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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-bsgenome-cfamiliaris-ucsc-canfam2-masked 1.3.99
Propagated dependencies: r-bsgenome-cfamiliaris-ucsc-canfam2@1.4.0 r-bsgenome@1.80.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://bioconductor.org/packages/BSgenome.Cfamiliaris.UCSC.canFam2.masked
Licenses: Artistic License 2.0
Build system: r
Synopsis: Full masked genome sequences for Canis lupus familiaris (UCSC version canFam2)
Description:

Full genome sequences for Canis lupus familiaris (Dog) as provided by UCSC (canFam2, May 2005) and stored in Biostrings objects. The sequences are the same as in BSgenome.Cfamiliaris.UCSC.canFam2, except that each of them has the 4 following masks on top: (1) the mask of assembly gaps (AGAPS mask), (2) the mask of intra-contig ambiguities (AMB mask), (3) the mask of repeats from RepeatMasker (RM mask), and (4) the mask of repeats from Tandem Repeats Finder (TRF mask). Only the AGAPS and AMB masks are "active" by default.

r-bsgenome-mmusculus-ucsc-mm8-masked 1.3.99
Propagated dependencies: r-bsgenome-mmusculus-ucsc-mm8@1.4.0 r-bsgenome@1.80.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://bioconductor.org/packages/BSgenome.Mmusculus.UCSC.mm8.masked
Licenses: Artistic License 2.0
Build system: r
Synopsis: Full masked genome sequences for Mus musculus (UCSC version mm8)
Description:

Full genome sequences for Mus musculus (Mouse) as provided by UCSC (mm8, Feb. 2006) and stored in Biostrings objects. The sequences are the same as in BSgenome.Mmusculus.UCSC.mm8, except that each of them has the 4 following masks on top: (1) the mask of assembly gaps (AGAPS mask), (2) the mask of intra-contig ambiguities (AMB mask), (3) the mask of repeats from RepeatMasker (RM mask), and (4) the mask of repeats from Tandem Repeats Finder (TRF mask). Only the AGAPS and AMB masks are "active" by default.

r-bsgenome-cneoformansvargrubiikn99-ncbi-asm221672v1 1.0.0
Propagated dependencies: r-bsgenome@1.80.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://bioconductor.org/packages/BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1
Licenses: Artistic License 2.0
Build system: r
Synopsis: Full Genome Sequence for Cryptococcus neoformans var. grubii KN99 (ASM221672v1)
Description:

Full genome sequences for Cryptococcus neoformans var. grubii KN99 (assembly ASM221672v1 assembly accession GCA_002216725.1).

r-batchqc 2.8.0
Propagated dependencies: r-umap@0.2.10.0 r-tidyverse@2.0.0 r-tidyr@1.3.2 r-tibble@3.3.1 r-sva@3.60.0 r-summarizedexperiment@1.42.0 r-shinythemes@1.2.0 r-shinyjs@2.1.1 r-shiny@1.13.0 r-scran@1.40.0 r-s4vectors@0.50.1 r-reshape2@1.4.5 r-reader@1.1.0 r-rcolorbrewer@1.1-3 r-pheatmap@1.0.13 r-matrixstats@1.5.0 r-mass@7.3-65 r-limma@3.68.3 r-harman@1.40.0 r-ggpubr@0.6.3 r-ggplot2@4.0.3 r-ggnewscale@0.5.2 r-ggdendro@0.2.0 r-fnn@1.1.4.1 r-edger@4.10.0 r-ebseq@2.10.0 r-dplyr@1.2.1 r-deseq2@1.52.0 r-data-table@1.18.4
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://github.com/wejlab/BatchQC
Licenses: Expat
Build system: r
Synopsis: Batch Effects Quality Control Software
Description:

Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA.

r-bg2 1.12.0
Propagated dependencies: r-memoise@2.0.1 r-matrix@1.7-5 r-mass@7.3-65 r-ga@3.2.5 r-caret@7.0-1
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://bioconductor.org/packages/BG2
Licenses: FSDG-compatible
Build system: r
Synopsis: Performs Bayesian GWAS analysis for non-Gaussian data using BG2
Description:

This package is built to perform GWAS analysis for non-Gaussian data using BG2. The BG2 method uses penalized quasi-likelihood along with nonlocal priors in a two step manner to identify SNPs in GWAS analysis. The research related to this package was supported in part by National Science Foundation awards DMS 1853549 and DMS 2054173.

r-bloodcancermultiomics2017 1.32.0
Propagated dependencies: r-tibble@3.3.1 r-survival@3.8-6 r-summarizedexperiment@1.42.0 r-scales@1.4.0 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-ipflasso@1.1 r-gtable@0.3.6 r-glmnet@5.0 r-ggplot2@4.0.3 r-ggdendro@0.2.0 r-dplyr@1.2.1 r-devtools@2.5.2 r-deseq2@1.52.0 r-biobase@2.72.0 r-beeswarm@0.4.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://bioconductor.org/packages/BloodCancerMultiOmics2017
Licenses: LGPL 3+
Build system: r
Synopsis: "Drug-perturbation-based stratification of blood cancer" by Dietrich S, Oleś M, Lu J et al. - experimental data and complete analysis
Description:

The package contains data of the Primary Blood Cancer Encyclopedia (PACE) project together with a complete executable transcript of the statistical analysis and reproduces figures presented in the paper "Drug-perturbation-based stratification of blood cancer" by Dietrich S, Oleś M, Lu J et al., J. Clin. Invest. (2018) 128(1):427-445. doi:10.1172/JCI93801.

r-blima 1.46.0
Propagated dependencies: r-rcpp@1.1.1-1.1 r-biocgenerics@0.58.1 r-biobase@2.72.0 r-beadarray@2.62.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://bitbucket.org/kulvait/blima
Licenses: GPL 3
Build system: r
Synopsis: Tools for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level
Description:

Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It also provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing.

r-bsgenome-btaurus-ucsc-bostau4 1.4.0
Propagated dependencies: r-bsgenome@1.80.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://bioconductor.org/packages/BSgenome.Btaurus.UCSC.bosTau4
Licenses: Artistic License 2.0
Build system: r
Synopsis: Full genome sequences for Bos taurus (UCSC version bosTau4)
Description:

Full genome sequences for Bos taurus (Cow) as provided by UCSC (bosTau4, Oct. 2007) and stored in Biostrings objects.

r-blase 1.2.0
Propagated dependencies: r-viridis@0.6.5 r-summarizedexperiment@1.42.0 r-singlecellexperiment@1.34.0 r-seurat@5.5.0 r-scater@1.40.1 r-rlang@1.2.0 r-patchwork@1.3.2 r-mgcv@1.9-4 r-matrixgenerics@1.24.0 r-matrix@1.7-5 r-lsa@0.73.4 r-ggplot2@4.0.3 r-dplyr@1.2.1 r-boot@1.3-32 r-biocparallel@1.46.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://andrewmccluskey-uog.github.io/blase/
Licenses: GPL 3+
Build system: r
Synopsis: Bulk Linking Analysis for Single-cell Experiments
Description:

BLASE is a method for finding where bulk RNA-seq data lies on a single-cell pseudotime trajectory. It uses a fast and understandable approach based on Spearman correlation, with bootstrapping to provide confidence. BLASE can be used to "date" bulk RNA-seq data, annotate cell types in scRNA-seq, and help correct for developmental phenotype differences in bulk RNA-seq experiments.

r-batchcorr 1.2.0
Propagated dependencies: r-summarizedexperiment@1.42.0 r-reshape@0.8.10 r-mclust@6.1.2 r-biocparallel@1.46.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://github.com/MetaboComp/batchCorr
Licenses: GPL 2
Build system: r
Synopsis: Within And Between Batch Correction Of LC-MS Metabolomics Data
Description:

From the perspective of metabolites as the continuation of the central dogma of biology, metabolomics provides the closest link to many phenotypes of interest. This makes metabolomics research promising in teasing apart the complexities of living systems. However, due to experimental reasons, the data includes non-biological variation which limits quality and reproducibility, especially if the data is obtained from several batches. The batchCorr package reduces unwanted variation by way of between-batch alignment, within-batch drift correction and between-batch normalization using batch-specific quality control samples and long-term reference QC samples. Please see the associated article for more thorough descriptions of algorithms.

