Showing 32 of total 32 results (show query)
bioc
clustifyr:Classifier for Single-cell RNA-seq Using Cell Clusters
Package designed to aid in classifying cells from single-cell RNA sequencing data using external reference data (e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A variety of correlation based methods and gene list enrichment methods are provided to assist cell type assignment.
Maintained by Rui Fu. Last updated 5 months ago.
singlecellannotationsequencingmicroarraygeneexpressionassign-identitiesclustersmarker-genesrna-seqsingle-cell-rna-seq
120 stars 9.63 score 296 scriptsbioc
DEGreport:Report of DEG analysis
Creation of ready-to-share figures of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene.
Maintained by Lorena Pantano. Last updated 5 months ago.
differentialexpressionvisualizationrnaseqreportwritinggeneexpressionimmunooncologybioconductordifferential-expressionqcreportrna-seqsmallrna
24 stars 9.42 score 354 scripts 1 dependentsbioc
OUTRIDER:OUTRIDER - OUTlier in RNA-Seq fInDER
Identification of aberrant gene expression in RNA-seq data. Read count expectations are modeled by an autoencoder to control for confounders in the data. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. Furthermore, OUTRIDER provides useful plotting functions to analyze and visualize the results.
Maintained by Christian Mertes. Last updated 5 months ago.
immunooncologyrnaseqtranscriptomicsalignmentsequencinggeneexpressiongeneticscount-datadiagnosticsexpression-analysismendelian-geneticsoutlier-detectionrna-seqopenblascpp
50 stars 9.07 score 110 scripts 1 dependentsbioc
bambu:Context-Aware Transcript Quantification from Long Read RNA-Seq data
bambu is a R package for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis such as differential gene expression or transcript usage.
Maintained by Ying Chen. Last updated 2 months ago.
alignmentcoveragedifferentialexpressionfeatureextractiongeneexpressiongenomeannotationgenomeassemblyimmunooncologylongreadmultiplecomparisonnormalizationrnaseqregressionsequencingsoftwaretranscriptiontranscriptomicsbambubioconductorlong-readsnanoporenanopore-sequencingrna-seqrna-seq-analysistranscript-quantificationtranscript-reconstructioncpp
203 stars 9.04 score 91 scripts 1 dependentsbioc
M3Drop:Michaelis-Menten Modelling of Dropouts in single-cell RNASeq
This package fits a model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. Also includes an method for calculating exact Pearson residuals in UMI-tagged data using a library-size aware negative binomial model.
Maintained by Tallulah Andrews. Last updated 5 months ago.
rnaseqsequencingtranscriptomicsgeneexpressionsoftwaredifferentialexpressiondimensionreductionfeatureextractionhuman-cell-atlasrna-seqsingle-cellsingle-cell-rna-seq
29 stars 8.53 score 119 scripts 2 dependentsbioc
FRASER:Find RAre Splicing Events in RNA-Seq Data
Detection of rare aberrant splicing events in transcriptome profiles. Read count ratio expectations are modeled by an autoencoder to control for confounding factors in the data. Given these expectations, the ratios are assumed to follow a beta-binomial distribution with a junction specific dispersion. Outlier events are then identified as read-count ratios that deviate significantly from this distribution. FRASER is able to detect alternative splicing, but also intron retention. The package aims to support diagnostics in the field of rare diseases where RNA-seq is performed to identify aberrant splicing defects.
Maintained by Christian Mertes. Last updated 5 months ago.
rnaseqalternativesplicingsequencingsoftwaregeneticscoverageaberrant-splicingdiagnosticsoutlier-detectionrare-diseaserna-seqsplicingopenblascpp
41 stars 8.50 score 155 scriptsbioc
BgeeDB:Annotation and gene expression data retrieval from Bgee database. TopAnat, an anatomical entities Enrichment Analysis tool for UBERON ontology
A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns.
Maintained by Julien Wollbrett. Last updated 5 months ago.
softwaredataimportsequencinggeneexpressionmicroarraygogenesetenrichmentbioinformaticsenrichment-analysisrna-seqscrna-seqsingle-cell
15 stars 8.46 score 19 scripts 1 dependentsbioc
hermes:Preprocessing, analyzing, and reporting of RNA-seq data
Provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed `RNA-seq` data. Data can be imported from `SummarizedExperiment` as well as `matrix` objects and can be annotated from `BioMart`. Filtering for genes without too low expression or containing required annotations, as well as filtering for samples with sufficient correlation to other samples or total number of reads is supported. The standard normalization methods including cpm, rpkm and tpm can be used, and 'DESeq2` as well as voom differential expression analyses are available.
