Showing 16 of total 16 results (show query)
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SummarizedExperiment:A container (S4 class) for matrix-like assays
The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples.
Maintained by Hervé Pagès. Last updated 5 months ago.
geneticsinfrastructuresequencingannotationcoveragegenomeannotationbioconductor-packagecore-package
34 stars 16.84 score 8.6k scripts 1.2k 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
BASiCS:Bayesian Analysis of Single-Cell Sequencing data
Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend.
Maintained by Catalina Vallejos. Last updated 5 months ago.
immunooncologynormalizationsequencingrnaseqsoftwaregeneexpressiontranscriptomicssinglecelldifferentialexpressionbayesiancellbiologybioconductor-packagegene-expressionrcpprcpparmadilloscrna-seqsingle-cellopenblascppopenmp
83 stars 10.14 score 368 scripts 1 dependentsbioc
MicrobiotaProcess:A comprehensive R package for managing and analyzing microbiome and other ecological data within the tidy framework
MicrobiotaProcess is an R package for analysis, visualization and biomarker discovery of microbial datasets. It introduces MPSE class, this make it more interoperable with the existing computing ecosystem. Moreover, it introduces a tidy microbiome data structure paradigm and analysis grammar. It provides a wide variety of microbiome data analysis procedures under the unified and common framework (tidy-like framework).
Maintained by Shuangbin Xu. Last updated 5 months ago.
visualizationmicrobiomesoftwaremultiplecomparisonfeatureextractionmicrobiome-analysismicrobiome-data
186 stars 9.70 score 126 scripts 1 dependentsbioc
SpatialFeatureExperiment:Integrating SpatialExperiment with Simple Features in sf
A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used.
Maintained by Lambda Moses. Last updated 2 months ago.
datarepresentationtranscriptomicsspatial
49 stars 9.40 score 322 scripts 1 dependentsbioc
RaggedExperiment:Representation of Sparse Experiments and Assays Across Samples
This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. The RaggedExperiment class derives from a GRangesList representation and provides a semblance of a rectangular dataset.
Maintained by Marcel Ramos. Last updated 4 months ago.
infrastructuredatarepresentationcopynumbercore-packagedata-structuremutationsu24ca289073
4 stars 8.93 score 76 scripts 14 dependentsbioc
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
51 stars 6.66 score 15 scriptsbioc
SpliceWiz:interactive analysis and visualization of alternative splicing in R
The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across conditions. SpliceWiz features a shiny-based GUI facilitating interactive data exploration of results including gene ontology enrichment. It is performance optimized with multi-threaded processing of BAM files and a new COV file format for fast recall of sequencing coverage. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization.
Maintained by Alex Chit Hei Wong. Last updated 17 days ago.
softwaretranscriptomicsrnaseqalternativesplicingcoveragedifferentialsplicingdifferentialexpressionguisequencingcppopenmp
16 stars 6.41 score 8 scriptsbioc
CoreGx:Classes and Functions to Serve as the Basis for Other 'Gx' Packages
A collection of functions and classes which serve as the foundation for our lab's suite of R packages, such as 'PharmacoGx' and 'RadioGx'. This package was created to abstract shared functionality from other lab package releases to increase ease of maintainability and reduce code repetition in current and future 'Gx' suite programs. Major features include a 'CoreSet' class, from which 'RadioSet' and 'PharmacoSet' are derived, along with get and set methods for each respective slot. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, as well as: Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., Freeman, M., Selby, H., Gendoo, D., Grossman, P., Beck, A., Aerts, H., Lupien, M., Goldenberg, A. (2015) <doi:10.1093/bioinformatics/btv723>. Manem, V., Labie, M., Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M., Abazeed, M., Haibe-Kains, B., Bratman, S. (2018) <doi:10.1101/449793>.
Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.
softwarepharmacogenomicsclassificationsurvival
6.36 score 63 scripts 6 dependentsbioc
plyxp:Data masks for SummarizedExperiment enabling dplyr-like manipulation
The package provides `rlang` data masks for the SummarizedExperiment class. The enables the evaluation of unquoted expression in different contexts of the SummarizedExperiment object with optional access to other contexts. The goal for `plyxp` is for evaluation to feel like a data.frame object without ever needing to unwind to a rectangular data.frame.
Maintained by Justin Landis. Last updated 12 days ago.
annotationgenomeannotationtranscriptomics
4 stars 5.88 score 6 scriptsbioc
TENxIO:Import methods for 10X Genomics files
Provides a structured S4 approach to importing data files from the 10X pipelines. It mainly supports Single Cell Multiome ATAC + Gene Expression data among other data types. The main Bioconductor data representations used are SingleCellExperiment and RaggedExperiment.
Maintained by Marcel Ramos. Last updated 4 months ago.
softwareinfrastructuredataimportsinglecellbioconductor-packageu24ca289073
5.77 score 7 scripts 3 dependentsbioc
regsplice:L1-regularization based methods for detection of differential splicing
Statistical methods for detection of differential splicing (differential exon usage) in RNA-seq and exon microarray data, using L1-regularization (lasso) to improve power.
Maintained by Lukas M. Weber. Last updated 5 months ago.
immunooncologyalternativesplicingdifferentialexpressiondifferentialsplicingsequencingrnaseqmicroarrayexonarrayexperimentaldesignsoftware
3 stars 5.69 score 27 scriptsbioc
QTLExperiment:S4 classes for QTL summary statistics and metadata
QLTExperiment defines an S4 class for storing and manipulating summary statistics from QTL mapping experiments in one or more states. It is based on the 'SummarizedExperiment' class and contains functions for creating, merging, and subsetting objects. 'QTLExperiment' also stores experiment metadata and has checks in place to ensure that transformations apply correctly.
Maintained by Amelia Dunstone. Last updated 10 days ago.
functionalgenomicsdataimportdatarepresentationinfrastructuresequencingsnpsoftware
2 stars 5.32 score 14 scripts 1 dependentsbioc
zitools:Analysis of zero-inflated count data
zitools allows for zero inflated count data analysis by either using down-weighting of excess zeros or by replacing an appropriate proportion of excess zeros with NA. Through overloading frequently used statistical functions (such as mean, median, standard deviation), plotting functions (such as boxplots or heatmap) or differential abundance tests, it allows a wide range of downstream analyses for zero-inflated data in a less biased manner. This becomes applicable in the context of microbiome analyses, where the data is often overdispersed and zero-inflated, therefore making data analysis extremly challenging.
Maintained by Carlotta Meyring. Last updated 5 months ago.
softwarestatisticalmethodmicrobiome
4.60 score 6 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 scriptsbioc
bnbc:Bandwise normalization and batch correction of Hi-C data
Tools to normalize (several) Hi-C data from replicates.
Maintained by Kipper Fletez-Brant. Last updated 5 months ago.
hicpreprocessingnormalizationsoftwarecpp
1 stars 3.88 score 15 scripts