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Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

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where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

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r-synaptome-data 0.99.6
Propagated dependencies: r-annotationhub@4.0.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/synaptome.data
Licenses: Artistic License 2.0
Build system: r
Synopsis: AnnotationData for Synaptome.DB package
Description:

The package provides access to the copy of the Synaptic proteome database. It was designed as an accompaniment for Synaptome.DB package. Database provides information for specific synaptic genes and allows building the protein-protein interaction graph for gene sets, synaptic compartments, and brain regions. In the current update we added 6 more synaptic proteome studies, which resulted in total of 64 studies. We introduced Synaptic Vesicle as a separate compartment. We also added coding mutations for Autistic Spectral disorder and Epilepsy collected from publicly available databases.

r-spsimseq 1.20.0
Propagated dependencies: r-wgcna@1.73 r-singlecellexperiment@1.32.0 r-phyloseq@1.54.0 r-mvtnorm@1.3-3 r-limma@3.66.0 r-hmisc@5.2-4 r-fitdistrplus@1.2-4 r-edger@4.8.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/CenterForStatistics-UGent/SPsimSeq
Licenses: GPL 2
Build system: r
Synopsis: Semi-parametric simulation tool for bulk and single-cell RNA sequencing data
Description:

SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size.

r-spatialdecon 1.20.1
Propagated dependencies: r-seuratobject@5.2.0 r-repmis@0.5.1 r-matrix@1.7-4 r-lognormreg@0.5-0 r-geomxtools@3.14.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SpatialDecon
Licenses: Expat
Build system: r
Synopsis: Deconvolution of mixed cells from spatial and/or bulk gene expression data
Description:

Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data", Danaher (2022). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data.

r-serumstimulation 1.46.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/serumStimulation
Licenses: GPL 2+
Build system: r
Synopsis: serumStimulation is a data package which is used by examples in package pcaGoPromoter
Description:

This package contains 13 micro array data results from a serum stimulation experiment.

r-splicewiz 1.12.0
Dependencies: zlib@1.3.1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/alexchwong/SpliceWiz
Licenses: Expat
Build system: r
Synopsis: interactive analysis and visualization of alternative splicing in R
Description:

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.

r-sosta 1.2.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/sgunz/sosta
Licenses: FSDG-compatible
Build system: r
Synopsis: package for the analysis of anatomical tissue structures in spatial omics data
Description:

sosta (Spatial Omics STructure Analysis) is a package for analyzing spatial omics data to explore tissue organization at the anatomical structure level. It reconstructs anatomically relevant structures based on molecular features or cell types. It further calculates a range of metrics at the structure level to quantitatively describe tissue architecture. The package is designed to integrate with other packages for the analysis of spatial omics data.

r-snphood 1.40.0
Propagated dependencies: r-variantannotation@1.56.0 r-summarizedexperiment@1.40.0 r-scales@1.4.0 r-s4vectors@0.48.0 r-rsamtools@2.26.0 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-lattice@0.22-7 r-iranges@2.44.0 r-gridextra@2.3 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-genomeinfodb@1.46.0 r-deseq2@1.50.2 r-data-table@1.17.8 r-cluster@2.1.8.1 r-checkmate@2.3.3 r-biostrings@2.78.0 r-biocparallel@1.44.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SNPhood
Licenses: LGPL 3+
Build system: r
Synopsis: SNPhood: Investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data
Description:

To date, thousands of single nucleotide polymorphisms (SNPs) have been found to be associated with complex traits and diseases. However, the vast majority of these disease-associated SNPs lie in the non-coding part of the genome, and are likely to affect regulatory elements, such as enhancers and promoters, rather than function of a protein. Thus, to understand the molecular mechanisms underlying genetic traits and diseases, it becomes increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin environment or transcription factor (TF) binding. Towards this aim, we developed SNPhood, a user-friendly *Bioconductor* R package to investigate and visualize the local neighborhood of a set of SNPs of interest for NGS data such as chromatin marks or transcription factor binding sites from ChIP-Seq or RNA- Seq experiments. SNPhood comprises a set of easy-to-use functions to extract, normalize and summarize reads for a genomic region, perform various data quality checks, normalize read counts using additional input files, and to cluster and visualize the regions according to the binding pattern. The regions around each SNP can be binned in a user-defined fashion to allow for analysis of very broad patterns as well as a detailed investigation of specific binding shapes. Furthermore, SNPhood supports the integration with genotype information to investigate and visualize genotype-specific binding patterns. Finally, SNPhood can be employed for determining, investigating, and visualizing allele-specific binding patterns around the SNPs of interest.

