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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-mulcom 1.60.0
Propagated dependencies: r-fields@16.3.1 r-biobase@2.68.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/Mulcom
Licenses: GPL 2
Synopsis: Calculates Mulcom test
Description:

Identification of differentially expressed genes and false discovery rate (FDR) calculation by Multiple Comparison test.

r-metabomxtr 1.44.0
Propagated dependencies: r-plyr@1.8.9 r-optimx@2025-4.9 r-multtest@2.64.0 r-ggplot2@3.5.2 r-formula@1.2-5 r-biocparallel@1.42.0 r-biobase@2.68.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/metabomxtr
Licenses: GPL 2
Synopsis: package to run mixture models for truncated metabolomics data with normal or lognormal distributions
Description:

The functions in this package return optimized parameter estimates and log likelihoods for mixture models of truncated data with normal or lognormal distributions.

r-metapone 1.16.0
Propagated dependencies: r-markdown@2.0 r-ggrepel@0.9.6 r-ggplot2@3.5.2 r-fields@16.3.1 r-fgsea@1.34.0 r-fdrtool@1.2.18 r-biocparallel@1.42.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/metapone
Licenses: Artistic License 2.0
Synopsis: Conducts pathway test of metabolomics data using a weighted permutation test
Description:

The package conducts pathway testing from untargetted metabolomics data. It requires the user to supply feature-level test results, from case-control testing, regression, or other suitable feature-level tests for the study design. Weights are given to metabolic features based on how many metabolites they could potentially match to. The package can combine positive and negative mode results in pathway tests.

r-mu19ksubccdf 2.18.0
Propagated dependencies: r-annotationdbi@1.70.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/mu19ksubccdf
Licenses: LGPL 2.0+
Synopsis: mu19ksubccdf
Description:

This package provides a package containing an environment representing the Mu19KsubC.CDF file.

r-mouseagingdata 1.6.0
Propagated dependencies: r-singlecellexperiment@1.30.1 r-experimenthub@2.16.0 r-annotationhub@3.16.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://github.com/ccb-hms/MouseAgingData
Licenses: Artistic License 2.0
Synopsis: Multi-omics data access for studies investigating the effects of aging
Description:

The MouseAgingData package provides analysis-ready data resources from different studies focused on aging and rejuvenation in mice. The package currently provides two 10x Genomics single-cell RNA-seq datasets. The first study profiled the aging mouse brain measured across 37,089 cells (Ximerakis et al., 2019). The second study investigated parabiosis by profiling a total of 105,329 cells (Ximerakis & Holton et al., 2023). The datasets are provided as SingleCellExperiment objects and provide raw UMI counts and cell metadata.

r-mouse-db0 3.22.0
Propagated dependencies: r-annotationdbi@1.70.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/mouse.db0
Licenses: Artistic License 2.0
Synopsis: Base Level Annotation databases for mouse
Description:

Base annotation databases for mouse, intended ONLY to be used by AnnotationDbi to produce regular annotation packages.

r-maqcsubset 1.48.0
Propagated dependencies: r-lumi@2.60.0 r-biobase@2.68.0 r-affy@1.86.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/MAQCsubset
Licenses: Artistic License 2.0
Synopsis: Experimental Data Package: MAQCsubset
Description:

Data Package automatically created on Sun Nov 19 15:59:29 2006.

r-microbiomeprofiler 1.16.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://github.com/YuLab-SMU/MicrobiomeProfiler/
Licenses: GPL 2
Synopsis: An R/shiny package for microbiome functional enrichment analysis
Description:

This is an R/shiny package to perform functional enrichment analysis for microbiome data. This package was based on clusterProfiler. Moreover, MicrobiomeProfiler support KEGG enrichment analysis, COG enrichment analysis, Microbe-Disease association enrichment analysis, Metabo-Pathway analysis.

r-marinerdata 1.10.0
Propagated dependencies: r-experimenthub@2.16.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/marinerData
Licenses: GPL 3
Synopsis: ExperimentHub data for the mariner package
Description:

Subsampled Hi-C in HEK cells expressing the NHA9 fusion with an F to S mutated IDR ("FS") or without any mutations to the IDR ("Wildtype" or "WT"). These files are used for testing mariner functions and some examples.

