Showing 157 of total 157 results (show query)
sciviews
pastecs:Package for Analysis of Space-Time Ecological Series
Regularisation, decomposition and analysis of space-time series. The pastecs R package is a PNEC-Art4 and IFREMER (Benoit Beliaeff <Benoit.Beliaeff@ifremer.fr>) initiative to bring PASSTEC 2000 functionalities to R.
Maintained by Philippe Grosjean. Last updated 1 years ago.
44.3 match 4 stars 10.34 score 2.1k scripts 13 dependents
virmar
finnishgrid:'Fingrid Open Data API' R Client
R API client package for 'Fingrid Open Data' on the electricity market and the power system. get_data() function holds the main application logic to retrieve time-series data. API calls require free user account registration.
Maintained by Markus Virtanen. Last updated 9 months ago.
36.2 match 2 stars 5.24 score 175 scripts
bioc
singscore:Rank-based single-sample gene set scoring method
A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level.
Maintained by Malvika Kharbanda. Last updated 5 months ago.
softwaregeneexpressiongenesetenrichmentbioinformatics
13.5 match 41 stars 10.03 score 124 scripts 4 dependentsbioc
fgsea:Fast Gene Set Enrichment Analysis
The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction.
Maintained by Alexey Sergushichev. Last updated 3 months ago.
geneexpressiondifferentialexpressiongenesetenrichmentpathwayscpp
6.2 match 387 stars 16.25 score 3.9k scripts 101 dependentsbioc
RegEnrich:Gene regulator enrichment analysis
This package is a pipeline to identify the key gene regulators in a biological process, for example in cell differentiation and in cell development after stimulation. There are four major steps in this pipeline: (1) differential expression analysis; (2) regulator-target network inference; (3) enrichment analysis; and (4) regulators scoring and ranking.
Maintained by Weiyang Tao. Last updated 5 months ago.
geneexpressiontranscriptomicsrnaseqtwochanneltranscriptiongenetargetnetworkenrichmentdifferentialexpressionnetworknetworkinferencegenesetenrichmentfunctionalprediction
21.2 match 3.82 score 22 scriptsbioc
Moonlight2R:Identify oncogenes and tumor suppressor genes from omics data
The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). We present an updated version of the R/bioconductor package called MoonlightR, namely Moonlight2R, which returns a list of candidate driver genes for specific cancer types on the basis of omics data integration. The Moonlight framework contains a primary layer where gene expression data and information about biological processes are integrated to predict genes called oncogenic mediators, divided into putative tumor suppressors and putative oncogenes. This is done through functional enrichment analyses, gene regulatory networks and upstream regulator analyses to score the importance of well-known biological processes with respect to the studied cancer type. By evaluating the effect of the oncogenic mediators on biological processes or through random forests, the primary layer predicts two putative roles for the oncogenic mediators: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As gene expression data alone is not enough to explain the deregulation of the genes, a second layer of evidence is needed. We have automated the integration of a secondary mutational layer through new functionalities in Moonlight2R. These functionalities analyze mutations in the cancer cohort and classifies these into driver and passenger mutations using the driver mutation prediction tool, CScape-somatic. Those oncogenic mediators with at least one driver mutation are retained as the driver genes. As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, Moonlight2R can be used to discover OCGs and TSGs in the same cancer type. This may for instance help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV). In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. An additional mechanistic layer evaluates if there are mutations affecting the protein stability of the transcription factors (TFs) of the TSGs and OCGs, as that may have an effect on the expression of the genes.
Maintained by Matteo Tiberti. Last updated 2 months ago.
dnamethylationdifferentialmethylationgeneregulationgeneexpressionmethylationarraydifferentialexpressionpathwaysnetworksurvivalgenesetenrichmentnetworkenrichment
11.6 match 5 stars 6.59 score 43 scriptsbioc
DifferentialRegulation:Differentially regulated genes from scRNA-seq data
DifferentialRegulation is a method for detecting differentially regulated genes between two groups of samples (e.g., healthy vs. disease, or treated vs. untreated samples), by targeting differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. From a mathematical point of view, DifferentialRegulation accounts for the sample-to-sample variability, and embeds multiple samples in a Bayesian hierarchical model. Furthermore, our method also deals with two major sources of mapping uncertainty: i) 'ambiguous' reads, compatible with both spliced and unspliced versions of a gene, and ii) reads mapping to multiple genes. In particular, ambiguous reads are treated separately from spliced and unsplced reads, while reads that are compatible with multiple genes are allocated to the gene of origin. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques (Metropolis-within-Gibbs).
Maintained by Simone Tiberi. Last updated 5 months ago.
differentialsplicingbayesiangeneticsrnaseqsequencingdifferentialexpressiongeneexpressionmultiplecomparisonsoftwaretranscriptionstatisticalmethodvisualizationsinglecellgenetargetopenblascpp
13.2 match 10 stars 5.30 score 4 scripts
inrae
airGRiwrm:'airGR' Integrated Water Resource Management
Semi-distributed Precipitation-Runoff Modeling based on 'airGR' package models integrating human infrastructures and their managements.
Maintained by David Dorchies. Last updated 6 months ago.
10.4 match 6.34 score 45 scriptsbioc
target:Predict Combined Function of Transcription Factors
Implement the BETA algorithm for infering direct target genes from DNA-binding and perturbation expression data Wang et al. (2013) <doi: 10.1038/nprot.2013.150>. Extend the algorithm to predict the combined function of two DNA-binding elements from comprable binding and expression data.
Maintained by Mahmoud Ahmed. Last updated 5 months ago.
softwarestatisticalmethodtranscriptionalgorithmchip-seqdna-bindinggene-regulationtranscription-factors
7.5 match 4 stars 7.79 score 1.3k scriptsbioc
scMultiSim:Simulation of Multi-Modality Single Cell Data Guided By Gene Regulatory Networks and Cell-Cell Interactions
scMultiSim simulates paired single cell RNA-seq, single cell ATAC-seq and RNA velocity data, while incorporating mechanisms of gene regulatory networks, chromatin accessibility and cell-cell interactions. It allows users to tune various parameters controlling the amount of each biological factor, variation of gene-expression levels, the influence of chromatin accessibility on RNA sequence data, and so on. It can be used to benchmark various computational methods for single cell multi-omics data, and to assist in experimental design of wet-lab experiments.
Maintained by Hechen Li. Last updated 5 months ago.
singlecelltranscriptomicsgeneexpressionsequencingexperimentaldesign
8.1 match 23 stars 7.15 score 11 scriptsbioc
TFARM:Transcription Factors Association Rules Miner
It searches for relevant associations of transcription factors with a transcription factor target, in specific genomic regions. It also allows to evaluate the Importance Index distribution of transcription factors (and combinations of transcription factors) in association rules.
Maintained by Liuba Nausicaa Martino. Last updated 5 months ago.
biologicalquestioninfrastructurestatisticalmethodtranscription
13.6 match 4.00 score 2 scriptsbioc
mitch:Multi-Contrast Gene Set Enrichment Analysis
mitch is an R package for multi-contrast enrichment analysis. At itโs heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. We have an established routine for pathway enrichment of Infinium Methylation Array data (see vignette). The main strengths of mitch are that it can import datasets easily from many upstream tools and has advanced plotting features to visualise these enrichments.
Maintained by Mark Ziemann. Last updated 4 months ago.
geneexpressiongenesetenrichmentsinglecelltranscriptomicsepigeneticsproteomicsdifferentialexpressionreactomednamethylationmethylationarraygene-regulationgene-seq-analysispathway-analysis
7.5 match 16 stars 7.06 score 15 scripts
cailab-tamu
scTenifoldNet:Construct and Compare scGRN from Single-Cell Transcriptomic Data
A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See <doi:10.1016/j.patter.2020.100139> for more details.
Maintained by Daniel Osorio. Last updated 2 months ago.
differential-regulation-analysisgene-regulatory-networksmanifold-alignmentsingle-celltensor-decomposition
8.4 match 22 stars 5.63 score 65 scripts 1 dependentsbioc
MoonlightR:Identify oncogenes and tumor suppressor genes from omics data
Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments.
Maintained by Matteo Tiberti. Last updated 5 months ago.
dnamethylationdifferentialmethylationgeneregulationgeneexpressionmethylationarraydifferentialexpressionpathwaysnetworksurvivalgenesetenrichmentnetworkenrichment
7.1 match 17 stars 6.57 scorebioc
debrowser:Interactive Differential Expresion Analysis Browser
Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps.
Maintained by Alper Kucukural. Last updated 5 months ago.
sequencingchipseqrnaseqdifferentialexpressiongeneexpressionclusteringimmunooncology
5.6 match 61 stars 7.80 score 65 scripts
hanjunwei-lab
SubtypeDrug:Prioritization of Candidate Cancer Subtype Specific Drugs
A systematic biology tool was developed to prioritize cancer subtype-specific drugs by integrating genetic perturbation, drug action, biological pathway, and cancer subtype. The capabilities of this tool include inferring patient-specific subpathway activity profiles in the context of gene expression profiles with subtype labels, calculating differentially expressed subpathways based on cultured human cells treated with drugs in the 'cMap' (connectivity map) database, prioritizing cancer subtype specific drugs according to drug-disease reverse association score based on subpathway, and visualization of results (Castelo (2013) <doi:10.1186/1471-2105-14-7>; Han et al (2019) <doi:10.1093/bioinformatics/btz894>; Lamb and Justin (2006) <doi:10.1126/science.1132939>). Please cite using <doi:10.1093/bioinformatics/btab011>.
Maintained by Junwei Han. Last updated 1 years ago.
