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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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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-singlecellalleleexperiment 1.6.0
Propagated dependencies: r-summarizedexperiment@1.38.1 r-singlecellexperiment@1.30.1 r-s4vectors@0.46.0 r-matrix@1.7-3 r-delayedarray@0.34.1 r-biocparallel@1.42.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/AGImkeller/SingleCellAlleleExperiment
Licenses: Expat
Synopsis: S4 Class for Single Cell Data with Allele and Functional Levels for Immune Genes
Description:

Defines a S4 class that is based on SingleCellExperiment. In addition to the usual gene layer the object can also store data for immune genes such as HLAs, Igs and KIRs at allele and functional level. The package is part of a workflow named single-cell ImmunoGenomic Diversity (scIGD), that firstly incorporates allele-aware quantification data for immune genes. This new data can then be used with the here implemented data structure and functionalities for further data handling and data analysis.

r-setools 1.24.0
Propagated dependencies: r-sva@3.56.0 r-summarizedexperiment@1.38.1 r-sechm@1.18.0 r-s4vectors@0.46.0 r-pheatmap@1.0.12 r-openxlsx@4.2.8 r-matrix@1.7-3 r-edger@4.6.2 r-deseq2@1.48.1 r-data-table@1.17.4 r-circlize@0.4.16 r-biocparallel@1.42.0
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+
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-scatac-explorer 1.16.0
Propagated dependencies: r-zellkonverter@1.18.0 r-singlecellexperiment@1.30.1 r-s4vectors@0.46.0 r-matrix@1.7-3 r-data-table@1.17.4 r-biocfilecache@2.16.0
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
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-selex 1.42.0
Dependencies: openjdk@24.0.1
Propagated dependencies: r-rjava@1.0-11 r-biostrings@2.76.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bussemakerlab.org/site/software/
Licenses: FSDG-compatible
Synopsis: Functions for analyzing SELEX-seq data
Description:

This package provides tools for quantifying DNA binding specificities based on SELEX-seq data.

r-streamer 1.56.0
Propagated dependencies: r-rbgl@1.84.0 r-graph@1.86.0 r-biocgenerics@0.54.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/Streamer
Licenses: Artistic License 2.0
Synopsis: Enabling stream processing of large files
Description:

Large data files can be difficult to work with in R, where data generally resides in memory. This package encourages a style of programming where data is streamed from disk into R via a `producer and through a series of `consumers that, typically reduce the original data to a manageable size. The package provides useful Producer and Consumer stream components for operations such as data input, sampling, indexing, and transformation; see package?Streamer for details.

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
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-seqgate 1.20.0
Propagated dependencies: r-summarizedexperiment@1.38.1 r-s4vectors@0.46.0 r-genomicranges@1.60.0 r-biocmanager@1.30.25
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SeqGate
Licenses: GPL 2+
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-spiat 1.12.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://trigosteam.github.io/SPIAT/
Licenses: FSDG-compatible
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.

r-site2target 1.2.0
Propagated dependencies: r-s4vectors@0.46.0 r-mass@7.3-65 r-iranges@2.42.0 r-genomicranges@1.60.0 r-genomeinfodb@1.44.0 r-biocgenerics@0.54.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/Site2Target
Licenses: GPL 2
Synopsis: An R package to associate peaks and target genes
Description:

Statistics implemented for both peak-wise and gene-wise associations. In peak-wise associations, the p-value of the target genes of a given set of peaks are calculated. Negative binomial or Poisson distributions can be used for modeling the unweighted peaks targets and log-nromal can be used to model the weighted peaks. In gene-wise associations a table consisting of a set of genes, mapped to specific peaks, is generated using the given rules.

r-sincell 1.42.0
Propagated dependencies: r-tsp@1.2-5 r-statmod@1.5.0 r-scatterplot3d@0.3-44 r-rtsne@0.17 r-reshape2@1.4.4 r-rcpp@1.0.14 r-proxy@0.4-27 r-mass@7.3-65 r-igraph@2.1.4 r-ggplot2@3.5.2 r-fields@16.3.1 r-fastica@1.2-7 r-entropy@1.3.2 r-cluster@2.1.8.1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: http://bioconductor.org/
Licenses: GPL 2+
Synopsis: R package for the statistical assessment of cell state hierarchies from single-cell RNA-seq data
Description:

Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies.

