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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-rlassocox 1.18.0
Propagated dependencies: r-survival@3.8-3 r-matrix@1.7-4 r-igraph@2.2.1 r-glmnet@4.1-10
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RLassoCox
Licenses: Artistic License 2.0
Build system: r
Synopsis: reweighted Lasso-Cox by integrating gene interaction information
Description:

RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types.

r-ragene11sttranscriptcluster-db 8.8.0
Propagated dependencies: r-org-rn-eg-db@3.22.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/ragene11sttranscriptcluster.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Affymetrix ragene11 annotation data (chip ragene11sttranscriptcluster)
Description:

Affymetrix ragene11 annotation data (chip ragene11sttranscriptcluster) assembled using data from public repositories.

r-raids 1.8.0
Propagated dependencies: r-variantannotation@1.56.0 r-stringr@1.6.0 r-snprelate@1.44.0 r-s4vectors@0.48.0 r-rsamtools@2.26.0 r-rlang@1.1.6 r-proc@1.19.0.1 r-matrixgenerics@1.22.0 r-iranges@2.44.0 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-genesis@2.40.0 r-gdsfmt@1.46.0 r-ensembldb@2.34.0 r-dplyr@1.1.4 r-class@7.3-23 r-bsgenome@1.78.0 r-annotationfilter@1.34.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://krasnitzlab.github.io/RAIDS/
Licenses: FSDG-compatible
Build system: r
Synopsis: Robust Ancestry Inference using Data Synthesis
Description:

This package implements specialized algorithms that enable genetic ancestry inference from various cancer sequences sources (RNA, Exome and Whole-Genome sequences). This package also implements a simulation algorithm that generates synthetic cancer-derived data. This code and analysis pipeline was designed and developed for the following publication: Belleau, P et al. Genetic Ancestry Inference from Cancer-Derived Molecular Data across Genomic and Transcriptomic Platforms. Cancer Res 1 January 2023; 83 (1): 49–58.

r-r10kcod-db 3.4.0
Propagated dependencies: r-org-rn-eg-db@3.22.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/r10kcod.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Codelink UniSet Rat I Bioarray (~10 000 rat gene targets) annotation data (chip r10kcod)
Description:

Codelink UniSet Rat I Bioarray (~10 000 rat gene targets) annotation data (chip r10kcod) assembled using data from public repositories.

r-rigraphlib 1.2.0
Propagated dependencies: r-biocmake@1.2.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/libscran/Rigraphlib
Licenses: GPL 3
Build system: r
Synopsis: igraph library as an R package
Description:

Vendors the igraph C source code and builds it into a static library. Other Bioconductor packages can link to libigraph.a in their own C/C++ code. This is intended for packages wrapping C/C++ libraries that depend on the igraph C library and cannot be easily adapted to use the igraph R package.

r-sclane 1.0.0
Propagated dependencies: r-withr@3.0.2 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-scales@1.4.0 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-purrr@1.2.0 r-mpath@0.4-2.26 r-matrix@1.7-4 r-mass@7.3-65 r-magrittr@2.0.4 r-glmmtmb@1.1.13 r-glm2@1.2.1 r-ggplot2@4.0.1 r-geem@0.10.1 r-gamlss@5.5-0 r-future@1.68.0 r-furrr@0.3.1 r-foreach@1.5.2 r-dplyr@1.1.4 r-dosnow@1.0.20 r-broom-mixed@0.2.9.6 r-bigstatsr@1.6.2
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/jr-leary7/scLANE
Licenses: Expat
Build system: r
Synopsis: Model Gene Expression Dynamics with Spline-Based NB GLMs, GEEs, & GLMMs
Description:

Our scLANE model uses truncated power basis spline models to build flexible, interpretable models of single cell gene expression over pseudotime or latent time. The modeling architectures currently supported are Negative-binomial GLMs, GEEs, & GLMMs. Downstream analysis functionalities include model comparison, dynamic gene clustering, smoothed counts generation, gene set enrichment testing, & visualization.

