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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-scarray-sat 1.9.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-seuratobject@5.2.0 r-seurat@5.3.1 r-scarray@1.18.0 r-s4vectors@0.48.0 r-matrix@1.7-4 r-gdsfmt@1.46.0 r-delayedarray@0.36.0 r-biocsingular@1.26.1 r-biocparallel@1.44.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SCArray.sat
Licenses: GPL 3
Build system: r
Synopsis: Large-scale single-cell RNA-seq data analysis using GDS files and Seurat
Description:

Extends the Seurat classes and functions to support Genomic Data Structure (GDS) files as a DelayedArray backend for data representation. It relies on the implementation of GDS-based DelayedMatrix in the SCArray package to represent single cell RNA-seq data. The common optimized algorithms leveraging GDS-based and single cell-specific DelayedMatrix (SC_GDSMatrix) are implemented in the SCArray package. SCArray.sat introduces a new SCArrayAssay class (derived from the Seurat Assay), which wraps raw counts, normalized expressions and scaled data matrix based on GDS-specific DelayedMatrix. It is designed to integrate seamlessly with the Seurat package to provide common data analysis in the SeuratObject-based workflow. Compared with Seurat, SCArray.sat significantly reduces the memory usage without downsampling and can be applied to very large datasets.

r-svm2crmdata 1.42.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SVM2CRMdata
Licenses: LGPL 2.0+
Build system: r
Synopsis: An example dataset for use with the SVM2CRM package
Description:

An example dataset for use with the SVM2CRM package.

r-suitor 1.12.0
Propagated dependencies: r-ggplot2@4.0.1 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SUITOR
Licenses: GPL 2
Build system: r
Synopsis: Selecting the number of mutational signatures through cross-validation
Description:

An unsupervised cross-validation method to select the optimal number of mutational signatures. A data set of mutational counts is split into training and validation data.Signatures are estimated in the training data and then used to predict the mutations in the validation data.

r-sevenc 1.30.0
Propagated dependencies: r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-rtracklayer@1.70.0 r-readr@2.1.6 r-purrr@1.2.0 r-iranges@2.44.0 r-interactionset@1.38.0 r-genomicranges@1.62.0 r-data-table@1.17.8 r-boot@1.3-32 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/ibn-salem/sevenC
Licenses: GPL 3
Build system: r
Synopsis: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs
Description:

Chromatin looping is an essential feature of eukaryotic genomes and can bring regulatory sequences, such as enhancers or transcription factor binding sites, in the close physical proximity of regulated target genes. Here, we provide sevenC, an R package that uses protein binding signals from ChIP-seq and sequence motif information to predict chromatin looping events. Cross-linking of proteins that bind close to loop anchors result in ChIP-seq signals at both anchor loci. These signals are used at CTCF motif pairs together with their distance and orientation to each other to predict whether they interact or not. The resulting chromatin loops might be used to associate enhancers or transcription factor binding sites (e.g., ChIP-seq peaks) to regulated target genes.

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

r-sigcheck 2.42.0
Propagated dependencies: r-survival@3.8-3 r-mlinterfaces@1.90.0 r-e1071@1.7-16 r-biocparallel@1.44.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SigCheck
Licenses: Artistic License 2.0
Build system: r
Synopsis: Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata
Description:

While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata.

r-stemhypoxia 1.46.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37761
Licenses: FSDG-compatible
Build system: r
Synopsis: Differentiation of Human Embryonic Stem Cells under Hypoxia gene expression dataset by Prado-Lopez et al. (2010)
Description:

Expression profiling using microarray technology to prove if Hypoxia Promotes Efficient Differentiation of Human Embryonic Stem Cells to Functional Endothelium by Prado-Lopez et al. (2010) Stem Cells 28:407-418. Full data available at Gene Expression Omnibus series GSE37761.

r-specl 1.44.0
Propagated dependencies: r-seqinr@4.2-36 r-rsqlite@2.4.4 r-protviz@0.7.9 r-dbi@1.2.3
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: http://bioconductor.org/packages/specL/
Licenses: GPL 3
Build system: r
Synopsis: specL - Prepare Peptide Spectrum Matches for Use in Targeted Proteomics
Description:

provides a functions for generating spectra libraries that can be used for MRM SRM MS workflows in proteomics. The package provides a BiblioSpec reader, a function which can add the protein information using a FASTA formatted amino acid file, and an export method for using the created library in the Spectronaut software. The package is developed, tested and used at the Functional Genomics Center Zurich <https://fgcz.ch>.