r-moe430bprobe 2.18.0
Propagated dependencies: r-annotationdbi@1.70.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/moe430bprobe
Licenses: LGPL 2.0+
Synopsis: Probe sequence data for microarrays of type moe430b
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 MOE430B\_probe\_tab.

r-msstatsqc 2.28.0
Propagated dependencies: r-qcmetrics@1.48.0 r-plotly@4.10.4 r-msnbase@2.34.1 r-ggplot2@3.5.2 r-ggextra@0.10.1 r-dplyr@1.1.4
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: http://msstats.org/msstatsqc
Licenses: FSDG-compatible
Synopsis: Longitudinal system suitability monitoring and quality control for proteomic experiments
Description:

MSstatsQC is an R package which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments.

r-mirage 1.52.0
Propagated dependencies: r-s4vectors@0.46.0 r-biocmanager@1.30.25 r-biocgenerics@0.54.0 r-biobase@2.68.0 r-annotationdbi@1.70.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/MiRaGE
Licenses: GPL 2+ GPL 3+
Synopsis: MiRNA Ranking by Gene Expression
Description:

The package contains functions for inferece of target gene regulation by miRNA, based on only target gene expression profile.

r-metabinr 1.12.0
Dependencies: openjdk@24.0.1
Propagated dependencies: r-rjava@1.0-11
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://github.com/gkanogiannis/metabinR
Licenses: GPL 3
Synopsis: Abundance and Compositional Based Binning of Metagenomes
Description:

Provide functions for performing abundance and compositional based binning on metagenomic samples, directly from FASTA or FASTQ files. Functions are implemented in Java and called via rJava. Parallel implementation that operates directly on input FASTA/FASTQ files for fast execution.

r-multiclust 1.40.0
Propagated dependencies: r-survival@3.8-3 r-mclust@6.1.1 r-dendextend@1.19.0 r-ctc@1.82.0 r-cluster@2.1.8.1 r-amap@0.8-20
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/multiClust
Licenses: GPL 2+
Synopsis: multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles
Description:

Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies.

r-mspurity 1.36.0
Propagated dependencies: r-stringr@1.5.1 r-rsqlite@2.3.11 r-reshape2@1.4.4 r-rcpp@1.0.14 r-plyr@1.8.9 r-mzr@2.42.0 r-magrittr@2.0.3 r-ggplot2@3.5.2 r-foreach@1.5.2 r-fastcluster@1.3.0 r-dplyr@1.1.4 r-dosnow@1.0.20 r-dbplyr@2.5.0 r-dbi@1.2.3
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://github.com/computational-metabolomics/msPurity/
Licenses: FSDG-compatible
Synopsis: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry Based Fragmentation in Metabolomics
Description:

msPurity R package was developed to: 1) Assess the spectral quality of fragmentation spectra by evaluating the "precursor ion purity". 2) Process fragmentation spectra. 3) Perform spectral matching. What is precursor ion purity? -What we call "Precursor ion purity" is a measure of the contribution of a selected precursor peak in an isolation window used for fragmentation. The simple calculation involves dividing the intensity of the selected precursor peak by the total intensity of the isolation window. When assessing MS/MS spectra this calculation is done before and after the MS/MS scan of interest and the purity is interpolated at the recorded time of the MS/MS acquisition. Additionally, isotopic peaks can be removed, low abundance peaks are removed that are thought to have limited contribution to the resulting MS/MS spectra and the isolation efficiency of the mass spectrometer can be used to normalise the intensities used for the calculation.

r-mogene20stprobeset-db 8.8.0
Propagated dependencies: r-org-mm-eg-db@3.21.0 r-annotationdbi@1.70.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/mogene20stprobeset.db
Licenses: Artistic License 2.0
Synopsis: Affymetrix mogene20 annotation data (chip mogene20stprobeset)
Description:

Affymetrix mogene20 annotation data (chip mogene20stprobeset) assembled using data from public repositories.

r-moanin 1.18.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/moanin
Licenses: FSDG-compatible
Synopsis: An R Package for Time Course RNASeq Data Analysis
Description:

Simple and efficient workflow for time-course gene expression data, built on publictly available open-source projects hosted on CRAN and bioconductor. moanin provides helper functions for all the steps required for analysing time-course data using functional data analysis: (1) functional modeling of the timecourse data; (2) differential expression analysis; (3) clustering; (4) downstream analysis.