10.9 match 2 stars 4.00 score 3 scripts
epiverse-trace
linelist:Tagging and Validating Epidemiological Data
Provides tools to help storing and handling case line list data. The 'linelist' class adds a tagging system to classical 'data.frame' objects to identify key epidemiological data such as dates of symptom onset, epidemiological case definition, age, gender or disease outcome. Once tagged, these variables can be seamlessly used in downstream analyses, making data pipelines more robust and reliable.
Maintained by Hugo Gruson. Last updated 22 days ago.
datadata-structuresepidemiologyepiverseoutbreakssdg-3structured-data
4.8 match 8 stars 8.80 score 61 scripts 2 dependents
openintrostat
openintro:Datasets and Supplemental Functions from 'OpenIntro' Textbooks and Labs
Supplemental functions and data for 'OpenIntro' resources, which includes open-source textbooks and resources for introductory statistics (<https://www.openintro.org/>). The package contains datasets used in our open-source textbooks along with custom plotting functions for reproducing book figures. Note that many functions and examples include color transparency; some plotting elements may not show up properly (or at all) when run in some versions of Windows operating system.
Maintained by Mine รetinkaya-Rundel. Last updated 2 months ago.
3.6 match 240 stars 11.39 score 6.0k scriptsbioc
MOMA:Multi Omic Master Regulator Analysis
This package implements the inference of candidate master regulator proteins from multi-omics' data (MOMA) algorithm, as well as ancillary analysis and visualization functions.
Maintained by Sunny Jones. Last updated 5 months ago.
softwarenetworkenrichmentnetworkinferencenetworkfeatureextractionclusteringfunctionalgenomicstranscriptomicssystemsbiology
6.4 match 6 stars 6.19 score 13 scriptsepiverse-trace
cfr:Estimate Disease Severity and Case Ascertainment
Estimate the severity of a disease and ascertainment of cases, as discussed in Nishiura et al. (2009) <doi:10.1371/journal.pone.0006852>.
Maintained by Adam Kucharski. Last updated 16 days ago.
case-fatality-rateepidemic-modellingepidemiologyepiversehealth-outcomesoutbreak-analysissdg-3
4.8 match 13 stars 8.15 score 35 scriptsbioc
miRspongeR:Identification and analysis of miRNA sponge regulation
This package provides several functions to explore miRNA sponge (also called ceRNA or miRNA decoy) regulation from putative miRNA-target interactions or/and transcriptomics data (including bulk, single-cell and spatial gene expression data). It provides eight popular methods for identifying miRNA sponge interactions, and an integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of miRNA sponge modules, and conduct survival analysis of miRNA sponge modules. By using a sample control variable strategy, it provides a function to infer sample-specific miRNA sponge interactions. In terms of sample-specific miRNA sponge interactions, it implements three similarity methods to construct sample-sample correlation network.
Maintained by Junpeng Zhang. Last updated 5 months ago.
geneexpressionbiomedicalinformaticsnetworkenrichmentsurvivalmicroarraysoftwaresinglecellspatialrnaseqcernamirnasponge
6.5 match 5 stars 5.88 score 8 scripts
garciadejalon
FlowRegEnvCost:The Environmental Costs of Flow Regulation
An application to calculate the daily environmental costs of river flow regulation by dams based on Garcรญa de Jalon et al. 2017 <doi:10.1007/s11269-017-1663-0>.
Maintained by Javier Martinez-Lopez. Last updated 7 years ago.
12.1 match 2 stars 3.00 score 8 scripts
jbryer
likert:Analysis and Visualization Likert Items
An approach to analyzing Likert response items, with an emphasis on visualizations. The stacked bar plot is the preferred method for presenting Likert results. Tabular results are also implemented along with density plots to assist researchers in determining whether Likert responses can be used quantitatively instead of qualitatively. See the likert(), summary.likert(), and plot.likert() functions to get started.
Maintained by Jason Bryer. Last updated 3 years ago.
3.5 match 310 stars 10.22 score 480 scripts 2 dependentsbioc
InPAS:Identify Novel Alternative PolyAdenylation Sites (PAS) from RNA-seq data
Alternative polyadenylation (APA) is one of the important post- transcriptional regulation mechanisms which occurs in most human genes. InPAS facilitates the discovery of novel APA sites and the differential usage of APA sites from RNA-Seq data. It leverages cleanUpdTSeq to fine tune identified APA sites by removing false sites.
Maintained by Jianhong Ou. Last updated 2 months ago.
alternative polyadenylationdifferential polyadenylation site usagerna-seqgene regulationtranscription
8.0 match 4.30 score 1 scriptsbioc
tRanslatome:Comparison between multiple levels of gene expression
Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots.
Maintained by Toma Tebaldi. Last updated 5 months ago.
cellbiologygeneregulationregulationgeneexpressiondifferentialexpressionmicroarrayhighthroughputsequencingqualitycontrolgomultiplecomparisonsbioinformatics
10.0 match 3.30 score 2 scriptsbioc
GSRI:Gene Set Regulation Index
The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI).
Maintained by Julian Gehring. Last updated 5 months ago.
microarraytranscriptiondifferentialexpressiongenesetenrichmentgeneregulation
9.9 match 3.30 score 2 scriptsbioc
ROntoTools:R Onto-Tools suite
Suite of tools for functional analysis.
Maintained by Sorin Draghici. Last updated 5 months ago.
networkanalysismicroarraygraphsandnetworks
6.1 match 5.10 score 15 scripts 2 dependentsbioc
terapadog:Translational Efficiency Regulation Analysis using the PADOG Method
This package performs a Gene Set Analysis with the approach adopted by PADOG on the genes that are reported as translationally regulated (ie. exhibit a significant change in TE) by the DeltaTE package. It can be used on its own to see the impact of translation regulation on gene sets, but it is also integrated as an additional analysis method within ReactomeGSA, where results are further contextualised in terms of pathways and directionality of the change.
Maintained by Gionmattia Carancini. Last updated 11 days ago.
riboseqtranscriptomicsgenesetenrichmentgeneregulationreactomesoftware
8.0 match 3.90 scorebioc
RTNduals:Analysis of co-regulation and inference of 'dual regulons'
RTNduals is a tool that searches for possible co-regulatory loops between regulon pairs generated by the RTN package. It compares the shared targets in order to infer 'dual regulons', a new concept that tests whether regulators can co-operate or compete in influencing targets.
Maintained by Mauro Castro. Last updated 5 months ago.
generegulationgeneexpressionnetworkenrichmentnetworkinferencegraphandnetwork
7.9 match 3.78 score 2 scripts 1 dependentsbioc
epidecodeR:epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation
epidecodeR is a package capable of analysing impact of degree of DNA/RNA epigenetic chemical modifications on dysregulation of genes or proteins. This package integrates chemical modification data generated from a host of epigenomic or epitranscriptomic techniques such as ChIP-seq, ATAC-seq, m6A-seq, etc. and dysregulated gene lists in the form of differential gene expression, ribosome occupancy or differential protein translation and identify impact of dysregulation of genes caused due to varying degrees of chemical modifications associated with the genes. epidecodeR generates cumulative distribution function (CDF) plots showing shifts in trend of overall log2FC between genes divided into groups based on the degree of modification associated with the genes. The tool also tests for significance of difference in log2FC between groups of genes.
Maintained by Kandarp Joshi. Last updated 5 months ago.
differentialexpressiongeneregulationhistonemodificationfunctionalpredictiontranscriptiongeneexpressionepitranscriptomicsepigeneticsfunctionalgenomicssystemsbiologytranscriptomicschiponchipdifferential-expressiongenomicsgenomics-visualization
5.7 match 5 stars 4.70 score 1 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 8 days ago.
networkinferencenetworkgeneregulationgeneexpressiontranscriptionmicroarraygraphandnetworkgene-regulatory-networktranscription-factors
3.3 match 105 stars 7.98 score
federicogiorgi
corto:Inference of Gene Regulatory Networks
We present 'corto' (Correlation Tool), a simple package to infer gene regulatory networks and visualize master regulators from gene expression data using DPI (Data Processing Inequality) and bootstrapping to recover edges. An initial step is performed to calculate all significant edges between a list of source nodes (centroids) and target genes. Then all triplets containing two centroids and one target are tested in a DPI step which removes edges. A bootstrapping process then calculates the robustness of the network, eventually re-adding edges previously removed by DPI. The algorithm has been optimized to run outside a computing cluster, using a fast correlation implementation. The package finally provides functions to calculate network enrichment analysis from RNA-Seq and ATAC-Seq signatures as described in the article by Giorgi lab (2020) <doi:10.1093/bioinformatics/btaa223>.
Maintained by Federico M. Giorgi. Last updated 2 years ago.
4.2 match 20 stars 6.25 score 59 scriptsbioc
TBSignatureProfiler:Profile RNA-Seq Data Using TB Pathway Signatures
Gene signatures of TB progression, TB disease, and other TB disease states have been validated and published previously. This package aggregates known signatures and provides computational tools to enlist their usage on other datasets. The TBSignatureProfiler makes it easy to profile RNA-Seq data using these signatures and includes common signature profiling tools including ASSIGN, GSVA, and ssGSEA. Original models for some gene signatures are also available. A shiny app provides some functionality alongside for detailed command line accessibility.
Maintained by Aubrey R. Odom. Last updated 3 months ago.
geneexpressiondifferentialexpressionbioconductor-packagebiomarkersgene-signaturestuberculosis
3.5 match 12 stars 7.25 score 23 scriptsbioc
PolySTest:PolySTest: Detection of differentially regulated features. Combined statistical testing for data with few replicates and missing values
The complexity of high-throughput quantitative omics experiments often leads to low replicates numbers and many missing values. We implemented a new test to simultaneously consider missing values and quantitative changes, which we combined with well-performing statistical tests for high confidence detection of differentially regulated features. The package contains functions to run the test and to visualize the results.
