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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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r-seqc 1.44.0
Propagated dependencies: r-biobase@2.70.0
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
Build system: r
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-sagenhaft 1.80.0
Propagated dependencies: r-sparsem@1.84-2
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: http://www.bioinf.med.uni-goettingen.de
Licenses: GPL 2+
Build system: r
Synopsis: Collection of functions for reading and comparing SAGE libraries
Description:

This package implements several functions useful for analysis of gene expression data by sequencing tags as done in SAGE (Serial Analysis of Gene Expressen) data, i.e. extraction of a SAGE library from sequence files, sequence error correction, library comparison. Sequencing error correction is implementing using an Expectation Maximization Algorithm based on a Mixture Model of tag counts.

r-sspaths 1.24.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-rocr@1.0-11 r-mess@0.6.0 r-dml@1.1.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/ssPATHS
Licenses: Expat
Build system: r
Synopsis: ssPATHS: Single Sample PATHway Score
Description:

This package generates pathway scores from expression data for single samples after training on a reference cohort. The score is generated by taking the expression of a gene set (pathway) from a reference cohort and performing linear discriminant analysis to distinguish samples in the cohort that have the pathway augmented and not. The separating hyperplane is then used to score new samples.

r-spsimseq 1.20.0
Propagated dependencies: r-wgcna@1.73 r-singlecellexperiment@1.32.0 r-phyloseq@1.54.0 r-mvtnorm@1.3-3 r-limma@3.66.0 r-hmisc@5.2-4 r-fitdistrplus@1.2-4 r-edger@4.8.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/CenterForStatistics-UGent/SPsimSeq
Licenses: GPL 2
Build system: r
Synopsis: Semi-parametric simulation tool for bulk and single-cell RNA sequencing data
Description:

SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size.

r-scider 1.8.0
Propagated dependencies: r-uwot@0.2.4 r-summarizedexperiment@1.40.0 r-spatstat-geom@3.6-1 r-spatstat-explore@3.6-0 r-spatialpack@0.4-1 r-spatialexperiment@1.20.0 r-singlecellexperiment@1.32.0 r-shiny@1.11.1 r-sf@1.0-23 r-s4vectors@0.48.0 r-plotly@4.11.0 r-pheatmap@1.0.13 r-lwgeom@0.2-14 r-knitr@1.50 r-janitor@2.2.1 r-isoband@0.2.7 r-irlba@2.3.5.1 r-igraph@2.2.1 r-hexdensity@1.4.10 r-hexbin@1.28.5 r-ggplot2@4.0.1 r-dbscan@1.2.3 r-biocneighbors@2.4.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/ChenLaboratory/scider
Licenses: FSDG-compatible
Build system: r
Synopsis: Spatial cell-type inter-correlation by density in R
Description:

scider is an user-friendly R package providing functions to model the global density of cells in a slide of spatial transcriptomics data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. After modelling density, the package allows for serveral downstream analysis, including colocalization analysis, boundary detection analysis and differential density analysis.

r-sigspack 1.24.0
Propagated dependencies: r-variantannotation@1.56.0 r-summarizedexperiment@1.40.0 r-rtracklayer@1.70.0 r-quadprog@1.5-8 r-genomicranges@1.62.0 r-genomeinfodb@1.46.0 r-bsgenome@1.78.0 r-biostrings@2.78.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/bihealth/SigsPack
Licenses: GPL 3
Build system: r
Synopsis: Mutational Signature Estimation for Single Samples
Description:

Single sample estimation of exposure to mutational signatures. Exposures to known mutational signatures are estimated for single samples, based on quadratic programming algorithms. Bootstrapping the input mutational catalogues provides estimations on the stability of these exposures. The effect of the sequence composition of mutational context can be taken into account by normalising the catalogues.

r-scpipe 2.10.0
Dependencies: zlib@1.3.1
Propagated dependencies: r-vctrs@0.6.5 r-tidyr@1.3.1 r-tibble@3.3.0 r-testthat@3.3.0 r-summarizedexperiment@1.40.0 r-stringr@1.6.0 r-singlecellexperiment@1.32.0 r-scales@1.4.0 r-s4vectors@0.48.0 r-rtracklayer@1.70.0 r-rsubread@2.24.0 r-rsamtools@2.26.0 r-robustbase@0.99-6 r-rlang@1.1.6 r-rhtslib@3.6.0 r-reticulate@1.44.1 r-reshape@0.8.10 r-rcpp@1.1.0 r-purrr@1.2.0 r-org-mm-eg-db@3.22.0 r-org-hs-eg-db@3.22.0 r-multiassayexperiment@1.36.1 r-mclust@6.1.2 r-matrix@1.7-4 r-mass@7.3-65 r-magrittr@2.0.4 r-iranges@2.44.0 r-hash@2.2.6.3 r-glue@1.8.0 r-ggplot2@4.0.1 r-ggally@2.4.0 r-genomicranges@1.62.0 r-genomicalignments@1.46.0 r-flexmix@2.3-20 r-dropletutils@1.30.0 r-dplyr@1.1.4 r-data-table@1.17.8 r-biostrings@2.78.0 r-biomart@2.66.0 r-biocgenerics@0.56.0 r-basilisk@1.22.0 r-annotationdbi@1.72.0
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-svmdo 1.10.0
Propagated dependencies: r-survival@3.8-3 r-summarizedexperiment@1.40.0 r-sjmisc@2.8.11 r-shinytitle@0.1.0 r-shinyfiles@0.9.3 r-shiny@1.11.1 r-org-hs-eg-db@3.22.0 r-nortest@1.0-4 r-klar@1.7-3 r-golem@0.5.1 r-e1071@1.7-16 r-dt@0.34.0 r-dplyr@1.1.4 r-dose@4.4.0 r-data-table@1.17.8 r-catools@1.18.3 r-caret@7.0-1 r-bsda@1.2.2 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SVMDO
Licenses: GPL 3
Build system: r
Synopsis: Identification of Tumor-Discriminating mRNA Signatures via Support Vector Machines Supported by Disease Ontology
Description:

