Showing 62 of total 62 results (show query)
bioc
celda:CEllular Latent Dirichlet Allocation
Celda is a suite of Bayesian hierarchical models for clustering single-cell RNA-sequencing (scRNA-seq) data. It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included.
Maintained by Joshua Campbell. Last updated 2 months ago.
singlecellgeneexpressionclusteringsequencingbayesianimmunooncologydataimportcppopenmp
147 stars 10.47 score 256 scripts 2 dependentsbioc
scRepertoire:A toolkit for single-cell immune receptor profiling
scRepertoire is a toolkit for processing and analyzing single-cell T-cell receptor (TCR) and immunoglobulin (Ig). The scRepertoire framework supports use of 10x, AIRR, BD, MiXCR, Omniscope, TRUST4, and WAT3R single-cell formats. The functionality includes basic clonal analyses, repertoire summaries, distance-based clustering and interaction with the popular Seurat and SingleCellExperiment/Bioconductor R workflows.
Maintained by Nick Borcherding. Last updated 14 days ago.
softwareimmunooncologysinglecellclassificationannotationsequencingcpp
327 stars 10.42 score 240 scriptsbioc
singleCellTK:Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data
The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk.
Maintained by Joshua David Campbell. Last updated 1 months ago.
singlecellgeneexpressiondifferentialexpressionalignmentclusteringimmunooncologybatcheffectnormalizationqualitycontroldataimportgui
182 stars 10.17 score 252 scriptsbioc
DEGreport:Report of DEG analysis
Creation of ready-to-share figures of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene.
Maintained by Lorena Pantano. Last updated 5 months ago.
differentialexpressionvisualizationrnaseqreportwritinggeneexpressionimmunooncologybioconductordifferential-expressionqcreportrna-seqsmallrna
24 stars 9.42 score 354 scripts 1 dependents
cbielow
PTXQC:Quality Report Generation for MaxQuant and mzTab Results
Generates Proteomics (PTX) quality control (QC) reports for shotgun LC-MS data analyzed with the MaxQuant software suite (from .txt files) or mzTab files (ideally from OpenMS 'QualityControl' tool). Reports are customizable (target thresholds, subsetting) and available in HTML or PDF format. Published in J. Proteome Res., Proteomics Quality Control: Quality Control Software for MaxQuant Results (2015) <doi:10.1021/acs.jproteome.5b00780>.
Maintained by Chris Bielow. Last updated 1 years ago.
drag-and-drophacktoberfestheatmapmatch-between-runsmaxquantmetricmztabopenmsproteomicsquality-controlquality-metricsreport
42 stars 9.35 score 105 scripts 1 dependents
ropensci
iheatmapr:Interactive, Complex Heatmaps
Make complex, interactive heatmaps. 'iheatmapr' includes a modular system for iteratively building up complex heatmaps, as well as the iheatmap() function for making relatively standard heatmaps.
Maintained by Alan OCallaghan. Last updated 8 months ago.
heatmapplotlyinteractive-visualizationsdata-visualizationhtmlwidgetspeer-reviewed
267 stars 9.08 score 99 scripts 1 dependentsbioc
BatchQC:Batch Effects Quality Control Software
Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA.
Maintained by Jessica Anderson. Last updated 15 days ago.
batcheffectgraphandnetworkmicroarraynormalizationprincipalcomponentsequencingsoftwarevisualizationqualitycontrolrnaseqpreprocessingdifferentialexpressionimmunooncology
7 stars 9.06 score 54 scripts
rlbarter
superheat:A Graphical Tool for Exploring Complex Datasets Using Heatmaps
A system for generating extendable and customizable heatmaps for exploring complex datasets, including big data and data with multiple data types.
Maintained by Rebecca Barter. Last updated 5 years ago.
238 stars 8.81 score 438 scripts 4 dependentsbioc
ngsReports:Load FastqQC reports and other NGS related files
This package provides methods and object classes for parsing FastQC reports and output summaries from other NGS tools into R. As well as parsing files, multiple plotting methods have been implemented for visualising the parsed data. Plots can be generated as static ggplot objects or interactive plotly objects.