Maintained by Veit Schwรคmmle. Last updated 4 months ago.
massspectrometryproteomicssoftwaredifferentialexpression
5.1 match 4.95 score 12 scripts
january3
tmod:Feature Set Enrichment Analysis for Metabolomics and Transcriptomics
Methods and feature set definitions for feature or gene set enrichment analysis in transcriptional and metabolic profiling data. Package includes tests for enrichment based on ranked lists of features, functions for visualisation and multivariate functional analysis. See Zyla et al (2019) <doi:10.1093/bioinformatics/btz447>.
Maintained by January Weiner. Last updated 2 months ago.
3.5 match 3 stars 6.88 score 168 scripts 1 dependents
sonsoleslp
tna:Transition Network Analysis (TNA)
Provides tools for performing Transition Network Analysis (TNA) to study relational dynamics, including functions for building and plotting TNA models, calculating centrality measures, and identifying dominant events and patterns. TNA statistical techniques (e.g., bootstrapping and permutation tests) ensure the reliability of observed insights and confirm that identified dynamics are meaningful. See (Saqr et al., 2025) <doi:10.1145/3706468.3706513> for more details on TNA.
Maintained by Sonsoles Lรณpez-Pernas. Last updated 2 days ago.
educational-data-mininglearning-analyticsmarkov-modeltemporal-analysis
3.8 match 4 stars 6.48 score 5 scripts
afukushima
TFactSR:Enrichment Approach to Predict Which Transcription Factors are Regulated
R implementation of 'TFactS' to predict which are the transcription factors (TFs), regulated in a biological condition based on lists of differentially expressed genes (DEGs) obtained from transcriptome experiments. This package is based on the 'TFactS' concept by Essaghir et al. (2010) <doi:10.1093/nar/gkq149> and expands it. It allows users to perform 'TFactS'-like enrichment approach. The package can import and use the original catalogue file from the 'TFactS' as well as users' defined catalogues of interest that are not supported by 'TFactS' (e.g., Arabidopsis).
Maintained by Atsushi Fukushima. Last updated 2 years ago.
networksoftwaredifferentialexpressiongenetargetgeneexpressionmicroarrayrnaseqtranscriptionnetworkenrichment
6.3 match 3.70 score 3 scriptsbioc
decoupleR:decoupleR: Ensemble of computational methods to infer biological activities from omics data
Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase.
Maintained by Pau Badia-i-Mompel. Last updated 5 months ago.
differentialexpressionfunctionalgenomicsgeneexpressiongeneregulationnetworksoftwarestatisticalmethodtranscription
2.1 match 230 stars 11.27 score 316 scripts 3 dependents
scmethods
scregclust:Reconstructing the Regulatory Programs of Target Genes in scRNA-Seq Data
Implementation of the scregclust algorithm described in Larsson, Held, et al. (2024) <doi:10.1038/s41467-024-53954-3> which reconstructs regulatory programs of target genes in scRNA-seq data. Target genes are clustered into modules and each module is associated with a linear model describing the regulatory program.
Maintained by Felix Held. Last updated 2 months ago.
clusteringregulatory-programsscrna-seq-analysiscppopenmp
3.6 match 9 stars 6.45 score 21 scripts
humaniverse
asylum:Data on Asylum and Resettlement for the UK
Data on Asylum and Resettlement for the UK, provided by the Home Office <https://www.gov.uk/government/statistical-data-sets/immigration-system-statistics-data-tables>.
Maintained by Matthew Gwynfryn Thomas. Last updated 17 days ago.
4.5 match 3 stars 4.99 score 36 scripts
middleton-lab
abd:The Analysis of Biological Data
The abd package contains data sets and sample code for The Analysis of Biological Data by Michael Whitlock and Dolph Schluter (2009; Roberts & Company Publishers).
Maintained by Kevin M. Middleton. Last updated 11 months ago.
4.0 match 6 stars 5.53 score 182 scripts 1 dependentsbioc
EnrichmentBrowser:Seamless navigation through combined results of set-based and network-based enrichment analysis
The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways.
Maintained by Ludwig Geistlinger. Last updated 5 months ago.
immunooncologymicroarrayrnaseqgeneexpressiondifferentialexpressionpathwaysgraphandnetworknetworkgenesetenrichmentnetworkenrichmentvisualizationreportwriting
2.3 match 20 stars 9.37 score 164 scripts 3 dependents
lightbluetitan
usdatasets:A Comprehensive Collection of U.S. Datasets
Provides a diverse collection of U.S. datasets encompassing various fields such as crime, economics, education, finance, energy, healthcare, and more. It serves as a valuable resource for researchers and analysts seeking to perform in-depth analyses and derive insights from U.S.-specific data.
Maintained by Renzo Caceres Rossi. Last updated 5 months ago.
3.6 match 7 stars 5.99 score 141 scriptsbioc
regutools:regutools: an R package for data extraction from RegulonDB
RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools provides researchers with the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks.
Maintained by Joselyn Chavez. Last updated 3 months ago.
generegulationgeneexpressionsystemsbiologynetworknetworkinferencevisualizationtranscriptionbioconductorcdsbregulondb
4.1 match 4 stars 5.20 score 6 scriptsbioc
APAlyzer:A toolkit for APA analysis using RNA-seq data
Perform 3'UTR APA, Intronic APA and gene expression analysis using RNA-seq data.
Maintained by Ruijia Wang. Last updated 5 months ago.
sequencingrnaseqdifferentialexpressiongeneexpressiongeneregulationannotationdataimportsoftwareative-polyadenylationbioinformatics-toolrna-seq
3.7 match 8 stars 5.81 score 9 scripts
papatheodorou-group
scGOclust:Measuring Cell Type Similarity with Gene Ontology in Single-Cell RNA-Seq
Traditional methods for analyzing single cell RNA-seq datasets focus solely on gene expression, but this package introduces a novel approach that goes beyond this limitation. Using Gene Ontology terms as features, the package allows for the functional profile of cell populations, and comparison within and between datasets from the same or different species. Our approach enables the discovery of previously unrecognized functional similarities and differences between cell types and has demonstrated success in identifying cell types' functional correspondence even between evolutionarily distant species.
Maintained by Yuyao Song. Last updated 1 years ago.
4.4 match 9 stars 4.80 score 14 scripts
sebdejean
CCA:Canonical Correlation Analysis
Provides a set of functions that extend the 'cancor' function with new numerical and graphical outputs. It also include a regularized extension of the canonical correlation analysis to deal with datasets with more variables than observations.
Maintained by Sรฉbastien Dรฉjean. Last updated 2 years ago.
4.3 match 4.98 score 334 scripts 4 dependentsbioc
GeneNetworkBuilder:GeneNetworkBuilder: a bioconductor package for building regulatory network using ChIP-chip/ChIP-seq data and Gene Expression Data
Appliation for discovering direct or indirect targets of transcription factors using ChIP-chip or ChIP-seq, and microarray or RNA-seq gene expression data. Inputting a list of genes of potential targets of one TF from ChIP-chip or ChIP-seq, and the gene expression results, GeneNetworkBuilder generates a regulatory network of the TF.
Maintained by Jianhong Ou. Last updated 9 days ago.
sequencingmicroarraygraphandnetworkcpp
5.3 match 3.77 score 17 scripts
wouterbeukema
ectotemp:Quantitative Estimates of Small Ectotherm Temperature Regulation Effectiveness
Easy and rapid quantitative estimation of small terrestrial ectotherm temperature regulation effectiveness in R. ectotemp is built on classical formulas that evaluate temperature regulation by means of various indices, inaugurated by Hertz et al. (1993) <doi: 10.1086/285573>. Options for bootstrapping and permutation testing are included to test hypotheses about divergence between organisms, species or populations.
Maintained by Wouter Beukema. Last updated 5 years ago.
7.2 match 2.70 score 7 scriptsbioc
ceRNAnetsim:Regulation Simulator of Interaction between miRNA and Competing RNAs (ceRNA)
This package simulates regulations of ceRNA (Competing Endogenous) expression levels after a expression level change in one or more miRNA/mRNAs. The methodolgy adopted by the package has potential to incorparate any ceRNA (circRNA, lincRNA, etc.) into miRNA:target interaction network. The package basically distributes miRNA expression over available ceRNAs where each ceRNA attracks miRNAs proportional to its amount. But, the package can utilize multiple parameters that modify miRNA effect on its target (seed type, binding energy, binding location, etc.). The functions handle the given dataset as graph object and the processes progress via edge and node variables.
Maintained by Selcen Ari Yuka. Last updated 5 months ago.
networkinferencesystemsbiologynetworkgraphandnetworktranscriptomicscernamirnanetwork-biologynetwork-simulatortcgatidygraphtidyverse
3.4 match 4 stars 5.76 score 12 scripts
cran
BICORN:Integrative Inference of De Novo Cis-Regulatory Modules
Prior transcription factor binding knowledge and target gene expression data are integrated in a Bayesian framework for functional cis-regulatory module inference. Using Gibbs sampling, we iteratively estimate transcription factor associations for each gene, regulation strength for each binding event and the hidden activity for each transcription factor.
Maintained by Xi Chen. Last updated 7 years ago.
