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).

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graphandnetworknetworkpathwayssoftwarethirdpartyclientdataimportdatarepresentationgenesignalinggeneregulationsystemsbiologytranscriptomicssinglecellannotationkeggquarto

7.40 score 4 dependents 378 scripts

tradeSeq - trajectory-based differential expression analysis for sequencing data

tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM.

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clusteringregressiontimecoursedifferentialexpressiongeneexpressionrnaseqsequencingsoftwaresinglecelltranscriptomicsmultiplecomparisonvisualization

7.21 score 776 scripts

scGate - Marker-Based Cell Type Purification for Single-Cell Sequencing Data

A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. 'scGate' automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. Briefly, 'scGate' takes as input: i) a gene expression matrix stored in a 'Seurat' object and ii) a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry. 'scGate' evaluates the strength of signature marker expression in each cell using the rank-based method 'UCell', and then performs k-nearest neighbor (kNN) smoothing by calculating the mean 'UCell' score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest. See the related publication Andreatta et al. (2022) <doi:10.1093/bioinformatics/btac141>.

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5.10 score 1 dependents 209 scripts 503 downloads

scClustEval - Single Cell Clustering Evaluation and Optimization Framework

A comprehensive framework for evaluating and optimizing single-cell RNA-seq clustering results using self-projection machine learning approaches. The package implements an iterative optimization strategy that merges poorly discriminated clusters based on confusion matrix analysis, achieving robust and reliable cell type identification. Features include multiple classifier support (logistic regression, random forest, SVM, etc.), ROC curve analysis, confusion matrix visualization, and seamless integration with Seurat objects. This is an R implementation inspired by the SCCAF Python package.

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openblascppopenmp

4.81 score 129 scripts

scTenifoldKnk - In-Silico Knockout Experiments from Single-Cell Gene Regulatory Networks

A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene's outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene's function in the analyzed cells. This version includes C++ acceleration with Eigen library, cross-platform compatibility (macOS, Linux, Windows), and Seurat v4/v5 support.

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cpp

4.64 score 3 stars 36 scripts 1.6k downloads

SCEVAN - Single CEll Variational Aneuploidy aNalysis

SCEVAN automatically classifies cells in scRNA-seq data by segregating non-malignant cells of tumor microenvironment from malignant cells. It also infers copy number profiles of malignant cells, identifies subclonal structures and analyzes specific and shared alterations of each subpopulation.

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4.47 score 73 scripts

SCENT - Single Cell Entropy for Estimating Differentiation Potency

Estimates differentiation potency of single cells from scRNA-Seq data using signaling entropy on protein interaction networks. Implements both the full Signaling Entropy Rate (SR) and the fast CCAT approximation. Based on the method described in Teschendorff AE, Enver T (2017) <doi:10.1038/ncomms15599>.

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openblascpp

4.38 score 120 scripts

multinichenetr - MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data

This package allows you the investigate differences in intercellular communication between multiple conditions of interest. It is a flexible framework that enables multi-criteria prioritization of cell-cell communication patterns from scRNAseq datasets with complex experimental designs. These datasets can contain multiple samples (e.g. patients) over different groups of interest (e.g. disease subtypes). With MultiNicheNet, you can now better analyze the differences in cell-cell signaling between the different groups of interest.

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4.24 score 116 scripts

SecAct - Secreted Signaling Activity Inference

Inferring secreted protein activities at bulk, single-cell, and spatial levels. SecAct uses ridge regression with permutation-based significance testing to infer the activity of over 1000 secreted proteins from gene expression profiles.

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complexheatmapgsl

4.20 score 35 scripts

iTALK - Characterize and Illustrate Intercellular Communication

iTALK, a computational approach to characterize, compare, and illustrate intercellular communication signals in the multicellular ecosystem using either bulk RNA sequencing data or single cell RNAseq data. iTALK can in principle be used to dissect the complexity, diversity, and dynamics of cell-cell communication from a wide range of cellular processes.