Maintained by Stevie Pederson. Last updated 3 days ago.
22 stars 7.99 score 99 scriptsbioc
netZooR:Unified methods for the inference and analysis of gene regulatory networks
netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information.
Maintained by Tara Eicher. Last updated 15 days ago.
networkinferencenetworkgeneregulationgeneexpressiontranscriptionmicroarraygraphandnetworkgene-regulatory-networktranscription-factors
105 stars 7.98 scorebioc
AneuFinder:Analysis of Copy Number Variation in Single-Cell-Sequencing Data
AneuFinder implements functions for copy-number detection, breakpoint detection, and karyotype and heterogeneity analysis in single-cell whole genome sequencing and strand-seq data.
Maintained by Aaron Taudt. Last updated 7 days ago.
immunooncologysoftwaresequencingsinglecellcopynumbervariationgenomicvariationhiddenmarkovmodelwholegenomecpp
18 stars 7.90 score 37 scripts
sollano
forestmangr:Forest Mensuration and Management
Processing forest inventory data with methods such as simple random sampling, stratified random sampling and systematic sampling. There are also functions for yield and growth predictions and model fitting, linear and nonlinear grouped data fitting, and statistical tests. References: Kershaw Jr., Ducey, Beers and Husch (2016). <doi:10.1002/9781118902028>.
Maintained by Sollano Rabelo Braga. Last updated 4 months ago.
17 stars 7.89 score 378 scriptsbioc
BioNERO:Biological Network Reconstruction Omnibus
BioNERO aims to integrate all aspects of biological network inference in a single package, including data preprocessing, exploratory analyses, network inference, and analyses for biological interpretations. BioNERO can be used to infer gene coexpression networks (GCNs) and gene regulatory networks (GRNs) from gene expression data. Additionally, it can be used to explore topological properties of protein-protein interaction (PPI) networks. GCN inference relies on the popular WGCNA algorithm. GRN inference is based on the "wisdom of the crowds" principle, which consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. As all steps of network analyses are included in this package, BioNERO makes users avoid having to learn the syntaxes of several packages and how to communicate between them. Finally, users can also identify consensus modules across independent expression sets and calculate intra and interspecies module preservation statistics between different networks.
Maintained by Fabricio Almeida-Silva. Last updated 5 months ago.
softwaregeneexpressiongeneregulationsystemsbiologygraphandnetworkpreprocessingnetworknetworkinference
27 stars 7.69 score 50 scripts 1 dependents
danlwarren
rwty:R We There Yet? Visualizing MCMC Convergence in Phylogenetics
Implements various tests, visualizations, and metrics for diagnosing convergence of MCMC chains in phylogenetics. It implements and automates many of the functions of the AWTY package in the R environment, as well as a host of other functions. Warren, Geneva, and Lanfear (2017), <doi:10.1093/molbev/msw279>.
Maintained by Dan Warren. Last updated 7 days ago.
30 stars 7.32 score 117 scriptsbioc
isomiRs:Analyze isomiRs and miRNAs from small RNA-seq
Characterization of miRNAs and isomiRs, clustering and differential expression.
Maintained by Lorena Pantano. Last updated 5 months ago.
mirnarnaseqdifferentialexpressionclusteringimmunooncologyanalyze-isomirsbioconductorisomirs
8 stars 6.97 score 43 scriptsbioc
mnem:Mixture Nested Effects Models
Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm.
Maintained by Martin Pirkl. Last updated 8 days ago.
pathwayssystemsbiologynetworkinferencenetworkrnaseqpooledscreenssinglecellcrispratacseqdnaseqgeneexpressioncpp
4 stars 6.81 score 15 scripts 4 dependents
wencke
GOplot:Visualization of Functional Analysis Data
Implementation of multilayered visualizations for enhanced graphical representation of functional analysis data. It combines and integrates omics data derived from expression and functional annotation enrichment analyses. Its plotting functions have been developed with an hierarchical structure in mind: starting from a general overview to identify the most enriched categories (modified bar plot, bubble plot) to a more detailed one displaying different types of relevant information for the molecules in a given set of categories (circle plot, chord plot, cluster plot, Venn diagram, heatmap).
