performance refers to finding lesions, i.e., true positives, while simultaneously not finding false positive locations. Classification performance measures the ability to distinguish between true and false positive locations. Knowing these separate performances allows principled optimization of reader or AI system performance. This package supersedes Windows JAFROC (jackknife alternative FROC) software V4.2.1, <https://github.com/dpc10ster/WindowsJafroc>. Package functions are organized as follows. Data file related function names are preceded by 'Df', curve fitting functions by 'Fit', included data sets by 'dataset', plotting functions by 'Plot', significance testing functions by 'St', sample size related functions by 'Ss', data simulation functions by 'Simulate' and utility functions by 'Util'. Implemented are figures of merit (FOMs) for quantifying performance and functions for visualizing empirical or fitted operating characteristics: e.g., ROC, FROC, alternative FROC (AFROC) and weighted AFROC (wAFROC) curves. For fully crossed study designs significance testing of reader-averaged FOM differences between modalities is implemented via either Dorfman-Berbaum-Metz or the Obuchowski-Rockette methods. Also implemented is single modality analysis, which allows comparison of performance of a group of radiologists to a specified value, or comparison of AI to a group of radiologists interpreting the same cases. Crossed-modality analysis is implemented wherein there are two crossed modality factors and the aim is to determined performance in each modality factor averaged over all levels of the second factor. Sample size estimation tools are provided for ROC and FROC studies; these use estimates of the relevant variances from a pilot study to predict required numbers of readers and cases in a pivotal study to achieve the desired power. Utility and data file manipulation functions allow data to be read in any of the currently used input formats, including Excel, and the results of the analysis can be viewed in text or Excel output files. The methods are illustrated with several included datasets from the author's collaborations. This update includes improvements to the code, some as a result of user-reported bugs and new feature requests, and others discovered during ongoing testing and code simplification.

Maintained by Dev Chakraborty. Last updated 5 months ago.

ai-optimizationartificial-intelligence-algorithmscomputer-aided-diagnosisfroc-analysisroc-analysistarget-classificationtarget-localizationcpp

19 stars 5.69 score 65 scripts

pik-piam

mrland:MadRaT land data package

The package provides land related data via the madrat framework.

Maintained by Jan Philipp Dietrich. Last updated 9 days ago.

5.59 score 3 scripts 4 dependents