Maintained by Daniel Sabanés Bové. Last updated 5 months ago.
rnaseqdifferentialexpressionnormalizationpreprocessingqualitycontrolrna-seqstatistical-engineering
11 stars 7.77 score 48 scripts 1 dependents
zjufanlab
scCATCH:Single Cell Cluster-Based Annotation Toolkit for Cellular Heterogeneity
An automatic cluster-based annotation pipeline based on evidence-based score by matching the marker genes with known cell markers in tissue-specific cell taxonomy reference database for single-cell RNA-seq data. See Shao X, et al (2020) <doi:10.1016/j.isci.2020.100882> for more details.
Maintained by Xin Shao. Last updated 2 years ago.
cell-markerscluster-annotationmarker-genesrna-seqsequencingseuratsingle-celltranscriptometranscriptomics
226 stars 7.13 score 75 scriptsbioc
ideal:Interactive Differential Expression AnaLysis
This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. Support for reproducibility of the whole analysis is provided by means of a template report which gets automatically compiled and can be stored/shared.
Maintained by Federico Marini. Last updated 3 months ago.
immunooncologygeneexpressiondifferentialexpressionrnaseqsequencingvisualizationqualitycontrolguigenesetenrichmentreportwritingshinyappsbioconductordifferential-expressionreproducible-researchrna-seqrna-seq-analysisshinyuser-friendly
29 stars 6.78 score 5 scripts
person-c
easybio:Comprehensive Single-Cell Annotation and Transcriptomic Analysis Toolkit
Provides a comprehensive toolkit for single-cell annotation with the 'CellMarker2.0' database (see Xia Li, Peng Wang, Yunpeng Zhang (2023) <doi: 10.1093/nar/gkac947>). Streamlines biological label assignment in single-cell RNA-seq data and facilitates transcriptomic analysis, including preparation of TCGA<https://portal.gdc.cancer.gov/> and GEO<https://www.ncbi.nlm.nih.gov/geo/> datasets, differential expression analysis and visualization of enrichment analysis results. Additional utility functions support various bioinformatics workflows. See Wei Cui (2024) <doi: 10.1101/2024.09.14.609619> for more details.
Maintained by Wei Cui. Last updated 26 days ago.
limmageoqueryedgerfgseabioinformaticscellmarker2gsearna-seqsingle-cell
10 stars 6.62 score 35 scriptsbioc
SpotClean:SpotClean adjusts for spot swapping in spatial transcriptomics data
SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontaminate the spot swapping effect, thus increase the sensitivity and precision of downstream analyses.
Maintained by Zijian Ni. Last updated 5 months ago.
dataimportrnaseqsequencinggeneexpressionspatialsinglecelltranscriptomicspreprocessingrna-seqspatial-transcriptomics
31 stars 6.52 score 36 scriptsbioc
RAIDS:Accurate Inference of Genetic Ancestry from Cancer Sequences
This package implements specialized algorithms that enable genetic ancestry inference from various cancer sequences sources (RNA, Exome and Whole-Genome sequences). This package also implements a simulation algorithm that generates synthetic cancer-derived data. This code and analysis pipeline was designed and developed for the following publication: Belleau, P et al. Genetic Ancestry Inference from Cancer-Derived Molecular Data across Genomic and Transcriptomic Platforms. Cancer Res 1 January 2023; 83 (1): 49–58.
Maintained by Pascal Belleau. Last updated 5 months ago.
geneticssoftwaresequencingwholegenomeprincipalcomponentgeneticvariabilitydimensionreductionbiocviewsancestrycancer-genomicsexome-sequencinggenomicsinferencer-languagerna-seqrna-sequencingwhole-genome-sequencing
5 stars 6.23 score 19 scripts
katrionagoldmann
volcano3D:3D Volcano Plots and Polar Plots for Three-Class Data
Generates interactive plots for analysing and visualising three-class high dimensional data. It is particularly suited to visualising differences in continuous attributes such as gene/protein/biomarker expression levels between three groups. Differential gene/biomarker expression analysis between two classes is typically shown as a volcano plot. However, with three groups this type of visualisation is particularly difficult to interpret. This package generates 3D volcano plots and 3-way polar plots for easier interpretation of three-class data.
Maintained by Katriona Goldmann. Last updated 2 years ago.
bioinformaticsdifferential-expressiondifferential-expression-analysisgene-expressioninteractiveomicsplotlyrna-seqtranscriptomicsvolcanoplots
36 stars 5.90 score 37 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
9 stars 5.86 score 9 scriptsbioc
BgeeCall:Automatic RNA-Seq present/absent gene expression calls generation
BgeeCall allows to generate present/absent gene expression calls without using an arbitrary cutoff like TPM<1. Calls are generated based on reference intergenic sequences. These sequences are generated based on expression of all RNA-Seq libraries of each species integrated in Bgee (https://bgee.org).