7.9 match 2.30 score
ropensci
tidyhydat:Extract and Tidy Canadian 'Hydrometric' Data
Provides functions to access historical and real-time national 'hydrometric' data from Water Survey of Canada data sources (<https://dd.weather.gc.ca/hydrometric/csv/> and <https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>) and then applies tidy data principles.
Maintained by Sam Albers. Last updated 4 days ago.
citzgovernment-datahydrologyhydrometricstidy-datawater-resources
1.8 match 71 stars 9.59 score 202 scripts 3 dependents
mathiasambuehl
cna:Causal Modeling with Coincidence Analysis
Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>, and generalized in Baumgartner & Ambuehl (2018) <doi:10.1017/psrm.2018.45>. CNA is designed to recover INUS-causation from data, which is particularly relevant for analyzing processes featuring conjunctural causation (component causation) and equifinality (alternative causation). CNA is currently the only method for INUS-discovery that allows for multiple effects (outcomes/endogenous factors), meaning it can analyze common-cause and causal chain structures.
Maintained by Mathias Ambuehl. Last updated 8 months ago.
3.6 match 1 stars 4.78 score 45 scripts 3 dependentsbioc
trigger:Transcriptional Regulatory Inference from Genetics of Gene ExpRession
This R package provides tools for the statistical analysis of integrative genomic data that involve some combination of: genotypes, high-dimensional intermediate traits (e.g., gene expression, protein abundance), and higher-order traits (phenotypes). The package includes functions to: (1) construct global linkage maps between genetic markers and gene expression; (2) analyze multiple-locus linkage (epistasis) for gene expression; (3) quantify the proportion of genome-wide variation explained by each locus and identify eQTL hotspots; (4) estimate pair-wise causal gene regulatory probabilities and construct gene regulatory networks; and (5) identify causal genes for a quantitative trait of interest.
Maintained by John D. Storey. Last updated 5 months ago.
geneexpressionsnpgeneticvariabilitymicroarraygenetics
5.0 match 3.30 score 3 scriptsbioc
RIVER:R package for RIVER (RNA-Informed Variant Effect on Regulation)
An implementation of a probabilistic modeling framework that jointly analyzes personal genome and transcriptome data to estimate the probability that a variant has regulatory impact in that individual. It is based on a generative model that assumes that genomic annotations, such as the location of a variant with respect to regulatory elements, determine the prior probability that variant is a functional regulatory variant, which is an unobserved variable. The functional regulatory variant status then influences whether nearby genes are likely to display outlier levels of gene expression in that person. See the RIVER website for more information, documentation and examples.
Maintained by Yungil Kim. Last updated 5 months ago.
geneexpressiongeneticvariabilitysnptranscriptionfunctionalpredictiongeneregulationgenomicvariationbiomedicalinformaticsfunctionalgenomicsgeneticssystemsbiologytranscriptomicsbayesianclusteringtranscriptomevariantregressionfunctional-variantsvariant
2.8 match 11 stars 5.52 score 5 scripts
jiang-junyao
CACIMAR:cross-species analysis of cell identities, markers and regulations
A toolkit to perform cross-species analysis based on scRNA-seq data. CACIMAR contains 5 main features. (1) identify Markers in each cluster. (2) Cell type annotaion (3) identify conserved markers. (4) identify conserved cell types. (5) identify conserved modules of regulatory networks.
Maintained by Junyao Jiang. Last updated 3 months ago.
cross-species-analysisscrna-seq
2.9 match 12 stars 5.26 score 6 scriptsbioc
RTN:RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons
A transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons.
Maintained by Mauro Castro. Last updated 5 months ago.
transcriptionnetworknetworkinferencenetworkenrichmentgeneregulationgeneexpressiongraphandnetworkgenesetenrichmentgeneticvariability
2.5 match 5.80 score 53 scripts 2 dependentsbioc
epiregulon:Gene regulatory network inference from single cell epigenomic data
Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.
Maintained by Xiaosai Yao. Last updated 6 days ago.
singlecellgeneregulationnetworkinferencenetworkgeneexpressiontranscriptiongenetargetcpp
2.2 match 14 stars 6.67 score 17 scriptsbioc
PanomiR:Detection of miRNAs that regulate interacting groups of pathways
PanomiR is a package to detect miRNAs that target groups of pathways from gene expression data. This package provides functionality for generating pathway activity profiles, determining differentially activated pathways between user-specified conditions, determining clusters of pathways via the PCxN package, and generating miRNAs targeting clusters of pathways. These function can be used separately or sequentially to analyze RNA-Seq data.
Maintained by Pourya Naderi. Last updated 5 months ago.
geneexpressiongenesetenrichmentgenetargetmirnapathways
2.9 match 3 stars 4.89 score 13 scriptsbioc
AffiXcan:A Functional Approach To Impute Genetically Regulated Expression
Impute a GReX (Genetically Regulated Expression) for a set of genes in a sample of individuals, using a method based on the Total Binding Affinity (TBA). Statistical models to impute GReX can be trained with a training dataset where the real total expression values are known.
Maintained by Alessandro Lussana. Last updated 5 months ago.
geneexpressiontranscriptiongeneregulationdimensionreductionregressionprincipalcomponent
3.4 match 4.00 scorebioc
NCIgraph:Pathways from the NCI Pathways Database
Provides various methods to load the pathways from the NCI Pathways Database in R graph objects and to re-format them.
Maintained by Laurent Jacob. Last updated 5 months ago.
3.2 match 4.26 score 10 scripts 1 dependents
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.
lipidomicsmassspectrometrynormalizationqualitycontrolvisualizationbioconductor
1.7 match 29 stars 7.44 score 40 scriptsbioc
xcore:xcore expression regulators inference
xcore is an R package for transcription factor activity modeling based on known molecular signatures and user's gene expression data. Accompanying xcoredata package provides a collection of molecular signatures, constructed from publicly available ChiP-seq experiments. xcore use ridge regression to model changes in expression as a linear combination of molecular signatures and find their unknown activities. Obtained, estimates can be further tested for significance to select molecular signatures with the highest predicted effect on the observed expression changes.
Maintained by Maciej Migdaล. Last updated 5 months ago.
geneexpressiongeneregulationepigeneticsregressionsequencing
3.1 match 4.00 score 8 scriptsbioc
INSPEcT:Modeling RNA synthesis, processing and degradation with RNA-seq data
INSPEcT (INference of Synthesis, Processing and dEgradation rates from Transcriptomic data) RNA-seq data in time-course experiments or steady-state conditions, with or without the support of nascent RNA data.
Maintained by Stefano de Pretis. Last updated 5 months ago.
sequencingrnaseqgeneregulationtimecoursesystemsbiology
2.8 match 4.38 score 9 scripts
javanderwal
readapra:Download and Tidy Data from the Australian Prudential Regulation Authority
Download the latest data from the Australian Prudential Regulation Authority <https://www.apra.gov.au/> and import it into R as a tidy data frame.
Maintained by Jarrod van der Wal. Last updated 21 days ago.
3.4 match 3.65 scorebioc
GSReg:Gene Set Regulation (GS-Reg)
A package for gene set analysis based on the variability of expressions as well as a method to detect Alternative Splicing Events . It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. For detecting Differentially Spliced genes, it provides an implementation of the Spliced-EVA (SEVA).
Maintained by Bahman Afsari. Last updated 5 months ago.
generegulationpathwaysgeneexpressiongeneticvariabilitygenesetenrichmentalternativesplicing
3.0 match 3.98 score 16 scriptsbioc
gINTomics:Multi-Omics data integration
gINTomics is an R package for Multi-Omics data integration and visualization. gINTomics is designed to detect the association between the expression of a target and of its regulators, taking into account also their genomics modifications such as Copy Number Variations (CNV) and methylation. What is more, gINTomics allows integration results visualization via a Shiny-based interactive app.
Maintained by Angelo Velle. Last updated 5 months ago.
geneexpressionrnaseqmicroarrayvisualizationcopynumbervariationgenetargetquarto
2.3 match 3 stars 5.08 score 3 scriptsbioc
fCI:f-divergence Cutoff Index for Differential Expression Analysis in Transcriptomics and Proteomics
(f-divergence Cutoff Index), is to find DEGs in the transcriptomic & proteomic data, and identify DEGs by computing the difference between the distribution of fold-changes for the control-control and remaining (non-differential) case-control gene expression ratio data. fCI provides several advantages compared to existing methods.
Maintained by Shaojun Tang. Last updated 5 months ago.
3.4 match 3.30 score 5 scriptshanjunwei-lab
DRviaSPCN:Drug Repurposing in Cancer via a Subpathway Crosstalk Network
A systematic biology tool was developed to repurpose drugs via a subpathway crosstalk network. The operation modes include 1) calculating centrality scores of SPs in the context of gene expression data to reflect the influence of SP crosstalk, 2) evaluating drug-disease reverse association based on disease- and drug-induced SPs weighted by the SP crosstalk, 3) identifying cancer candidate drugs through perturbation analysis. There are also several functions used to visualize the results.
Maintained by Junwei Han. Last updated 2 months ago.
5.6 match 2.00 score 5 scripts
shixinrui
SubpathwayLNCE:Identify Signal Subpathways Competitively Regulated by LncRNAs Based on ceRNA Theory
Identify dysfunctional subpathways competitively regulated by lncRNAs through integrating lncRNA-mRNA expression profile and pathway topologies.
Maintained by Xinrui Shi. Last updated 9 years ago.
statisticssupathwayslncrnasenrichment analysiscerna
5.0 match 2.00 score 2 scriptsbioc
ELMER:Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes
ELMER is designed to use DNA methylation and gene expression from a large number of samples to infere regulatory element landscape and transcription factor network in primary tissue.