Maintained by Wencke Walter. Last updated 8 years ago.
21 stars 6.62 score 235 scriptsbioc
artMS:Analytical R tools for Mass Spectrometry
artMS provides a set of tools for the analysis of proteomics label-free datasets. It takes as input the MaxQuant search result output (evidence.txt file) and performs quality control, relative quantification using MSstats, downstream analysis and integration. artMS also provides a set of functions to re-format and make it compatible with other analytical tools, including, SAINTq, SAINTexpress, Phosfate, and PHOTON. Check [http://artms.org](http://artms.org) for details.
Maintained by David Jimenez-Morales. Last updated 5 months ago.
proteomicsdifferentialexpressionbiomedicalinformaticssystemsbiologymassspectrometryannotationqualitycontrolgenesetenrichmentclusteringnormalizationimmunooncologymultiplecomparisonanalysisanalyticalap-msbioconductorbioinformaticsmass-spectrometryphosphoproteomicspost-translational-modificationquantitative-analysis
14 stars 6.41 score 13 scriptsbioc
Linnorm:Linear model and normality based normalization and transformation method (Linnorm)
Linnorm is an algorithm for normalizing and transforming RNA-seq, single cell RNA-seq, ChIP-seq count data or any large scale count data. It has been independently reviewed by Tian et al. on Nature Methods (https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM.
Maintained by Shun Hang Yip. Last updated 5 months ago.
immunooncologysequencingchipseqrnaseqdifferentialexpressiongeneexpressiongeneticsnormalizationsoftwaretranscriptionbatcheffectpeakdetectionclusteringnetworksinglecellcpp
6.26 score 61 scripts 5 dependents
tirgit
missCompare:Intuitive Missing Data Imputation Framework
Offers a convenient pipeline to test and compare various missing data imputation algorithms on simulated and real data. These include simpler methods, such as mean and median imputation and random replacement, but also include more sophisticated algorithms already implemented in popular R packages, such as 'mi', described by Su et al. (2011) <doi:10.18637/jss.v045.i02>; 'mice', described by van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; 'missForest', described by Stekhoven and Buhlmann (2012) <doi:10.1093/bioinformatics/btr597>; 'missMDA', described by Josse and Husson (2016) <doi:10.18637/jss.v070.i01>; and 'pcaMethods', described by Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>. The central assumption behind 'missCompare' is that structurally different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation algorithms. 'missCompare' takes measurements of your dataset and sets up a sandbox to try a curated list of standard and sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns. 'missCompare' will also impute your real-life dataset for you after the selection of the best performing algorithm in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you assess imputation performance.
Maintained by Tibor V. Varga. Last updated 4 years ago.
comparisoncomparison-benchmarksimputationimputation-algorithmimputation-methodsimputationskolmogorov-smirnovmissingmissing-datamissing-data-imputationmissing-status-checkmissing-valuesmissingnesspost-imputation-diagnosticsrmse
39 stars 5.89 score 40 scriptsbioc
epiNEM:epiNEM
epiNEM is an extension of the original Nested Effects Models (NEM). EpiNEM is able to take into account double knockouts and infer more complex network signalling pathways. It is tailored towards large scale double knock-out screens.
Maintained by Martin Pirkl. Last updated 5 months ago.
pathwayssystemsbiologynetworkinferencenetwork
1 stars 5.83 score 1 scripts 3 dependentsbioc
benchdamic:Benchmark of differential abundance methods on microbiome data
Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization.
Maintained by Matteo Calgaro. Last updated 4 months ago.
metagenomicsmicrobiomedifferentialexpressionmultiplecomparisonnormalizationpreprocessingsoftwarebenchmarkdifferential-abundance-methods
8 stars 5.78 score 8 scriptsbioc
scRNAseqApp:A single-cell RNAseq Shiny app-package
The scRNAseqApp is a Shiny app package designed for interactive visualization of single-cell data. It is an enhanced version derived from the ShinyCell, repackaged to accommodate multiple datasets. The app enables users to visualize data containing various types of information simultaneously, facilitating comprehensive analysis. Additionally, it includes a user management system to regulate database accessibility for different users.