Maintained by Julien Wollbrett. Last updated 5 months ago.
softwaregeneexpressionrnaseqbiologygene-expressiongene-levelintergenic-regionspresent-absent-callsrna-seqrna-seq-librariesscrna-seq
3 stars 5.56 score 9 scriptsbioc
HybridExpress:Comparative analysis of RNA-seq data for hybrids and their progenitors
HybridExpress can be used to perform comparative transcriptomics analysis of hybrids (or allopolyploids) relative to their progenitor species. The package features functions to perform exploratory analyses of sample grouping, identify differentially expressed genes in hybrids relative to their progenitors, classify genes in expression categories (N = 12) and classes (N = 5), and perform functional analyses. We also provide users with graphical functions for the seamless creation of publication-ready figures that are commonly used in the literature.
Maintained by Fabricio Almeida-Silva. Last updated 5 months ago.
softwarefunctionalgenomicsgeneexpressiontranscriptomicsrnaseqclassificationdifferentialexpressiongene-expressionhybridpolyploidyrna-seq
14 stars 5.32 score 2 scriptsbioc
SplicingFactory:Splicing Diversity Analysis for Transcriptome Data
The SplicingFactory R package uses transcript-level expression values to analyze splicing diversity based on various statistical measures, like Shannon entropy or the Gini index. These measures can quantify transcript isoform diversity within samples or between conditions. Additionally, the package analyzes the isoform diversity data, looking for significant changes between conditions.
Maintained by Endre Sebestyen. Last updated 5 months ago.
transcriptomicsrnaseqdifferentialsplicingalternativesplicingtranscriptomevariantgini-indexrna-seqshannon-entropysimpson-indexsplicing
4 stars 5.20 score 1 scriptsbioc
RNAAgeCalc:A multi-tissue transcriptional age calculator
It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data.
Maintained by Xu Ren. Last updated 5 months ago.
rnaseqgeneexpressionbiological-ageelastic-netgene-expressiongenotype-tissue-expressionpredictionregularized-regressionrna-seq
8 stars 5.20 score 10 scriptsbioc
maSigPro:Significant Gene Expression Profile Differences in Time Course Gene Expression Data
maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments.
Maintained by Maria Jose Nueda. Last updated 5 months ago.
microarrayrna-seqdifferential expressiontimecourse
5.18 score 76 scripts
aalhendi1707
countToFPKM:Convert Counts to Fragments per Kilobase of Transcript per Million (FPKM)
Implements the algorithm described in Trapnell,C. et al. (2010) <doi: 10.1038/nbt.1621>. This function takes read counts matrix of RNA-Seq data, feature lengths which can be retrieved using 'biomaRt' package, and the mean fragment lengths which can be calculated using the 'CollectInsertSizeMetrics(Picard)' tool. It then returns a matrix of FPKM normalised data by library size and feature effective length. It also provides the user with a quick and reliable function to generate FPKM heatmap plot of the highly variable features in RNA-Seq dataset.
Maintained by Ahmed Alhendi. Last updated 4 years ago.
gene-expressionnormalizationrna-seq
62 stars 5.09 score 20 scriptsbioc
icetea:Integrating Cap Enrichment with Transcript Expression Analysis
icetea (Integrating Cap Enrichment with Transcript Expression Analysis) provides functions for end-to-end analysis of multiple 5'-profiling methods such as CAGE, RAMPAGE and MAPCap, beginning from raw reads to detection of transcription start sites using replicates. It also allows performing differential TSS detection between group of samples, therefore, integrating the mRNA cap enrichment information with transcript expression analysis.
Maintained by Vivek Bhardwaj. Last updated 5 months ago.
immunooncologytranscriptiongeneexpressionsequencingrnaseqtranscriptomicsdifferentialexpressioncageexpressionrna-seq
2 stars 5.08 score 7 scriptsbioc
scQTLtools:An R package for single-cell eQTL analysis and visualization
This package specializes in analyzing and visualizing eQTL at the single-cell level. It can read gene expression matrices or Seurat data, or SingleCellExperiment object along with genotype data. It offers a function for cis-eQTL analysis to detect eQTL within a given range, and another function to fit models with three methods. Using this package, users can also generate single-cell level visualization result.
Maintained by Xiaofeng Wu. Last updated 3 days ago.
softwaregeneexpressiongeneticvariabilitysnpdifferentialexpressiongenomicvariationvariantdetectiongeneticsfunctionalgenomicssystemsbiologyregressionsinglecellnormalizationvisualizationrna-seqsc-eqtl
3 stars 5.02 scorebioc
VaSP:Quantification and Visualization of Variations of Splicing in Population
Discovery of genome-wide variable alternative splicing events from short-read RNA-seq data and visualizations of gene splicing information for publication-quality multi-panel figures in a population. (Warning: The visualizing function is removed due to the dependent package Sushi deprecated. If you want to use it, please change back to an older version.)