Maintained by Tiago Chedraoui Silva. Last updated 5 months ago.
dnamethylationgeneexpressionmotifannotationsoftwaregeneregulationtranscriptionnetwork
1.3 match 7.42 score 176 scriptsbioc
FindIT2:find influential TF and Target based on multi-omics data
This package implements functions to find influential TF and target based on different input type. It have five module: Multi-peak multi-gene annotaion(mmPeakAnno module), Calculate regulation potential(calcRP module), Find influential Target based on ChIP-Seq and RNA-Seq data(Find influential Target module), Find influential TF based on different input(Find influential TF module), Calculate peak-gene or peak-peak correlation(peakGeneCor module). And there are also some other useful function like integrate different source information, calculate jaccard similarity for your TF.
Maintained by Guandong Shang. Last updated 5 months ago.
softwareannotationchipseqatacseqgeneregulationmultiplecomparisongenetarget
1.8 match 6 stars 5.26 score 7 scriptsbioc
CausalR:Causal network analysis methods
Causal network analysis methods for regulator prediction and network reconstruction from genome scale data.
Maintained by Glyn Bradley. Last updated 5 months ago.
immunooncologysystemsbiologynetworkgraphandnetworknetwork inferencetranscriptomicsproteomicsdifferentialexpressionrnaseqmicroarray
2.5 match 3.60 score 7 scriptsbioc
GENIE3:GEne Network Inference with Ensemble of trees
This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data.
Maintained by Van Anh Huynh-Thu. Last updated 5 months ago.
networkinferencesystemsbiologydecisiontreeregressionnetworkgraphandnetworkgeneexpression
1.2 match 7.62 score 180 scripts 4 dependentsbioc
BulkSignalR:Infer Ligand-Receptor Interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics
Inference of ligand-receptor (LR) interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics. BulkSignalR bases its inferences on the LRdb database included in our other package, SingleCellSignalR available from Bioconductor. It relies on a statistical model that is specific to bulk data sets. Different visualization and data summary functions are proposed to help navigating prediction results.
Maintained by Jean-Philippe Villemin. Last updated 3 months ago.
networkrnaseqsoftwareproteomicstranscriptomicsnetworkinferencespatial
1.8 match 5.22 score 15 scripts
core-bioinformatics
bulkAnalyseR:Interactive Shiny App for Bulk Sequencing Data
Given an expression matrix from a bulk sequencing experiment, pre-processes it and creates a shiny app for interactive data analysis and visualisation. The app contains quality checks, differential expression analysis, volcano and cross plots, enrichment analysis and gene regulatory network inference, and can be customised to contain more panels by the user.
Maintained by Ilias Moutsopoulos. Last updated 1 years ago.
2.0 match 27 stars 4.47 score 11 scriptsbioc
MiRaGE:MiRNA Ranking by Gene Expression
The package contains functions for inferece of target gene regulation by miRNA, based on only target gene expression profile.
Maintained by Y-h. Taguchi. Last updated 5 months ago.
immunooncologymicroarraygeneexpressionrnaseqsequencingsage
2.3 match 3.85 score 35 scriptsbioc
MOSim:Multi-Omics Simulation (MOSim)
MOSim package simulates multi-omic experiments that mimic regulatory mechanisms within the cell, allowing flexible experimental design including time course and multiple groups.
Maintained by Sonia Tarazona. Last updated 5 months ago.
softwaretimecourseexperimentaldesignrnaseqcpp
1.2 match 9 stars 7.46 score 11 scripts
kjhealy
gssrdoc:Document General Social Survey Variable
The General Social Survey (GSS) is a long-running, mostly annual survey of US households. It is administered by the National Opinion Research Center (NORC). This package contains the a tibble with information on the survey variables, together with every variable documented as an R help page. For more information on the GSS see \url{http://gss.norc.org}.
Maintained by Kieran Healy. Last updated 11 months ago.
3.8 match 2.28 score 38 scriptsbioc
chipenrich:Gene Set Enrichment For ChIP-seq Peak Data
ChIP-Enrich and Poly-Enrich perform gene set enrichment testing using peaks called from a ChIP-seq experiment. The method empirically corrects for confounding factors such as the length of genes, and the mappability of the sequence surrounding genes.
Maintained by Kai Wang. Last updated 4 days ago.
immunooncologychipseqepigeneticsfunctionalgenomicsgenesetenrichmenthistonemodificationregression
1.7 match 4.94 score 29 scriptsbioc
coMET:coMET: visualisation of regional epigenome-wide association scan (EWAS) results and DNA co-methylation patterns
Visualisation of EWAS results in a genomic region. In addition to phenotype-association P-values, coMET also generates plots of co-methylation patterns and provides a series of annotation tracks. It can be used to other omic-wide association scans as lon:g as the data can be translated to genomic level and for any species.
Maintained by Tiphaine Martin. Last updated 2 days ago.
softwaredifferentialmethylationvisualizationsequencinggeneticsfunctionalgenomicsmicroarraymethylationarraymethylseqchipseqdnaseqriboseqrnaseqexomeseqdnamethylationgenomewideassociationmotifannotation
1.9 match 4.41 score 17 scriptsbioc
tigre:Transcription factor Inference through Gaussian process Reconstruction of Expression
The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF.
Maintained by Antti Honkela. Last updated 5 months ago.
microarraytimecoursegeneexpressiontranscriptiongeneregulationnetworkinferencebayesian
1.8 match 4.38 score 6 scriptsbioc
CTexploreR:Explores Cancer Testis Genes
The CTexploreR package re-defines the list of Cancer Testis/Germline (CT) genes. It is based on publicly available RNAseq databases (GTEx, CCLE and TCGA) and summarises CT genes' main characteristics. Several visualisation functions allow to explore their expression in different types of tissues and cancer cells, or to inspect the methylation status of their promoters in normal tissues.
Maintained by Axelle Loriot. Last updated 5 months ago.
transcriptomicsepigeneticsdifferentialexpressiongeneexpressiondnamethylationexperimenthubsoftwaredataimportbioconductor
1.5 match 5.02 score 2 scriptsbioc
groHMM:GRO-seq Analysis Pipeline
A pipeline for the analysis of GRO-seq data.
Maintained by Tulip Nandu. Last updated 2 days ago.
1.7 match 1 stars 4.48 score 25 scripts
ftwkoopmans
goat:Gene Set Analysis Using the Gene Set Ordinal Association Test
Perform gene set enrichment analyses using the Gene set Ordinal Association Test (GOAT) algorithm and visualize your results. Koopmans, F. (2024) <doi:10.1038/s42003-024-06454-5>.
Maintained by Frank Koopmans. Last updated 22 days ago.
bioinformaticsgeneset-enrichmentgeneset-enrichment-analysiscppopenmp
1.6 match 10 stars 4.40 score 8 scripts
yannabraham
Radviz:Project Multidimensional Data in 2D Space
An implementation of the radviz projection in R. It enables the visualization of multidimensional data while maintaining the relation to the original dimensions. This package provides functions to create and plot radviz projections, and a number of summary plots that enable comparison and analysis. For reference see Ankerst *et al.* (1996) (<https://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.1811>) for original implementation, see Di Caro *et al* (2012) (<https://link.springer.com/chapter/10.1007/978-3-642-13672-6_13>) for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) (<doi:10.1016/j.jbi.2007.03.010>) for the original Freeviz implementation.
Maintained by Yann Abraham. Last updated 3 years ago.
high-dimensional-dataradvizsciencevisualizationcpp
1.1 match 10 stars 6.19 score 52 scripts
kwb-r
r2q:Connectable Separate Sewer System to Small Surface Waters - An Immission Based Assessment
The R package is used to define a tolerable pollutant input into small surface waters via rainwater runoff. It assigns a maximal connectable urban area to the surface water. For planning areas, different scenarios regarding the connection of surfaces to the separate sewer system and runoff water treatment can be calculated.
Maintained by Malte Zamzow. Last updated 2 years ago.
1.7 match 4.00 score 2 scriptsbioc
ChIPseeker:ChIPseeker for ChIP peak Annotation, Comparison, and Visualization
This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes.
Maintained by Guangchuang Yu. Last updated 5 months ago.
annotationchipseqsoftwarevisualizationmultiplecomparisonatac-seqchip-seqcomparisonepigeneticsepigenomics
0.5 match 234 stars 13.02 score 1.6k scripts 5 dependentsbioc
diggit:Inference of Genetic Variants Driving Cellular Phenotypes
Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm
Maintained by Mariano J Alvarez. Last updated 5 months ago.
systemsbiologynetworkenrichmentgeneexpressionfunctionalpredictiongeneregulation
2.0 match 3.30 score 3 scripts
arcolombo
imcExperiment:Mass Cytometry S4 Class Structure Pipeline for Images
Containerizes cytometry data and allows for S4 class structure to extend slots related to cell morphology, spatial coordinates, phenotype network information, and unique cellular labeling.
Maintained by Anthony Colombo. Last updated 4 years ago.
softwareworkflowstepmultiplecomparisonimc
1.7 match 3.70 score 5 scriptsbioc
mistyR:Multiview Intercellular SpaTial modeling framework
mistyR is an implementation of the Multiview Intercellular SpaTialmodeling framework (MISTy). MISTy is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. Each MISTy view is then analyzed for its contribution to the total expression of each marker and is explained in terms of the interactions with other measurements that led to the observed contribution.
