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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
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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-drawproteins 1.30.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/brennanpincardiff/drawProteins
Licenses: Expat
Build system: r
Synopsis: Package to Draw Protein Schematics from Uniprot API output
Description:

This package draws protein schematics from Uniprot API output. From the JSON returned by the GET command, it creates a dataframe from the Uniprot Features API. This dataframe can then be used by geoms based on ggplot2 and base R to draw protein schematics.

r-delayedtensor 1.16.0
Propagated dependencies: r-sparsearray@1.10.2 r-s4arrays@1.10.0 r-rtensor@1.4.9 r-matrix@1.7-4 r-irlba@2.3.5.1 r-hdf5array@1.38.0 r-einsum@0.1.2 r-delayedrandomarray@1.18.0 r-delayedarray@0.36.0 r-biocsingular@1.26.1
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DelayedTensor
Licenses: Artistic License 2.0
Build system: r
Synopsis: R package for sparse and out-of-core arithmetic and decomposition of Tensor
Description:

DelayedTensor operates Tensor arithmetic directly on DelayedArray object. DelayedTensor provides some generic function related to Tensor arithmetic/decompotision and dispatches it on the DelayedArray class. DelayedTensor also suppors Tensor contraction by einsum function, which is inspired by numpy einsum.

r-dapardata 1.40.0
Propagated dependencies: r-msnbase@2.36.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: http://www.prostar-proteomics.org/
Licenses: GPL 2
Build system: r
Synopsis: Data accompanying the DAPAR and Prostar packages
Description:

Mass-spectrometry based UPS proteomics data sets from Ramus C, Hovasse A, Marcellin M, Hesse AM, Mouton-Barbosa E, Bouyssie D, Vaca S, Carapito C, Chaoui K, Bruley C, Garin J, Cianferani S, Ferro M, Dorssaeler AV, Burlet-Schiltz O, Schaeffer C, Coute Y, Gonzalez de Peredo A. Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods. Data Brief. 2015 Dec 17;6:286-94 and Giai Gianetto, Q., Combes, F., Ramus, C., Bruley, C., Coute, Y., Burger, T. (2016). Calibration plot for proteomics: A graphical tool to visually check the assumptions underlying FDR control in quantitative experiments. Proteomics, 16(1), 29-32.

r-dyebiasexamples 1.50.0
Propagated dependencies: r-marray@1.88.0 r-geoquery@2.78.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: http://www.holstegelab.nl/publications/margaritis_lijnzaad
Licenses: GPL 3
Build system: r
Synopsis: Example data for the dyebias package, which implements the GASSCO method
Description:

Data for the dyebias package, consisting of 4 self-self hybrizations of self-spotted yeast slides, as well as data from Array Express accession E-MTAB-32.

r-depmap 1.24.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/depmap
Licenses: Artistic License 2.0
Build system: r
Synopsis: Cancer Dependency Map Data Package
Description:

The depmap package is a data package that accesses datsets from the Broad Institute DepMap cancer dependency study using ExperimentHub. Datasets from the most current release are available, including RNAI and CRISPR-Cas9 gene knockout screens quantifying the genetic dependency for select cancer cell lines. Additional datasets are also available pertaining to the log copy number of genes for select cell lines, protein expression of cell lines as measured by reverse phase protein lysate microarray (RPPA), Transcript Per Million (TPM) data, as well as supplementary datasets which contain metadata and mutation calls for the other datasets found in the current release. The 19Q3 release adds the drug_dependency dataset, that contains cancer cell line dependency data with respect to drug and drug-candidate compounds. The 20Q2 release adds the proteomic dataset that contains quantitative profiling of proteins via mass spectrometry. This package will be updated on a quarterly basis to incorporate the latest Broad Institute DepMap Public cancer dependency datasets. All data made available in this package was generated by the Broad Institute DepMap for research purposes and not intended for clinical use. This data is distributed under the Creative Commons license (Attribution 4.0 International (CC BY 4.0)).

r-diggitdata 1.42.0
Propagated dependencies: r-viper@1.44.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/diggitdata
Licenses: FSDG-compatible
Build system: r
Synopsis: Example data for the diggit package
Description:

This package provides expression profile and CNV data for glioblastoma from TCGA, and transcriptional and post-translational regulatory networks assembled with the ARACNe and MINDy algorithms, respectively.

r-debrowser 1.38.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/UMMS-Biocore/debrowser
Licenses: FSDG-compatible
Build system: r
Synopsis: Interactive Differential Expresion Analysis Browser
Description:

Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps.

r-deltacapturec 1.24.0
Propagated dependencies: r-tictoc@1.2.1 r-summarizedexperiment@1.40.0 r-iranges@2.44.0 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-deseq2@1.50.2
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/deltaCaptureC
Licenses: Expat
Build system: r
Synopsis: This Package Discovers Meso-scale Chromatin Remodeling from 3C Data
Description:

This package discovers meso-scale chromatin remodelling from 3C data. 3C data is local in nature. It givens interaction counts between restriction enzyme digestion fragments and a preferred viewpoint region. By binning this data and using permutation testing, this package can test whether there are statistically significant changes in the interaction counts between the data from two cell types or two treatments.

r-degraph 1.62.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DEGraph
Licenses: GPL 3
Build system: r
Synopsis: Two-sample tests on a graph
Description:

DEGraph implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results.

r-dmcfb 1.24.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DMCFB
Licenses: GPL 3
Build system: r
Synopsis: Differentially Methylated Cytosines via a Bayesian Functional Approach
Description:

DMCFB is a pipeline for identifying differentially methylated cytosines using a Bayesian functional regression model in bisulfite sequencing data. By using a functional regression data model, it tries to capture position-specific, group-specific and other covariates-specific methylation patterns as well as spatial correlation patterns and unknown underlying models of methylation data. It is robust and flexible with respect to the true underlying models and inclusion of any covariates, and the missing values are imputed using spatial correlation between positions and samples. A Bayesian approach is adopted for estimation and inference in the proposed method.

r-duoclustering2018 1.28.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DuoClustering2018
Licenses: FSDG-compatible
Build system: r
Synopsis: Data, Clustering Results and Visualization Functions From Duò et al (2018)
Description:

Preprocessed experimental and simulated scRNA-seq data sets used for evaluation of clustering methods for scRNA-seq data in Duò et al (2018). Also contains results from applying several clustering methods to each of the data sets, and functions for plotting method performance.

r-difflogo 2.34.0
Propagated dependencies: r-cba@0.2-25
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/mgledi/DiffLogo/
Licenses: GPL 2+
Build system: r
Synopsis: DiffLogo: A comparative visualisation of biooligomer motifs
Description:

DiffLogo is an easy-to-use tool to visualize motif differences.

r-dino 1.16.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-singlecellexperiment@1.32.0 r-seurat@5.3.1 r-scran@1.38.0 r-s4vectors@0.48.0 r-matrixstats@1.5.0 r-matrix@1.7-4 r-biocsingular@1.26.1 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/JBrownBiostat/Dino
Licenses: GPL 3
Build system: r
Synopsis: Normalization of Single-Cell mRNA Sequencing Data
Description:

Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such as those produced by 10X genomics and other uninque molecular identifier (UMI) based microfluidics protocols for which the depth-dependent proportion of zeros in the raw expression data can otherwise present a challenge.

r-davidtiling 1.50.0
Propagated dependencies: r-tilingarray@1.88.0 r-go-db@3.22.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: http://www.ebi.ac.uk/huber
Licenses: LGPL 2.0+
Build system: r
Synopsis: Data and analysis scripts for David, Huber et al. yeast tiling array paper
Description:

This package contains the data for the paper by L. David et al. in PNAS 2006 (PMID 16569694): 8 CEL files of Affymetrix genechips, an ExpressionSet object with the raw feature data, a probe annotation data structure for the chip and the yeast genome annotation (GFF file) that was used. In addition, some custom-written analysis functions are provided, as well as R scripts in the scripts directory.

r-derfinderplot 1.44.0
Propagated dependencies: r-seqinfo@1.0.0 r-scales@1.4.0 r-s4vectors@0.48.0 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-plyr@1.8.9 r-limma@3.66.0 r-iranges@2.44.0 r-ggplot2@4.0.1 r-ggbio@1.58.0 r-genomicranges@1.62.0 r-genomicfeatures@1.62.0 r-genomeinfodb@1.46.0 r-derfinder@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/leekgroup/derfinderPlot
Licenses: Artistic License 2.0
Build system: r
Synopsis: Plotting functions for derfinder
Description:

This package provides plotting functions for results from the derfinder package. This helps separate the graphical dependencies required for making these plots from the core functionality of derfinder.

