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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-muscdata 1.24.0
Propagated dependencies: r-singlecellexperiment@1.32.0 r-experimenthub@3.0.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://github.com/HelenaLC/muscData
Licenses: Expat
Build system: r
Synopsis: Multi-sample multi-group scRNA-seq data
Description:

Data package containing a collection of multi-sample multi-group scRNA-seq datasets in SingleCellExperiment Bioconductor object format.

r-miqc 1.18.0
Propagated dependencies: r-singlecellexperiment@1.32.0 r-ggplot2@4.0.1 r-flexmix@2.3-20
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://github.com/greenelab/miQC
Licenses: Modified BSD
Build system: r
Synopsis: Flexible, probabilistic metrics for quality control of scRNA-seq data
Description:

Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA encoded genes (mtDNA) and (ii) if a small number of genes are detected. miQC is data-driven QC metric that jointly models both the proportion of reads mapping to mtDNA and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset.

r-mouse430a2frmavecs 1.3.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/mouse430a2frmavecs
Licenses: GPL 2+
Build system: r
Synopsis: Vectors used by frma for microarrays of type mouse430a2
Description:

This package was created by frmaTools version 1.19.3 and hgu133ahsentrezgcdf version 19.0.0.

r-mu19ksubbcdf 2.18.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/mu19ksubbcdf
Licenses: LGPL 2.0+
Build system: r
Synopsis: mu19ksubbcdf
Description:

This package provides a package containing an environment representing the Mu19KsubB.CDF file.

r-m20kcod-db 3.4.0
Propagated dependencies: r-org-mm-eg-db@3.22.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/m20kcod.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Codelink UniSet Mouse 20k I Bioarray annotation data (chip m20kcod)
Description:

Codelink UniSet Mouse 20k I Bioarray annotation data (chip m20kcod) assembled using data from public repositories.

r-moonlight2r 1.8.1
Propagated dependencies: r-withr@3.0.2 r-tidyr@1.3.1 r-tidyheatmap@1.13.1 r-tibble@3.3.0 r-stringr@1.6.0 r-seqminer@9.7 r-rtracklayer@1.70.0 r-rlang@1.1.6 r-rismed@2.3.0 r-readr@2.1.6 r-rcolorbrewer@1.1-3 r-randomforest@4.7-1.2 r-qpdf@1.4.1 r-purrr@1.2.0 r-parmigene@1.1.1 r-org-hs-eg-db@3.22.0 r-magrittr@2.0.4 r-hiver@0.4.0 r-gplots@3.2.0 r-ggplot2@4.0.1 r-geoquery@2.78.0 r-genomicranges@1.62.0 r-fuzzyjoin@0.1.6.1 r-foreach@1.5.2 r-experimenthub@3.0.0 r-epimix@1.12.0 r-easypubmed@3.1.6 r-dplyr@1.1.4 r-dose@4.4.0 r-doparallel@1.0.17 r-data-table@1.17.8 r-complexheatmap@2.26.0 r-clusterprofiler@4.18.2 r-circlize@0.4.16 r-biocgenerics@0.56.0 r-biobase@2.70.0 r-annotationhub@4.0.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://github.com/ELELAB/Moonlight2R
Licenses: GPL 3
Build system: r
Synopsis: Identify oncogenes and tumor suppressor genes from omics data
Description:

The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). We present an updated version of the R/bioconductor package called MoonlightR, namely Moonlight2R, which returns a list of candidate driver genes for specific cancer types on the basis of omics data integration. The Moonlight framework contains a primary layer where gene expression data and information about biological processes are integrated to predict genes called oncogenic mediators, divided into putative tumor suppressors and putative oncogenes. This is done through functional enrichment analyses, gene regulatory networks and upstream regulator analyses to score the importance of well-known biological processes with respect to the studied cancer type. By evaluating the effect of the oncogenic mediators on biological processes or through random forests, the primary layer predicts two putative roles for the oncogenic mediators: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As gene expression data alone is not enough to explain the deregulation of the genes, a second layer of evidence is needed. We have automated the integration of a secondary mutational layer through new functionalities in Moonlight2R. These functionalities analyze mutations in the cancer cohort and classifies these into driver and passenger mutations using the driver mutation prediction tool, CScape-somatic. Those oncogenic mediators with at least one driver mutation are retained as the driver genes. As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, Moonlight2R can be used to discover OCGs and TSGs in the same cancer type. This may for instance help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV). In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. An additional mechanistic layer evaluates if there are mutations affecting the protein stability of the transcription factors (TFs) of the TSGs and OCGs, as that may have an effect on the expression of the genes.

