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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-synergyfinder 3.18.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: http://www.synergyfinder.org
Licenses: FSDG-compatible
Synopsis: Calculate and Visualize Synergy Scores for Drug Combinations
Description:

Efficient implementations for analyzing pre-clinical multiple drug combination datasets. It provides efficient implementations for 1.the popular synergy scoring models, including HSA, Loewe, Bliss, and ZIP to quantify the degree of drug combination synergy; 2. higher order drug combination data analysis and synergy landscape visualization for unlimited number of drugs in a combination; 3. statistical analysis of drug combination synergy and sensitivity with confidence intervals and p-values; 4. synergy barometer for harmonizing multiple synergy scoring methods to provide a consensus metric of synergy; 5. evaluation of synergy and sensitivity simultaneously to provide an unbiased interpretation of the clinical potential of the drug combinations. Based on this package, we also provide a web application (http://www.synergyfinder.org) for users who prefer graphical user interface.

r-sfedata 1.12.0
Propagated dependencies: r-experimenthub@2.16.0 r-biocfilecache@2.16.0 r-annotationhub@3.16.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/pachterlab/SFEData
Licenses: Artistic License 2.0
Synopsis: Example SpatialFeatureExperiment datasets
Description:

Example spatial transcriptomics datasets with Simple Feature annotations as SpatialFeatureExperiment objects. Technologies include Visium, slide-seq, Nanostring CoxMX, Vizgen MERFISH, and 10X Xenium. Tissues include mouse skeletal muscle, human melanoma metastasis, human lung, breast cancer, and mouse liver.

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-scale4c 1.32.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/Scale4C
Licenses: LGPL 3
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-spotclean 1.12.0
Propagated dependencies: r-viridis@0.6.5 r-tibble@3.2.1 r-summarizedexperiment@1.38.1 r-spatialexperiment@1.18.1 r-seurat@5.3.0 r-s4vectors@0.46.0 r-rlang@1.1.6 r-rjson@0.2.23 r-rhdf5@2.52.0 r-readbitmap@0.1.5 r-rcolorbrewer@1.1-3 r-matrix@1.7-3 r-ggplot2@3.5.2 r-dplyr@1.1.4
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/zijianni/SpotClean
Licenses: GPL 3
Synopsis: SpotClean adjusts for spot swapping in spatial transcriptomics data
Description:

SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontaminate the spot swapping effect, thus increase the sensitivity and precision of downstream analyses.

r-sarks 1.22.0
Dependencies: openjdk@24.0.1
Propagated dependencies: r-rjava@1.0-11 r-iranges@2.42.0 r-cluster@2.1.8.1 r-biostrings@2.76.0 r-binom@1.1-1.1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797
Licenses: Modified BSD
Synopsis: Suffix Array Kernel Smoothing for discovery of correlative sequence motifs and multi-motif domains
Description:

Suffix Array Kernel Smoothing (see https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797), or SArKS, identifies sequence motifs whose presence correlates with numeric scores (such as differential expression statistics) assigned to the sequences (such as gene promoters). SArKS smooths over sequence similarity, quantified by location within a suffix array based on the full set of input sequences. A second round of smoothing over spatial proximity within sequences reveals multi-motif domains. Discovered motifs can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing.

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-smite 1.38.0
Propagated dependencies: r-scales@1.4.0 r-s4vectors@0.46.0 r-reactome-db@1.92.0 r-plyr@1.8.9 r-org-hs-eg-db@3.21.0 r-keggrest@1.48.0 r-iranges@2.42.0 r-igraph@2.1.4 r-hmisc@5.2-3 r-goseq@1.60.0 r-ggplot2@3.5.2 r-genomicranges@1.60.0 r-genelendatabase@1.44.0 r-bionet@1.68.0 r-biobase@2.68.0 r-annotationdbi@1.70.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/GreallyLab/SMITE
Licenses: FSDG-compatible
Synopsis: Significance-based Modules Integrating the Transcriptome and Epigenome
Description:

This package builds on the Epimods framework which facilitates finding weighted subnetworks ("modules") on Illumina Infinium 27k arrays using the SpinGlass algorithm, as implemented in the iGraph package. We have created a class of gene centric annotations associated with p-values and effect sizes and scores from any researchers prior statistical results to find functional modules.

r-seqc 1.44.0
Propagated dependencies: r-biobase@2.68.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: http://bioconductor.org/packages/release/data/experiment/html/seqc.html
Licenses: GPL 3
Synopsis: RNA-seq data generated from SEQC (MAQC-III) study
Description:

The SEQC/MAQC-III Consortium has produced benchmark RNA-seq data for the assessment of RNA sequencing technologies and data analysis methods (Nat Biotechnol, 2014). Billions of sequence reads have been generated from ten different sequencing sites. This package contains the summarized read count data for ~2000 sequencing libraries. It also includes all the exon-exon junctions discovered from the study. TaqMan RT-PCR data for ~1000 genes and ERCC spike-in sequence data are included in this package as well.

r-shdz-db 3.2.3
Propagated dependencies: r-org-hs-eg-db@3.21.0 r-annotationdbi@1.70.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SHDZ.db
Licenses: Artistic License 2.0
Synopsis: SHDZ http://genome-www5.stanford.edu/ Annotation Data (SHDZ)
Description:

SHDZ http://genome-www5.stanford.edu/ Annotation Data (SHDZ) assembled using data from public repositories.

r-scthi-data 1.22.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/scTHI.data
Licenses: GPL 2
Synopsis: The package contains examples of single cell data used in vignettes and examples of the scTHI package; data contain both tumor cells and immune cells from public dataset of glioma
Description:

Data for the vignette and tutorial of the package scTHI.

r-survtype 1.26.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/survtype
Licenses: Artistic License 2.0
Synopsis: Subtype Identification with Survival Data
Description:

Subtypes are defined as groups of samples that have distinct molecular and clinical features. Genomic data can be analyzed for discovering patient subtypes, associated with clinical data, especially for survival information. This package is aimed to identify subtypes that are both clinically relevant and biologically meaningful.