r-cellscape 1.36.0
Propagated dependencies: r-stringr@1.6.0 r-reshape2@1.4.5 r-jsonlite@2.0.0 r-htmlwidgets@1.6.4 r-gtools@3.9.5 r-dplyr@1.2.1
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/cellscape
Licenses: GPL 3
Build system: r
Synopsis: Explores single cell copy number profiles in the context of a single cell tree
Description:

CellScape facilitates interactive browsing of single cell clonal evolution datasets. The tool requires two main inputs: (i) the genomic content of each single cell in the form of either copy number segments or targeted mutation values, and (ii) a single cell phylogeny. Phylogenetic formats can vary from dendrogram-like phylogenies with leaf nodes to evolutionary model-derived phylogenies with observed or latent internal nodes. The CellScape phylogeny is flexibly input as a table of source-target edges to support arbitrary representations, where each node may or may not have associated genomic data. The output of CellScape is an interactive interface displaying a single cell phylogeny and a cell-by-locus genomic heatmap representing the mutation status in each cell for each locus.

r-cellmigration 1.20.0
Propagated dependencies: r-vioplot@0.5.1 r-tiff@0.1-12 r-spatialtools@1.0.5 r-sp@2.2-1 r-reshape2@1.4.5 r-matrixstats@1.5.0 r-hmisc@5.2-5 r-foreach@1.5.2 r-fme@1.3.6.4 r-factominer@2.14 r-doparallel@1.0.17
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/ocbe-uio/cellmigRation/
Licenses: GPL 2
Build system: r
Synopsis: Track Cells, Analyze Cell Trajectories and Compute Migration Statistics
Description:

Import TIFF images of fluorescently labeled cells, and track cell movements over time. Parallelization is supported for image processing and for fast computation of cell trajectories. In-depth analysis of cell trajectories is enabled by 15 trajectory analysis functions.

r-cellmapperdata 1.38.0
Propagated dependencies: r-experimenthub@3.2.0 r-cellmapper@1.38.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/CellMapperData
Licenses: Artistic License 2.0
Build system: r
Synopsis: Pre-processed data for use with the CellMapper package
Description:

Experiment data package. Contains microarray data from several large expression compendia that have been pre-processed for use with the CellMapper package. This pre-processed data is recommended for routine searches using the CellMapper package.

r-chipsim 1.66.0
Propagated dependencies: r-xvector@0.52.0 r-shortread@1.70.0 r-iranges@2.46.0 r-biostrings@2.80.1
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/ChIPsim
Licenses: GPL 2+
Build system: r
Synopsis: Simulation of ChIP-seq experiments
Description:

This package provides a general framework for the simulation of ChIP-seq data. Although currently focused on nucleosome positioning the package is designed to support different types of experiments.

r-cosmiq 1.46.0
Propagated dependencies: r-xcms@4.10.0 r-rcpp@1.1.1-1.1 r-pracma@2.4.6 r-massspecwavelet@1.78.0 r-faahko@1.52.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: http://www.bioconductor.org/packages/devel/bioc/html/cosmiq.html
Licenses: GPL 3
Build system: r
Synopsis: cosmiq - COmbining Single Masses Into Quantities
Description:

cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step.

r-catscradle 1.6.0
Propagated dependencies: r-summarizedexperiment@1.42.0 r-stringr@1.6.0 r-spatialexperiment@1.22.0 r-singlecellexperiment@1.34.0 r-seuratobject@5.4.0 r-seurat@5.5.0 r-s4vectors@0.50.1 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@26.1.0 r-matrix@1.7-5 r-igraph@2.3.1 r-ggplot2@4.0.3 r-geometry@0.5.2 r-ebimage@4.54.0 r-data-table@1.18.4 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-crisprbase 1.16.0
Propagated dependencies: r-stringr@1.6.0 r-s4vectors@0.50.1 r-iranges@2.46.0 r-genomicranges@1.64.0 r-biostrings@2.80.1 r-biocgenerics@0.58.1
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/crisprVerse/crisprBase
Licenses: Expat
Build system: r
Synopsis: Base functions and classes for CRISPR gRNA design
Description:

This package provides S4 classes for general nucleases, CRISPR nucleases, CRISPR nickases, and base editors.Several CRISPR-specific genome arithmetic functions are implemented to help extract genomic coordinates of spacer and protospacer sequences. Commonly-used CRISPR nuclease objects are provided that can be readily used in other packages. Both DNA- and RNA-targeting nucleases are supported.

r-cottoncdf 2.18.0
Propagated dependencies: r-annotationdbi@1.74.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/cottoncdf
Licenses: LGPL 2.0+
Build system: r
Synopsis: cottoncdf
Description:

This package provides a package containing an environment representing the Cotton.cdf file.

r-cmapr 1.24.0
Propagated dependencies: r-summarizedexperiment@1.42.0 r-rhdf5@2.56.0 r-matrixstats@1.5.0 r-flowcore@2.24.0 r-data-table@1.18.4
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/cmap/cmapR
Licenses: FSDG-compatible
Build system: r
Synopsis: CMap Tools in R
Description:

The Connectivity Map (CMap) is a massive resource of perturbational gene expression profiles built by researchers at the Broad Institute and funded by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Please visit https://clue.io for more information. The cmapR package implements methods to parse, manipulate, and write common CMap data objects, such as annotated matrices and collections of gene sets.

r-clusterjudge 1.34.0
Propagated dependencies: r-latticeextra@0.6-31 r-lattice@0.22-9 r-jsonlite@2.0.0 r-infotheo@1.2.0.1 r-httr@1.4.8
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/ClusterJudge
Licenses: Artistic License 2.0
Build system: r
Synopsis: Judging Quality of Clustering Methods using Mutual Information
Description:

ClusterJudge implements the functions, examples and other software published as an algorithm by Gibbons, FD and Roth FP. The article is called "Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation" and it appeared in Genome Research, vol. 12, pp1574-1581 (2002). See package?ClusterJudge for an overview.

r-cfdnakit 1.10.0
Propagated dependencies: r-s4vectors@0.50.1 r-rsamtools@2.28.0 r-rlang@1.2.0 r-qdnaseq@1.48.0 r-pscbs@0.68.0 r-magrittr@2.0.5 r-iranges@2.46.0 r-ggplot2@4.0.3 r-genomicranges@1.64.0 r-genomeinfodb@1.48.0 r-dplyr@1.2.1 r-biobase@2.72.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/cfdnakit
Licenses: GPL 3
Build system: r
Synopsis: Fragmen-length analysis package from high-throughput sequencing of cell-free DNA (cfDNA)
Description:

This package provides basic functions for analyzing shallow whole-genome sequencing (~0.3X or more) of cell-free DNA (cfDNA). The package basically extracts the length of cfDNA fragments and aids the vistualization of fragment-length information. The package also extract fragment-length information per non-overlapping fixed-sized bins and used it for calculating ctDNA estimation score (CES).

r-celegans-db 3.13.0
Propagated dependencies: r-org-ce-eg-db@3.22.0 r-annotationdbi@1.74.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/celegans.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Affymetrix Affymetrix Celegans Array annotation data (chip celegans)
Description:

Affymetrix Affymetrix Celegans Array annotation data (chip celegans) assembled using data from public repositories.

r-caen 1.20.0
Propagated dependencies: r-summarizedexperiment@1.42.0 r-poiclaclu@1.0.2.1
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/CAEN
Licenses: GPL 2
Build system: r
Synopsis: Category encoding method for selecting feature genes for the classification of single-cell RNA-seq
Description:

With the development of high-throughput techniques, more and more gene expression analysis tend to replace hybridization-based microarrays with the revolutionary technology.The novel method encodes the category again by employing the rank of samples for each gene in each class. We then consider the correlation coefficient of gene and class with rank of sample and new rank of category. The highest correlation coefficient genes are considered as the feature genes which are most effective to classify the samples.

r-clustersignificance 1.40.0
Propagated dependencies: r-scatterplot3d@0.3-45 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.

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