Maintained by Jianhong Ou. Last updated 20 days ago.
visualizationsinglecellrnaseqinteractive-visualizationsmultiple-usersshiny-appssingle-cell-rna-seq
4 stars 5.76 score 3 scriptsbioc
sangeranalyseR:sangeranalyseR: a suite of functions for the analysis of Sanger sequence data in R
This package builds on sangerseqR to allow users to create contigs from collections of Sanger sequencing reads. It provides a wide range of options for a number of commonly-performed actions including read trimming, detecting secondary peaks, and detecting indels using a reference sequence. All parameters can be adjusted interactively either in R or in the associated Shiny applications. There is extensive online documentation, and the package can outputs detailed HTML reports, including chromatograms.
Maintained by Kuan-Hao Chao. Last updated 1 months ago.
geneticsalignmentsequencingsangerseqpreprocessingqualitycontrolvisualizationgui
5.76 score 46 scriptsbioc
CEMiTool:Co-expression Modules identification Tool
The CEMiTool package unifies the discovery and the analysis of coexpression gene modules in a fully automatic manner, while providing a user-friendly html report with high quality graphs. Our tool evaluates if modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group. Additionally, CEMiTool is able to integrate transcriptomic data with interactome information, identifying the potential hubs on each network.
Maintained by Helder Nakaya. Last updated 5 months ago.
geneexpressiontranscriptomicsgraphandnetworkmrnamicroarrayrnaseqnetworknetworkenrichmentpathwaysimmunooncology
5.58 score 38 scripts
rezakj
iCellR:Analyzing High-Throughput Single Cell Sequencing Data
A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.
Maintained by Alireza Khodadadi-Jamayran. Last updated 9 months ago.
10xgenomics3dbatch-normalizationcell-type-classificationcite-seqclusteringclustering-algorithmdiffusion-mapsdropouticellrimputationintractive-graphnormalizationpseudotimescrna-seqscvdj-seqsingel-cell-sequencingumapcpp
121 stars 5.56 score 7 scripts 1 dependentsbioc
GeDi:Defining and visualizing the distances between different genesets
The package provides different distances measurements to calculate the difference between genesets. Based on these scores the genesets are clustered and visualized as graph. This is all presented in an interactive Shiny application for easy usage.
Maintained by Annekathrin Nedwed. Last updated 5 months ago.
guigenesetenrichmentsoftwaretranscriptionrnaseqvisualizationclusteringpathwaysreportwritinggokeggreactomeshinyapps
1 stars 5.36 score 22 scriptsbioc
biotmle:Targeted Learning with Moderated Statistics for Biomarker Discovery
Tools for differential expression biomarker discovery based on microarray and next-generation sequencing data that leverage efficient semiparametric estimators of the average treatment effect for variable importance analysis. Estimation and inference of the (marginal) average treatment effects of potential biomarkers are computed by targeted minimum loss-based estimation, with joint, stable inference constructed across all biomarkers using a generalization of moderated statistics for use with the estimated efficient influence function. The procedure accommodates the use of ensemble machine learning for the estimation of nuisance functions.
Maintained by Nima Hejazi. Last updated 5 months ago.
regressiongeneexpressiondifferentialexpressionsequencingmicroarrayrnaseqimmunooncologybioconductorbioconductor-packagebioconductor-packagesbioinformaticsbiomarker-discoverybiostatisticscausal-inferencecomputational-biologymachine-learningstatisticstargeted-learning
5 stars 5.30 score 5 scripts
c4tb
shinyExprPortal:A Configurable 'shiny' Portal for Sharing Analysis of Molecular Expression Data
Enables deploying configuration file-based 'shiny' apps with minimal programming for interactive exploration and analysis showcase of molecular expression data. For exploration, supports visualization of correlations between rows of an expression matrix and a table of observations, such as clinical measures, and comparison of changes in expression over time. For showcase, enables visualizing the results of differential expression from package such as 'limma', co-expression modules from 'WGCNA' and lower dimensional projections.
