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bioc
mistyR:Multiview Intercellular SpaTial modeling framework
mistyR is an implementation of the Multiview Intercellular SpaTialmodeling framework (MISTy). MISTy is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. Each MISTy view is then analyzed for its contribution to the total expression of each marker and is explained in terms of the interactions with other measurements that led to the observed contribution.
Maintained by Jovan Tanevski. Last updated 5 months ago.
softwarebiomedicalinformaticscellbiologysystemsbiologyregressiondecisiontreesinglecellspatialbioconductorbiologyintercellularmachine-learningmodularmolecular-biologymultiviewspatial-transcriptomics
14.8 match 51 stars 7.87 score 160 scriptsbioc
OmnipathR:OmniPath web service client and more
A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github).
Maintained by Denes Turei. Last updated 19 days ago.
graphandnetworknetworkpathwayssoftwarethirdpartyclientdataimportdatarepresentationgenesignalinggeneregulationsystemsbiologytranscriptomicssinglecellannotationkeggcomplexesenzyme-ptmnetworksnetworks-biologyomnipathproteinsquarto
5.7 match 126 stars 9.90 score 226 scripts 2 dependentsstemangiola
tidyseurat:Brings Seurat to the Tidyverse
It creates an invisible layer that allow to see the 'Seurat' object as tibble and interact seamlessly with the tidyverse.
Maintained by Stefano Mangiola. Last updated 8 months ago.
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsdplyrggplot2pcapurrrsctseuratsingle-cellsingle-cell-rna-seqtibbletidyrtidyversetranscriptstsneumap
3.3 match 158 stars 9.66 score 398 scripts 1 dependentsjuergenknauer
bigleaf:Physical and Physiological Ecosystem Properties from Eddy Covariance Data
Calculation of physical (e.g. aerodynamic conductance, surface temperature), and physiological (e.g. canopy conductance, water-use efficiency) ecosystem properties from eddy covariance data and accompanying meteorological measurements. Calculations assume the land surface to behave like a 'big-leaf' and return bulk ecosystem/canopy variables.
Maintained by Juergen Knauer. Last updated 8 months ago.
4.0 match 7.23 score 124 scripts 17 dependentsbioc
tidySingleCellExperiment:Brings SingleCellExperiment to the Tidyverse
'tidySingleCellExperiment' is an adapter that abstracts the 'SingleCellExperiment' container in the form of a 'tibble'. This allows *tidy* data manipulation, nesting, and plotting. For example, a 'tidySingleCellExperiment' is directly compatible with functions from 'tidyverse' packages `dplyr` and `tidyr`, as well as plotting with `ggplot2` and `plotly`. In addition, the package provides various utility functions specific to single-cell omics data analysis (e.g., aggregation of cell-level data to pseudobulks).
Maintained by Stefano Mangiola. Last updated 5 months ago.
assaydomaininfrastructurernaseqdifferentialexpressionsinglecellgeneexpressionnormalizationclusteringqualitycontrolsequencingbioconductordplyrggplot2plotlysingle-cell-rna-seqsingle-cell-sequencingsinglecellexperimenttibbletidyrtidyverse
3.3 match 36 stars 8.86 score 125 scripts 2 dependentscyrillagger
scDiffCom:Differential Analysis of Intercellular Communication from scRNA-Seq Data
Analysis tools to investigate changes in intercellular communication from scRNA-seq data. Using a Seurat object as input, the package infers which cell-cell interactions are present in the dataset and how these interactions change between two conditions of interest (e.g. young vs old). It relies on an internal database of ligand-receptor interactions (available for human, mouse and rat) that have been gathered from several published studies. Detection and differential analyses rely on permutation tests. The package also contains several tools to perform over-representation analysis and visualize the results. See Lagger, C. et al. (2023) <doi:10.1038/s43587-023-00514-x> for a full description of the methodology.
Maintained by Cyril Lagger. Last updated 1 years ago.
5.2 match 21 stars 4.25 score 17 scriptswangminyu
ISAT:Extract Cell Density and Nearest Distance Based on 'PerkinElmer InForm' Software Output
Reads the output of the 'PerkinElmer InForm' software <http://www.perkinelmer.com/product/inform-cell-analysis-one-seat-cls135781>. In addition to cell-density count, it can derive statistics of intercellular spatial distance for each cell-type.
Maintained by Minyu Wang. Last updated 7 years ago.
0.5 match 1 stars 3.00 score 4 scripts