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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-clariomsrattranscriptcluster-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/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/clariomsrattranscriptcluster.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Affymetrix clariomsrat annotation data (chip clariomsrattranscriptcluster)
Description:

Affymetrix clariomsrat annotation data (chip clariomsrattranscriptcluster) assembled using data from public repositories.

r-ctsge 1.36.0
Propagated dependencies: r-stringr@1.6.0 r-shiny@1.11.1 r-reshape2@1.4.5 r-limma@3.66.0 r-ggplot2@4.0.1 r-ccapp@0.3.5
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/michalsharabi/ctsGE
Licenses: GPL 2
Build system: r
Synopsis: Clustering of Time Series Gene Expression data
Description:

Methodology for supervised clustering of potentially many predictor variables, such as genes etc., in time series datasets Provides functions that help the user assigning genes to predefined set of model profiles.

r-cfassay 1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/CFAssay
Licenses: LGPL 2.0+
Build system: r
Synopsis: Statistical analysis for the Colony Formation Assay
Description:

The package provides functions for calculation of linear-quadratic cell survival curves and for ANOVA of experimental 2-way designs along with the colony formation assay.

r-codelink 1.78.0
Propagated dependencies: r-limma@3.66.0 r-biocgenerics@0.56.0 r-biobase@2.70.0 r-annotate@1.88.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/ddiez/codelink
Licenses: GPL 2
Build system: r
Synopsis: Manipulation of Codelink microarray data
Description:

This package facilitates reading, preprocessing and manipulating Codelink microarray data. The raw data must be exported as text file using the Codelink software.

r-cnviz 1.18.0
Propagated dependencies: r-shiny@1.11.1 r-scales@1.4.0 r-plotly@4.11.0 r-magrittr@2.0.4 r-karyoploter@1.36.0 r-genomicranges@1.62.0 r-dt@0.34.0 r-dplyr@1.1.4 r-copynumberplots@1.26.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/CNViz
Licenses: Artistic License 2.0
Build system: r
Synopsis: Copy Number Visualization
Description:

CNViz takes probe, gene, and segment-level log2 copy number ratios and launches a Shiny app to visualize your sample's copy number profile. You can also integrate loss of heterozygosity (LOH) and single nucleotide variant (SNV) data.

r-cbpmanager 1.18.0
Propagated dependencies: r-vroom@1.6.6 r-shinydashboard@0.7.3 r-shinycssloaders@1.1.0 r-shinybs@0.61.1 r-shiny@1.11.1 r-rlang@1.1.6 r-rintrojs@0.3.4 r-reticulate@1.44.1 r-rapportools@1.2 r-plyr@1.8.9 r-markdown@2.0 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-htmltools@0.5.8.1 r-dt@0.34.0 r-dplyr@1.1.4 r-basilisk@1.22.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://arsenij-ust.github.io/cbpManager/index.html
Licenses: FSDG-compatible
Build system: r
Synopsis: Generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics
Description:

This R package provides an R Shiny application that enables the user to generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics. Create cancer studies and edit its metadata. Upload mutation data of a patient that will be concatenated to the data_mutation_extended.txt file of the study. Create and edit clinical patient data, sample data, and timeline data. Create custom timeline tracks for patients.

r-clustirr 1.8.0
Propagated dependencies: r-visnetwork@2.1.4 r-tidyr@1.3.1 r-stringdist@0.9.15 r-stanheaders@2.32.10 r-scales@1.4.0 r-rstantools@2.5.0 r-rstan@2.32.7 r-reshape2@1.4.5 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-posterior@1.6.1 r-igraph@2.2.1 r-ggplot2@4.0.1 r-ggforce@0.5.0 r-future-apply@1.20.0 r-future@1.68.0 r-dplyr@1.1.4 r-bh@1.87.0-1
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/snaketron/ClustIRR
Licenses: FSDG-compatible
Build system: r
Synopsis: Clustering of immune receptor repertoires
Description:

ClustIRR analyzes repertoires of B- and T-cell receptors. It starts by identifying communities of immune receptors with similar specificities, based on the sequences of their complementarity-determining regions (CDRs). Next, it employs a Bayesian probabilistic models to quantify differential community occupancy (DCO) between repertoires, allowing the identification of expanding or contracting communities in response to e.g. infection or cancer treatment.