Maintained by Jovan Tanevski. Last updated 5 months ago.
softwarebiomedicalinformaticscellbiologysystemsbiologyregressiondecisiontreesinglecellspatialbioconductorbiologyintercellularmachine-learningmodularmolecular-biologymultiviewspatial-transcriptomics
0.8 match 51 stars 7.87 score 160 scriptscran
CeRNASeek:Identification and Analysis of ceRNA Regulation
Provides several functions to identify and analyse miRNA sponge, including popular methods for identifying miRNA sponge interactions, two types of global ceRNA regulation prediction methods and four types of context-specific prediction methods( Li Y et al.(2017) <doi:10.1093/bib/bbx137>), which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. In addition, For predictive ceRNA relationship pairs, this package provides several downstream analysis algorithms, including regulatory network analysis and functional annotation analysis, as well as survival prognosis analysis based on expression of ceRNA ternary pair.
Maintained by Mengying Zhang. Last updated 5 years ago.
competing endogenous rna (cerna)geneexpressiontripletfunctionsoftware
5.6 match 1.00 scorebioc
geneplast:Evolutionary and plasticity analysis of orthologous groups
Geneplast is designed for evolutionary and plasticity analysis based on orthologous groups distribution in a given species tree. It uses Shannon information theory and orthologs abundance to estimate the Evolutionary Plasticity Index. Additionally, it implements the Bridge algorithm to determine the evolutionary root of a given gene based on its orthologs distribution.
Maintained by Mauro Castro. Last updated 5 months ago.
geneticsgeneregulationsystemsbiology
1.3 match 4.13 score 17 scripts
cogdisreslab
PAVER:PAVER: Pathway Analysis Visualization with Embedding Representations
Summary visualization using embedding representations to reveal underlying themes within sets of pathway terms.
Maintained by William G Ryan V. Last updated 8 months ago.
1.5 match 3.48 score 6 scriptscran
XDNUTS:Discontinuous Hamiltonian Monte Carlo with Varying Trajectory Length
Hamiltonian Monte Carlo for both continuous and discontinuous posterior distributions with customisable trajectory length termination criterion. See Nishimura et al. (2020) <doi:10.1093/biomet/asz083> for the original Discontinuous Hamiltonian Monte Carlo, Hoffman et al. (2014) <doi:10.48550/arXiv.1111.4246> and Betancourt (2016) <doi:10.48550/arXiv.1601.00225> for the definition of possible Hamiltonian Monte Carlo termination criteria.
Maintained by Paolo Manildo. Last updated 2 months ago.
1.8 match 2.78 scorebioc
tomoda:Tomo-seq data analysis
This package provides many easy-to-use methods to analyze and visualize tomo-seq data. The tomo-seq technique is based on cryosectioning of tissue and performing RNA-seq on consecutive sections. (Reference: Kruse F, Junker JP, van Oudenaarden A, Bakkers J. Tomo-seq: A method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol. 2016;135:299-307. doi:10.1016/bs.mcb.2016.01.006) The main purpose of the package is to find zones with similar transcriptional profiles and spatially expressed genes in a tomo-seq sample. Several visulization functions are available to create easy-to-modify plots.
Maintained by Wendao Liu. Last updated 5 months ago.
geneexpressionsequencingrnaseqtranscriptomicsspatialclusteringvisualization
1.3 match 4.00 score 2 scriptsyannabraham
bodenmiller:Profiling of Peripheral Blood Mononuclear Cells using CyTOF
This data package contains a subset of the Bodenmiller et al, Nat Biotech 2012 dataset for testing single cell, high dimensional analysis and visualization methods.
Maintained by Yann Abraham. Last updated 4 years ago.
bioinformaticscytofdatasetscience
1.1 match 2 stars 4.45 score 28 scriptsbioc
scMET:Bayesian modelling of cell-to-cell DNA methylation heterogeneity
High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression.
Maintained by Andreas C. Kapourani. Last updated 5 months ago.
immunooncologydnamethylationdifferentialmethylationdifferentialexpressiongeneexpressiongeneregulationepigeneticsgeneticsclusteringfeatureextractionregressionbayesiansequencingcoveragesinglecellbayesian-inferencegeneralised-linear-modelsheterogeneityhierarchical-modelsmethylation-analysissingle-cellcpp
0.8 match 20 stars 6.23 score 42 scriptscran
qtlhot:Inference for QTL Hotspots
Functions to infer co-mapping trait hotspots and causal models. Chaibub Neto E, Keller MP, Broman AF, Attie AD, Jansen RC, Broman KW, Yandell BS (2012) Quantile-based permutation thresholds for QTL hotspots. Genetics 191 : 1355-1365. <doi:10.1534/genetics.112.139451>. Chaibub Neto E, Broman AT, Keller MP, Attie AD, Zhang B, Zhu J, Yandell BS (2013) Modeling causality for pairs of phenotypes in system genetics. Genetics 193 : 1003-1013. <doi:10.1534/genetics.112.147124>.
Maintained by Brian S. Yandell. Last updated 7 years ago.
1.7 match 2.78 score 30 scriptsbioc
SubCellBarCode:SubCellBarCode: Integrated workflow for robust mapping and visualizing whole human spatial proteome
Mass-Spectrometry based spatial proteomics have enabled the proteome-wide mapping of protein subcellular localization (Orre et al. 2019, Molecular Cell). SubCellBarCode R package robustly classifies proteins into corresponding subcellular localization.
Maintained by Taner Arslan. Last updated 5 months ago.
proteomicsmassspectrometryclassification
1.1 match 3.78 score 1 scriptsbioc
sevenC:Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs
Chromatin looping is an essential feature of eukaryotic genomes and can bring regulatory sequences, such as enhancers or transcription factor binding sites, in the close physical proximity of regulated target genes. Here, we provide sevenC, an R package that uses protein binding signals from ChIP-seq and sequence motif information to predict chromatin looping events. Cross-linking of proteins that bind close to loop anchors result in ChIP-seq signals at both anchor loci. These signals are used at CTCF motif pairs together with their distance and orientation to each other to predict whether they interact or not. The resulting chromatin loops might be used to associate enhancers or transcription factor binding sites (e.g., ChIP-seq peaks) to regulated target genes.
Maintained by Jonas Ibn-Salem. Last updated 5 months ago.
dna3dstructurechipchipcoveragedataimportepigeneticsfunctionalgenomicsclassificationregressionchipseqhicannotation3d-genomechip-seqchromatin-interactionhi-cpredictionsequence-motiftranscription-factors
0.8 match 12 stars 5.38 score 3 scriptsbioc
dreamlet:Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs
Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.
Maintained by Gabriel Hoffman. Last updated 5 months ago.
rnaseqgeneexpressiondifferentialexpressionbatcheffectqualitycontrolregressiongenesetenrichmentgeneregulationepigeneticsfunctionalgenomicstranscriptomicsnormalizationsinglecellpreprocessingsequencingimmunooncologysoftwarecpp
0.5 match 12 stars 8.09 score 128 scriptsbioc
eisaR:Exon-Intron Split Analysis (EISA) in R
Exon-intron split analysis (EISA) uses ordinary RNA-seq data to measure changes in mature RNA and pre-mRNA reads across different experimental conditions to quantify transcriptional and post-transcriptional regulation of gene expression. For details see Gaidatzis et al., Nat Biotechnol 2015. doi: 10.1038/nbt.3269. eisaR implements the major steps of EISA in R.
Maintained by Michael Stadler. Last updated 2 months ago.
transcriptiongeneexpressiongeneregulationfunctionalgenomicstranscriptomicsregressionrnaseq
0.5 match 16 stars 7.48 score 63 scripts
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 3 months ago.
annotationdifferentialexpressiongeneexpressiongenesetenrichmentgographandnetworkmultiplecomparisonpathwaysreactomesoftwaretranscriptionvisualizationenrichmentenrichment-analysisfunctional-enrichment-analysisgene-set-enrichmentontologiestranscriptomicscpp
0.5 match 28 stars 7.36 score 34 scriptscran
QCApro:Advanced Functionality for Performing and Evaluating Qualitative Comparative Analysis
Provides advanced functionality for performing configurational comparative research with Qualitative Comparative Analysis (QCA), including crisp-set, multi-value, and fuzzy-set QCA. It also offers advanced tools for sensitivity diagnostics and methodological evaluations of QCA.
Maintained by Alrik Thiem. Last updated 7 years ago.
3.6 match 1 stars 1.00 scorehanjunwei-lab
MiRSEA:'MicroRNA' Set Enrichment Analysis
The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.
Maintained by Junwei Han. Last updated 5 years ago.
statisticspathwaysmicrornaenrichment analysis
0.8 match 4.51 score 16 scripts
daklor
netcontrol:Control Theory Methods for Networks
Implementations of various control theory methods for use in brain and psychological networks. Contains controllability statistics from Pasqualetti, Zampieri & Bullo (2014) <doi:10.1109/TCNS.2014.2310254>, optimal control algorithms from Lewis, Vrabie & Syrmos (2012, ISBN:978-0-470-63349-6), and various utilities.
Maintained by Teague R. Henry. Last updated 5 years ago.
2.0 match 1 stars 1.70 score 3 scriptsbioc
proActiv:Estimate Promoter Activity from RNA-Seq data
Most human genes have multiple promoters that control the expression of different isoforms. The use of these alternative promoters enables the regulation of isoform expression pre-transcriptionally. Alternative promoters have been found to be important in a wide number of cell types and diseases. proActiv is an R package that enables the analysis of promoters from RNA-seq data. proActiv uses aligned reads as input, and generates counts and normalized promoter activity estimates for each annotated promoter. In particular, proActiv accepts junction files from TopHat2 or STAR or BAM files as inputs. These estimates can then be used to identify which promoter is active, which promoter is inactive, and which promoters change their activity across conditions. proActiv also allows visualization of promoter activity across conditions.