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rnasequencingsinglecellgeneexpressioncellbiology

4.16 score 73 scripts

nichenetr - Modeling Intercellular Communication by Linking Ligands to Target Genes with NicheNet

NicheNet is a computational framework for modeling intercellular communication by linking extracellular ligands to intracellular gene regulatory changes. It integrates prior knowledge of ligand-receptor interactions, signaling pathways, and transcription factor regulation to predict which ligands from sender cells affect gene expression in receiver cells. This enables mechanistic hypothesis generation about cell-cell communication events observed in transcriptomic data.

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cpp

4.04 score 1 dependents 732 scripts

SafeMapper - Fault-Tolerant Functional Programming with Automatic Checkpointing

Provides drop-in replacements for 'purrr' and 'furrr' mapping functions with built-in fault tolerance, automatic checkpointing, and seamless recovery capabilities. When long-running computations are interrupted due to errors, system crashes, or other failures, simply re-run the same code to automatically resume from the last checkpoint. Ideal for large-scale data processing, API calls, web scraping, and other time-intensive operations where reliability is critical.

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3.98 score 1 stars 12 scripts 1 downloads

Connectome - Single Cell Connectomics

A toolkit for exploring cell-cell connectivity patterns based on ligand and receptor expression in heterogeneous single-cell datasets. Implements methods for constructing, filtering, and visualizing connectomic networks from single-cell RNA sequencing data. Supports multiple species via the FANTOM5 ligand-receptor database. Methods described in Raredon et al (2022) <doi:10.1038/s41598-022-07959-x>.

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3.94 score 35 scripts

SpaTalk - Spatially Resolved Cell-Cell Communication Inference for Spatial Transcriptomics

Infers spatially resolved cell-cell communications from spatial transcriptomics data using graph network and knowledge graph approaches. Supports both single-cell resolution and spot-based spatial transcriptomics platforms. Provides cell type deconvolution, ligand-receptor interaction analysis, and downstream pathway inference.

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cpp

3.93 score 34 scripts

METAFLUX - High-Performance Metabolic Flux Analysis from Bulk and Single-Cell RNA-seq Data

A performance-optimized implementation of METAFlux for characterizing cellular metabolism from bulk and single-cell RNA-sequencing data. Uses genome-scale metabolic modeling with Human-GEM to derive 13,082 metabolic fluxes through flux balance analysis (FBA). Features parallel computing, Rcpp acceleration, and optimized algorithms for 10-20x faster computation.

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cpp

3.89 score 1 stars 31 scripts

CellOracleR - In Silico Gene Perturbation Analysis with Single-Cell Data

An R implementation of the CellOracle framework for in silico gene perturbation analysis and gene regulatory network (GRN) inference from single-cell RNA-seq data. Predicts cell state transitions in response to transcription factor perturbations by combining GRN models with single-cell expression data. Key features include motif analysis for base GRN construction, cluster-specific GRN inference using regularized regression, perturbation simulation with signal propagation, and comprehensive visualization of predicted cell fate changes. Based on the methodology described in Kamimoto et al. (2023) <doi:10.15252/msb.202211547>.

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openblascppopenmp

3.88 score 3 stars 7 scripts

recall - Calibrated Clustering with Artificial Variables to Avoid Over-Clustering in Single-Cell RNA-Sequencing

recall (Calibrated Clustering with Artificial Variables) is a method for protecting against over-clustering by controlling for the impact of double-dipping. The approach can be applied to any clustering algorithm (implemented are the Louvain and Leiden algorithms with plans for K-means, and hierarchical clustering algorithms). The method provides state-of-the-art clustering performance and can rapidly analyze large-scale scRNA-seq studies and is compatible with the Seurat library (V4 and V5).

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3.81 score 32 scripts

scMetaLink - Single-Cell Metabolite-Mediated Cell Communication Analysis

A comprehensive framework for inferring metabolite-mediated cell-cell communication from single-cell transcriptomic data. scMetaLink integrates metabolite production potential via enzyme expression, metabolite sensing capability via receptor and transporter expression, and secretion potential to construct intercellular metabolic communication networks. The package leverages the MetalinksDB database containing 41894 metabolite-protein interactions covering 1128 metabolites and 4374 proteins. Key features include probabilistic inference of metabolite production, receptor-mediated metabolite sensing quantification, permutation-based statistical testing with multiple hypothesis correction, pathway-level aggregation analysis, and publication-ready visualization.