Maintained by Rafael Henkin. Last updated 8 months ago.
bioinformaticsdata-analysistranscriptomics
5 stars 5.30 score 8 scriptsbioc
decontX:Decontamination of single cell genomics data
This package contains implementation of DecontX (Yang et al. 2020), a decontamination algorithm for single-cell RNA-seq, and DecontPro (Yin et al. 2023), a decontamination algorithm for single cell protein expression data. DecontX is a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. DecontPro is a Bayesian method that estimates the level of contamination from ambient and background sources in CITE-seq ADT dataset and decontaminate the dataset.
Maintained by Joshua Campbell. Last updated 2 months ago.
4.94 score 29 scriptsbioc
evaluomeR:Evaluation of Bioinformatics Metrics
Evaluating the reliability of your own metrics and the measurements done on your own datasets by analysing the stability and goodness of the classifications of such metrics.
Maintained by José Antonio Bernabé-Díaz. Last updated 5 months ago.
clusteringclassificationfeatureextractionassessmentclustering-evaluationevaluomeevaluomermetrics
4.82 score 33 scripts
korap
RKorAPClient:'KorAP' Web Service Client Package
A client package that makes the 'KorAP' web service API accessible from R. The corpus analysis platform 'KorAP' has been developed as a scientific tool to make potentially large, stratified and multiply annotated corpora, such as the 'German Reference Corpus DeReKo' or the 'Corpus of the Contemporary Romanian Language CoRoLa', accessible for linguists to let them verify hypotheses and to find interesting patterns in real language use. The 'RKorAPClient' package provides access to 'KorAP' and the corpora behind it for user-created R code, as a programmatic alternative to the 'KorAP' web user-interface. You can learn more about 'KorAP' and use it directly on 'DeReKo' at <https://korap.ids-mannheim.de/>.
Maintained by Marc Kupietz. Last updated 1 months ago.
6 stars 4.77 score 30 scriptsbioc
PhosR:A set of methods and tools for comprehensive analysis of phosphoproteomics data
PhosR is a package for the comprenhensive analysis of phosphoproteomic data. There are two major components to PhosR: processing and downstream analysis. PhosR consists of various processing tools for phosphoproteomics data including filtering, imputation, normalisation, and functional analysis for inferring active kinases and signalling pathways.
Maintained by Taiyun Kim. Last updated 5 months ago.
softwareresearchfieldproteomics
4.71 score 51 scriptsbioc
barcodetrackR:Functions for Analyzing Cellular Barcoding Data
barcodetrackR is an R package developed for the analysis and visualization of clonal tracking data. Data required is samples and tag abundances in matrix form. Usually from cellular barcoding experiments, integration site retrieval analyses, or similar technologies.
Maintained by Diego Alexander Espinoza. Last updated 5 months ago.
softwarevisualizationsequencing
5 stars 4.70 score 6 scriptsbioc
nempi:Inferring unobserved perturbations from gene expression data
Takes as input an incomplete perturbation profile and differential gene expression in log odds and infers unobserved perturbations and augments observed ones. The inference is done by iteratively inferring a network from the perturbations and inferring perturbations from the network. The network inference is done by Nested Effects Models.
Maintained by Martin Pirkl. Last updated 5 months ago.
softwaregeneexpressiondifferentialexpressiondifferentialmethylationgenesignalingpathwaysnetworkclassificationneuralnetworknetworkinferenceatacseqdnaseqrnaseqpooledscreenscrisprsinglecellsystemsbiology
2 stars 4.60 score 2 scriptsbioc
bnem:Training of logical models from indirect measurements of perturbation experiments
bnem combines the use of indirect measurements of Nested Effects Models (package mnem) with the Boolean networks of CellNOptR. Perturbation experiments of signalling nodes in cells are analysed for their effect on the global gene expression profile. Those profiles give evidence for the Boolean regulation of down-stream nodes in the network, e.g., whether two parents activate their child independently (OR-gate) or jointly (AND-gate).