Maintained by Joseph Lee. Last updated 5 months ago.
rnaseqgeneexpressiontranscriptionalternativesplicinggeneregulationdifferentialsplicingfunctionalgenomicsepigeneticstranscriptomicspreprocessingalternative-promotersgenomicspromoter-activitypromoter-annotationrna-seq-data
0.5 match 51 stars 6.66 score 15 scriptsbioc
RankProd:Rank Product method for identifying differentially expressed genes with application in meta-analysis
Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification.
Maintained by Francesco Del Carratore. Last updated 5 months ago.
differentialexpressionstatisticalmethodsoftwareresearchfieldmetabolomicslipidomicsproteomicssystemsbiologygeneexpressionmicroarraygenesignaling
0.5 match 6.46 score 81 scripts 6 dependentsbioc
GIGSEA:Genotype Imputed Gene Set Enrichment Analysis
We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal.
Maintained by Shijia Zhu. Last updated 5 months ago.
genesetenrichmentsnpvariantannotationgeneexpressiongeneregulationregressiondifferentialexpression
0.8 match 4.30 score 2 scripts
iheid-library
iheiddown:For Writing Geneva Graduate Institute Documents
A set of tools for writing documents according to Geneva Graduate Institute conventions and regulations. The most common use is for writing and compiling theses or thesis chapters, as drafts or for examination with correct preamble formatting. However, the package also offers users to create HTML presentation slides with 'xaringan', complete problem sets, format posters, and, for course instructors, prepare a syllabus. The package includes additional functions for institutional color palettes, an institutional 'ggplot' theme, a function for counting manuscript words, and a bibliographical analysis toolkit.
Maintained by James Hollway. Last updated 2 years ago.
0.5 match 11 stars 6.14 score 5 scriptshanjunwei-lab
SMDIC:Identification of Somatic Mutation-Driven Immune Cells
A computing tool is developed to automated identify somatic mutation-driven immune cells. The operation modes including: i) inferring the relative abundance matrix of tumor-infiltrating immune cells and integrating it with a particular gene mutation status, ii) detecting differential immune cells with respect to the gene mutation status and converting the abundance matrix of significant differential immune cell into two binary matrices (one for up-regulated and one for down-regulated), iii) identifying somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and iv) visualization of immune cell abundance of samples in different mutation status..
Maintained by Junwei Han. Last updated 5 months ago.
0.8 match 2 stars 4.00 score 5 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 3 days ago.
visualizationsinglecellrnaseqinteractive-visualizationsmultiple-usersshiny-appssingle-cell-rna-seq
0.5 match 4 stars 5.76 score 3 scriptsbioc
GRaNIE:GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using single-cell or bulk chromatin accessibility and RNA-seq data
Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have cell-type specific activity. This TF activity can be quantified with existing tools such as diffTF and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN using single-cell or bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open chromatin marks) and optionally (Capture) Hi-C data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach.
Maintained by Christian Arnold. Last updated 5 months ago.
softwaregeneexpressiongeneregulationnetworkinferencegenesetenrichmentbiomedicalinformaticsgeneticstranscriptomicsatacseqrnaseqgraphandnetworkregressiontranscriptionchipseq
0.5 match 5.40 score 24 scriptsbioc
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.
geneexpressiondifferentialexpressionpathwaysnetwork
0.5 match 6 stars 5.26 score 8 scriptsbioc
rScudo:Signature-based Clustering for Diagnostic Purposes
SCUDO (Signature-based Clustering for Diagnostic Purposes) is a rank-based method for the analysis of gene expression profiles for diagnostic and classification purposes. It is based on the identification of sample-specific gene signatures composed of the most up- and down-regulated genes for that sample. Starting from gene expression data, functions in this package identify sample-specific gene signatures and use them to build a graph of samples. In this graph samples are joined by edges if they have a similar expression profile, according to a pre-computed similarity matrix. The similarity between the expression profiles of two samples is computed using a method similar to GSEA. The graph of samples can then be used to perform community clustering or to perform supervised classification of samples in a testing set.
Maintained by Matteo Ciciani. Last updated 5 months ago.
geneexpressiondifferentialexpressionbiomedicalinformaticsclassificationclusteringgraphandnetworknetworkproteomicstranscriptomicssystemsbiologyfeatureextraction
0.5 match 4 stars 5.19 score 13 scripts
mharinga
spatialrisk:Calculating Spatial Risk
Methods for spatial risk calculations. It offers an efficient approach to determine the sum of all observations within a circle of a certain radius. This might be beneficial for insurers who are required (by a recent European Commission regulation) to determine the maximum value of insured fire risk policies of all buildings that are partly or fully located within a circle of a radius of 200m. See Church (1974) <doi:10.1007/BF01942293> for a description of the problem.
Maintained by Martin Haringa. Last updated 7 months ago.
actuarial-scienceinsurancesolvency-iispatialcpp
0.5 match 19 stars 5.06 score 30 scripts
divadnojnarg
CaPO4Sim:A Virtual Patient Simulator in the Context of Calcium and Phosphate Homeostasis
Explore calcium (Ca) and phosphate (Pi) homeostasis with two novel 'Shiny' apps, building upon on a previously published mathematical model written in C, to ensure efficient computations. The underlying model is accessible here <https://pubmed.ncbi.nlm.nih.gov/28747359/)>. The first application explores the fundamentals of Ca-Pi homeostasis, while the second provides interactive case studies for in-depth exploration of the topic, thereby seeking to foster student engagement and an integrative understanding of Ca-Pi regulation.
Maintained by David Granjon. Last updated 2 months ago.
0.5 match 40 stars 4.92 score 14 scriptsbioc
epiregulon.extra:Companion package to epiregulon with additional plotting, differential and graph functions
Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.
Maintained by Xiaosai Yao. Last updated 6 days ago.
generegulationnetworkgeneexpressiontranscriptionchiponchipdifferentialexpressiongenetargetnormalizationgraphandnetwork
0.5 match 4.90 score 10 scriptscailab-tamu
scTenifoldKnk:In-Silico Knockout Experiments from Single-Cell Gene Regulatory Networks
A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the geneโs outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the geneโs function in the analyzed cells.
Maintained by Daniel Osorio. Last updated 2 months ago.
functional-genomicsgene-functiongene-knockoutgene-regulatory-networkvirtual-knockout-experiments
0.5 match 43 stars 4.85 score 11 scripts
lbbe-software
rbioacc:Inference and Prediction of ToxicoKinetic (TK) Models
The MOSAICbioacc application is a turnkey package providing bioaccumulation factors (BCF/BMF/BSAF) from a toxicokinetic (TK) model fitted to accumulation-depuration data. It is designed to fulfil the requirements of regulators when examining applications for market authorization of active substances. See Ratier et al. (2021) <doi:10.1101/2021.09.08.459421>.
Maintained by Virgile Baudrot. Last updated 1 years ago.
0.5 match 4.78 score 8 scripts
quantilma
quantdates:Manipulate Dates for Finance
Functions to manipulate dates and count days for quantitative finance analysis. The 'quantdates' package considers leap, holidays and business days for relevant calendars in a financial context to simplify quantitative finance calculations, consistent with International Swaps and Derivatives Association (ISDA) (2006) <https://www.isda.org/book/2006-isda-definitions/> regulations.
Maintained by Juan Pablo Bermudez. Last updated 9 months ago.
datesfinancequantitative-finance
0.5 match 2 stars 4.78 score 3 scripts 1 dependentsbioc
GenomicInteractionNodes:A R/Bioconductor package to detect the interaction nodes from HiC/HiChIP/HiCAR data
The GenomicInteractionNodes package can import interactions from bedpe file and define the interaction nodes, the genomic interaction sites with multiple interaction loops. The interaction nodes is a binding platform regulates one or multiple genes. The detected interaction nodes will be annotated for downstream validation.
Maintained by Jianhong Ou. Last updated 1 months ago.
0.5 match 4.70 score 1 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
0.5 match 2 stars 4.60 score 5 scriptsbioc
NoRCE:NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment
While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint to a functional association. We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast.
Maintained by Gulden Olgun. Last updated 5 months ago.
biologicalquestiondifferentialexpressiongenomeannotationgenesetenrichmentgenetargetgenomeassemblygo
0.5 match 1 stars 4.60 score 6 scripts
tacazares
SeedMatchR:Find Matches to Canonical SiRNA Seeds in Genomic Features
On-target gene knockdown using siRNA ideally results from binding fully complementary regions in mRNA transcripts to induce cleavage. Off-target siRNA gene knockdown can occur through several modes, one being a seed-mediated mechanism mimicking miRNA gene regulation. Seed-mediated off-target effects occur when the ~8 nucleotides at the 5โ end of the guide strand, called a seed region, bind the 3โ untranslated regions of mRNA, causing reduced translation. Experiments using siRNA knockdown paired with RNA-seq can be used to detect siRNA sequences with potential off-target effects driven by the seed region. 'SeedMatchR' provides tools for exploring and detecting potential seed-mediated off-target effects of siRNA in RNA-seq experiments. 'SeedMatchR' is designed to extend current differential expression analysis tools, such as 'DESeq2', by annotating results with predicted seed matches. Using publicly available data, we demonstrate the ability of 'SeedMatchR' to detect cumulative changes in differential gene expression attributed to siRNA seed regions.
