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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-scdiagnostics 1.4.0
Propagated dependencies: r-transport@0.15-4 r-summarizedexperiment@1.40.0 r-stringr@1.6.0 r-singlecellexperiment@1.32.0 r-scales@1.4.0 r-rlang@1.1.6 r-ranger@0.17.0 r-matrix@1.7-4 r-mass@7.3-65 r-isotree@0.6.1-4 r-igraph@2.2.1 r-ggridges@0.5.7 r-ggplot2@4.0.1 r-ggally@2.4.0 r-fnn@1.1.4.1 r-cramer@0.9-4 r-bluster@1.20.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/ccb-hms/scDiagnostics
Licenses: Artistic License 2.0
Build system: r
Synopsis: Cell type annotation diagnostics
Description:

The scDiagnostics package provides diagnostic plots to assess the quality of cell type assignments from single cell gene expression profiles. The implemented functionality allows to assess the reliability of cell type annotations, investigate gene expression patterns, and explore relationships between different cell types in query and reference datasets allowing users to detect potential misalignments between reference and query datasets. The package also provides visualization capabilities for diagnostics purposes.

r-splatter 1.34.0
Propagated dependencies: r-withr@3.0.2 r-summarizedexperiment@1.40.0 r-singlecellexperiment@1.32.0 r-scuttle@1.20.0 r-s4vectors@0.48.0 r-rlang@1.1.6 r-matrixstats@1.5.0 r-locfit@1.5-9.12 r-fitdistrplus@1.2-4 r-edger@4.8.0 r-crayon@1.5.3 r-checkmate@2.3.3 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/splatter/
Licenses: FSDG-compatible
Build system: r
Synopsis: Simple Simulation of Single-cell RNA Sequencing Data
Description:

Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets.

r-sevenbridges 1.40.0
Propagated dependencies: r-yaml@2.3.10 r-uuid@1.2-1 r-stringr@1.6.0 r-s4vectors@0.48.0 r-objectproperties@0.6.8 r-jsonlite@2.0.0 r-httr@1.4.7 r-docopt@0.7.2 r-data-table@1.17.8 r-curl@7.0.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://www.sevenbridges.com
Licenses: ASL 2.0 FSDG-compatible
Build system: r
Synopsis: Seven Bridges Platform API Client and Common Workflow Language Tool Builder in R
Description:

R client and utilities for Seven Bridges platform API, from Cancer Genomics Cloud to other Seven Bridges supported platforms.

r-struct 1.22.1
Propagated dependencies: r-summarizedexperiment@1.40.0 r-s4vectors@0.48.0 r-rols@3.6.1 r-ontologyindex@2.12 r-knitr@1.50
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/struct
Licenses: GPL 3
Build system: r
Synopsis: Statistics in R Using Class-based Templates
Description:

Defines and includes a set of class-based templates for developing and implementing data processing and analysis workflows, with a strong emphasis on statistics and machine learning. The templates can be used and where needed extended to wrap tools and methods from other packages into a common standardised structure to allow for effective and fast integration. Model objects can be combined into sequences, and sequences nested in iterators using overloaded operators to simplify and improve readability of the code. Ontology lookup has been integrated and implemented to provide standardised definitions for methods, inputs and outputs wrapped using the class-based templates.

r-spanorm 1.4.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-spatialexperiment@1.20.0 r-singlecellexperiment@1.32.0 r-seuratobject@5.2.0 r-scran@1.38.0 r-s4vectors@0.48.0 r-rlang@1.1.6 r-matrixstats@1.5.0 r-matrix@1.7-4 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://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-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-seqsetvis 1.30.0
Propagated dependencies: r-upsetr@1.4.0 r-seqinfo@1.0.0 r-scales@1.4.0 r-s4vectors@0.48.0 r-rtracklayer@1.70.0 r-rsamtools@2.26.0 r-rcolorbrewer@1.1-3 r-png@0.1-8 r-pbmcapply@1.5.1 r-pbapply@1.7-4 r-limma@3.66.0 r-iranges@2.44.0 r-ggplotify@0.1.3 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-genomicalignments@1.46.0 r-eulerr@7.0.4 r-data-table@1.17.8 r-cowplot@1.2.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/seqsetvis
Licenses: Expat
Build system: r
Synopsis: Set Based Visualizations for Next-Gen Sequencing Data
Description:

seqsetvis enables the visualization and analysis of sets of genomic sites in next gen sequencing data. Although seqsetvis was designed for the comparison of mulitple ChIP-seq samples, this package is domain-agnostic and allows the processing of multiple genomic coordinate files (bed-like files) and signal files (bigwig files pileups from bam file). seqsetvis has multiple functions for fetching data from regions into a tidy format for analysis in data.table or tidyverse and visualization via ggplot2.