r-spqn 1.22.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-matrixstats@1.5.0 r-ggridges@0.5.7 r-ggplot2@4.0.1 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/hansenlab/spqn
Licenses: Artistic License 2.0
Build system: r
Synopsis: Spatial quantile normalization
Description:

The spqn package implements spatial quantile normalization (SpQN). This method was developed to remove a mean-correlation relationship in correlation matrices built from gene expression data. It can serve as pre-processing step prior to a co-expression analysis.

r-supersigs 1.18.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://tomasettilab.github.io/supersigs/
Licenses: GPL 3
Build system: r
Synopsis: Supervised mutational signatures
Description:

Generate SuperSigs (supervised mutational signatures) from single nucleotide variants in the cancer genome. Functions included in the package allow the user to learn supervised mutational signatures from their data and apply them to new data. The methodology is based on the one described in Afsari (2021, ELife).

r-scifer 1.12.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/rodrigarc/scifer
Licenses: Expat
Build system: r
Synopsis: Scifer: Single-Cell Immunoglobulin Filtering of Sanger Sequences
Description:

Have you ever index sorted cells in a 96 or 384-well plate and then sequenced using Sanger sequencing? If so, you probably had some struggles to either check the electropherogram of each cell sequenced manually, or when you tried to identify which cell was sorted where after sequencing the plate. Scifer was developed to solve this issue by performing basic quality control of Sanger sequences and merging flow cytometry data from probed single-cell sorted B cells with sequencing data. scifer can export summary tables, fasta files, electropherograms for visual inspection, and generate reports.

r-spia 2.62.0
Propagated dependencies: r-kegggraph@1.70.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: http://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1
Licenses: FSDG-compatible
Build system: r
Synopsis: Signaling Pathway Impact Analysis (SPIA) using combined evidence of pathway over-representation and unusual signaling perturbations
Description:

This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study.

r-similarpeak 1.42.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/adeschen/similaRpeak
Licenses: Artistic License 2.0
Build system: r
Synopsis: Metrics to estimate a level of similarity between two ChIP-Seq profiles
Description:

This package calculates metrics which quantify the level of similarity between ChIP-Seq profiles. More specifically, the package implements six pseudometrics specialized in pattern similarity detection in ChIP-Seq profiles.

r-spanorm 1.4.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bhuvad.github.io/SpaNorm
Licenses: GPL 3+
Build system: r
Synopsis: Spatially-aware normalisation for spatial transcriptomics data
Description:

This package implements the spatially aware library size normalisation algorithm, SpaNorm. SpaNorm normalises out library size effects while retaining biology through the modelling of smooth functions for each effect. Normalisation is performed in a gene- and cell-/spot- specific manner, yielding library size adjusted data.

r-schiccompare 1.2.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/dozmorovlab/ScHiCcompare
Licenses: Expat
Build system: r
Synopsis: Differential Analysis of Single-cell Hi-C Data
Description:

This package provides functions for differential chromatin interaction analysis between two single-cell Hi-C data groups. It includes tools for imputation, normalization, and differential analysis of chromatin interactions. The package implements pooling techniques for imputation and offers methods to normalize and test for differential interactions across single-cell Hi-C datasets.

r-seahtrue 1.4.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://vcjdeboer.github.io/seahtrue/
Licenses: Artistic License 2.0
Build system: r
Synopsis: Seahtrue revives XF data for structured data analysis
Description:

Seahtrue organizes oxygen consumption and extracellular acidification analysis data from experiments performed on an XF analyzer into structured nested tibbles.This allows for detailed processing of raw data and advanced data visualization and statistics. Seahtrue introduces an open and reproducible way to analyze these XF experiments. It uses file paths to .xlsx files. These .xlsx files are supplied by the userand are generated by the user in the Wave software from Agilent from the assay result files (.asyr). The .xlsx file contains different sheets of important data for the experiment; 1. Assay Information - Details about how the experiment was set up. 2. Rate Data - Information about the OCR and ECAR rates. 3. Raw Data - The original raw data collected during the experiment. 4. Calibration Data - Data related to calibrating the instrument. Seahtrue focuses on getting the specific data needed for analysis. Once this data is extracted, it is prepared for calculations through preprocessing. To make sure everything is accurate, both the initial data and the preprocessed data go through thorough checks.

r-saigegds 2.10.1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/AbbVie-ComputationalGenomics/SAIGEgds
Licenses: GPL 3
Build system: r
Synopsis: Scalable Implementation of Generalized mixed models using GDS files in Phenome-Wide Association Studies
Description:

Scalable implementation of generalized mixed models with highly optimized C++ implementation and integration with Genomic Data Structure (GDS) files. It is designed for single variant tests and set-based aggregate tests in large-scale Phenome-wide Association Studies (PheWAS) with millions of variants and samples, controlling for sample structure and case-control imbalance. The implementation is based on the SAIGE R package (v0.45, Zhou et al. 2018 and Zhou et al. 2020), and it is extended to include the state-of-the-art ACAT-O set-based tests. Benchmarks show that SAIGEgds is significantly faster than the SAIGE R package.

r-single-mtec-transcriptomes 1.38.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/Single.mTEC.Transcriptomes
Licenses: LGPL 2.0+
Build system: r
Synopsis: Single Cell Transcriptome Data and Analysis of Mouse mTEC cells
Description:

This data package contains the code used to analyse the single-cell RNA-seq and the bulk ATAC-seq data from the manuscript titled: Single-cell transcriptome analysis reveals coordinated ectopic-gene expression patterns in medullary thymic epithelial cells. This paper was published in Nature Immunology 16,933-941(2015). The data objects provided in this package has been pre-processed: the raw data files can be downloaded from ArrayExpress under the accession identifiers E-MTAB-3346 and E-MTAB-3624. The vignette of this data package provides a documented and reproducible workflow that includes the code that was used to generate each statistic and figure from the manuscript.

r-ssnappy 1.14.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://wenjun-liu.github.io/sSNAPPY/
Licenses: GPL 3
Build system: r
Synopsis: Single Sample directioNAl Pathway Perturbation analYsis
Description:

This package provides a single sample pathway perturbation testing method for RNA-seq data. The method propagates changes in gene expression down gene-set topologies to compute single-sample directional pathway perturbation scores that reflect potential direction of change. Perturbation scores can be used to test significance of pathway perturbation at both individual-sample and treatment levels.

r-sccb2 1.20.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-singlecellexperiment@1.32.0 r-seurat@5.3.1 r-rhdf5@2.54.0 r-matrix@1.7-4 r-iterators@1.0.14 r-foreach@1.5.2 r-edger@4.8.0 r-dropletutils@1.30.0 r-doparallel@1.0.17
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/zijianni/scCB2
Licenses: GPL 3
Build system: r
Synopsis: CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data
Description:

scCB2 is an R package implementing CB2 for distinguishing real cells from empty droplets in droplet-based single cell RNA-seq experiments (especially for 10x Chromium). It is based on clustering similar barcodes and calculating Monte-Carlo p-value for each cluster to test against background distribution. This cluster-level test outperforms single-barcode-level tests in dealing with low count barcodes and homogeneous sequencing library, while keeping FDR well controlled.