r-clusterfoldsimilarity 1.6.0
Propagated dependencies: r-singlecellexperiment@1.32.0 r-seuratobject@5.2.0 r-seurat@5.3.1 r-scales@1.4.0 r-reshape2@1.4.5 r-matrix@1.7-4 r-igraph@2.2.1 r-ggplot2@4.0.1 r-ggdendro@0.2.0 r-dplyr@1.1.4 r-cowplot@1.2.0 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/ClusterFoldSimilarity
Licenses: Artistic License 2.0
Build system: r
Synopsis: Calculate similarity of clusters from different single cell samples using foldchanges
Description:

This package calculates a similarity coefficient using the fold changes of shared features (e.g. genes) among clusters of different samples/batches/datasets. The similarity coefficient is calculated using the dot-product (Hadamard product) of every pairwise combination of Fold Changes between a source cluster i of sample/dataset n and all the target clusters j in sample/dataset m.

r-crupr 1.2.0
Propagated dependencies: r-txdb-mmusculus-ucsc-mm9-knowngene@3.2.2 r-txdb-mmusculus-ucsc-mm10-knowngene@3.10.0 r-txdb-hsapiens-ucsc-hg38-knowngene@3.22.0 r-txdb-hsapiens-ucsc-hg19-knowngene@3.22.1 r-summarizedexperiment@1.40.0 r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-rtracklayer@1.70.0 r-rsamtools@2.26.0 r-reshape2@1.4.5 r-randomforest@4.7-1.2 r-preprocesscore@1.72.0 r-matrixstats@1.5.0 r-magrittr@2.0.4 r-iranges@2.44.0 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-genomicfeatures@1.62.0 r-genomicalignments@1.46.0 r-fs@1.6.6 r-dplyr@1.1.4 r-biocparallel@1.44.0 r-bamsignals@1.42.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/akbariomgba/crupR
Licenses: GPL 3
Build system: r
Synopsis: An R package to predict condition-specific enhancers from ChIP-seq data
Description:

An R package that offers a workflow to predict condition-specific enhancers from ChIP-seq data. The prediction of regulatory units is done in four main steps: Step 1 - the normalization of the ChIP-seq counts. Step 2 - the prediction of active enhancers binwise on the whole genome. Step 3 - the condition-specific clustering of the putative active enhancers. Step 4 - the detection of possible target genes of the condition-specific clusters using RNA-seq counts.

r-causalr 1.42.0
Propagated dependencies: r-igraph@2.2.1
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/CausalR
Licenses: GPL 2+
Build system: r
Synopsis: Causal network analysis methods
Description:

Causal network analysis methods for regulator prediction and network reconstruction from genome scale data.

r-cellbench 1.26.0
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-singlecellexperiment@1.32.0 r-rlang@1.1.6 r-rappdirs@0.3.3 r-purrr@1.2.0 r-memoise@2.0.1 r-magrittr@2.0.4 r-lubridate@1.9.4 r-glue@1.8.0 r-dplyr@1.1.4 r-biocparallel@1.44.0 r-biocgenerics@0.56.0 r-biocfilecache@3.0.0 r-assertthat@0.2.1
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/shians/cellbench
Licenses: GPL 3
Build system: r
Synopsis: Construct Benchmarks for Single Cell Analysis Methods
Description:

This package contains infrastructure for benchmarking analysis methods and access to single cell mixture benchmarking data. It provides a framework for organising analysis methods and testing combinations of methods in a pipeline without explicitly laying out each combination. It also provides utilities for sampling and filtering SingleCellExperiment objects, constructing lists of functions with varying parameters, and multithreaded evaluation of analysis methods.

r-chipenrich-data 2.34.0
Propagated dependencies: r-s4vectors@0.48.0 r-rtracklayer@1.70.0 r-readr@2.1.6 r-iranges@2.44.0 r-genomicranges@1.62.0 r-genomeinfodb@1.46.0 r-biocgenerics@0.56.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/chipenrich.data
Licenses: GPL 3
Build system: r
Synopsis: Companion package to chipenrich
Description:

Supporting data for the chipenrich package. Includes pre-defined gene sets, gene locus definitions, and mappability estimates.