r-synmut 1.26.0
Propagated dependencies: r-stringr@1.5.1 r-seqinr@4.2-36 r-biostrings@2.76.0 r-biocgenerics@0.54.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/Koohoko/SynMut
Licenses: GPL 2
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-singlemoleculefootprintingdata 1.18.0
Propagated dependencies: r-experimenthub@2.16.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SingleMoleculeFootprintingData
Licenses: GPL 3
Synopsis: Data supporting the SingleMoleculeFootprinting pkg
Description:

This Data package contains data objcets relevanat for the SingleMoleculeFootprinting package. More specifically, it contains one example of aligned sequencing data (.bam & .bai) necessary to run the SingleMoleculeFootprinting vignette. Additionally, we provide data that are essential for some functions to work correctly such as BaitCapture() and SampleCorrelation().

r-spikein 1.52.0
Propagated dependencies: r-affy@1.86.0
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
Synopsis: Affymetrix Spike-In Experiment Data
Description:

This package contains the HGU133 and HGU95 spikein experiment data.

r-segmenter 1.16.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/segmenter
Licenses: GPL 3
Synopsis: Perform Chromatin Segmentation Analysis in R by Calling ChromHMM
Description:

Chromatin segmentation analysis transforms ChIP-seq data into signals over the genome. The latter represents the observed states in a multivariate Markov model to predict the chromatin's underlying states. ChromHMM, written in Java, integrates histone modification datasets to learn the chromatin states de-novo. The goal of this package is to call chromHMM from within R, capture the output files in an S4 object and interface to other relevant Bioconductor analysis tools. In addition, segmenter provides functions to test, select and visualize the output of the segmentation.

r-sc3 1.38.0
Propagated dependencies: r-writexls@6.8.0 r-summarizedexperiment@1.38.1 r-singlecellexperiment@1.30.1 r-shiny@1.10.0 r-s4vectors@0.46.0 r-rrcov@1.7-7 r-rocr@1.0-11 r-robustbase@0.99-4-1 r-rcpparmadillo@14.4.3-1 r-rcpp@1.0.14 r-pheatmap@1.0.12 r-ggplot2@3.5.2 r-foreach@1.5.2 r-e1071@1.7-16 r-dorng@1.8.6.2 r-doparallel@1.0.17 r-cluster@2.1.8.1 r-biocgenerics@0.54.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/hemberg-lab/SC3
Licenses: GPL 3
Synopsis: Single-Cell Consensus Clustering
Description:

This package provides a tool for unsupervised clustering and analysis of single cell RNA-Seq data.

r-scanmir 1.16.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/scanMiR
Licenses: GPL 3
Synopsis: scanMiR
Description:

This package provides a set of tools for working with miRNA affinity models (KdModels), efficiently scanning for miRNA binding sites, and predicting target repression. It supports scanning using miRNA seeds, full miRNA sequences (enabling 3 alignment) and KdModels, and includes the prediction of slicing and TDMD sites. Finally, it includes utility and plotting functions (e.g. for the visual representation of miRNA-target alignment).

r-screcover 1.26.0
Propagated dependencies: r-saver@1.1.2 r-rsvd@1.0.5 r-pscl@1.5.9 r-preseqr@4.0.0 r-penalized@0.9-52 r-matrix@1.7-3 r-mass@7.3-65 r-kernlab@0.9-33 r-gamlss@5.4-22 r-foreach@1.5.2 r-doparallel@1.0.17 r-biocparallel@1.42.0 r-bbmle@1.0.25.1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://miaozhun.github.io/scRecover
Licenses: GPL 2+ GPL 3+
Synopsis: scRecover for imputation of single-cell RNA-seq data
Description:

scRecover is an R package for imputation of single-cell RNA-seq (scRNA-seq) data. It will detect and impute dropout values in a scRNA-seq raw read counts matrix while keeping the real zeros unchanged, since there are both dropout zeros and real zeros in scRNA-seq data. By combination with scImpute, SAVER and MAGIC, scRecover not only detects dropout and real zeros at higher accuracy, but also improve the downstream clustering and visualization results.

r-scanvis 1.24.0
Propagated dependencies: r-rtracklayer@1.68.0 r-rcurl@1.98-1.17 r-plotrix@3.8-4 r-iranges@2.42.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SCANVIS
Licenses: FSDG-compatible
Synopsis: SCANVIS - a tool for SCoring, ANnotating and VISualizing splice junctions
Description:

SCANVIS is a set of annotation-dependent tools for analyzing splice junctions and their read support as predetermined by an alignment tool of choice (for example, STAR aligner). SCANVIS assesses each junction's relative read support (RRS) by relating to the context of local split reads aligning to annotated transcripts. SCANVIS also annotates each splice junction by indicating whether the junction is supported by annotation or not, and if not, what type of junction it is (e.g. exon skipping, alternative 5 or 3 events, Novel Exons). Unannotated junctions are also futher annotated by indicating whether it induces a frame shift or not. SCANVIS includes a visualization function to generate static sashimi-style plots depicting relative read support and number of split reads using arc thickness and arc heights, making it easy for users to spot well-supported junctions. These plots also clearly delineate unannotated junctions from annotated ones using designated color schemes, and users can also highlight splice junctions of choice. Variants and/or a read profile are also incoroporated into the plot if the user supplies variants in bed format and/or the BAM file. One further feature of the visualization function is that users can submit multiple samples of a certain disease or cohort to generate a single plot - this occurs via a "merge" function wherein junction details over multiple samples are merged to generate a single sashimi plot, which is useful when contrasting cohorots (eg. disease vs control).

r-scdataviz 1.20.0
Propagated dependencies: r-umap@0.2.10.0 r-singlecellexperiment@1.30.1 r-seurat@5.3.0 r-scales@1.4.0 r-s4vectors@0.46.0 r-reshape2@1.4.4 r-rcolorbrewer@1.1-3 r-matrixstats@1.5.0 r-mass@7.3-65 r-ggrepel@0.9.6 r-ggplot2@3.5.2 r-flowcore@2.20.0 r-corrplot@0.95
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/kevinblighe/scDataviz
Licenses: GPL 3
Synopsis: scDataviz: single cell dataviz and downstream analyses
Description:

In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a plug and play feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz. Finally, the graphics in scDataviz are generated via the ggplot engine, which means that users can add on features to these with ease.

r-spqn 1.22.0
Propagated dependencies: r-summarizedexperiment@1.38.1 r-matrixstats@1.5.0 r-ggridges@0.5.6 r-ggplot2@3.5.2 r-biocgenerics@0.54.0
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
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-spatialfeatureexperiment 1.12.1
Propagated dependencies: r-zeallot@0.2.0 r-terra@1.8-50 r-summarizedexperiment@1.38.1 r-spdep@1.3-11 r-spatialreg@1.3-6 r-spatialexperiment@1.18.1 r-singlecellexperiment@1.30.1 r-sfheaders@0.4.4 r-sf@1.0-21 r-s4vectors@0.46.0 r-rlang@1.1.6 r-rjson@0.2.23 r-matrix@1.7-3 r-lifecycle@1.0.4 r-ebimage@4.50.0 r-dropletutils@1.28.0 r-data-table@1.17.4 r-biocparallel@1.42.0 r-biocneighbors@2.2.0 r-biocgenerics@0.54.0 r-biobase@2.68.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/pachterlab/SpatialFeatureExperiment
Licenses: Artistic License 2.0
Synopsis: Integrating SpatialExperiment with Simple Features in sf
Description:

This package provides 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.

r-scanmirdata 1.16.0
Propagated dependencies: r-scanmir@1.16.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/scanMiRData
Licenses: GPL 3
Synopsis: miRNA Affinity models for the scanMiR package
Description:

This package contains companion data to the scanMiR package. It contains `KdModel` (miRNA 12-mer binding affinity models) collections corresponding to all human, mouse and rat mirbase miRNAs. See the scanMiR package for details.

r-simbu 1.12.0
Propagated dependencies: r-tidyr@1.3.1 r-summarizedexperiment@1.38.1 r-sparsematrixstats@1.20.0 r-reticulate@1.42.0 r-rcurl@1.98-1.17 r-rcolorbrewer@1.1-3 r-proxyc@0.5.2 r-phyloseq@1.52.0 r-matrix@1.7-3 r-ggplot2@3.5.2 r-dplyr@1.1.4 r-data-table@1.17.4 r-biocparallel@1.42.0 r-basilisk@1.20.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/omnideconv/SimBu
Licenses: FSDG-compatible
Synopsis: Simulate Bulk RNA-seq Datasets from Single-Cell Datasets
Description:

SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations.

r-sugarcaneprobe 2.18.0
Propagated dependencies: r-annotationdbi@1.70.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/sugarcaneprobe
Licenses: LGPL 2.0+
Synopsis: Probe sequence data for microarrays of type sugarcane
Description:

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was Sugar\_Cane\_probe\_tab.

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Total results: 45109