It is an easy-to-use GUI using disease information for detecting tumor/normal sample discriminating gene sets from differentially expressed genes. Our approach is based on an iterative algorithm filtering genes with disease ontology enrichment analysis and wilk and wilks lambda criterion connected to SVM classification model construction. Along with gene set extraction, SVMDO also provides individual prognostic marker detection. The algorithm is designed for FPKM and RPKM normalized RNA-Seq transcriptome datasets.

r-sparrow 1.16.0
Propagated dependencies: r-viridis@0.6.5 r-plotly@4.11.0 r-matrix@1.7-4 r-limma@3.66.0 r-irlba@2.3.5.1 r-gseabase@1.72.0 r-ggplot2@4.0.1 r-edger@4.8.0 r-delayedmatrixstats@1.32.0 r-data-table@1.17.8 r-complexheatmap@2.26.0 r-circlize@0.4.16 r-checkmate@2.3.3 r-biocset@1.24.0 r-biocparallel@1.44.0 r-biocgenerics@0.56.0 r-babelgene@22.9
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/lianos/sparrow
Licenses: Expat
Build system: r
Synopsis: Take command of set enrichment analyses through a unified interface
Description:

This package provides a unified interface to a variety of GSEA techniques from different bioconductor packages. Results are harmonized into a single object and can be interrogated uniformly for quick exploration and interpretation of results. Interactive exploration of GSEA results is enabled through a shiny app provided by a sparrow.shiny sibling package.

r-spatialfda 1.2.0
Propagated dependencies: r-tidyr@1.3.1 r-summarizedexperiment@1.40.0 r-spatstat-geom@3.6-1 r-spatstat-explore@3.6-0 r-spatialexperiment@1.20.0 r-scales@1.4.0 r-s4vectors@0.48.0 r-refund@0.1-38 r-purrr@1.2.0 r-patchwork@1.3.2 r-ggplot2@4.0.1 r-fda@6.3.0 r-experimenthub@3.0.0 r-dplyr@1.1.4
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/mjemons/spatialFDA
Licenses: FSDG-compatible
Build system: r
Synopsis: Tool for Spatial Multi-sample Comparisons
Description:

spatialFDA is a package to calculate spatial statistics metrics. The package takes a SpatialExperiment object and calculates spatial statistics metrics using the package spatstat. Then it compares the resulting functions across samples/conditions using functional additive models as implemented in the package refund. Furthermore, it provides exploratory visualisations using functional principal component analysis, as well implemented in refund.

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-summix 2.16.0
Propagated dependencies: r-visnetwork@2.1.4 r-tidyselect@1.2.1 r-tibble@3.3.0 r-scales@1.4.0 r-randomcolor@1.1.0.1 r-nloptr@2.2.1 r-magrittr@2.0.4 r-dplyr@1.1.4 r-bedassle@1.6.1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/Summix
Licenses: Expat
Build system: r
Synopsis: Summix2: A suite of methods to estimate, adjust, and leverage substructure in genetic summary data
Description:

This package contains the Summix2 method for estimating and adjusting for substructure in genetic summary allele frequency data. The function summix() estimates reference group proportions using a mixture model. The adjAF() function produces adjusted allele frequencies for an observed group with reference group proportions matching a target individual or sample. The summix_local() function estimates local ancestry mixture proportions and performs selection scans in genetic summary data.

r-spikeinsubset 1.50.0
Propagated dependencies: r-biobase@2.70.0 r-affy@1.88.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SpikeInSubset
Licenses: LGPL 2.0+
Build system: r
Synopsis: Part of Affymetrix's Spike-In Experiment Data
Description:

Includes probe-level and expression data for the HGU133 and HGU95 spike-in experiments.