r-cbioportaldata 2.22.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-tcgautils@1.30.1 r-summarizedexperiment@1.40.0 r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-rtcgatoolbox@2.40.0 r-readr@2.1.6 r-raggedexperiment@1.34.0 r-multiassayexperiment@1.36.1 r-iranges@2.44.0 r-httr@1.4.7 r-genomicranges@1.62.0 r-dplyr@1.1.4 r-digest@0.6.39 r-biocfilecache@3.0.0 r-biocbaseutils@1.12.0 r-anvil@1.22.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/waldronlab/cBioPortalData
Licenses: AGPL 3
Build system: r
Synopsis: Exposes and Makes Available Data from the cBioPortal Web Resources
Description:

The cBioPortalData R package accesses study datasets from the cBio Cancer Genomics Portal. It accesses the data either from the pre-packaged zip / tar files or from the API interface that was recently implemented by the cBioPortal Data Team. The package can provide data in either tabular format or with MultiAssayExperiment object that uses familiar Bioconductor data representations.

r-clariomsrathttranscriptcluster-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/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/clariomsrathttranscriptcluster.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Affymetrix clariomsratht annotation data (chip clariomsrathttranscriptcluster)
Description:

Affymetrix clariomsratht annotation data (chip clariomsrathttranscriptcluster) assembled using data from public repositories.

r-cleanuprnaseq 1.4.0
Propagated dependencies: r-tximport@1.38.1 r-summarizedexperiment@1.40.0 r-seqinfo@1.0.0 r-rsubread@2.24.0 r-rsamtools@2.26.0 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-r6@2.6.1 r-qsmooth@1.26.0 r-pheatmap@1.0.13 r-limma@3.66.0 r-kernsmooth@2.23-26 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-ensembldb@2.34.0 r-edger@4.8.0 r-deseq2@1.50.2 r-bsgenome@1.78.0 r-biostrings@2.78.0 r-biocgenerics@0.56.0 r-annotationfilter@1.34.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/CleanUpRNAseq
Licenses: GPL 3
Build system: r
Synopsis: Detect and Correct Genomic DNA Contamination in RNA-seq Data
Description:

RNA-seq data generated by some library preparation methods, such as rRNA-depletion-based method and the SMART-seq method, might be contaminated by genomic DNA (gDNA), if DNase I disgestion is not performed properly during RNA preparation. CleanUpRNAseq is developed to check if RNA-seq data is suffered from gDNA contamination. If so, it can perform correction for gDNA contamination and reduce false discovery rate of differentially expressed genes.

r-cellmapper 1.36.0
Propagated dependencies: r-s4vectors@0.48.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/CellMapper
Licenses: Artistic License 2.0
Build system: r
Synopsis: Predict genes expressed selectively in specific cell types
Description:

This package infers cell type-specific expression based on co-expression similarity with known cell type marker genes. Can make accurate predictions using publicly available expression data, even when a cell type has not been isolated before.

r-categorycompare 1.54.0
Propagated dependencies: r-rcy3@2.30.0 r-hwriter@1.3.2.1 r-gseabase@1.72.0 r-graph@1.88.0 r-gostats@2.76.0 r-colorspace@2.1-2 r-category@2.76.0 r-biocgenerics@0.56.0 r-biobase@2.70.0 r-annotationdbi@1.72.0 r-annotate@1.88.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/rmflight/categoryCompare
Licenses: GPL 2
Build system: r
Synopsis: Meta-analysis of high-throughput experiments using feature annotations
Description:

Calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested).

r-compspot 1.8.0
Propagated dependencies: r-plotly@4.11.0 r-magrittr@2.0.4 r-gridextra@2.3 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-data-table@1.17.8
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/sydney-grant/compSPOT
Licenses: Artistic License 2.0
Build system: r
Synopsis: compSPOT: Tool for identifying and comparing significantly mutated genomic hotspots
Description:

Clonal cell groups share common mutations within cancer, precancer, and even clinically normal appearing tissues. The frequency and location of these mutations may predict prognosis and cancer risk. It has also been well established that certain genomic regions have increased sensitivity to acquiring mutations. Mutation-sensitive genomic regions may therefore serve as markers for predicting cancer risk. This package contains multiple functions to establish significantly mutated hotspots, compare hotspot mutation burden between samples, and perform exploratory data analysis of the correlation between hotspot mutation burden and personal risk factors for cancer, such as age, gender, and history of carcinogen exposure. This package allows users to identify robust genomic markers to help establish cancer risk.