r-methylseekr 1.50.0
Propagated dependencies: r-rtracklayer@1.68.0 r-mhsmm@0.4.21 r-iranges@2.42.0 r-genomicranges@1.60.0 r-genomeinfodb@1.44.0 r-geneplotter@1.86.0 r-bsgenome@1.76.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/MethylSeekR
Licenses: FSDG-compatible
Synopsis: Segmentation of Bis-seq data
Description:

This is a package for the discovery of regulatory regions from Bis-seq data.

r-mapfx 1.6.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://github.com/HsiaoChiLiao/MAPFX
Licenses: GPL 2
Synopsis: MAssively Parallel Flow cytometry Xplorer (MAPFX): A Toolbox for Analysing Data from the Massively-Parallel Cytometry Experiments
Description:

MAPFX is an end-to-end toolbox that pre-processes the raw data from MPC experiments (e.g., BioLegend's LEGENDScreen and BD Lyoplates assays), and further imputes the ‘missing’ infinity markers in the wells without those measurements. The pipeline starts by performing background correction on raw intensities to remove the noise from electronic baseline restoration and fluorescence compensation by adapting a normal-exponential convolution model. Unwanted technical variation, from sources such as well effects, is then removed using a log-normal model with plate, column, and row factors, after which infinity markers are imputed using the informative backbone markers as predictors. The completed dataset can then be used for clustering and other statistical analyses. Additionally, MAPFX can be used to normalise data from FFC assays as well.

r-mircompdata 1.40.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/miRcompData
Licenses: GPL 3 FSDG-compatible
Synopsis: Data used in the miRcomp package
Description:

Raw amplification data from a large microRNA mixture / dilution study. These data are used by the miRcomp package to assess the performance of methods that estimate expression from the amplification curves.

r-mousethymusageing 1.18.0
Propagated dependencies: r-summarizedexperiment@1.38.1 r-singlecellexperiment@1.30.1 r-s4vectors@0.46.0 r-experimenthub@2.16.0 r-biocgenerics@0.54.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/MouseThymusAgeing
Licenses: GPL 3
Synopsis: Single-cell Transcriptomics Data of the Ageing Mouse Thymus
Description:

This package provides data access to counts matrices and meta-data for single-cell RNA sequencing data of thymic epithlial cells across mouse ageing using SMARTseq2 and 10X Genommics chemistries. Access is provided as a data package via ExperimentHub. It is designed to facilitate the re-use of data from Baran-Gale _et al._ in a consistent format that includes relevant and informative meta-data.

r-mgu74bv2probe 2.18.0
Propagated dependencies: r-annotationdbi@1.70.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/mgu74bv2probe
Licenses: LGPL 2.0+
Synopsis: Probe sequence data for microarrays of type mgu74bv2
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 MG-U74Bv2\_probe\_tab.

r-methimpute 1.32.0
Propagated dependencies: r-reshape2@1.4.4 r-rcpp@1.0.14 r-minpack-lm@1.2-4 r-iranges@2.42.0 r-ggplot2@3.5.2 r-genomicranges@1.60.0 r-genomeinfodb@1.44.0 r-data-table@1.17.4 r-biostrings@2.76.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/methimpute
Licenses: Artistic License 2.0
Synopsis: Imputation-guided re-construction of complete methylomes from WGBS data
Description:

This package implements functions for calling methylation for all cytosines in the genome.

r-motifcounter 1.34.0
Propagated dependencies: r-biostrings@2.76.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/motifcounter
Licenses: GPL 2
Synopsis: R package for analysing TFBSs in DNA sequences
Description:

motifcounter provides motif matching, motif counting and motif enrichment functionality based on position frequency matrices. The main features of the packages include the utilization of higher-order background models and accounting for self-overlapping motif matches when determining motif enrichment. The background model allows to capture dinucleotide (or higher-order nucleotide) composition adequately which may reduced model biases and misleading results compared to using simple GC background models. When conducting a motif enrichment analysis based on the motif match count, the package relies on a compound Poisson distribution or alternatively a combinatorial model. These distribution account for self-overlapping motif structures as exemplified by repeat-like or palindromic motifs, and allow to determine the p-value and fold-enrichment for a set of observed motif matches.

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