Showing 17 of total 17 results (show query)
bioc
DESeq2:Differential gene expression analysis based on the negative binomial distribution
Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.
Maintained by Michael Love. Last updated 24 days ago.
sequencingrnaseqchipseqgeneexpressiontranscriptionnormalizationdifferentialexpressionbayesianregressionprincipalcomponentclusteringimmunooncologyopenblascpp
375 stars 16.11 score 17k scripts 115 dependentsbioc
BiocGenerics:S4 generic functions used in Bioconductor
The package defines many S4 generic functions used in Bioconductor.
Maintained by Hervé Pagès. Last updated 2 months ago.
infrastructurebioconductor-packagecore-package
12 stars 14.22 score 612 scripts 2.2k dependentsbioc
SingleCellExperiment:S4 Classes for Single Cell Data
Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries.
Maintained by Davide Risso. Last updated 22 days ago.
immunooncologydatarepresentationdataimportinfrastructuresinglecell
13.53 score 15k scripts 285 dependentsbioc
EDASeq:Exploratory Data Analysis and Normalization for RNA-Seq
Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010).
Maintained by Davide Risso. Last updated 5 months ago.
immunooncologysequencingrnaseqpreprocessingqualitycontroldifferentialexpression
5 stars 10.24 score 594 scripts 9 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 dependents
cmmr
rbiom:Read/Write, Analyze, and Visualize 'BIOM' Data
A toolkit for working with Biological Observation Matrix ('BIOM') files. Read/write all 'BIOM' formats. Compute rarefaction, alpha diversity, and beta diversity (including 'UniFrac'). Summarize counts by taxonomic level. Subset based on metadata. Generate visualizations and statistical analyses. CPU intensive operations are coded in C for speed.
Maintained by Daniel P. Smith. Last updated 12 days ago.
15 stars 9.07 score 117 scripts 6 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 dependentsbioc
chromVAR:Chromatin Variation Across Regions
Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments.
Maintained by Alicia Schep. Last updated 5 months ago.
singlecellsequencinggeneregulationimmunooncologycpp
7.31 score 772 scriptsbioc
isomiRs:Analyze isomiRs and miRNAs from small RNA-seq
Characterization of miRNAs and isomiRs, clustering and differential expression.
Maintained by Lorena Pantano. Last updated 5 months ago.
mirnarnaseqdifferentialexpressionclusteringimmunooncologyanalyze-isomirsbioconductorisomirs
8 stars 6.97 score 43 scriptsbioc
autonomics:Unified Statistical Modeling of Omics Data
This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). It provides a fast enrichment analysis implementation. And an intuitive contrastogram visualisation to summarize contrast effects in complex designs.
Maintained by Aditya Bhagwat. Last updated 2 months ago.
softwaredataimportpreprocessingdimensionreductionprincipalcomponentregressiondifferentialexpressiongenesetenrichmenttranscriptomicstranscriptiongeneexpressionrnaseqmicroarrayproteomicsmetabolomicsmassspectrometry
5.95 score 5 scriptsbioc
TCseq:Time course sequencing data analysis
Quantitative and differential analysis of epigenomic and transcriptomic time course sequencing data, clustering analysis and visualization of the temporal patterns of time course data.
Maintained by Mengjun Wu. Last updated 5 months ago.
epigeneticstimecoursesequencingchipseqrnaseqdifferentialexpressionclusteringvisualization
5.92 score 28 scripts 1 dependentsbioc
SGSeq:Splice event prediction and quantification from RNA-seq data
SGSeq is a software package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation.
Maintained by Leonard Goldstein. Last updated 5 months ago.
alternativesplicingimmunooncologyrnaseqtranscription
5.91 score 45 scripts 3 dependentsbioc
PhIPData:Container for PhIP-Seq Experiments
PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters.
Maintained by Athena Chen. Last updated 5 months ago.
infrastructuredatarepresentationsequencingcoverage
6 stars 5.26 score 6 scripts 1 dependentsbioc
mariner:Mariner: Explore the Hi-Cs
Tools for manipulating paired ranges and working with Hi-C data in R. Functionality includes manipulating/merging paired regions, generating paired ranges, extracting/aggregating interactions from `.hic` files, and visualizing the results. Designed for compatibility with plotgardener for visualization.
Maintained by Eric Davis. Last updated 5 months ago.
functionalgenomicsvisualizationhic
4.89 score 77 scriptsbioc
ABSSeq:ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences
Inferring differential expression genes by absolute counts difference between two groups, utilizing Negative binomial distribution and moderating fold-change according to heterogeneity of dispersion across expression level.
Maintained by Wentao Yang. Last updated 5 months ago.
4.62 score 1 scripts 1 dependentsbioc
SeqGSEA:Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing
The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively.
Maintained by Xi Wang. Last updated 5 months ago.
sequencingrnaseqgenesetenrichmentgeneexpressiondifferentialexpressiondifferentialsplicingimmunooncology
4.34 score 11 scriptsbioc
ccfindR:Cancer Clone Finder
A collection of tools for cancer genomic data clustering analyses, including those for single cell RNA-seq. Cell clustering and feature gene selection analysis employ Bayesian (and maximum likelihood) non-negative matrix factorization (NMF) algorithm. Input data set consists of RNA count matrix, gene, and cell bar code annotations. Analysis outputs are factor matrices for multiple ranks and marginal likelihood values for each rank. The package includes utilities for downstream analyses, including meta-gene identification, visualization, and construction of rank-based trees for clusters.
Maintained by Jun Woo. Last updated 5 months ago.
transcriptomicssinglecellimmunooncologybayesianclusteringgslcpp
4.00 score 9 scripts