Showing 13 of total 13 results (show query)
bioc
edgeR:Empirical Analysis of Digital Gene Expression Data in R
Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.
Maintained by Yunshun Chen. Last updated 19 days ago.
alternativesplicingbatcheffectbayesianbiomedicalinformaticscellbiologychipseqclusteringcoveragedifferentialexpressiondifferentialmethylationdifferentialsplicingdnamethylationepigeneticsfunctionalgenomicsgeneexpressiongenesetenrichmentgeneticsimmunooncologymultiplecomparisonnormalizationpathwaysproteomicsqualitycontrolregressionrnaseqsagesequencingsinglecellsystemsbiologytimecoursetranscriptiontranscriptomicsopenblas
13.40 score 17k scripts 255 dependents
richardli
SUMMER:Small-Area-Estimation Unit/Area Models and Methods for Estimation in R
Provides methods for spatial and spatio-temporal smoothing of demographic and health indicators using survey data, with particular focus on estimating and projecting under-five mortality rates, described in Mercer et al. (2015) <doi:10.1214/15-AOAS872>, Li et al. (2019) <doi:10.1371/journal.pone.0210645>, Wu et al. (DHS Spatial Analysis Reports No. 21, 2021), and Li et al. (2023) <doi:10.48550/arXiv.2007.05117>.
Maintained by Zehang R Li. Last updated 3 months ago.
bayesian-inferencesmall-area-estimationspace-time
23 stars 10.28 score 134 scripts 2 dependentsbioc
rWikiPathways:rWikiPathways - R client library for the WikiPathways API
Use this package to interface with the WikiPathways API. It provides programmatic access to WikiPathways content in multiple data and image formats, including official monthly release files and convenient GMT read/write functions.
Maintained by Egon Willighagen. Last updated 5 months ago.
visualizationgraphandnetworkthirdpartyclientnetworkmetabolomicsbioinformaticsdata-accesspathways
15 stars 9.23 score 131 scripts 3 dependentsbioc
SCnorm:Normalization of single cell RNA-seq data
This package implements SCnorm — a method to normalize single-cell RNA-seq data.
Maintained by Rhonda Bacher. Last updated 5 months ago.
normalizationrnaseqsinglecellimmunooncology
47 stars 8.46 score 76 scriptsbioc
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
Trendy:Breakpoint analysis of time-course expression data
Trendy implements segmented (or breakpoint) regression models to estimate breakpoints which represent changes in expression for each feature/gene in high throughput data with ordered conditions.
Maintained by Rhonda Bacher. Last updated 5 months ago.
timecoursernaseqregressionimmunooncology
6 stars 6.53 score 14 scriptsbioc
COMPASS:Combinatorial Polyfunctionality Analysis of Single Cells
COMPASS is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination.
Maintained by Greg Finak. Last updated 5 months ago.
immunooncologyflowcytometrycpp
7 stars 6.51 score 42 scriptsbioc
segmentSeq:Methods for identifying small RNA loci from high-throughput sequencing data
High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery.
Maintained by Samuel Granjeaud. Last updated 5 months ago.
multiplecomparisonsequencingalignmentdifferentialexpressionqualitycontroldataimport
6.17 score 42 scriptsbioc
normr:Normalization and difference calling in ChIP-seq data
Robust normalization and difference calling procedures for ChIP-seq and alike data. Read counts are modeled jointly as a binomial mixture model with a user-specified number of components. A fitted background estimate accounts for the effect of enrichment in certain regions and, therefore, represents an appropriate null hypothesis. This robust background is used to identify significantly enriched or depleted regions.
Maintained by Johannes Helmuth. Last updated 5 months ago.
bayesiandifferentialpeakcallingclassificationdataimportchipseqripseqfunctionalgenomicsgeneticsmultiplecomparisonnormalizationpeakdetectionpreprocessingalignmentcppopenmp
11 stars 5.93 score 13 scripts
bioc
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
5.40 score 24 scripts
bentaylor1
lgcp:Log-Gaussian Cox Process
Spatial and spatio-temporal modelling of point patterns using the log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal, multivariate and aggregated point processes using Markov chain Monte Carlo. See Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle (2015) <doi:10.18637/jss.v063.i07>.
Maintained by Benjamin M. Taylor. Last updated 1 years ago.
3.59 score 27 scriptsbioc
qsea:IP-seq data analysis and vizualization
qsea (quantitative sequencing enrichment analysis) was developed as the successor of the MEDIPS package for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, qsea provides several functionalities for the analysis of other kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential enrichment between groups of samples.
Maintained by Matthias Lienhard. Last updated 5 months ago.
sequencingdnamethylationcpgislandchipseqpreprocessingnormalizationqualitycontrolvisualizationcopynumbervariationchiponchipdifferentialmethylation
3.30 score 7 scripts