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softwaresinglecelltranscriptomicscellbiologymetabolomicsnetworkvisualizationgeneexpressionspatial

3.78 score 3 stars 10 scripts

scPAS - Single-Cell Phenotype-Associated Subpopulation Identifier

Identifies phenotype-associated cell subpopulations from single-cell RNA-seq data by integrating bulk RNA-seq data with phenotype information. This package uses network-regularized sparse regression to quantify the strength of association between each cell and a phenotype (e.g., disease stage, tumor metastasis, treatment response, survival outcomes). Compatible with both Seurat v4 (4.0.0+) and Seurat v5 (5.0.0+). The method supports Gaussian (continuous), binomial (binary), and Cox (survival) regression families. Full cross-platform compatibility (Windows, macOS, Linux).

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cpp

3.78 score 10 scripts

NOVA - Network Of Versatile Cell-Cell Communication Analysis

NOVA (Network Of Versatile cell-cell Analysis) is a high-performance R package for predicting and visualizing cell-to-cell communication networks from single-cell and bulk transcriptomic data. It leverages connectomeDB2020, the most comprehensive manually curated ligand-receptor interaction database, to identify signaling interactions between cell types. NOVA features vectorized computations, parallel processing, and seamless integration with Seurat (V4/V5) for efficient analysis of large-scale datasets.

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cppopenmp

3.73 score 1 stars 18 scripts 475 downloads

SVG - Spatially Variable Genes Detection Methods for Spatial Transcriptomics

A unified framework for detecting spatially variable genes (SVGs) in spatial transcriptomics data. This package integrates multiple state-of-the-art SVG detection methods including MERINGUE (Moran's I based spatial autocorrelation), Giotto binSpect (binary spatial enrichment test), SPARK-X (non-parametric kernel-based test), and nnSVG (nearest-neighbor Gaussian processes). Each method is implemented with optimized performance through vectorization, parallelization, and C++ acceleration where applicable. Methods are described in Miller et al. (2021) <doi:10.1101/gr.271288.120>, Dries et al. (2021) <doi:10.1186/s13059-021-02286-2>, Zhu et al. (2021) <doi:10.1186/s13059-021-02404-0>, and Weber et al. (2023) <doi:10.1038/s41467-023-39748-z>.

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openblascpp

3.70 score 10 scripts 173 downloads

SCORPION - Single Cell Oriented Reconstruction of PANDA Individual Optimized Networks

Constructs gene regulatory networks from single-cell gene expression data using the PANDA (Passing Attributes between Networks for Data Assimilation) algorithm.

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openblascppopenmp

3.62 score 21 scripts 557 downloads

TorchDecon - Deep Learning-Based Cell Type Deconvolution Using torch

A deep neural network ensemble approach for cell type deconvolution of bulk RNA-seq data. TorchDecon uses simulated bulk samples generated from single-cell RNA-seq data to train deep neural networks that predict cell type fractions. This package is an R-native implementation based on the Scaden algorithm, built on the torch framework (LibTorch C++ backend) for GPU acceleration and cross-platform compatibility. Seamlessly integrates with Seurat objects (v4 and v5 compatible).

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3.60 score 1 stars 5 scripts

MultiK - Multi-Resolution Consensus Clustering for Single-Cell RNA-seq

Identifies optimal cluster numbers in single-cell RNA-seq data through subsampling-based consensus clustering. MultiK performs repeated subsampling and clustering across multiple resolution parameters, then evaluates clustering stability using the Proportion of Ambiguous Clustering (PAC) metric. Statistical significance of cluster separability is assessed using SigClust.