r-sbgnview 1.24.0
Propagated dependencies: r-xml2@1.5.0 r-summarizedexperiment@1.40.0 r-sbgnview-data@1.24.0 r-rsvg@2.7.0 r-rmarkdown@2.30 r-rdpack@2.6.4 r-pathview@1.50.0 r-knitr@1.50 r-keggrest@1.50.0 r-igraph@2.2.1 r-httr@1.4.7 r-bookdown@0.45 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/datapplab/SBGNview
Licenses: AGPL 3
Build system: r
Synopsis: "SBGNview: Data Analysis, Integration and Visualization on SBGN Pathways"
Description:

SBGNview is a tool set for pathway based data visalization, integration and analysis. SBGNview is similar and complementary to the widely used Pathview, with the following key features: 1. Pathway definition by the widely adopted Systems Biology Graphical Notation (SBGN); 2. Supports multiple major pathway databases beyond KEGG (Reactome, MetaCyc, SMPDB, PANTHER, METACROP) and user defined pathways; 3. Covers 5,200 reference pathways and over 3,000 species by default; 4. Extensive graphics controls, including glyph and edge attributes, graph layout and sub-pathway highlight; 5. SBGN pathway data manipulation, processing, extraction and analysis.

r-sanityr 1.0.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-singlecellexperiment@1.32.0 r-scuttle@1.20.0 r-s4vectors@0.48.0 r-rcpp@1.1.0 r-matrixgenerics@1.22.0 r-biocparallel@1.44.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/TeoSakel/SanityR
Licenses: GPL 3+
Build system: r
Synopsis: R/Bioconductor interface to the Sanity model gene expression analysis
Description:

a Bayesian normalization procedure derived from first principles. Sanity estimates expression values and associated error bars directly from raw unique molecular identifier (UMI) counts without any tunable parameters.

r-signaturesearch 1.24.0
Propagated dependencies: r-visnetwork@2.1.4 r-tibble@3.3.0 r-summarizedexperiment@1.40.0 r-scales@1.4.0 r-rsqlite@2.4.4 r-rhdf5@2.54.0 r-reshape2@1.4.5 r-readr@2.1.6 r-reactome-db@1.94.0 r-rcpp@1.1.0 r-qvalue@2.42.0 r-org-hs-eg-db@3.22.0 r-matrix@1.7-4 r-magrittr@2.0.4 r-hdf5array@1.38.0 r-gseabase@1.72.0 r-ggplot2@4.0.1 r-fgsea@1.36.0 r-fastmatch@1.1-6 r-experimenthub@3.0.0 r-dplyr@1.1.4 r-dose@4.4.0 r-delayedarray@0.36.0 r-data-table@1.17.8 r-clusterprofiler@4.18.2 r-biocparallel@1.44.0 r-biocgenerics@0.56.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/yduan004/signatureSearch/
Licenses: Artistic License 2.0
Build system: r
Synopsis: Environment for Gene Expression Searching Combined with Functional Enrichment Analysis
Description:

This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods.

r-somnibus 1.18.0
Propagated dependencies: r-yaml@2.3.10 r-vgam@1.1-13 r-tidyr@1.3.1 r-s4vectors@0.48.0 r-rtracklayer@1.70.0 r-reshape2@1.4.5 r-mgcv@1.9-4 r-matrix@1.7-4 r-iranges@2.44.0 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-genomeinfodb@1.46.0 r-data-table@1.17.8 r-bsseq@1.46.0 r-biocmanager@1.30.27 r-annotatr@1.36.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/kaiqiong/SOMNiBUS
Licenses: Expat
Build system: r
Synopsis: Smooth modeling of bisulfite sequencing
Description:

This package aims to analyse count-based methylation data on predefined genomic regions, such as those obtained by targeted sequencing, and thus to identify differentially methylated regions (DMRs) that are associated with phenotypes or traits. The method is built a rich flexible model that allows for the effects, on the methylation levels, of multiple covariates to vary smoothly along genomic regions. At the same time, this method also allows for sequencing errors and can adjust for variability in cell type mixture.

r-scgps 1.24.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-singlecellexperiment@1.32.0 r-rcppparallel@5.1.11-1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-locfit@1.5-9.12 r-glmnet@4.1-10 r-ggplot2@4.0.1 r-fastcluster@1.3.0 r-dynamictreecut@1.63-1 r-dplyr@1.1.4 r-deseq2@1.50.2 r-caret@7.0-1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/scGPS
Licenses: GPL 3
Build system: r
Synopsis: complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation)
Description:

The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations.