Maintained by Martin Pirkl. Last updated 5 months ago.
pathwayssystemsbiologynetworkinferencenetworkgeneexpressiongeneregulationpreprocessing
2 stars 4.60 score 5 scriptsbioc
RESOLVE:RESOLVE: An R package for the efficient analysis of mutational signatures from cancer genomes
Cancer is a genetic disease caused by somatic mutations in genes controlling key biological functions such as cellular growth and division. Such mutations may arise both through cell-intrinsic and exogenous processes, generating characteristic mutational patterns over the genome named mutational signatures. The study of mutational signatures have become a standard component of modern genomics studies, since it can reveal which (environmental and endogenous) mutagenic processes are active in a tumor, and may highlight markers for therapeutic response. Mutational signatures computational analysis presents many pitfalls. First, the task of determining the number of signatures is very complex and depends on heuristics. Second, several signatures have no clear etiology, casting doubt on them being computational artifacts rather than due to mutagenic processes. Last, approaches for signatures assignment are greatly influenced by the set of signatures used for the analysis. To overcome these limitations, we developed RESOLVE (Robust EStimation Of mutationaL signatures Via rEgularization), a framework that allows the efficient extraction and assignment of mutational signatures. RESOLVE implements a novel algorithm that enables (i) the efficient extraction, (ii) exposure estimation, and (iii) confidence assessment during the computational inference of mutational signatures.
Maintained by Luca De Sano. Last updated 8 days ago.
biomedicalinformaticssomaticmutation
1 stars 4.60 score 3 scriptsbioc
dce:Pathway Enrichment Based on Differential Causal Effects
Compute differential causal effects (dce) on (biological) networks. Given observational samples from a control experiment and non-control (e.g., cancer) for two genes A and B, we can compute differential causal effects with a (generalized) linear regression. If the causal effect of gene A on gene B in the control samples is different from the causal effect in the non-control samples the dce will differ from zero. We regularize the dce computation by the inclusion of prior network information from pathway databases such as KEGG.
Maintained by Kim Philipp Jablonski. Last updated 4 months ago.
softwarestatisticalmethodgraphandnetworkregressiongeneexpressiondifferentialexpressionnetworkenrichmentnetworkkeggbioconductorcausality
13 stars 4.59 score 4 scripts
hpetren
chemodiv:Analysing Chemodiversity of Phytochemical Data
Quantify and visualise various measures of chemical diversity and dissimilarity, for phytochemical compounds and other sets of chemical composition data. Importantly, these measures can incorporate biosynthetic and/or structural properties of the chemical compounds, resulting in a more comprehensive quantification of diversity and dissimilarity. For details, see Petrén, Köllner and Junker (2023) <doi:10.1111/nph.18685>.
Maintained by Hampus Petrén. Last updated 2 years ago.
5 stars 4.57 score 15 scriptsbioc
MAPFX:MAssively Parallel Flow cytometry Xplorer (MAPFX): A Toolbox for Analysing Data from the Massively-Parallel Cytometry Experiments
MAPFX is an end-to-end toolbox that pre-processes the raw data from MPC experiments (e.g., BioLegend's LEGENDScreen and BD Lyoplates assays), and further imputes the ‘missing’ infinity markers in the wells without those measurements. The pipeline starts by performing background correction on raw intensities to remove the noise from electronic baseline restoration and fluorescence compensation by adapting a normal-exponential convolution model. Unwanted technical variation, from sources such as well effects, is then removed using a log-normal model with plate, column, and row factors, after which infinity markers are imputed using the informative backbone markers as predictors. The completed dataset can then be used for clustering and other statistical analyses. Additionally, MAPFX can be used to normalise data from FFC assays as well.
Maintained by Hsiao-Chi Liao. Last updated 5 months ago.
softwareflowcytometrycellbasedassayssinglecellproteomicsclustering
1 stars 4.54 scorebioc
ClusterFoldSimilarity:Calculate similarity of clusters from different single cell samples using foldchanges
This package calculates a similarity coefficient using the fold changes of shared features (e.g. genes) among clusters of different samples/batches/datasets. The similarity coefficient is calculated using the dot-product (Hadamard product) of every pairwise combination of Fold Changes between a source cluster i of sample/dataset n and all the target clusters j in sample/dataset m
Maintained by Oscar Gonzalez-Velasco. Last updated 5 months ago.
singlecellclusteringfeatureextractiongraphandnetworkgenetargetrnaseq
4.34 score 11 scriptsbioc
cageminer:Candidate Gene Miner
This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments.