r-spotclean 1.12.0
Propagated dependencies: r-viridis@0.6.5 r-tibble@3.3.0 r-summarizedexperiment@1.40.0 r-spatialexperiment@1.20.0 r-seurat@5.3.1 r-s4vectors@0.48.0 r-rlang@1.1.6 r-rjson@0.2.23 r-rhdf5@2.54.0 r-readbitmap@0.1.5 r-rcolorbrewer@1.1-3 r-matrix@1.7-4 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/zijianni/SpotClean
Licenses: GPL 3
Build system: r
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-sconify 1.30.0
Propagated dependencies: r-tibble@3.3.0 r-rtsne@0.17 r-readr@2.1.6 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-fnn@1.1.4.1 r-flowcore@2.22.0 r-dplyr@1.1.4
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-synapsis 1.16.0
Propagated dependencies: r-ebimage@4.52.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/synapsis
Licenses: Expat
Build system: r
Synopsis: An R package to automate the analysis of double-strand break repair during meiosis
Description:

Synapsis is a Bioconductor software package for automated (unbiased and reproducible) analysis of meiotic immunofluorescence datasets. The primary functions of the software can i) identify cells in meiotic prophase that are labelled by a synaptonemal complex axis or central element protein, ii) isolate individual synaptonemal complexes and measure their physical length, iii) quantify foci and co-localise them with synaptonemal complexes, iv) measure interference between synaptonemal complex-associated foci. The software has applications that extend to multiple species and to the analysis of other proteins that label meiotic prophase chromosomes. The software converts meiotic immunofluorescence images into R data frames that are compatible with machine learning methods. Given a set of microscopy images of meiotic spread slides, synapsis crops images around individual single cells, counts colocalising foci on strands on a per cell basis, and measures the distance between foci on any given strand.

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-spktools 1.66.0
Propagated dependencies: r-rcolorbrewer@1.1-3 r-gtools@3.9.5 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: http://bioconductor.org
Licenses: GPL 2+
Build system: r
Synopsis: Methods for Spike-in Arrays
Description:

The package contains functions that can be used to compare expression measures on different array platforms.

r-simlr 1.36.0
Propagated dependencies: r-rspectra@0.16-2 r-rcppannoy@0.0.22 r-rcpp@1.1.0 r-pracma@2.4.6 r-matrix@1.7-4
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/BatzoglouLabSU/SIMLR
Licenses: FSDG-compatible
Build system: r
Synopsis: Single-cell Interpretation via Multi-kernel LeaRning (SIMLR)
Description:

Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization.

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-scmultisim 1.6.0
Propagated dependencies: r-zeallot@0.2.0 r-summarizedexperiment@1.40.0 r-rtsne@0.17 r-rlang@1.1.6 r-phytools@2.5-2 r-matrixstats@1.5.0 r-mass@7.3-65 r-markdown@2.0 r-kernelknn@1.1.6 r-igraph@2.2.1 r-gplots@3.2.0 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dplyr@1.1.4 r-crayon@1.5.3 r-biocparallel@1.44.0 r-assertthat@0.2.1 r-ape@5.8-1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://zhanglabgt.github.io/scMultiSim/
Licenses: Artistic License 2.0
Build system: r
Synopsis: Simulation of Multi-Modality Single Cell Data Guided By Gene Regulatory Networks and Cell-Cell Interactions
Description:

scMultiSim simulates paired single cell RNA-seq, single cell ATAC-seq and RNA velocity data, while incorporating mechanisms of gene regulatory networks, chromatin accessibility and cell-cell interactions. It allows users to tune various parameters controlling the amount of each biological factor, variation of gene-expression levels, the influence of chromatin accessibility on RNA sequence data, and so on. It can be used to benchmark various computational methods for single cell multi-omics data, and to assist in experimental design of wet-lab experiments.

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-snplocs-hsapiens-dbsnp149-grch38 0.99.21
Propagated dependencies: r-s4vectors@0.48.0 r-iranges@2.44.0 r-genomicranges@1.62.0 r-genomeinfodb@1.46.0 r-bsgenome@1.78.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SNPlocs.Hsapiens.dbSNP149.GRCh38
Licenses: Artistic License 2.0
Build system: r
Synopsis: SNP locations for Homo sapiens (dbSNP Build 149)
Description:

SNP locations and alleles for Homo sapiens extracted from NCBI dbSNP Build 149. The source data files used for this package were created by NCBI between November 8-12, 2016, and contain SNPs mapped to reference genome GRCh38.p7 (a patched version of GRCh38 that doesn't alter chromosomes 1-22, X, Y, MT). Note that these SNPs can be "injected" in BSgenome.Hsapiens.NCBI.GRCh38 or in BSgenome.Hsapiens.UCSC.hg38.