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3.60 score 20 scripts

MOFSR - Multi-Omics Fusion for Subtype Recognition

A comprehensive toolkit for integrating multi-modal biological data to discover disease subtypes and biological mechanisms. MOFSR provides 15 state-of-the-art multi-omics clustering algorithms (SNF, wSNF, CPCA, iClusterBayes, IntNMF, LRAcluster, MCIA, MOFA, NEMO, PINSPlus, RGCCA, SGCCA, CIMLR, BCC, LateFusion), 17 classification methods, parallel computing support, comprehensive visualization (UMAP, heatmaps, survival curves), data preprocessing (normalization, filtering, batch correction, QC), feature selection with bootstrap validation, and cluster quality assessment. All core algorithms are implemented internally for maximum compatibility, cross-platform support, and optimized performance. Works on Windows, macOS, and Linux without external dependencies for core functionality.

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3.54 score 14 scripts

scaper - Single Cell Transcriptomics-Level Cytokine Activity Prediction and Estimation

Generates cell-level cytokine activity estimates using relevant information from gene sets constructed with the 'CytoSig' and the 'Reactome' databases and scored using the modified 'Variance-adjusted Mahalanobis (VAM)' framework for single-cell RNA-sequencing (scRNA-seq) data. 'CytoSig' database is described in: Jiang at al., (2021) <doi:10.1038/s41592-021-01274-5>. 'Reactome' database is described in: Gillespie et al., (2021) <doi:10.1093/nar/gkab1028>. The 'VAM' method is outlined in: Frost (2020) <doi:10.1093/nar/gkaa582>.

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3.51 score 13 scripts 564 downloads

CellProgramMapper - Map Single Cells to Reference Gene Expression Programs

Maps single-cell RNA sequencing data to reference gene expression programs (GEPs) using non-negative matrix factorization. Enables cell type annotation and state characterization by projecting query cells onto pre-built or custom reference programs. Includes tools for building consensus references from multiple datasets. Features C++ accelerated NNLS solvers and built-in machine learning models for cell type prediction.

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openblascppopenmp

3.48 score 1 stars 12 scripts

scVeloR - RNA Velocity Analysis in R

A comprehensive R implementation of RNA velocity analysis based on the scvelo Python package. This package enables estimation of RNA velocities for single-cell RNA-seq data, supporting deterministic (steady-state), stochastic, and dynamical models. The dynamical model uses an Expectation-Maximization algorithm to infer full transcriptional kinetics including transcription, splicing, and degradation rates. The package integrates seamlessly with Seurat objects and provides visualization functions for velocity embedding, stream plots, and phase portraits.

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openblascpp

3.48 score 1 stars 8 scripts

darwin - Multi-Objective Gene Selection Using Evolutionary Algorithms

Automatic gene selection for bulk RNA-seq deconvolution using multi-objective optimization. Implements the NSGA-II algorithm to simultaneously minimize correlation and maximize distance between cell type expression profiles, yielding Pareto-optimal gene subsets. Supports Seurat objects (V4 and V5), SingleCellExperiment, and standard matrix inputs. Includes built-in deconvolution methods and parallel computing support.

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openblascpp

3.48 score 10 scripts

hdWGCNA - High-dimensional Weighted Gene Co-expression Network Analysis

hdWGCNA is an R package for performing weighted gene co-expression network analysis in high dimensional -omics such as single-cell RNA-seq or spatial transcriptomics. It provides tools for constructing metacells, building co-expression networks, computing module eigengenes, and performing various downstream analyses including module trait correlation, module preservation, and transcription factor network analysis.

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3.43 score 544 scripts

cellGeometry - Geometric Single Cell Deconvolution

Deconvolution of bulk RNA-Sequencing data into proportions of cells based on a reference single-cell RNA-Sequencing dataset using high-dimensional geometric methodology.

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3.40 score 44 scripts 11k downloads

scFOCAL - Integration of Drug and Single-Cell Gene Expression Signatures

scFOCAL integrates small molecules transcriptional consensus signatures with single-cell gene expression data to identify cell states targeted or resistant to different perturbations. This information is leveraged by the scFOCAL combination index, which identifies synergistic combinations that maximize the discordance across a heterogenous tumor.

