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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.

API method:

GET /api/packages?search=hello&page=1&limit=20

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.

If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-snm 1.58.0
Propagated dependencies: r-lme4@1.1-37 r-corpcor@1.6.10
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/snm
Licenses: LGPL 2.0+
Build system: r
Synopsis: Supervised Normalization of Microarrays
Description:

SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest.

r-slqpcr 1.76.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SLqPCR
Licenses: GPL 2+
Build system: r
Synopsis: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH
Description:

This package provides functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH.

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-spatialomicsoverlay 1.10.0
Propagated dependencies: r-xml@3.99-0.20 r-stringr@1.6.0 r-scattermore@1.2 r-s4vectors@0.48.0 r-readxl@1.4.5 r-rbioformats@1.10.0 r-plotrix@3.8-13 r-pbapply@1.7-4 r-magick@2.9.0 r-ggtext@0.1.2 r-ggplot2@4.0.1 r-geomxtools@3.14.0 r-ebimage@4.52.0 r-dplyr@1.1.4 r-data-table@1.17.8 r-biocfilecache@3.0.0 r-biobase@2.70.0 r-base64enc@0.1-3
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SpatialOmicsOverlay
Licenses: Expat
Build system: r
Synopsis: Spatial Overlay for Omic Data from Nanostring GeoMx Data
Description:

This package provides tools for NanoString Technologies GeoMx Technology. Package to easily graph on top of an OME-TIFF image. Plotting annotations can range from tissue segment to gene expression.

r-scpipe 2.10.0
Dependencies: zlib@1.3.1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/LuyiTian/scPipe
Licenses: GPL 2+
Build system: r
Synopsis: Pipeline for single cell multi-omic data pre-processing
Description:

This package provides a preprocessing pipeline for single cell RNA-seq/ATAC-seq data that starts from the fastq files and produces a feature count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols.

r-supercellcyto 1.0.0
Propagated dependencies: r-supercell@1.1 r-matrix@1.7-4 r-data-table@1.17.8 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://phipsonlab.github.io/SuperCellCyto/
Licenses: FSDG-compatible
Build system: r
Synopsis: SuperCell For Cytometry Data
Description:

SuperCellCyto provides the ability to summarise cytometry data into supercells by merging together cells that are similar in their marker expressions using the SuperCell package.

r-sctensor 2.20.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/scTensor
Licenses: Artistic License 2.0
Build system: r
Synopsis: Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition
Description:

The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor.

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-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-saureuscdf 2.18.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/saureuscdf
Licenses: LGPL 2.0+
Build system: r
Synopsis: saureuscdf
Description:

This package provides a package containing an environment representing the S_aureus.cdf file.

r-shinybiocloader 1.0.0
Propagated dependencies: r-shiny@1.11.1 r-htmltools@0.5.8.1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/Bioconductor/shinybiocloader
Licenses: Artistic License 2.0
Build system: r
Synopsis: Use a Shiny Bioconductor CSS loader
Description:

Add a Bioconductor themed CSS loader to your shiny app. It is based on the shinycustomloader R package. Use a spinning Bioconductor note loader to enhance your shiny app loading screen. This package is intended for developer use.

r-svp 1.2.1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/YuLab-SMU/SVP
Licenses: GPL 3
Build system: r
Synopsis: Predicting cell states and their variability in single-cell or spatial omics data
Description:

SVP uses the distance between cells and cells, features and features, cells and features in the space of MCA to build nearest neighbor graph, then uses random walk with restart algorithm to calculate the activity score of gene sets (such as cell marker genes, kegg pathway, go ontology, gene modules, transcription factor or miRNA target sets, reactome pathway, ...), which is then further weighted using the hypergeometric test results from the original expression matrix. To detect the spatially or single cell variable gene sets or (other features) and the spatial colocalization between the features accurately, SVP provides some global and local spatial autocorrelation method to identify the spatial variable features. SVP is developed based on SingleCellExperiment class, which can be interoperable with the existing computing ecosystem.

r-scbn 1.28.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SCBN
Licenses: GPL 2
Build system: r
Synopsis: statistical normalization method and differential expression analysis for RNA-seq data between different species
Description:

This package provides a scale based normalization (SCBN) method to identify genes with differential expression between different species. It takes into account the available knowledge of conserved orthologous genes and the hypothesis testing framework to detect differentially expressed orthologous genes. The method on this package are described in the article A statistical normalization method and differential expression analysis for RNA-seq data between different species by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang (2018, pending publication).

r-scale4c 1.32.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-smoothie@1.0-4 r-iranges@2.44.0 r-genomicranges@1.62.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/Scale4C
Licenses: LGPL 3
Build system: r
Synopsis: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data
Description:

Scale4C is an R/Bioconductor package for scale-space transformation and visualization of 4C-seq data. The scale-space transformation is a multi-scale visualization technique to transform a 2D signal (e.g. 4C-seq reads on a genomic interval of choice) into a tesselation in the scale space (2D, genomic position x scale factor) by applying different smoothing kernels (Gauss, with increasing sigma). This transformation allows for explorative analysis and comparisons of the data's structure with other samples.