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
Build system: r
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-saser 1.6.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-s4vectors@0.48.0 r-prroc@1.4 r-matrixgenerics@1.22.0 r-mass@7.3-65 r-limma@3.66.0 r-iranges@2.44.0 r-igraph@2.2.1 r-genomicranges@1.62.0 r-genomicfeatures@1.62.0 r-genomicalignments@1.46.0 r-edger@4.8.0 r-dplyr@1.1.4 r-deseq2@1.50.2 r-data-table@1.17.8 r-biocparallel@1.44.0 r-biocgenerics@0.56.0 r-aspli@2.20.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/statOmics/saseR
Licenses: Artistic License 2.0
Build system: r
Synopsis: Scalable Aberrant Splicing and Expression Retrieval
Description:

saseR is a highly performant and fast framework for aberrant expression and splicing analyses. The main functions are: \itemize\item \code\linkBamtoAspliCounts - Process BAM files to ASpli counts \item \code\linkconvertASpli - Get gene, bin or junction counts from ASpli SummarizedExperiment \item \code\linkcalculateOffsets - Create an offsets assays for aberrant expression or splicing analysis \item \code\linksaseRfindEncodingDim - Estimate the optimal number of latent factors to include when estimating the mean expression \item \code\linksaseRfit - Parameter estimation of the negative binomial distribution and compute p-values for aberrant expression and splicing For information upon how to use these functions, check out our vignette at \urlhttps://github.com/statOmics/saseR/blob/main/vignettes/Vignette.Rmd and the saseR paper: Segers, A. et al. (2023). Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses. bioRxiv. \urlhttps://doi.org/10.1101/2023.06.29.547014.

r-scbn 1.28.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SCBN
Licenses: GPL 2
Build system: r
Synopsis: statistical normalization method and differential expression analysis for RNA-seq data between different species
Description:

This package provides a scale based normalization (SCBN) method to identify genes with differential expression between different species. It takes into account the available knowledge of conserved orthologous genes and the hypothesis testing framework to detect differentially expressed orthologous genes. The method on this package are described in the article A statistical normalization method and differential expression analysis for RNA-seq data between different species by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang (2018, pending publication).

r-semdist 1.44.0
Propagated dependencies: r-go-db@3.22.0 r-annotationdbi@1.72.0 r-annotate@1.88.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: http://github.com/iangonzalez/SemDist
Licenses: GPL 2+
Build system: r
Synopsis: Information Accretion-based Function Predictor Evaluation
Description:

This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor.

r-segmentseq 2.44.0
Propagated dependencies: r-shortread@1.68.0 r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-rsamtools@2.26.0 r-iranges@2.44.0 r-genomicranges@1.62.0 r-bayseq@2.44.0 r-abind@1.4-8
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/samgg/segmentSeq
Licenses: GPL 3
Build system: r
Synopsis: Methods for identifying small RNA loci from high-throughput sequencing data
Description:

High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery.

r-signifinder 1.12.0
Propagated dependencies: r-viridis@0.6.5 r-txdb-hsapiens-ucsc-hg38-knowngene@3.22.0 r-txdb-hsapiens-ucsc-hg19-knowngene@3.22.1 r-survminer@0.5.1 r-survival@3.8-3 r-summarizedexperiment@1.40.0 r-spatialexperiment@1.20.0 r-scales@1.4.0 r-rcolorbrewer@1.1-3 r-patchwork@1.3.2 r-org-hs-eg-db@3.22.0 r-openair@2.19.0 r-maxstat@0.7-26 r-matrixstats@1.5.0 r-magrittr@2.0.4 r-iranges@2.44.0 r-gsva@2.4.1 r-ggridges@0.5.7 r-ggplot2@4.0.1 r-ensembldb@2.34.0 r-dplyr@1.1.4 r-dgeobj-utils@1.0.6 r-cowplot@1.2.0 r-consensusov@1.32.0 r-complexheatmap@2.26.0 r-biocgenerics@0.56.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/CaluraLab/signifinder
Licenses: AGPL 3
Build system: r
Synopsis: Collection and implementation of public transcriptional cancer signatures
Description:

signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows to compute a variety of signatures coming from public literature, based on gene expression values, and return single-sample (-cell/-spot) scores. Currently, signifinder collects more than 70 distinct signatures, relating to multiple tumors and multiple cancer processes.

r-screencounter 1.10.0
Dependencies: zlib@1.3.1
Propagated dependencies: r-summarizedexperiment@1.40.0 r-s4vectors@0.48.0 r-rcpp@1.1.0 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/crisprVerse/screenCounter
Licenses: Expat
Build system: r
Synopsis: Counting Reads in High-Throughput Sequencing Screens
Description:

This package provides functions for counting reads from high-throughput sequencing screen data (e.g., CRISPR, shRNA) to quantify barcode abundance. Currently supports single barcodes in single- or paired-end data, and combinatorial barcodes in paired-end data.

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Total results: 68658