r-maizecdf 2.18.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/maizecdf
Licenses: LGPL 2.0+
Build system: r
Synopsis: maizecdf
Description:

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

r-mu11ksubb-db 3.13.0
Propagated dependencies: r-org-mm-eg-db@3.22.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/mu11ksubb.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Affymetrix Affymetrix Mu11KsubB Array annotation data (chip mu11ksubb)
Description:

Affymetrix Affymetrix Mu11KsubB Array annotation data (chip mu11ksubb) assembled using data from public repositories.

r-moe430bprobe 2.18.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/moe430bprobe
Licenses: LGPL 2.0+
Build system: r
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-massarray 1.62.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/MassArray
Licenses: FSDG-compatible
Build system: r
Synopsis: Analytical Tools for MassArray Data
Description:

This package is designed for the import, quality control, analysis, and visualization of methylation data generated using Sequenom's MassArray platform. The tools herein contain a highly detailed amplicon prediction for optimal assay design. Also included are quality control measures of data, such as primer dimer and bisulfite conversion efficiency estimation. Methylation data are calculated using the same algorithms contained in the EpiTyper software package. Additionally, automatic SNP-detection can be used to flag potentially confounded data from specific CG sites. Visualization includes barplots of methylation data as well as UCSC Genome Browser-compatible BED tracks. Multiple assays can be positionally combined for integrated analysis.

r-mirtarrnaseq 1.18.0
Propagated dependencies: r-viridis@0.6.5 r-reshape2@1.4.5 r-r-utils@2.13.0 r-purrr@1.2.0 r-pscl@1.5.9 r-pheatmap@1.0.13 r-mass@7.3-65 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-data-table@1.17.8 r-corrplot@0.95 r-catools@1.18.3 r-assertthat@0.2.1
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/mirTarRnaSeq
Licenses: Expat
Build system: r
Synopsis: mirTarRnaSeq
Description:

mirTarRnaSeq R package can be used for interactive mRNA miRNA sequencing statistical analysis. This package utilizes expression or differential expression mRNA and miRNA sequencing results and performs interactive correlation and various GLMs (Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis between mRNA and miRNA expriments. These experiments can be time point experiments, and or condition expriments.

r-mouse430a2probe 2.18.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/mouse430a2probe
Licenses: LGPL 2.0+
Build system: r
Synopsis: Probe sequence data for microarrays of type mouse430a2
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 Mouse430A\_2\_probe\_tab.

r-metams 1.46.0
Propagated dependencies: r-xcms@4.8.0 r-robustbase@0.99-6 r-matrix@1.7-4 r-camera@1.66.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://github.com/yguitton/metaMS
Licenses: GPL 2+
Build system: r
Synopsis: MS-based metabolomics annotation pipeline
Description:

MS-based metabolomics data processing and compound annotation pipeline.

r-msstatslobd 1.18.0
Propagated dependencies: r-rcpp@1.1.0 r-minpack-lm@1.2-4 r-ggplot2@4.0.1
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/MSstatsLOBD
Licenses: Artistic License 2.0
Build system: r
Synopsis: Assay characterization: estimation of limit of blanc(LoB) and limit of detection(LOD)
Description:

The MSstatsLOBD package allows calculation and visualization of limit of blac (LOB) and limit of detection (LOD). We define the LOB as the highest apparent concentration of a peptide expected when replicates of a blank sample containing no peptides are measured. The LOD is defined as the measured concentration value for which the probability of falsely claiming the absence of a peptide in the sample is 0.05, given a probability 0.05 of falsely claiming its presence. These functionalities were previously a part of the MSstats package. The methodology is described in Galitzine (2018) <doi:10.1074/mcp.RA117.000322>.

r-motif2site 1.14.0
Propagated dependencies: r-s4vectors@0.48.0 r-mixtools@2.0.0.1 r-mass@7.3-65 r-iranges@2.44.0 r-genomicranges@1.62.0 r-genomicalignments@1.46.0 r-genomeinfodb@1.46.0 r-edger@4.8.0 r-bsgenome@1.78.0 r-biostrings@2.78.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/Motif2Site
Licenses: GPL 2
Build system: r
Synopsis: Detect binding sites from motifs and ChIP-seq experiments, and compare binding sites across conditions
Description:

Detect binding sites using motifs IUPAC sequence or bed coordinates and ChIP-seq experiments in bed or bam format. Combine/compare binding sites across experiments, tissues, or conditions. All normalization and differential steps are done using TMM-GLM method. Signal decomposition is done by setting motifs as the centers of the mixture of normal distribution curves.