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-schot 1.22.0
Propagated dependencies: r-summarizedexperiment@1.38.1 r-singlecellexperiment@1.30.1 r-s4vectors@0.46.0 r-reshape@0.8.9 r-matrix@1.7-3 r-iranges@2.42.0 r-igraph@2.1.4 r-ggplot2@3.5.2 r-ggforce@0.4.2 r-biocparallel@1.42.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/scHOT
Licenses: GPL 3
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-sbmlr 2.6.0
Propagated dependencies: r-xml@3.99-0.18 r-desolve@1.40
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: http://epbi-radivot.cwru.edu/SBMLR/SBMLR.html
Licenses: GPL 2
Synopsis: SBML-R Interface and Analysis Tools
Description:

This package contains a systems biology markup language (SBML) interface to R.

r-splicinggraphs 1.50.1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SplicingGraphs
Licenses: Artistic License 2.0
Synopsis: Create, manipulate, visualize splicing graphs, and assign RNA-seq reads to them
Description:

This package allows the user to create, manipulate, and visualize splicing graphs and their bubbles based on a gene model for a given organism. Additionally it allows the user to assign RNA-seq reads to the edges of a set of splicing graphs, and to summarize them in different ways.

r-swathxtend 2.32.0
Propagated dependencies: r-venndiagram@1.7.3 r-openxlsx@4.2.8 r-lattice@0.22-7 r-e1071@1.7-16
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SwathXtend
Licenses: GPL 2
Synopsis: SWATH extended library generation and statistical data analysis
Description:

This package contains utility functions for integrating spectral libraries for SWATH and statistical data analysis for SWATH generated data.

r-spikeli 2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/spikeLI
Licenses: GPL 2
Synopsis: Affymetrix Spike-in Langmuir Isotherm Data Analysis Tool
Description:

SpikeLI is a package that performs the analysis of the Affymetrix spike-in data using the Langmuir Isotherm. The aim of this package is to show the advantages of a physical-chemistry based analysis of the Affymetrix microarray data compared to the traditional methods. The spike-in (or Latin square) data for the HGU95 and HGU133 chipsets have been downloaded from the Affymetrix web site. The model used in the spikeLI package is described in details in E. Carlon and T. Heim, Physica A 362, 433 (2006).

r-scvir 1.10.0
Propagated dependencies: r-summarizedexperiment@1.38.1 r-singlecellexperiment@1.30.1 r-shiny@1.10.0 r-scater@1.36.0 r-s4vectors@0.46.0 r-reticulate@1.42.0 r-pheatmap@1.0.12 r-matrixgenerics@1.20.0 r-limma@3.64.1 r-biocfilecache@2.16.0 r-basilisk@1.20.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/vjcitn/scviR
Licenses: Artistic License 2.0
Synopsis: experimental inferface from R to scvi-tools
Description:

This package defines interfaces from R to scvi-tools. A vignette works through the totalVI tutorial for analyzing CITE-seq data. Another vignette compares outputs of Chapter 12 of the OSCA book with analogous outputs based on totalVI quantifications. Future work will address other components of scvi-tools, with a focus on building understanding of probabilistic methods based on variational autoencoders.

r-sipsic 1.10.0
Propagated dependencies: r-singlecellexperiment@1.30.1 r-matrix@1.7-3
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://www.genome.org/cgi/doi/10.1101/gr.278431.123
Licenses: FSDG-compatible
Synopsis: Calculate Pathway Scores for Each Cell in scRNA-Seq Data
Description:

Infer biological pathway activity of cells from single-cell RNA-sequencing data by calculating a pathway score for each cell (pathway genes are specified by the user). It is recommended to have the data in Transcripts-Per-Million (TPM) or Counts-Per-Million (CPM) units for best results. Scores may change when adding cells to or removing cells off the data. SiPSiC stands for Single Pathway analysis in Single Cells.

r-spotlight 1.14.0
Propagated dependencies: r-sparsematrixstats@1.20.0 r-singlecellexperiment@1.30.1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.0.14 r-matrix@1.7-3 r-ggplot2@3.5.2
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/MarcElosua/SPOTlight
Licenses: GPL 3
Synopsis: `SPOTlight`: Spatial Transcriptomics Deconvolution
Description:

`SPOTlight` provides a method to deconvolute spatial transcriptomics spots using a seeded NMF approach along with visualization tools to assess the results. Spatially resolved gene expression profiles are key to understand tissue organization and function. However, novel spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots).

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-sharedobject 1.24.0
Propagated dependencies: r-rcpp@1.0.14 r-biocgenerics@0.54.0 r-bh@1.87.0-1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SharedObject
Licenses: GPL 3
Synopsis: Sharing R objects across multiple R processes without memory duplication
Description:

This package is developed for facilitating parallel computing in R. It is capable to create an R object in the shared memory space and share the data across multiple R processes. It avoids the overhead of memory dulplication and data transfer, which make sharing big data object across many clusters possible.

r-scddboost 1.12.0
Propagated dependencies: r-summarizedexperiment@1.38.1 r-singlecellexperiment@1.30.1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.0.14 r-oscope@1.38.0 r-mclust@6.1.1 r-ggplot2@3.5.2 r-ebseq@2.6.0 r-cluster@2.1.8.1 r-biocparallel@1.42.0 r-bh@1.87.0-1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/wiscstatman/scDDboost
Licenses: GPL 2+
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.

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Total results: 45109