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3.40 score 6 scripts

CytoSPACER - Mapping Single-Cell Transcriptomes to Spatial Transcriptomics Data

CytoSPACER is an R implementation of CytoSPACE, a computational strategy for assigning single-cell transcriptomes to in situ spatial transcriptomics (ST) data. The method solves single cell/spot assignment by minimizing a correlation-based cost function through a linear programming-based optimization routine using the Jonker-Volgenant algorithm. This package provides high-performance implementations using Rcpp and supports parallel processing across all platforms.

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cpp

3.40 score 7 scripts

SpaGER - Spatial Gene Expression Prediction using scRNA-seq

Integrates spatial transcriptomics data with single-cell RNA sequencing (scRNA-seq) data to predict expression of unmeasured genes in spatial data. Uses Principal Vectors (PVs) for domain adaptation followed by k-nearest neighbor weighted imputation. This R implementation provides identical results to the original Python SpaGE package with efficient C++ acceleration.

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openblascpp

3.38 score 1 stars 12 scripts

yaGST - Competitive gene set and regulon tests.

This is a collection of wrappers to the Wilcoxon test to run competitive gene set and regulon tests.

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3.36 score 14 stars 33 scripts

CellODE - Cellular Dynamics Inference Using Neural ODE

An R implementation for single-cell trajectory inference using Variational Autoencoder (VAE) and Neural Ordinary Differential Equations (Neural ODE). CellODE automatically infers cellular dynamics from single-cell RNA sequencing data, providing pseudotime estimation, latent space representation, and vector field analysis. The package is designed for seamless integration with Seurat objects and supports both Seurat V4 and V5.

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openblascppopenmp

3.30 score 4 scripts

MAGICR - Markov Affinity-Based Graph Imputation of Cells

Native R implementation of MAGIC (Markov Affinity-based Graph Imputation of Cells) for denoising and imputation of single-cell RNA sequencing data. MAGIC uses diffusion geometry to denoise single-cell data and fill in missing transcripts, as described in van Dijk et al. (2018) <doi:10.1016/j.cell.2018.05.061>. This package provides a pure R implementation with optional C++ acceleration, parallel computing support, and seamless integration with Seurat (v4 and v5) objects.

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openblascpp

3.30 score 1 stars 10 scripts

scPharm - Identification of Pharmacological Subpopulations of Single Cells for Precision Medicine in Cancers

A computational framework for single-cell RNA-seq data that integrates pharmacogenomics profiles to uncover therapeutic heterogeneity within tumors at single-cell resolution. The tool prioritizes tailored drugs and provides insights into combination therapy regimens and drug toxicity in cancers.

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openblascpp

3.30 score 4 scripts

COMMOTR - Screening Cell-Cell Communication in Spatial Transcriptomics via Collective Optimal Transport

Infers cell-cell communication in spatial transcriptomics data using collective optimal transport. The method models ligand-receptor interactions as optimal transport problems with spatial distance constraints, providing communication direction inference, cluster-level analysis, and comprehensive visualization. This is an R implementation based on the COMMOT algorithm (Cang et al., Nature Methods, 2023).

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openblascppopenmp

3.30 score 1 stars 4 scripts

fastCNV - Fast CNV Detection for Single-Cell and Spatial Transcriptomics

Fast and accurate detection of Copy Number Variations (CNVs) in single-cell RNA sequencing (scRNA-seq) and Spatial Transcriptomics (ST) data, including 10X Visium and Visium HD. Provides sliding window-based CNV inference, hierarchical clustering of CNV profiles, and publication-ready visualization. Compatible with Seurat 4.x/5.x.

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3.30 score 1 stars 10 scripts

liana - LIANA: a LIgand-receptor ANalysis frAmework

LIANA provides a number of methods and resources for ligand-receptor interaction inference from scRNA-seq data. This version is specifically designed for Seurat v4 and SeuratObject v4 compatibility. It includes improved Python environment handling and better integration with existing workflows.