r-scdataviz 1.20.0
Propagated dependencies: r-umap@0.2.10.0 r-singlecellexperiment@1.32.0 r-seurat@5.3.1 r-scales@1.4.0 r-s4vectors@0.48.0 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-matrixstats@1.5.0 r-mass@7.3-65 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-flowcore@2.22.0 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
Build system: r
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@1.40.0 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-subcellularspatialdata 1.6.0
Propagated dependencies: r-spatialexperiment@1.20.0 r-matrix@1.7-4 r-hexbin@1.28.5 r-dplyr@1.1.4
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://davislaboratory.github.io/SubcellularSpatialData
Licenses: GPL 3+
Build system: r
Synopsis: Annotated spatial transcriptomics datasets from 10x Xenium, NanoString CosMx and BGI STOmics
Description:

This is a data package that hosts annotated sub-cellular localised datasets from the STOmics, Xenium and CosMx platforms. Specifically, it hosts datasets analysed in the publication Bhuva et. al, 2024 titled "Library size confounds biology in spatial transcriptomics data". Raw transcript detections are hosted and functions to convert them to SpatialExperiment objects have been implemented.

r-scthi 1.22.0
Propagated dependencies: r-rtsne@0.17 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/scTHI
Licenses: GPL 2
Build system: r
Synopsis: Indentification of significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data
Description:

scTHI is an R package to identify active pairs of ligand-receptors from single cells in order to study,among others, tumor-host interactions. scTHI contains a set of signatures to classify cells from the tumor microenvironment.

r-spectraltad 1.26.0
Propagated dependencies: r-primme@3.2-6 r-matrix@1.7-4 r-magrittr@2.0.4 r-hiccompare@1.32.0 r-genomicranges@1.62.0 r-dplyr@1.1.4 r-cluster@2.1.8.1 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/dozmorovlab/SpectralTAD
Licenses: Expat
Build system: r
Synopsis: SpectralTAD: Hierarchical TAD detection using spectral clustering
Description:

SpectralTAD is an R package designed to identify Topologically Associated Domains (TADs) from Hi-C contact matrices. It uses a modified version of spectral clustering that uses a sliding window to quickly detect TADs. The function works on a range of different formats of contact matrices and returns a bed file of TAD coordinates. The method does not require users to adjust any parameters to work and gives them control over the number of hierarchical levels to be returned.

r-seq-hotspot 1.10.0
Propagated dependencies: r-r-utils@2.13.0 r-hash@2.2.6.3
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/sydney-grant/seq.hotSPOT
Licenses: Artistic License 2.0
Build system: r
Synopsis: Targeted sequencing panel design based on mutation hotspots
Description:

seq.hotSPOT provides a resource for designing effective sequencing panels to help improve mutation capture efficacy for ultradeep sequencing projects. Using SNV datasets, this package designs custom panels for any tissue of interest and identify the genomic regions likely to contain the most mutations. Establishing efficient targeted sequencing panels can allow researchers to study mutation burden in tissues at high depth without the economic burden of whole-exome or whole-genome sequencing. This tool was developed to make high-depth sequencing panels to study low-frequency clonal mutations in clinically normal and cancerous tissues.

r-sizepower 1.80.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/sizepower
Licenses: LGPL 2.0+
Build system: r
Synopsis: Sample Size and Power Calculation in Micorarray Studies
Description:

This package has been prepared to assist users in computing either a sample size or power value for a microarray experimental study. The user is referred to the cited references for technical background on the methodology underpinning these calculations. This package provides support for five types of sample size and power calculations. These five types can be adapted in various ways to encompass many of the standard designs encountered in practice.

r-stexampledata 1.18.0
Propagated dependencies: r-spatialexperiment@1.20.0 r-singlecellexperiment@1.32.0 r-experimenthub@3.0.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/lmweber/STexampleData
Licenses: Expat
Build system: r
Synopsis: Collection of spatial transcriptomics datasets in SpatialExperiment Bioconductor format
Description:

Collection of spatial transcriptomics datasets stored in SpatialExperiment Bioconductor format, for use in examples, demonstrations, and tutorials. The datasets are from several different platforms and have been sourced from various publicly available sources. Several datasets include images and/or reference annotation labels.

r-sparsenetgls 1.28.0
Propagated dependencies: r-matrix@1.7-4 r-mass@7.3-65 r-huge@1.3.5 r-glmnet@4.1-10
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/sparsenetgls
Licenses: GPL 3
Build system: r
Synopsis: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression
Description:

The package provides methods of combining the graph structure learning and generalized least squares regression to improve the regression estimation. The main function sparsenetgls() provides solutions for multivariate regression with Gaussian distributed dependant variables and explanatory variables utlizing multiple well-known graph structure learning approaches to estimating the precision matrix, and uses a penalized variance covariance matrix with a distance tuning parameter of the graph structure in deriving the sandwich estimators in generalized least squares (gls) regression. This package also provides functions for assessing a Gaussian graphical model which uses the penalized approach. It uses Receiver Operative Characteristics curve as a visualization tool in the assessment.

r-signer 2.12.0
Propagated dependencies: r-vgam@1.1-13 r-variantannotation@1.56.0 r-tidyr@1.3.1 r-tibble@3.3.0 r-survminer@0.5.1 r-survival@3.8-3 r-shinywidgets@0.9.0 r-shinydashboard@0.7.3 r-shinycssloaders@1.1.0 r-shiny@1.11.1 r-scales@1.4.0 r-rtracklayer@1.70.0 r-reshape2@1.4.5 r-readr@2.1.6 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 r-randomforest@4.7-1.2 r-pvclust@2.2-0 r-proxy@0.4-27 r-proc@1.19.0.1 r-ppclust@1.1.0.1 r-pmcmrplus@1.9.12 r-pheatmap@1.0.13 r-nmf@0.28 r-nloptr@2.2.1 r-maxstat@0.7-26 r-mass@7.3-65 r-magrittr@2.0.4 r-listenv@0.10.0 r-kknn@1.4.1 r-iranges@2.44.0 r-glmnet@4.1-10 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-genomeinfodb@1.46.0 r-future-apply@1.20.0 r-future@1.68.0 r-e1071@1.7-16 r-dt@0.34.0 r-dplyr@1.1.4 r-cowplot@1.2.0 r-clue@0.3-66 r-class@7.3-23 r-bsplus@0.1.5 r-bsgenome@1.78.0 r-broom@1.0.10 r-biostrings@2.78.0 r-biocgenerics@0.56.0 r-biocfilecache@3.0.0 r-ada@2.0-5
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/TojalLab/signeR
Licenses: GPL 3
Build system: r
Synopsis: Empirical Bayesian approach to mutational signature discovery
Description:

The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variation (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided.

r-spacetrooper 1.0.1
Propagated dependencies: r-summarizedexperiment@1.40.0 r-spatialexperimentio@1.2.0 r-spatialexperiment@1.20.0 r-sfheaders@0.4.5 r-sf@1.0-23 r-scuttle@1.20.0 r-scater@1.38.0 r-s4vectors@0.48.0 r-robustbase@0.99-6 r-rlang@1.1.6 r-rhdf5@2.54.0 r-glmnet@4.1-10 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-e1071@1.7-16 r-dropletutils@1.30.0 r-dplyr@1.1.4 r-data-table@1.17.8 r-cowplot@1.2.0 r-arrow@22.0.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/drighelli/SpaceTrooper
Licenses: Expat
Build system: r
Synopsis: SpaceTrooper performs Quality Control analysis of Image-Based spatial
Description:

SpaceTrooper performs Quality Control analysis using data driven GLM models of Image-Based spatial data, providing exploration plots, QC metrics computation, outlier detection. It implements a GLM strategy for the detection of low quality cells in imaging-based spatial data (Transcriptomics and Proteomics). It additionally implements several plots for the visualization of imaging based polygons through the ggplot2 package.

r-spatialfeatureexperiment 1.12.1
Propagated dependencies: r-zeallot@0.2.0 r-terra@1.8-86 r-summarizedexperiment@1.40.0 r-spdep@1.4-1 r-spatialreg@1.4-2 r-spatialexperiment@1.20.0 r-singlecellexperiment@1.32.0 r-sfheaders@0.4.5 r-sf@1.0-23 r-s4vectors@0.48.0 r-rlang@1.1.6 r-rjson@0.2.23 r-matrix@1.7-4 r-lifecycle@1.0.4 r-ebimage@4.52.0 r-dropletutils@1.30.0 r-data-table@1.17.8 r-biocparallel@1.44.0 r-biocneighbors@2.4.0 r-biocgenerics@0.56.0 r-biobase@2.70.0
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
Build system: r
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-selectksigs 1.22.0
Propagated dependencies: r-rcpp@1.1.0 r-magrittr@2.0.4 r-hilda@1.24.0 r-gtools@3.9.5
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/USCbiostats/selectKSigs
Licenses: GPL 3
Build system: r
Synopsis: Selecting the number of mutational signatures using a perplexity-based measure and cross-validation
Description:

This package provides a package to suggest the number of mutational signatures in a collection of somatic mutations using calculating the cross-validated perplexity score.

r-seahtrue 1.4.0
Propagated dependencies: r-validate@1.1.7 r-tidyxl@1.0.10 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-scales@1.4.0 r-rlang@1.1.6 r-readxl@1.4.5 r-readr@2.1.6 r-rcolorbrewer@1.1-3 r-purrr@1.2.0 r-lubridate@1.9.4 r-logger@0.4.1 r-janitor@2.2.1 r-glue@1.8.0 r-ggridges@0.5.7 r-ggplot2@4.0.1 r-forcats@1.0.1 r-dplyr@1.1.4 r-colorspace@2.1-2 r-cli@3.6.5
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-slalom 1.32.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-singlecellexperiment@1.32.0 r-rsvd@1.0.5 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-gseabase@1.72.0 r-ggplot2@4.0.1 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/slalom
Licenses: GPL 2
Build system: r
Synopsis: Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data
Description:

slalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors. The method uses Bayesian factor analysis with a latent variable model to identify active pathways (selected by the user, e.g. KEGG pathways) that explain variation in a single-cell RNA-seq dataset. This an R/C++ implementation of the f-scLVM Python package. See the publication describing the method at https://doi.org/10.1186/s13059-017-1334-8.

r-scaedata 1.6.0
Propagated dependencies: r-experimenthub@3.0.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/AGImkeller/scaeData
Licenses: Expat
Build system: r
Synopsis: Data Package for SingleCellAlleleExperiment
Description:

This package contains default datasets used by the Bioconductor package SingleCellAlleleExperiment. The raw FASTQ files were sourced from publicly accessible datasets provided by 10x Genomics. Subsequently, our scIGD snakemake workflow was employed to process these FASTQ files. The resulting output from scIGD constitutes to the contents of this data package.

r-sseq 1.48.0
Propagated dependencies: r-rcolorbrewer@1.1-3 r-catools@1.18.3
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/sSeq
Licenses: GPL 3+
Build system: r
Synopsis: Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size
Description:

The purpose of this package is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments. Gene expression is measured in counts of transcripts and modeled with the Negative Binomial (NB) distribution using a shrinkage approach for dispersion estimation. The method of moment (MM) estimates for dispersion are shrunk towards an estimated target, which minimizes the average squared difference between the shrinkage estimates and the initial estimates. The exact per-gene probability under the NB model is calculated, and used to test the hypothesis that the expected expression of a gene in two conditions identically follow a NB distribution.

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Total results: 68655