r-cellxgenedp 1.14.0
Propagated dependencies: r-shiny@1.11.1 r-rjsoncons@1.3.2 r-httr@1.4.7 r-dt@0.34.0 r-dplyr@1.1.4 r-curl@7.0.0 r-cli@3.6.5
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://mtmorgan.github.io/cellxgenedp/
Licenses: Artistic License 2.0
Build system: r
Synopsis: Discover and Access Single Cell Data Sets in the CELLxGENE Data Portal
Description:

The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to count matrix summaries. The cellxgenedp package provides an alternative, R-based inteface, allowind data discovery, viewing, and downloading.

r-ctdquerier 2.18.0
Propagated dependencies: r-stringr@1.6.0 r-stringdist@0.9.15 r-s4vectors@0.48.0 r-rcurl@1.98-1.17 r-igraph@2.2.1 r-gridextra@2.3 r-ggplot2@4.0.1 r-biocfilecache@3.0.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/CTDquerier
Licenses: Expat
Build system: r
Synopsis: Package for CTDbase data query, visualization and downstream analysis
Description:

Package to retrieve and visualize data from the Comparative Toxicogenomics Database (http://ctdbase.org/). The downloaded data is formated as DataFrames for further downstream analyses.

r-catscradle 1.4.2
Propagated dependencies: r-summarizedexperiment@1.40.0 r-stringr@1.6.0 r-spatialexperiment@1.20.0 r-singlecellexperiment@1.32.0 r-seuratobject@5.2.0 r-seurat@5.3.1 r-s4vectors@0.48.0 r-rfast@2.1.5.2 r-reshape2@1.4.5 r-rdist@0.0.5 r-pracma@2.4.6 r-pheatmap@1.0.13 r-networkd3@0.4.1 r-msigdbr@25.1.1 r-matrix@1.7-4 r-igraph@2.2.1 r-ggplot2@4.0.1 r-geometry@0.5.2 r-ebimage@4.52.0 r-data-table@1.17.8 r-abind@1.4-8
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/AnnaLaddach/CatsCradle
Licenses: Expat
Build system: r
Synopsis: This package provides methods for analysing spatial transcriptomics data and for discovering gene clusters
Description:

This package addresses two broad areas. It allows for in-depth analysis of spatial transcriptomic data by identifying tissue neighbourhoods. These are contiguous regions of tissue surrounding individual cells. CatsCradle allows for the categorisation of neighbourhoods by the cell types contained in them and the genes expressed in them. In particular, it produces Seurat objects whose individual elements are neighbourhoods rather than cells. In addition, it enables the categorisation and annotation of genes by producing Seurat objects whose elements are genes.

r-cogeqc 1.14.0
Propagated dependencies: r-scales@1.4.0 r-rlang@1.1.6 r-reshape2@1.4.5 r-patchwork@1.3.2 r-jsonlite@2.0.0 r-igraph@2.2.1 r-ggtree@4.0.1 r-ggplot2@4.0.1 r-ggbeeswarm@0.7.2 r-biostrings@2.78.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/almeidasilvaf/cogeqc
Licenses: GPL 3
Build system: r
Synopsis: Systematic quality checks on comparative genomics analyses
Description:

cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualization functions to explore QC summary statistics.

r-cellnoptr 1.56.0
Propagated dependencies: r-xml@3.99-0.20 r-stringr@1.6.0 r-stringi@1.8.7 r-rmarkdown@2.30 r-rgraphviz@2.54.0 r-rcurl@1.98-1.17 r-rbgl@1.86.0 r-igraph@2.2.1 r-graph@1.88.0 r-ggplot2@4.0.1
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/CellNOptR
Licenses: GPL 3
Build system: r
Synopsis: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data
Description:

This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network.

r-clustersignificance 1.38.0
Propagated dependencies: r-scatterplot3d@0.3-44 r-rcolorbrewer@1.1-3 r-princurve@2.1.6 r-pracma@2.4.6
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/jasonserviss/ClusterSignificance/
Licenses: GPL 3
Build system: r
Synopsis: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data
Description:

The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data. The term class clusters here refers to, clusters of points representing known classes in the data. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. ClusterSignificance accomplishes this by, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method.

Total results: 2909