r-systempipetools 1.18.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/systemPipeTools
Licenses: Artistic License 2.0
Build system: r
Synopsis: Tools for data visualization
Description:

systemPipeTools package extends the widely used systemPipeR (SPR) workflow environment with an enhanced toolkit for data visualization, including utilities to automate the data visualizaton for analysis of differentially expressed genes (DEGs). systemPipeTools provides data transformation and data exploration functions via scatterplots, hierarchical clustering heatMaps, principal component analysis, multidimensional scaling, generalized principal components, t-Distributed Stochastic Neighbor embedding (t-SNE), and MA and volcano plots. All these utilities can be integrated with the modular design of the systemPipeR environment that allows users to easily substitute any of these features and/or custom with alternatives.

r-splinetimer 1.38.0
Propagated dependencies: r-longitudinal@1.1.13 r-limma@3.66.0 r-igraph@2.2.1 r-gtools@3.9.5 r-gseabase@1.72.0 r-genenet@1.2.17 r-fis@1.38.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/splineTimeR
Licenses: GPL 3
Build system: r
Synopsis: Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction
Description:

This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks.

r-spatialde 1.16.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-spatialexperiment@1.20.0 r-scales@1.4.0 r-reticulate@1.44.1 r-matrix@1.7-4 r-gridextra@2.3 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-checkmate@2.3.3 r-basilisk@1.22.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/sales-lab/spatialDE
Licenses: Expat
Build system: r
Synopsis: R wrapper for SpatialDE
Description:

SpatialDE is a method to find spatially variable genes (SVG) from spatial transcriptomics data. This package provides wrappers to use the Python SpatialDE library in R, using reticulate and basilisk.

r-systempipeshiny 1.20.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://systempipe.org/sps
Licenses: GPL 3+
Build system: r
Synopsis: systemPipeShiny: An Interactive Framework for Workflow Management and Visualization
Description:

systemPipeShiny (SPS) extends the widely used systemPipeR (SPR) workflow environment with a versatile graphical user interface provided by a Shiny App. This allows non-R users, such as experimentalists, to run many systemPipeR’s workflow designs, control, and visualization functionalities interactively without requiring knowledge of R. Most importantly, SPS has been designed as a general purpose framework for interacting with other R packages in an intuitive manner. Like most Shiny Apps, SPS can be used on both local computers as well as centralized server-based deployments that can be accessed remotely as a public web service for using SPR’s functionalities with community and/or private data. The framework can integrate many core packages from the R/Bioconductor ecosystem. Examples of SPS’ current functionalities include: (a) interactive creation of experimental designs and metadata using an easy to use tabular editor or file uploader; (b) visualization of workflow topologies combined with auto-generation of R Markdown preview for interactively designed workflows; (d) access to a wide range of data processing routines; (e) and an extendable set of visualization functionalities. Complex visual results can be managed on a Canvas Workbench’ allowing users to organize and to compare plots in an efficient manner combined with a session snapshot feature to continue work at a later time. The present suite of pre-configured visualization examples. The modular design of SPR makes it easy to design custom functions without any knowledge of Shiny, as well as extending the environment in the future with contributions from the community.

r-scan-upc 2.52.0
Propagated dependencies: r-sva@3.58.0 r-oligo@1.74.0 r-mass@7.3-65 r-iranges@2.44.0 r-geoquery@2.78.0 r-foreach@1.5.2 r-biostrings@2.78.0 r-biobase@2.70.0 r-affyio@1.80.0 r-affy@1.88.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: http://bioconductor.org
Licenses: Expat
Build system: r
Synopsis: Single-channel array normalization (SCAN) and Universal exPression Codes (UPC)
Description:

SCAN is a microarray normalization method to facilitate personalized-medicine workflows. Rather than processing microarray samples as groups, which can introduce biases and present logistical challenges, SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-channel (e.g., Affymetrix) or two-channel (e.g., Agilent) microarrays. The Universal exPression Codes (UPC) method is an extension of SCAN that estimates whether a given gene/transcript is active above background levels in a given sample. The UPC method can be applied to one-channel or two-channel microarrays as well as to RNA-Seq read counts. Because UPC values are represented on the same scale and have an identical interpretation for each platform, they can be used for cross-platform data integration.

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