r-spotlight 1.14.0
Propagated dependencies: r-sparsematrixstats@1.22.0 r-singlecellexperiment@1.32.0 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-matrix@1.7-4 r-ggplot2@4.0.1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/MarcElosua/SPOTlight
Licenses: GPL 3
Build system: r
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-scoreinvhap 1.32.0
Propagated dependencies: r-variantannotation@1.56.0 r-summarizedexperiment@1.40.0 r-snpstats@1.60.0 r-genomicranges@1.62.0 r-biostrings@2.78.0 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/scoreInvHap
Licenses: FSDG-compatible
Build system: r
Synopsis: Get inversion status in predefined regions
Description:

scoreInvHap can get the samples inversion status of known inversions. scoreInvHap uses SNP data as input and requires the following information about the inversion: genotype frequencies in the different haplotypes, R2 between the region SNPs and inversion status and heterozygote genotypes in the reference. The package include this data for 21 inversions.

r-snphooddata 1.40.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SNPhoodData
Licenses: LGPL 3+
Build system: r
Synopsis: Additional and more complex example data for the SNPhood package
Description:

This companion package for SNPhood provides some example datasets of a larger size than allowed for the SNPhood package. They include full and real-world examples for performing analyses with the SNPhood package.

r-splicinggraphs 1.50.1
Propagated dependencies: r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-rsamtools@2.26.0 r-rgraphviz@2.54.0 r-iranges@2.44.0 r-igraph@2.2.1 r-graph@1.88.0 r-genomicranges@1.62.0 r-genomicfeatures@1.62.0 r-genomicalignments@1.46.0 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/SplicingGraphs
Licenses: Artistic License 2.0
Build system: r
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-switchde 1.36.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-singlecellexperiment@1.32.0 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/kieranrcampbell/switchde
Licenses: GPL 2+
Build system: r
Synopsis: Switch-like differential expression across single-cell trajectories
Description:

Inference and detection of switch-like differential expression across single-cell RNA-seq trajectories.

r-spatialde 1.16.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-spatialexperiment@1.20.0 r-scales@1.4.0 r-reticulate@1.44.1 r-matrix@1.7-4 r-gridextra@2.3 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-checkmate@2.3.3 r-basilisk@1.22.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/sales-lab/spatialDE
Licenses: Expat
Build system: r
Synopsis: R wrapper for SpatialDE
Description:

SpatialDE is a method to find spatially variable genes (SVG) from spatial transcriptomics data. This package provides wrappers to use the Python SpatialDE library in R, using reticulate and basilisk.

r-shdz-db 3.2.3
Propagated dependencies: r-org-hs-eg-db@3.22.0 r-annotationdbi@1.72.0
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
Build system: r
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-spatialdecon 1.20.0
Propagated dependencies: r-seuratobject@5.2.0 r-repmis@0.5.1 r-matrix@1.7-4 r-lognormreg@0.5-0 r-geomxtools@3.14.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SpatialDecon
Licenses: Expat
Build system: r
Synopsis: Deconvolution of mixed cells from spatial and/or bulk gene expression data
Description:

Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data", Danaher (2022). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data.

r-smite 1.38.0
Propagated dependencies: r-scales@1.4.0 r-s4vectors@0.48.0 r-reactome-db@1.94.0 r-plyr@1.8.9 r-org-hs-eg-db@3.22.0 r-keggrest@1.50.0 r-iranges@2.44.0 r-igraph@2.2.1 r-hmisc@5.2-4 r-goseq@1.62.0 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-genelendatabase@1.46.0 r-bionet@1.70.0 r-biobase@2.70.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/GreallyLab/SMITE
Licenses: FSDG-compatible
Build system: r
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-soybeanprobe 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/soybeanprobe
Licenses: LGPL 2.0+
Build system: r
Synopsis: Probe sequence data for microarrays of type soybean
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 Soybean\_probe\_tab.

r-stpipe 1.0.1
Dependencies: zlib@1.3.1
Propagated dependencies: r-yaml@2.3.10 r-umap@0.2.10.0 r-testthat@3.3.0 r-summarizedexperiment@1.40.0 r-spatialexperiment@1.20.0 r-singlecellexperiment@1.32.0 r-shiny@1.11.1 r-seuratobject@5.2.0 r-seurat@5.3.1 r-scpipe@2.10.0 r-rtsne@0.17 r-rsubread@2.24.0 r-rmarkdown@2.30 r-rhtslib@3.6.0 r-rhdf5lib@1.32.0 r-reticulate@1.44.1 r-rcpp@1.1.0 r-pbmcapply@1.5.1 r-ggplot2@4.0.1 r-dropletutils@1.30.0 r-dplyr@1.1.4 r-data-table@1.17.8 r-basilisk@1.22.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/mritchielab/stPipe
Licenses: GPL 3
Build system: r
Synopsis: Upstream pre-processing for Sequencing-Based Spatial Transcriptomics
Description:

This package serves as an upstream pipeline for pre-processing sequencing-based spatial transcriptomics data. Functions includes FASTQ trimming, BAM file reformatting, index building, spatial barcode detection, demultiplexing, gene count matrix generation with UMI deduplication, QC, and revelant visualization. Config is an essential input for most of the functions which aims to improve reproducibility.

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