nichenetr
Cell-cell communication predictor
Predicts ligand-receptor interactions based on gene expression data and integrates prior knowledge to model intercellular communication.
NicheNet: predict active ligand-target links between interacting cells
497 stars
14 watching
118 forks
Language: R
last commit: 6 months ago
Linked from 1 awesome list
cell-cell-communicationdata-integrationgene-expressionintercellular-communicationligand-receptorligand-targetnetwork-inferencerna-seqsingle-cell-omicssingle-cell-rna-seq
seandavi/awesome-single-cellRelated projects:
| Repository | Description | Stars |
|---|---|---|
 zjufanlab/spatalk | An R package that infers cell-cell communication and ligand-receptor-target networks from spatially resolved transcriptomic data | 61 |
 saezlab/liana | An R-based framework to analyze cell-cell communication from single-cell RNA-Seq data by integrating multiple methods and resources | 184 |
 cabsel/sincerities | Infers gene regulatory networks from time-stamped single cell transcriptional expression profiles using a statistical method | 12 |
 hkmztrk/deepdta | A system that predicts drug-target binding affinity using convolutional neural networks and protein sequences. | 228 |
 martaint/intercellar | An interactive R/Shiny app for analyzing cell-cell communication from single-cell transcriptomics data | 9 |
 muhaochen/seq_ppi | A deep learning framework for predicting protein-protein interactions based on sequence data | 89 |
 broadinstitute/cell-health | Predicts cell health from morphological profiles using machine learning and image analysis | 35 |
 inoue0426/drgat | This implementation uses Graph Attention Networks to predict drug response based on genetic influences in heterogeneous networks. | 3 |
 asmagen/robustsinglecell | An R-based software package for analyzing scRNA-seq data to identify robust cell subpopulations and compare population compositions across different tissues and experimental models. | 13 |
 lishuya17/monn | A framework for predicting pairwise non-covalent interactions and binding affinities between compounds and proteins using machine learning | 100 |
 irrationone/cellassign | Automated assignment of cell types in single-cell RNA-seq data based on marker genes and patient/batch effects | 197 |
| zjufanlab/sccatch | Automated tool for annotating cell types from single-cell RNA sequencing data based on marker genes | 219 |
 scverse/scirpy | Analyzes single-cell TCR and BCR data from scRNA-seq using Python | 222 |
 emergent-behaviors-in-biology/sctop | A tool for identifying cell phenotypes from single-cell RNA sequencing data by projecting onto known cell types | 3 |
 pinellolab/dictys | Reconstructs dynamic gene regulatory networks from single-cell RNA and ATAC-seq data to understand cell-type specific transcription factor regulation. | 111 |