r-microbiomedasim 1.24.0
Propagated dependencies: r-tmvtnorm@1.7 r-phyloseq@1.54.0 r-pbapply@1.7-4 r-mvtnorm@1.3-3 r-metagenomeseq@1.52.0 r-matrix@1.7-4 r-mass@7.3-65 r-ggplot2@4.0.1 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://github.com/williazo/microbiomeDASim
Licenses: Expat
Build system: r
Synopsis: Microbiome Differential Abundance Simulation
Description:

This package provides a toolkit for simulating differential microbiome data designed for longitudinal analyses. Several functional forms may be specified for the mean trend. Observations are drawn from a multivariate normal model. The objective of this package is to be able to simulate data in order to accurately compare different longitudinal methods for differential abundance.

r-mipp 1.82.0
Propagated dependencies: r-mass@7.3-65 r-e1071@1.7-16 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/
Licenses: GPL 2+
Build system: r
Synopsis: Misclassification Penalized Posterior Classification
Description:

This package finds optimal sets of genes that seperate samples into two or more classes.

r-mai 1.16.0
Propagated dependencies: r-tidyverse@2.0.0 r-summarizedexperiment@1.40.0 r-s4vectors@0.48.0 r-pcamethods@2.2.0 r-missforest@1.6.1 r-future-apply@1.20.0 r-future@1.68.0 r-foreach@1.5.2 r-e1071@1.7-16 r-doparallel@1.0.17 r-caret@7.0-1
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://github.com/KechrisLab/MAI
Licenses: GPL 3
Build system: r
Synopsis: Mechanism-Aware Imputation
Description:

This package provides a two-step approach to imputing missing data in metabolomics. Step 1 uses a random forest classifier to classify missing values as either Missing Completely at Random/Missing At Random (MCAR/MAR) or Missing Not At Random (MNAR). MCAR/MAR are combined because it is often difficult to distinguish these two missing types in metabolomics data. Step 2 imputes the missing values based on the classified missing mechanisms, using the appropriate imputation algorithms. Imputation algorithms tested and available for MCAR/MAR include Bayesian Principal Component Analysis (BPCA), Multiple Imputation No-Skip K-Nearest Neighbors (Multi_nsKNN), and Random Forest. Imputation algorithms tested and available for MNAR include nsKNN and a single imputation approach for imputation of metabolites where left-censoring is present.

r-mouse-db0 3.22.0
Propagated dependencies: r-annotationdbi@1.72.0
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
Build system: r
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-mpra 1.32.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-statmod@1.5.1 r-scales@1.4.0 r-s4vectors@0.48.0 r-limma@3.66.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://github.com/hansenlab/mpra
Licenses: Artistic License 2.0
Build system: r
Synopsis: Analyze massively parallel reporter assays
Description:

This package provides tools for data management, count preprocessing, and differential analysis in massively parallel report assays (MPRA).

r-mgug4120a-db 3.2.3
Propagated dependencies: r-org-mm-eg-db@3.22.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/mgug4120a.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Agilent annotation data (chip mgug4120a)
Description:

Agilent annotation data (chip mgug4120a) assembled using data from public repositories.

r-micrornaome 1.32.0
Propagated dependencies: r-summarizedexperiment@1.40.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/microRNAome
Licenses: GPL 2+
Build system: r
Synopsis: SummarizedExperiment for the microRNAome project
Description:

This package provides a SummarizedExperiment object of read counts for microRNAs across tissues, cell-types, and cancer cell-lines. The read count matrix was prepared and provided by the author of the study: Towards the human cellular microRNAome.

r-mogene10stv1probe 2.18.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/mogene10stv1probe
Licenses: LGPL 2.0+
Build system: r
Synopsis: Probe sequence data for microarrays of type mogene10stv1
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 MoGene-1\_0-st-v1\_probe\_tab.

r-metacyto 1.32.0
Propagated dependencies: r-tidyr@1.3.1 r-metafor@4.8-0 r-ggplot2@4.0.1 r-flowsom@2.18.0 r-flowcore@2.22.0 r-fastcluster@1.3.0 r-cluster@2.1.8.1
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/MetaCyto
Licenses: GPL 2+
Build system: r
Synopsis: MetaCyto: A package for meta-analysis of cytometry data
Description:

This package provides functions for preprocessing, automated gating and meta-analysis of cytometry data. It also provides functions that facilitate the collection of cytometry data from the ImmPort database.

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