Maintained by Fabrício Almeida-Silva. Last updated 5 months ago.
softwaresnpfunctionalpredictiongenomewideassociationgeneexpressionnetworkenrichmentvariantannotationfunctionalgenomicsnetwork
1 stars 4.30 score 5 scripts
priism-center
plotBart:Diagnostic and Plotting Functions to Supplement 'bartCause'
Functions to assist in diagnostics and plotting during the causal inference modeling process. Supplements the 'bartCause' package.
Maintained by Joseph Marlo. Last updated 10 months ago.
2 stars 4.30 score 20 scripts
arliph
SPARTAAS:Statistical Pattern Recognition and daTing using Archaeological Artefacts assemblageS
Statistical pattern recognition and dating using archaeological artefacts assemblages. Package of statistical tools for archaeology. hclustcompro(perioclust): Bellanger Lise, Coulon Arthur, Husi Philibrary(SPARTlippe (2021, ISBN:978-3-030-60103-4). mapclust: Bellanger Lise, Coulon Arthur, Husi Philippe (2021) <doi:10.1016/j.jas.2021.105431>. seriograph: Desachy Bruno (2004) <doi:10.3406/pica.2004.2396>. cerardat: Bellanger Lise, Husi Philippe (2012) <doi:10.1016/j.jas.2011.06.031>.
Maintained by Arthur Coulon. Last updated 10 months ago.
6 stars 4.14 score 46 scriptsbioc
qPLEXanalyzer:Tools for quantitative proteomics data analysis
Tools for TMT based quantitative proteomics data analysis.
Maintained by Ashley Sawle. Last updated 5 months ago.
immunooncologyproteomicsmassspectrometrynormalizationpreprocessingqualitycontroldataimport
1 stars 4.08 score 9 scriptsbioc
abseqR:Reporting and data analysis functionalities for Rep-Seq datasets of antibody libraries
AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. abseqR empowers the users of abseqPy (https://github.com/malhamdoosh/abseqPy) with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output.
Maintained by JiaHong Fong. Last updated 5 months ago.
sequencingvisualizationreportwritingqualitycontrolmultiplecomparison
4.00 score 3 scripts
jlp2duke
EnsCat:Clustering of categorical data
This package implements the clustering methods of categorical data discussed in Amiri, S., Clarke, B. and Clarke J. (2015). Clustering categorical data via ensembling dissimilarity matrices. arXiv:1506.07930.
Maintained by Saeid Amiri. Last updated 8 years ago.
5 stars 3.74 score 22 scripts
modeloriented
triplot:Explaining Correlated Features in Machine Learning Models
Tools for exploring effects of correlated features in predictive models. The predict_triplot() function delivers instance-level explanations that calculate the importance of the groups of explanatory variables. The model_triplot() function delivers data-level explanations. The generic plot function visualises in a concise way importance of hierarchical groups of predictors. All of the the tools are model agnostic, therefore works for any predictive machine learning models. Find more details in Biecek (2018) <arXiv:1806.08915>.
Maintained by Katarzyna Pekala. Last updated 4 years ago.
explanationsexplanatory-model-analysismachine-learningmodel-visualizationxai
9 stars 3.65 score 7 scripts
promidat
discoveR:Exploratory Data Analysis System
Performs an exploratory data analysis through a 'shiny' interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method.
Maintained by Oldemar Rodriguez. Last updated 2 years ago.
3 stars 3.03 score 18 scripts
gefeizhang
statVisual:Statistical Visualization Tools
Visualization functions in the applications of translational medicine (TM) and biomarker (BM) development to compare groups by statistically visualizing data and/or results of analyses, such as visualizing data by displaying in one figure different groups' histograms, boxplots, densities, scatter plots, error-bar plots, or trajectory plots, by displaying scatter plots of top principal components or dendrograms with data points colored based on group information, or visualizing volcano plots to check the results of whole genome analyses for gene differential expression.
