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https://github.com/arc85/celltalker
Inference of cell-cell communication from single-cell RNAseq data
https://github.com/arc85/celltalker
ligands receptors single-cell transcriptomics
Last synced: 3 months ago
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Inference of cell-cell communication from single-cell RNAseq data
- Host: GitHub
- URL: https://github.com/arc85/celltalker
- Owner: arc85
- License: other
- Created: 2019-08-25T21:06:20.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-06-26T20:25:45.000Z (over 1 year ago)
- Last Synced: 2024-06-18T11:32:46.387Z (5 months ago)
- Topics: ligands, receptors, single-cell, transcriptomics
- Language: R
- Homepage: https://arc85.github.io/celltalker/
- Size: 11.1 MB
- Stars: 43
- Watchers: 4
- Forks: 18
- Open Issues: 8
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
- awesome-cell-cell-communication - celltalker - [R]- celltalker can infer putative ligand and receptor interactions from single-cell RNAseq data. (Uncategorized / Uncategorized)
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# celltalker
The goal of celltalker is to infer putative ligand and receptor interactions from single-cell RNAseq data. This is accomplished by evaluating known cognate ligand/receptor interactions across groups of cells. Interactions are scored by jointly weighting the expression levels of ligands and receptors, and significance is evaluated by comparing to a background distribution of scrambled ligands and receptors.
A recent refactoring of this package has made the interface much simpler to use. We also provide a wrapper to the functionality of the [circlize package](https://jokergoo.github.io/circlize_book/book/) for creating circos plots of ligands and receptors.
## Installation
You can install the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("arc85/celltalker")
```
## TL;DR example use
Here's a basic use case for celltalker based on a subset of the 40,000 bone marrow single-cell dataset available from the Human Cell Atlas. Check out SeuratData [here](https://github.com/satijalab/seurat-data) to download the example dataset.
Note that we have identified canonical immune cell types and filtered the dataset for ease of analysis. A vignette documenting this processing is coming soon, but please utilize the **hca_bm_umap_cell_types** data.frame for the vignette below.
```{r example}
# Load packages
suppressMessages({
library(celltalker)
library(Seurat)
suppressWarnings(
library(SeuratData)
)
library(tidyverse)
})
# Load Human Cell Atlast Bone Marrow from SeuratData
data(hcabm40k)
# Filter cell and assign cell types to the dataset
# NB: hca_bm_umap_cell_types has cell types and UMAP embeddings
hca_bm <- hcabm40k[,rownames(hca_bm_umap_cell_types)]
hca_bm[["cell_types"]] <- hca_bm_umap_cell_types$cell_types
# Process data
hca_bm <- NormalizeData(hca_bm)
# Add UMAP coordinates
hca_bm[["umap"]] <- CreateDimReducObject(embeddings=as.matrix(hca_bm_umap_cell_types[,1:2]),
key="UMAP_",assay="RNA")
# View cell types
DimPlot(hca_bm,group.by="cell_types")
## Run celltalker
hca_bm_interactions <- celltalk(input_object=hca_bm,
metadata_grouping="cell_types",
ligand_receptor_pairs=ramilowski_pairs,
number_cells_required=100,
min_expression=1000,
max_expression=20000,
scramble_times=10)
## Identify top statistically significant interactions
top_stats <- hca_bm_interactions %>%
mutate(fdr=p.adjust(p_val,method="fdr")) %>%
filter(fdr<0.05) %>%
group_by(cell_type1) %>%
top_n(3,interact_ratio) %>%
ungroup()
## Generate a circos plot
colors_use <- RColorBrewer::brewer.pal(n=length(unique(hca_bm$cell_types)),"Set2")
circos_plot(ligand_receptor_frame=top_stats,
cell_group_colors=colors_use,
ligand_color="blue",
receptor_color="red",
cex_outer=0.5,
cex_inner=0.4)
```