r-surfr 1.6.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/auroramaurizio/SurfR
Licenses: FSDG-compatible
Build system: r
Synopsis: Surface Protein Prediction and Identification
Description:

Identify Surface Protein coding genes from a list of candidates. Systematically download data from GEO and TCGA or use your own data. Perform DGE on bulk RNAseq data. Perform Meta-analysis. Descriptive enrichment analysis and plots.

r-schot 1.22.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-singlecellexperiment@1.32.0 r-s4vectors@0.48.0 r-reshape@0.8.10 r-matrix@1.7-4 r-iranges@2.44.0 r-igraph@2.2.1 r-ggplot2@4.0.1 r-ggforce@0.5.0 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/scHOT
Licenses: GPL 3
Build system: r
Synopsis: single-cell higher order testing
Description:

Single cell Higher Order Testing (scHOT) is an R package that facilitates testing changes in higher order structure of gene expression along either a developmental trajectory or across space. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations; as well as accommodate any higher order measurement such as variability or correlation. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data.

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-scatac-explorer 1.16.0
Propagated dependencies: r-zellkonverter@1.20.0 r-singlecellexperiment@1.32.0 r-s4vectors@0.48.0 r-matrix@1.7-4 r-data-table@1.17.8 r-biocfilecache@3.0.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/scATAC.Explorer
Licenses: Artistic License 2.0
Build system: r
Synopsis: Collection of Single-cell ATAC Sequencing Datasets and Corresponding Metadata
Description:

This package provides a tool to search and download a collection of publicly available single cell ATAC-seq datasets and their metadata. scATAC-Explorer aims to act as a single point of entry for users looking to study single cell ATAC-seq data. Users can quickly search available datasets using the metadata table and download datasets of interest for immediate analysis within R.

r-spillr 1.6.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/spillR
Licenses: LGPL 3
Build system: r
Synopsis: Spillover Compensation in Mass Cytometry Data
Description:

Channel interference in mass cytometry can cause spillover and may result in miscounting of protein markers. We develop a nonparametric finite mixture model and use the mixture components to estimate the probability of spillover. We implement our method using expectation-maximization to fit the mixture model.

r-sitepath 1.26.0
Propagated dependencies: r-tidytree@0.4.6 r-seqinr@4.2-36 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 r-gridextra@2.3 r-ggtree@4.0.1 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-aplot@0.2.9 r-ape@5.8-1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://wuaipinglab.github.io/sitePath/
Licenses: Expat
Build system: r
Synopsis: Phylogeny-based sequence clustering with site polymorphism
Description:

Using site polymorphism is one of the ways to cluster DNA/protein sequences but it is possible for the sequences with the same polymorphism on a single site to be genetically distant. This package is aimed at clustering sequences using site polymorphism and their corresponding phylogenetic trees. By considering their location on the tree, only the structurally adjacent sequences will be clustered. However, the adjacent sequences may not necessarily have the same polymorphism. So a branch-and-bound like algorithm is used to minimize the entropy representing the purity of site polymorphism of each cluster.

r-simlr 1.36.0
Propagated dependencies: r-rspectra@0.16-2 r-rcppannoy@0.0.22 r-rcpp@1.1.0 r-pracma@2.4.6 r-matrix@1.7-4
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/BatzoglouLabSU/SIMLR
Licenses: FSDG-compatible
Build system: r
Synopsis: Single-cell Interpretation via Multi-kernel LeaRning (SIMLR)
Description:

Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization.

r-synaptome-db 0.99.17
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/synaptome.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Synamptosome Proteome Database
Description:

The package contains local copy of the Synaptic proteome database. On top of this it provide a set of utility R functions to query and analyse its content. It allows extraction of information for specific genes and building the protein-protein interaction graph for gene sets, synaptic compartments, and brain regions.

r-santa 2.46.0
Propagated dependencies: r-matrix@1.7-4 r-igraph@2.2.1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SANTA
Licenses: GPL 2+
Build system: r
Synopsis: Spatial Analysis of Network Associations
Description:

This package provides methods for measuring the strength of association between a network and a phenotype. It does this by measuring clustering of the phenotype across the network (Knet). Vertices can also be individually ranked by their strength of association with high-weight vertices (Knode).

r-spiat 1.12.1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://trigosteam.github.io/SPIAT/
Licenses: FSDG-compatible
Build system: r
Synopsis: Spatial Image Analysis of Tissues
Description:

SPIAT (**Sp**atial **I**mage **A**nalysis of **T**issues) is an R package with a suite of data processing, quality control, visualization and data analysis tools. SPIAT is compatible with data generated from single-cell spatial proteomics platforms (e.g. OPAL, CODEX, MIBI, cellprofiler). SPIAT reads spatial data in the form of X and Y coordinates of cells, marker intensities and cell phenotypes. SPIAT includes six analysis modules that allow visualization, calculation of cell colocalization, categorization of the immune microenvironment relative to tumor areas, analysis of cellular neighborhoods, and the quantification of spatial heterogeneity, providing a comprehensive toolkit for spatial data analysis.

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