Maintained by Huihui Yu. Last updated 5 months ago.
rnaseqalternativesplicingdifferentialsplicingstatisticalmethodvisualizationpreprocessingclusteringdifferentialexpressionkeggimmunooncology3s-scoresalternative-splicingballgownrna-seqsplicingsqtlstatistics
3 stars 4.78 score 3 scripts
vivianstats
MAAPER:Analysis of Alternative Polyadenylation Using 3' End-Linked Reads
A computational method developed for model-based analysis of alternative polyadenylation (APA) using 3' end-linked reads. It accurately assigns 3' RNA-seq reads to polyA sites through statistical modeling, and generates multiple statistics for APA analysis. Please also see Li WV, Zheng D, Wang R, Tian B (2021) <doi:10.1186/s13059-021-02429-5>.
Maintained by Wei Vivian Li. Last updated 4 years ago.
alternative-polyadenylationbioinformatics-toolrna-seq
9 stars 4.65 score 7 scriptsbioc
GEOfastq:Downloads ENA Fastqs With GEO Accessions
GEOfastq is used to download fastq files from the European Nucleotide Archive (ENA) starting with an accession from the Gene Expression Omnibus (GEO). To do this, sample metadata is retrieved from GEO and the Sequence Read Archive (SRA). SRA run accessions are then used to construct FTP and aspera download links for fastq files generated by the ENA.
Maintained by Alex Pickering. Last updated 5 months ago.
rnaseqdataimportbioinformaticsfastqgene-expressiongeorna-seq
4 stars 4.60 score 6 scriptsbioc
SVMDO:Identification of Tumor-Discriminating mRNA Signatures via Support Vector Machines Supported by Disease Ontology
It is an easy-to-use GUI using disease information for detecting tumor/normal sample discriminating gene sets from differentially expressed genes. Our approach is based on an iterative algorithm filtering genes with disease ontology enrichment analysis and wilk and wilks lambda criterion connected to SVM classification model construction. Along with gene set extraction, SVMDO also provides individual prognostic marker detection. The algorithm is designed for FPKM and RPKM normalized RNA-Seq transcriptome datasets.
Maintained by Mustafa Erhan Ozer. Last updated 5 months ago.
genesetenrichmentdifferentialexpressionguiclassificationrnaseqtranscriptomicssurvivalmachine-learningrna-seqshiny
4.60 score 2 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 5’ end of the guide strand, called a seed region, bind the 3’ 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
7 stars 4.54 score 7 scripts
dosorio
rPanglaoDB:Download and Merge Single-Cell RNA-Seq Data from the PanglaoDB Database
Download and merge labeled single-cell RNA-seq data from the PanglaoDB <https://panglaodb.se/> into a Seurat object.
Maintained by Daniel Osorio. Last updated 2 years ago.
data-integrationdata-miningrna-seqsingle-cellsingle-cell-rna-seq
26 stars 4.41 score 20 scriptsbioc
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 3 months ago.
alternative polyadenylationdifferential polyadenylation site usagerna-seqgene regulationtranscription
4.30 score 1 scriptsbioc
getDEE2:Programmatic access to the DEE2 RNA expression dataset
Digital Expression Explorer 2 (or DEE2 for short) is a repository of processed RNA-seq data in the form of counts. It was designed so that researchers could undertake re-analysis and meta-analysis of published RNA-seq studies quickly and easily. As of April 2020, over 1 million SRA datasets have been processed. This package provides an R interface to access these expression data. More information about the DEE2 project can be found at the project homepage (http://dee2.io) and main publication (https://doi.org/10.1093/gigascience/giz022).
Maintained by Mark Ziemann. Last updated 3 months ago.
geneexpressiontranscriptomicssequencingbioinformaticsdata-mininggenomicsrna-expressionrna-seq
4 stars 4.20 score 5 scriptsbioc
pram:Pooling RNA-seq datasets for assembling transcript models
Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology. Novel transcript discovery enabled by large collections of RNA-seq datasets has emerged as one of such analysis. To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a new R package named Pooling RNA-seq and Assembling Models (PRAM), which builds transcript models in intergenic regions from pooled RNA-seq datasets. This package includes functions for defining intergenic regions, extracting and pooling related RNA-seq alignments, predicting, selected, and evaluating transcript models.
Maintained by Peng Liu. Last updated 5 months ago.
softwaretechnologysequencingrnaseqbiologicalquestiongenepredictiongenomeannotationresearchfieldtranscriptomicsbioconductor-packagegenome-annotationrna-seqtranscript-model
1 stars 4.18 score 3 scripts