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scaterscransinglecellexperimentcomplexheatmapquarto

3.06 score 231 scripts

llmhelper - Unified Interface for Large Language Model Interactions

Provides a unified interface for interacting with Large Language Models (LLMs) through various providers including OpenAI <https://platform.openai.com/docs/api-reference>, Ollama <https://ollama.com/>, and other OpenAI-compatible APIs. Features include automatic connection testing, max_tokens limit auto-adjustment, structured JSON responses with schema validation, interactive JSON schema generation, prompt templating, and comprehensive diagnostics.

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3.00 score 2 scripts 136 downloads

BioTransition - Dynamic Network Biomarker Analysis for Critical Transitions

A comprehensive toolkit for detecting critical transitions and identifying dynamic network biomarkers (DNB) in biological systems. Critical transitions, characterized by sudden shifts between distinct states, are prevalent in complex biological processes including disease progression, cellular differentiation, and developmental transitions. This package implements seven complementary DNB methodologies: (1) conventional DNB (cDNB) based on the original DNB theory (Chen et al. 2012 <doi:10.1038/srep00342>); (2) topological DNB (tDNB), a novel approach utilizing network topology and scale-free properties; (3) landscape DNB (LDNB) for quantifying state transitions (Liu et al. 2019 <doi:10.1093/nsr/nwy162>); (4) local DNB (LcDNB) leveraging protein-protein interaction networks; (5) module-based DNB (MDNB) for modular analysis (Li et al. 2022 <doi:10.1016/j.xinn.2022.100364>); (6) time-series network module biomarker (TSNMB) for temporal dynamics (Zhong et al. 2022 <doi:10.1093/jmcb/mjac052>); and (7) time-series leading edge (TSLE) analysis (Liu et al. 2020 <doi:10.1093/bioinformatics/btz758>). Core computational routines are implemented in C++ via 'Rcpp' for optimal performance. Compatible with bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics data. Includes curated protein-protein interaction networks for human and mouse from the STRING database.

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softwarestatisticalmethodnetworksystemsbiologygeneexpressiontranscriptomicssinglecellspatialbiomedicalinformaticsdifferentialexpressioncpp

3.00 score

llmflow - Reasoning and Acting Workflow for Automated Data Analysis

Provides a framework for integrating Large Language Models ('LLMs') with R programming through workflow automation. Built on the 'ReAct' (Reasoning and Acting) architecture, enables bi-directional communication between 'LLMs' and R environments. Features include automated code generation and execution, intelligent error handling with retry mechanisms, persistent session management, structured JSON output validation, and context-aware conversation management.

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2.70 score 1 stars 1 scripts 151 downloads

FastCCCR - Fast Cell-Cell Communication Analysis with Statistical Framework

A high-performance R package for cell-cell communication (CCC) analysis in single-cell RNA sequencing data. FastCCCR implements an innovative statistical framework based on exact null distribution computation, Cauchy combination of multiple statistical methods, and reference-based inference. The package is optimized with Rcpp for computational efficiency and supports both Seurat V4 and V5 objects. Key features include: (1) Multiple scoring methods with Cauchy combination, (2) Reference panel construction and query inference, (3) Support for multiple ligand-receptor databases (CellPhoneDB, CellChat, NicheNet), (4) High-performance vectorized and parallel computation.

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cpp

2.48 score 3 stars 6 scripts

STRIDER - Spatial Transcriptomics Deconvolution and Integration in R

STRIDER (Spatial Transcriptomics deconvolutIon and integration in R) provides comprehensive tools for analyzing spatial transcriptomics data. The package implements topic modeling based deconvolution using Latent Dirichlet Allocation (LDA) to decompose spatial spots into cell type proportions. It also provides multi-sample integration using Fused Gromov-Wasserstein (FGW) optimal transport, spatial clustering with neighborhood awareness, and visualization functions. STRIDER is designed to work seamlessly with Seurat objects (v4 and v5) and supports various input formats including 10X Genomics HDF5 files.

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openblascppopenmp

1.81 score 13 scripts