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: 4 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 (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2023-06-26T20:25:45.000Z (12 months ago)
• Last Synced: 2024-01-14T19:41:51.783Z (5 months ago)
• Topics: ligands, receptors, single-cell, transcriptomics
• Language: R
• Homepage: https://arc85.github.io/celltalker/
• Size: 11.1 MB
• Stars: 44
• Watchers: 4
• Forks: 17
• Open Issues: 7
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE

Lists

• 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)

```