Maintained by Wenfei Zhang. Last updated 5 years ago.
3.00 score 3 scripts
cran
MultivariateAnalysis:Pacote Para Analise Multivariada
Package with multivariate analysis methodologies for experiment evaluation. The package estimates dissimilarity measures, builds dendrograms, obtains MANOVA, principal components, canonical variables, etc. (Pacote com metodologias de analise multivariada para avaliação de experimentos. O pacote estima medidas de dissimilaridade, construi de dendogramas, obtem a MANOVA, componentes principais, variaveis canonicas, etc.)
Maintained by Alcinei Mistico Azevedo. Last updated 12 months ago.
2.95 score
sciviews
exploreit:Exploratory Data Analysis for 'SciViews::R'
Multivariate analysis and data exploration for the 'SciViews::R' dialect.
Maintained by Philippe Grosjean. Last updated 11 months ago.
multivariate-analysissciviewsstatistical-methods
2.70 score 4 scripts
wjschne
WJSmisc:Miscellaneous functions from W. Joel Schneider
Several functions I find useful.
Maintained by W. Joel Schneider. Last updated 2 years ago.
5 stars 2.40 score 10 scripts
sgezan
ASRgenomics:Complementary Genomic Functions
Presents a series of molecular and genetic routines in the R environment with the aim of assisting in analytical pipelines before and after the use of 'asreml' or another library to perform analyses such as Genomic Selection or Genome-Wide Association Analyses. Methods and examples are described in Gezan, Oliveira, Galli, and Murray (2022) <https://asreml.kb.vsni.co.uk/wp-content/uploads/sites/3/ASRgenomics_Manual.pdf>.
Maintained by Salvador Gezan. Last updated 1 years ago.
1 stars 2.28 score 38 scripts
green-striped-gecko
dartR.popgen:Analysing 'SNP' and 'Silicodart' Data Generated by Genome-Wide Restriction Fragment Analysis
Facilitates the analysis of SNP (single nucleotide polymorphism) and silicodart (presence/absence) data. 'dartR.popgen' provides a suit of functions to analyse such data in a population genetics context. It provides several functions to calculate population genetic metrics and to study population structure. Quite a few functions need additional software to be able to run (gl.run.structure(), gl.blast(), gl.LDNe()). You find detailed description in the help pages how to download and link the packages so the function can run the software. 'dartR.popgen' is part of the the 'dartRverse' suit of packages. Gruber et al. (2018) <doi:10.1111/1755-0998.12745>. Mijangos et al. (2022) <doi:10.1111/2041-210X.13918>.
Maintained by Bernd Gruber. Last updated 9 months ago.
2.00 score 9 scripts
welch-lab
SiNMFiD:Supervised iNMF informed Deconvolution
A package for completing cell type deconvolution on bulk spatial transcriptomic data utilizing multiple reference scRNA-seq datasets.
Maintained by Joshua Sodicoff. Last updated 1 years ago.
2.00 score 1 scripts
heeringa0
visvow:Visible Vowels: Visualization of Vowel Variation
Visualizes vowel variation in f0, F1, F2, F3 and duration.
Maintained by Wilbert Heeringa. Last updated 1 years ago.
2.00 score 4 scripts
fdzul
boldenr:boldenr: a package designed to generate of Dengue Bulletin of Veracruz state
This package contains functions designed specifically to generate graphs, tables, heatmap, maps and hotspots that allow designing the dengue bulletin of the state of Veracruz.
Maintained by The package maintainer. Last updated 12 months ago.
1 stars 1.81 score 13 scripts
thezetner
Plasmidprofiler:Visualization of Plasmid Profile Results
Contains functions developed to combine the results of querying a plasmid database using short-read sequence typing with the results of a blast analysis against the query results.
Maintained by Adrian Zetner. Last updated 8 years ago.
1.43 score 27 scripts