Maintained by Tareian Cazares. Last updated 1 years ago.
deseq2-analysismirnarna-seqsirnatranscriptomics
0.5 match 7 stars 4.54 score 7 scripts
dcourvoisier
doremi:Dynamics of Return to Equilibrium During Multiple Inputs
Provides models to fit the dynamics of a regulated system experiencing exogenous inputs. The underlying models use differential equations and linear mixed-effects regressions to estimate the coefficients of the equation. With them, the functions can provide an estimated signal. The package provides simulation and analysis functions and also print, summary, plot and predict methods, adapted to the function outputs, for easy implementation and presentation of results.
Maintained by Mongin Denis. Last updated 3 years ago.
0.5 match 4.48 score 25 scripts 1 dependentsbioc
G4SNVHunter:Evaluating SNV-Induced Disruption of G-Quadruplex Structures
G-quadruplexes (G4s) are unique nucleic acid secondary structures predominantly found in guanine-rich regions and have been shown to be involved in various biological regulatory processes. G4SNVHunter is an R package designed to rapidly identify genomic sequences with G4-forming potential and accurately screen user-provided single nucleotide variants (also applicable to single nucleotide polymorphisms) that may destabilize these structures. This enables users to screen key variants for further experimental study, investigating how these variants may influence biological functions, such as gene regulation, by altering G4 formation.
Maintained by Rongxin Zhang. Last updated 3 months ago.
0.5 match 4.48 score 4 scriptsbioc
anota2seq:Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq
anota2seq provides analysis of translational efficiency and differential expression analysis for polysome-profiling and ribosome-profiling studies (two or more sample classes) quantified by RNA sequencing or DNA-microarray. Polysome-profiling and ribosome-profiling typically generate data for two RNA sources; translated mRNA and total mRNA. Analysis of differential expression is used to estimate changes within each RNA source (i.e. translated mRNA or total mRNA). Analysis of translational efficiency aims to identify changes in translation efficiency leading to altered protein levels that are independent of total mRNA levels (i.e. changes in translated mRNA that are independent of levels of total mRNA) or buffering, a mechanism regulating translational efficiency so that protein levels remain constant despite fluctuating total mRNA levels (i.e. changes in total mRNA that are independent of levels of translated mRNA). anota2seq applies analysis of partial variance and the random variance model to fulfill these tasks.
Maintained by Christian Oertlin. Last updated 5 months ago.
immunooncologygeneexpressiondifferentialexpressionmicroarraygenomewideassociationbatcheffectnormalizationrnaseqsequencinggeneregulationregression
0.5 match 4.28 score 12 scriptsbioc
OCplus:Operating characteristics plus sample size and local fdr for microarray experiments
This package allows to characterize the operating characteristics of a microarray experiment, i.e. the trade-off between false discovery rate and the power to detect truly regulated genes. The package includes tools both for planned experiments (for sample size assessment) and for already collected data (identification of differentially expressed genes).
Maintained by Alexander Ploner. Last updated 5 months ago.
microarraydifferentialexpressionmultiplecomparison
0.5 match 4.08 score 2 scriptsbioc
QuaternaryProd:Computes the Quaternary Dot Product Scoring Statistic for Signed and Unsigned Causal Graphs
QuaternaryProd is an R package that performs causal reasoning on biological networks, including publicly available networks such as STRINGdb. QuaternaryProd is an open-source alternative to commercial products such as Inginuity Pathway Analysis. For a given a set of differentially expressed genes, QuaternaryProd computes the significance of upstream regulators in the network by performing causal reasoning using the Quaternary Dot Product Scoring Statistic (Quaternary Statistic), Ternary Dot product Scoring Statistic (Ternary Statistic) and Fisher's exact test (Enrichment test). The Quaternary Statistic handles signed, unsigned and ambiguous edges in the network. Ambiguity arises when the direction of causality is unknown, or when the source node (e.g., a protein) has edges with conflicting signs for the same target gene. On the other hand, the Ternary Statistic provides causal reasoning using the signed and unambiguous edges only. The Vignette provides more details on the Quaternary Statistic and illustrates an example of how to perform causal reasoning using STRINGdb.
Maintained by Carl Tony Fakhry. Last updated 5 months ago.
graphandnetworkgeneexpressiontranscriptioncpp
0.5 match 4.00 score 1 scriptsbioc
SpeCond:Condition specific detection from expression data
This package performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions. It does so by fitting a mixture of normal distributions to the expression values. Conditions can be environmental conditions, different tissues, organs or any other sources that you wish to compare in terms of gene expression.
Maintained by Florence Cavalli. Last updated 5 months ago.
microarraydifferentialexpressionmultiplecomparisonclusteringreportwriting
0.5 match 3.89 score 13 scriptsbioc
les:Identifying Differential Effects in Tiling Microarray Data
The 'les' package estimates Loci of Enhanced Significance (LES) in tiling microarray data. These are regions of regulation such as found in differential transcription, CHiP-chip, or DNA modification analysis. The package provides a universal framework suitable for identifying differential effects in tiling microarray data sets, and is independent of the underlying statistics at the level of single probes.
Maintained by Julian Gehring. Last updated 5 months ago.
microarraydifferentialexpressionchipchipdnamethylationtranscription
0.5 match 3.78 score 3 scripts 1 dependents
raghvendra5688
RGBM:LS-TreeBoost and LAD-TreeBoost for Gene Regulatory Network Reconstruction
Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc).
Maintained by Raghvendra Mall. Last updated 2 years ago.
1.9 match 1.00 score 4 scripts
riazakhan94
loadshaper:Producing Load Shape with Target Peak and Load Factor
Modifying a load shape to match specific peak and load factor is a fundamental component for various power system planning and operation studies. This package is an efficient tool to modify a reference load shape while matching the desired peak and load factor. The package offers both linear and non-linear method, described in <https://rpubs.com/riazakhan94/load_shape_match_peak_energy>. The user can control the shape of the final load shape by regulating certain parameters. The package provides validation metrics for assessing the derived load shape in terms of preserving time series properties. It also offers powerful graphics, that allows the user to visually assess the derived load shape.
Maintained by Md Riaz Ahmed Khan. Last updated 3 years ago.
0.5 match 3.70 score 2 scripts
sritchie73
BootstrapQTL:Bootstrap cis-QTL Method that Corrects for the Winner's Curse
Identifies genome-related molecular traits with significant evidence of genetic regulation and performs a bootstrap procedure to correct estimated effect sizes for over-estimation present in cis-QTL mapping studies (The "Winner's Curse"), described in Huang QQ *et al.* 2018 <doi: 10.1093/nar/gky780>.
Maintained by Scott Ritchie. Last updated 4 years ago.
0.5 match 6 stars 3.48 score 4 scripts
devrekerd
TTAinterfaceTrendAnalysis:Temporal Trend Analysis Graphical Interface
This interface was created to develop a standard procedure to analyse temporal trend in the framework of the OSPAR convention. The analysis process run through 4 successive steps : 1) manipulate your data, 2) select the parameters you want to analyse, 3) build your regulated time series, 4) perform diagnosis and analysis and 5) read the results. Statistical analysis call other package function such as Kendall tests or cusum() function.
Maintained by David DEVREKER. Last updated 1 years ago.
0.5 match 3.36 score 2 scriptscran
SeqMADE:Network Module-Based Model in the Differential Expression Analysis for RNA-Seq
A network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq.
Maintained by Mingli Lei. Last updated 9 years ago.
1.7 match 1.00 scorecran
neuroimaGene:Transcriptomic Atlas of Neuroimaging Derived Phenotypes
Contains functions to query and visualize the Neuroimaging features associated with genetically regulated gene expression (GReX). The primary utility, neuroimaGene(), relies on a list of user-defined genes and returns a table of neuroimaging features (NIDPs) associated with each gene. This resource is designed to assist in the interpretation of genome-wide and transcriptome-wide association studies that evaluate brain related traits. Bledsoe (2024) <doi:10.1016/j.ajhg.2024.06.002>. In addition there are several visualization functions that generate summary plots and 2-dimensional visualizations of regional brain measures. Mowinckel (2020).
Maintained by Xavier Bledsoe. Last updated 3 months ago.
0.5 match 3.18 score
randel
GMAC:Genomic Mediation Analysis with Adaptive Confounding Adjustment
Performs genomic mediation analysis with adaptive confounding adjustment (GMAC) proposed by Yang et al. (2017) <doi:10.1101/078683>. It implements large scale mediation analysis and adaptively selects potential confounding variables to adjust for each mediation test from a pool of candidate confounders. The package is tailored for but not limited to genomic mediation analysis (e.g., cis-gene mediating trans-gene regulation pattern where an eQTL, its cis-linking gene transcript, and its trans-gene transcript play the roles as treatment, mediator and the outcome, respectively), restricting to scenarios with the presence of cis-association (i.e., treatment-mediator association) and random eQTL (i.e., treatment).
Maintained by Jiebiao Wang. Last updated 3 years ago.
0.5 match 1.48 score 3 scripts
z0on
KOGMWU:Functional Summary and Meta-Analysis of Gene Expression Data
Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed.
Maintained by Mikhail V. Matz. Last updated 6 years ago.
0.5 match 1.20 score 16 scriptsraghvendra5688
func2vis:Clean and Visualize Over Expression Results from 'ConsensusPathDB'
Provides functions to have visualization and clean-up of enriched gene ontologies (GO) terms, protein complexes and pathways (obtained from multiple databases) using 'ConsensusPathDB' from gene set over-expression analysis. Performs clustering of pathway based on similarity of over-expressed gene sets and visualizations similar to Ingenuity Pathway Analysis (IPA) when up and down regulated genes are known. The methods are described in a paper currently submitted by Orecchioni et al, 2020 in Nanoscale.
Maintained by Raghvendra Mall. Last updated 2 years ago.
0.5 match 1.00 score 2 scripts
