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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-shinymethyldata 1.30.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/shinyMethylData
Licenses: Artistic License 2.0
Build system: r
Synopsis: Example dataset of input data for shinyMethyl
Description:

Extracted data from 369 TCGA Head and Neck Cancer DNA methylation samples. The extracted data serve as an example dataset for the package shinyMethyl. Original samples are from 450k methylation arrays, and were obtained from The Cancer Genome Atlas (TCGA). 310 samples are from tumor, 50 are matched normals and 9 are technical replicates of a control cell line.

r-singlemoleculefootprinting 2.4.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://www.bioconductor.org/packages/release/bioc/html/SingleMoleculeFootprinting.html
Licenses: GPL 3
Build system: r
Synopsis: Analysis tools for Single Molecule Footprinting (SMF) data
Description:

SingleMoleculeFootprinting provides functions to analyze Single Molecule Footprinting (SMF) data. Following the workflow exemplified in its vignette, the user will be able to perform basic data analysis of SMF data with minimal coding effort. Starting from an aligned bam file, we show how to perform quality controls over sequencing libraries, extract methylation information at the single molecule level accounting for the two possible kind of SMF experiments (single enzyme or double enzyme), classify single molecules based on their patterns of molecular occupancy, plot SMF information at a given genomic location.

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-safe 3.50.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/safe
Licenses: GPL 2+
Build system: r
Synopsis: Significance Analysis of Function and Expression
Description:

SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions.

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-singlecellmultimodal 1.22.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-spatialexperiment@1.20.0 r-singlecellexperiment@1.32.0 r-s4vectors@0.48.0 r-multiassayexperiment@1.36.1 r-matrix@1.7-4 r-hdf5array@1.38.0 r-experimenthub@3.0.0 r-biocfilecache@3.0.0 r-biocbaseutils@1.12.0 r-annotationhub@4.0.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SingleCellMultiModal
Licenses: Artistic License 2.0
Build system: r
Synopsis: Integrating Multi-modal Single Cell Experiment datasets
Description:

SingleCellMultiModal is an ExperimentHub package that serves multiple datasets obtained from GEO and other sources and represents them as MultiAssayExperiment objects. We provide several multi-modal datasets including scNMT, 10X Multiome, seqFISH, CITEseq, SCoPE2, and others. The scope of the package is is to provide data for benchmarking and analysis. To cite, use the citation function and see <https://doi.org/10.1371/journal.pcbi.1011324>.

r-spikein 1.52.0
Propagated dependencies: r-affy@1.88.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SpikeIn
Licenses: Artistic License 2.0
Build system: r
Synopsis: Affymetrix Spike-In Experiment Data
Description:

This package contains the HGU133 and HGU95 spikein experiment data.

r-spacemarkers 2.0.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/DeshpandeLab/SpaceMarkers
Licenses: Expat
Build system: r
Synopsis: Spatial Interaction Markers
Description:

Spatial transcriptomic technologies have helped to resolve the connection between gene expression and the 2D orientation of tissues relative to each other. However, the limited single-cell resolution makes it difficult to highlight the most important molecular interactions in these tissues. SpaceMarkers, R/Bioconductor software, can help to find molecular interactions, by identifying genes associated with latent space interactions in spatial transcriptomics.

r-sracipe 2.2.0
Propagated dependencies: r-visnetwork@2.1.4 r-umap@0.2.10.0 r-summarizedexperiment@1.40.0 r-s4vectors@0.48.0 r-reshape2@1.4.5 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 r-mass@7.3-65 r-htmlwidgets@1.6.4 r-gridextra@2.3 r-gplots@3.2.0 r-ggplot2@4.0.1 r-future@1.68.0 r-foreach@1.5.2 r-dorng@1.8.6.2 r-dofuture@1.1.2 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/lusystemsbio/sRACIPE
Licenses: Expat
Build system: r
Synopsis: Systems biology tool to simulate gene regulatory circuits
Description:

sRACIPE implements a randomization-based method for gene circuit modeling. It allows us to study the effect of both the gene expression noise and the parametric variation on any gene regulatory circuit (GRC) using only its topology, and simulates an ensemble of models with random kinetic parameters at multiple noise levels. Statistical analysis of the generated gene expressions reveals the basin of attraction and stability of various phenotypic states and their changes associated with intrinsic and extrinsic noises. sRACIPE provides a holistic picture to evaluate the effects of both the stochastic nature of cellular processes and the parametric variation.

r-svaretro 1.16.6
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/svaRetro
Licenses: FSDG-compatible
Build system: r
Synopsis: Retrotransposed transcript detection from structural variants
Description:

svaRetro contains functions for detecting retrotransposed transcripts (RTs) from structural variant calls. It takes structural variant calls in GRanges of breakend notation and identifies RTs by exon-exon junctions and insertion sites. The candidate RTs are reported by events and annotated with information of the inserted transcripts.

r-setools 1.24.0
Propagated dependencies: r-sva@3.58.0 r-summarizedexperiment@1.40.0 r-sechm@1.18.0 r-s4vectors@0.48.0 r-pheatmap@1.0.13 r-openxlsx@4.2.8.1 r-matrix@1.7-4 r-edger@4.8.0 r-deseq2@1.50.2 r-data-table@1.17.8 r-circlize@0.4.16 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SEtools
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: SEtools: tools for working with SummarizedExperiment
Description:

This includes a set of convenience functions for working with the SummarizedExperiment class. Note that plotting functions historically in this package have been moved to the sechm package (see vignette for details).

r-speckle 1.10.0
Propagated dependencies: r-singlecellexperiment@1.32.0 r-seurat@5.3.1 r-limma@3.66.0 r-ggplot2@4.0.1 r-edger@4.8.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/speckle
Licenses: GPL 3
Build system: r
Synopsis: Statistical methods for analysing single cell RNA-seq data
Description:

The speckle package contains functions for the analysis of single cell RNA-seq data. The speckle package currently contains functions to analyse differences in cell type proportions. There are also functions to estimate the parameters of the Beta distribution based on a given counts matrix, and a function to normalise a counts matrix to the median library size. There are plotting functions to visualise cell type proportions and the mean-variance relationship in cell type proportions and counts. As our research into specialised analyses of single cell data continues we anticipate that the package will be updated with new functions.

r-simpleseg 1.12.0
Propagated dependencies: r-terra@1.8-86 r-summarizedexperiment@1.40.0 r-spatstat-geom@3.6-1 r-s4vectors@0.48.0 r-ebimage@4.52.0 r-cytomapper@1.22.0 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/simpleSeg
Licenses: GPL 3
Build system: r
Synopsis: package to perform simple cell segmentation
Description:

Image segmentation is the process of identifying the borders of individual objects (in this case cells) within an image. This allows for the features of cells such as marker expression and morphology to be extracted, stored and analysed. simpleSeg provides functionality for user friendly, watershed based segmentation on multiplexed cellular images in R based on the intensity of user specified protein marker channels. simpleSeg can also be used for the normalization of single cell data obtained from multiple images.

r-seqgate 1.20.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-s4vectors@0.48.0 r-genomicranges@1.62.0 r-biocmanager@1.30.27
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SeqGate
Licenses: GPL 2+
Build system: r
Synopsis: Filtering of Lowly Expressed Features
Description:

Filtering of lowly expressed features (e.g. genes) is a common step before performing statistical analysis, but an arbitrary threshold is generally chosen. SeqGate implements a method that rationalize this step by the analysis of the distibution of counts in replicate samples. The gate is the threshold above which sequenced features can be considered as confidently quantified.

r-somaticadata 1.48.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SomatiCAData
Licenses: Artistic License 2.0
Build system: r
Synopsis: An example cancer whole genome sequencing data for the SomatiCA package
Description:

An example cancer whole genome sequencing data for the SomatiCA package.

r-subseq 1.40.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: http://github.com/StoreyLab/subSeq
Licenses: Expat
Build system: r
Synopsis: Subsampling of high-throughput sequencing count data
Description:

Subsampling of high throughput sequencing count data for use in experiment design and analysis.

r-scoup 1.4.0
Propagated dependencies: r-matrix@1.7-4 r-biostrings@2.78.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/thsadiq/scoup
Licenses: GPL 2+
Build system: r
Synopsis: Simulate Codons with Darwinian Selection Modelled as an OU Process
Description:

An elaborate molecular evolutionary framework that facilitates straightforward simulation of codon genetic sequences subjected to different degrees and/or patterns of Darwinian selection. The model is built upon the fitness landscape paradigm of Sewall Wright, as popularised by the mutation-selection model of Halpern and Bruno. This enables realistic evolutionary process of living organisms to be reproducible seamlessly. For example, an Ornstein-Uhlenbeck fitness update algorithm is incorporated herein. Consequently, otherwise complex biological processes, such as the effect of the interplay between genetic drift and fitness landscape fluctuations on the inference of diversifying selection, may now be investigated with minimal effort. Frequency-dependent and stochastic fitness landscape update techniques are available.

r-subcellbarcode 1.26.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SubCellBarCode
Licenses: GPL 2
Build system: r
Synopsis: SubCellBarCode: Integrated workflow for robust mapping and visualizing whole human spatial proteome
Description:

Mass-Spectrometry based spatial proteomics have enabled the proteome-wide mapping of protein subcellular localization (Orre et al. 2019, Molecular Cell). SubCellBarCode R package robustly classifies proteins into corresponding subcellular localization.

r-sconify 1.30.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/Sconify
Licenses: Artistic License 2.0
Build system: r
Synopsis: toolkit for performing KNN-based statistics for flow and mass cytometry data
Description:

This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold.

r-soybeancdf 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/soybeancdf
Licenses: LGPL 2.0+
Build system: r
Synopsis: soybeancdf
Description:

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

r-synmut 1.26.0
Propagated dependencies: r-stringr@1.6.0 r-seqinr@4.2-36 r-biostrings@2.78.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/Koohoko/SynMut
Licenses: GPL 2
Build system: r
Synopsis: SynMut: Designing Synonymously Mutated Sequences with Different Genomic Signatures
Description:

There are increasing demands on designing virus mutants with specific dinucleotide or codon composition. This tool can take both dinucleotide preference and/or codon usage bias into account while designing mutants. It is a powerful tool for in silico designs of DNA sequence mutants.

r-semisup 1.34.0
Propagated dependencies: r-vgam@1.1-13
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/rauschenberger/semisup
Licenses: GPL 3
Build system: r
Synopsis: Semi-Supervised Mixture Model
Description:

This package implements a parametric semi-supervised mixture model. The permutation test detects markers with main or interactive effects, without distinguishing them. Possible applications include genome-wide association analysis and differential expression analysis.

r-scddboost 1.12.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/wiscstatman/scDDboost
Licenses: GPL 2+
Build system: r
Synopsis: compositional model to assess expression changes from single-cell rna-seq data
Description:

scDDboost is an R package to analyze changes in the distribution of single-cell expression data between two experimental conditions. Compared to other methods that assess differential expression, scDDboost benefits uniquely from information conveyed by the clustering of cells into cellular subtypes. Through a novel empirical Bayesian formulation it calculates gene-specific posterior probabilities that the marginal expression distribution is the same (or different) between the two conditions. The implementation in scDDboost treats gene-level expression data within each condition as a mixture of negative binomial distributions.

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.

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Total results: 69112