r-signaturesearchdata 1.24.0
Propagated dependencies: r-rhdf5@2.54.0 r-r-utils@2.13.0 r-magrittr@2.0.4 r-limma@3.66.0 r-experimenthub@3.0.0 r-dplyr@1.1.4 r-biobase@2.70.0 r-affy@1.88.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/signatureSearchData
Licenses: Artistic License 2.0
Build system: r
Synopsis: Datasets for signatureSearch package
Description:

CMAP/LINCS hdf5 databases and other annotations used for signatureSearch software package.

r-sketchr 1.6.0
Propagated dependencies: r-scales@1.4.0 r-rlang@1.1.6 r-reticulate@1.44.1 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-delayedarray@0.36.0 r-biobase@2.70.0 r-basilisk@1.22.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/fmicompbio/sketchR
Licenses: Expat
Build system: r
Synopsis: An R interface for python subsampling/sketching algorithms
Description:

This package provides an R interface for various subsampling algorithms implemented in python packages. Currently, interfaces to the geosketch and scSampler python packages are implemented. In addition it also provides diagnostic plots to evaluate the subsampling.

r-schex 1.24.0
Propagated dependencies: r-singlecellexperiment@1.32.0 r-rlang@1.1.6 r-hexbin@1.28.5 r-ggplot2@4.0.1 r-ggforce@0.5.0 r-entropy@1.3.2 r-dplyr@1.1.4 r-concaveman@1.2.0 r-cluster@2.1.8.1
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/SaskiaFreytag/schex
Licenses: GPL 3
Build system: r
Synopsis: Hexbin plots for single cell omics data
Description:

Builds hexbin plots for variables and dimension reduction stored in single cell omics data such as SingleCellExperiment. The ideas used in this package are based on the excellent work of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas Lumley.

r-seqgsea 1.50.0
Propagated dependencies: r-doparallel@1.0.17 r-deseq2@1.50.2 r-biomart@2.66.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/SeqGSEA
Licenses: GPL 3+
Build system: r
Synopsis: Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing
Description:

The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively.

r-scdesign3 1.8.0
Propagated dependencies: r-viridis@0.6.5 r-umap@0.2.10.0 r-tibble@3.3.0 r-summarizedexperiment@1.40.0 r-sparsemvn@0.2.2 r-singlecellexperiment@1.32.0 r-pbmcapply@1.5.1 r-mvtnorm@1.3-3 r-mgcv@1.9-4 r-mclust@6.1.2 r-matrixstats@1.5.0 r-matrix@1.7-4 r-irlba@2.3.5.1 r-ggplot2@4.0.1 r-gamlss-dist@6.1-1 r-gamlss@5.5-0 r-dplyr@1.1.4 r-coop@0.6-3 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/SONGDONGYUAN1994/scDesign3
Licenses: Expat
Build system: r
Synopsis: unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics
Description:

We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.

r-statial 1.12.0
Propagated dependencies: r-treekor@1.18.0 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-summarizedexperiment@1.40.0 r-stringr@1.6.0 r-spatstat-geom@3.6-1 r-spatstat-explore@3.6-0 r-spatialexperiment@1.20.0 r-singlecellexperiment@1.32.0 r-s4vectors@0.48.0 r-ranger@0.17.0 r-purrr@1.2.0 r-plotly@4.11.0 r-magrittr@2.0.4 r-limma@3.66.0 r-ggplot2@4.0.1 r-edger@4.8.0 r-dplyr@1.1.4 r-data-table@1.17.8 r-concaveman@1.2.0 r-cluster@2.1.8.1 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/Statial
Licenses: GPL 3
Build system: r
Synopsis: package to identify changes in cell state relative to spatial associations
Description:

Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with.

r-smartphos 1.0.0
Propagated dependencies: r-xml@3.99-0.20 r-vsn@3.78.0 r-tidyr@1.3.1 r-tibble@3.3.0 r-summarizedexperiment@1.40.0 r-stringr@1.6.0 r-shinywidgets@0.9.0 r-shinythemes@1.2.0 r-shinyjs@2.1.0 r-shinybs@0.61.1 r-shiny@1.11.1 r-rlang@1.1.6 r-proda@1.24.0 r-plotly@4.11.0 r-piano@2.26.0 r-pheatmap@1.0.13 r-multiassayexperiment@1.36.1 r-mscoreutils@1.21.0 r-missforest@1.6.1 r-matrixstats@1.5.0 r-magrittr@2.0.4 r-limma@3.66.0 r-imputelcmd@2.1 r-ggplot2@4.0.1 r-ggbeeswarm@0.7.2 r-factoextra@1.0.7 r-e1071@1.7-16 r-dt@0.34.0 r-dplyr@1.1.4 r-dorng@1.8.6.2 r-doparallel@1.0.17 r-decoupler@2.16.0 r-data-table@1.17.8 r-cowplot@1.2.0 r-biocparallel@1.44.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://lu-group-ukhd.github.io/SmartPhos/
Licenses: GPL 3
Build system: r
Synopsis: phosphoproteomics data analysis package with an interactive ShinyApp
Description:

To facilitate and streamline phosphoproteomics data analysis, we developed SmartPhos, an R package for the pre-processing, quality control, and exploratory analysis of phosphoproteomics data generated by MaxQuant and Spectronaut. The package can be used either through the R command line or through an interactive ShinyApp called SmartPhos Explorer. The package contains methods such as normalization and normalization correction, transformation, imputation, batch effect correction, PCA, heatmap, differential expression, time-series clustering, gene set enrichment analysis, and kinase activity inference.

r-spectraql 1.4.0
Propagated dependencies: r-spectra@1.20.0 r-protgenerics@1.42.0 r-mscoreutils@1.21.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://github.com/RforMassSpectrometry/SpectraQL
Licenses: Artistic License 2.0
Build system: r
Synopsis: MassQL support for Spectra
Description:

The Mass Spec Query Language (MassQL) is a domain-specific language enabling to express a query and retrieve mass spectrometry (MS) data in a more natural and understandable way for MS users. It is inspired by SQL and is by design programming language agnostic. The SpectraQL package adds support for the MassQL query language to R, in particular to MS data represented by Spectra objects. Users can thus apply MassQL expressions to analyze and retrieve specific data from Spectra objects.

r-ssnappy 1.14.0
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-summarizedexperiment@1.40.0 r-stringr@1.6.0 r-rlang@1.1.6 r-reshape2@1.4.5 r-pheatmap@1.0.13 r-org-hs-eg-db@3.22.0 r-magrittr@2.0.4 r-igraph@2.2.1 r-gtools@3.9.5 r-graphite@1.56.0 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-ggforce@0.5.0 r-edger@4.8.0 r-dplyr@1.1.4
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://wenjun-liu.github.io/sSNAPPY/
Licenses: GPL 3
Build system: r
Synopsis: Single Sample directioNAl Pathway Perturbation analYsis
Description:

This package provides a single sample pathway perturbation testing method for RNA-seq data. The method propagates changes in gene expression down gene-set topologies to compute single-sample directional pathway perturbation scores that reflect potential direction of change. Perturbation scores can be used to test significance of pathway perturbation at both individual-sample and treatment levels.

r-synaptome-data 0.99.6
Propagated dependencies: r-annotationhub@4.0.0
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://bioconductor.org/packages/synaptome.data
Licenses: Artistic License 2.0
Build system: r
Synopsis: AnnotationData for Synaptome.DB package
Description:

The package provides access to the copy of the Synaptic proteome database. It was designed as an accompaniment for Synaptome.DB package. Database provides information for specific synaptic genes and allows building the protein-protein interaction graph for gene sets, synaptic compartments, and brain regions. In the current update we added 6 more synaptic proteome studies, which resulted in total of 64 studies. We introduced Synaptic Vesicle as a separate compartment. We also added coding mutations for Autistic Spectral disorder and Epilepsy collected from publicly available databases.

r-spatialheatmap 2.16.3
Propagated dependencies: r-xml2@1.5.0 r-tibble@3.3.0 r-summarizedexperiment@1.40.0 r-spscomps@0.3.4.0 r-singlecellexperiment@1.32.0 r-shinydashboard@0.7.3 r-shiny@1.11.1 r-s4vectors@0.48.0 r-rsvg@2.7.0 r-reshape2@1.4.5 r-matrix@1.7-4 r-igraph@2.2.1 r-grimport@0.9-7 r-gridextra@2.3 r-ggplotify@0.1.3 r-ggplot2@4.0.1 r-genefilter@1.92.0 r-edger@4.8.0 r-dplyr@1.1.4 r-data-table@1.17.8
Channel: guix-bioc
Location: guix-bioc/packages/s.scm (guix-bioc packages s)
Home page: https://spatialheatmap.org
Licenses: Artistic License 2.0
Build system: r
Synopsis: spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Large-Scale Data Extensions
Description:

The spatialHeatmap package offers the primary functionality for visualizing cell-, tissue- and organ-specific assay data in spatial anatomical images. Additionally, it provides extended functionalities for large-scale data mining routines and co-visualizing bulk and single-cell data. A description of the project is available here: https://spatialheatmap.org.

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