r-dandelionr 1.2.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://www.github.com/tuonglab/dandelionR/
Licenses: Expat
Build system: r
Synopsis: Single-cell Immune Repertoire Trajectory Analysis in R
Description:

dandelionR is an R package for performing single-cell immune repertoire trajectory analysis, based on the original python implementation. It provides the necessary functions to interface with scRepertoire and a custom implementation of an absorbing Markov chain for pseudotime inference, inspired by the Palantir Python package.

r-dnabarcodecompatibility 1.26.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://dnabarcodecompatibility.pasteur.fr/
Licenses: FSDG-compatible
Build system: r
Synopsis: Tool for Optimizing Combinations of DNA Barcodes Used in Multiplexed Experiments on Next Generation Sequencing Platforms
Description:

The package allows one to obtain optimised combinations of DNA barcodes to be used for multiplex sequencing. In each barcode combination, barcodes are pooled with respect to Illumina chemistry constraints. Combinations can be filtered to keep those that are robust against substitution and insertion/deletion errors thereby facilitating the demultiplexing step. In addition, the package provides an optimiser function to further favor the selection of barcode combinations with least heterogeneity in barcode usage.

r-dvddata 1.46.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DvDdata
Licenses: GPL 3
Build system: r
Synopsis: Drug versus Disease Data
Description:

Data package which provides default drug and disease expression profiles for the DvD package.

r-dep 1.32.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DEP
Licenses: Artistic License 2.0
Build system: r
Synopsis: Differential Enrichment analysis of Proteomics data
Description:

This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package.

r-depinfer 1.14.0
Propagated dependencies: r-matrixstats@1.5.0 r-glmnet@4.1-10 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DepInfeR
Licenses: GPL 3
Build system: r
Synopsis: Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling
Description:

DepInfeR integrates two experimentally accessible input data matrices: the drug sensitivity profiles of cancer cell lines or primary tumors ex-vivo (X), and the drug affinities of a set of proteins (Y), to infer a matrix of molecular protein dependencies of the cancers (ß). DepInfeR deconvolutes the protein inhibition effect on the viability phenotype by using regularized multivariate linear regression. It assigns a “dependence coefficient” to each protein and each sample, and therefore could be used to gain a causal and accurate understanding of functional consequences of genomic aberrations in a heterogeneous disease, as well as to guide the choice of pharmacological intervention for a specific cancer type, sub-type, or an individual patient. For more information, please read out preprint on bioRxiv: https://doi.org/10.1101/2022.01.11.475864.

r-deedeeexperiment 1.0.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-singlecellexperiment@1.32.0 r-s4vectors@0.48.0 r-limma@3.66.0 r-edger@4.8.0 r-deseq2@1.50.2 r-cli@3.6.5
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/imbeimainz/DeeDeeExperiment
Licenses: Expat
Build system: r
Synopsis: DeeDeeExperiment: An S4 Class for managing and exploring omics analysis results
Description:

DeeDeeExperiment is an S4 class extending the SingleCellExperiment class, designed to integrate and manage omics analysis results. It introduces two dedicated slots to store Differential Expression Analysis (DEA) results and Functional Enrichment Analysis (FEA) results, providing a structured approach for downstream analysis.

r-desingle 1.30.0
Propagated dependencies: r-vgam@1.1-13 r-pscl@1.5.9 r-maxlik@1.5-2.1 r-matrix@1.7-4 r-mass@7.3-65 r-gamlss@5.5-0 r-biocparallel@1.44.0 r-bbmle@1.0.25.1
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://miaozhun.github.io/DEsingle/
Licenses: GPL 2
Build system: r
Synopsis: DEsingle for detecting three types of differential expression in single-cell RNA-seq data
Description:

DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially expressed genes between two groups of single cells, with regard to different expression status (DEs), differential expression abundance (DEa), and general differential expression (DEg). DEsingle employs Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different biological functions.

r-degnorm 1.20.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DegNorm
Licenses: LGPL 3+
Build system: r
Synopsis: DegNorm: degradation normalization for RNA-seq data
Description:

This package performs degradation normalization in bulk RNA-seq data to improve differential expression analysis accuracy. It provides estimates for each gene within each sample.

r-demand 1.40.0
Propagated dependencies: r-kernsmooth@2.23-26
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DeMAND
Licenses: FSDG-compatible
Build system: r
Synopsis: DeMAND
Description:

DEMAND predicts Drug MoA by interrogating a cell context specific regulatory network with a small number (N >= 6) of compound-induced gene expression signatures, to elucidate specific proteins whose interactions in the network is dysregulated by the compound.

Total results: 2911