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https://github.com/Zhu-JC/IGAN
A method for Inferring cell-cell communication pathways represented by spatial gene associations based on spatial transcriptomic data
https://github.com/Zhu-JC/IGAN
Last synced: about 2 months ago
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A method for Inferring cell-cell communication pathways represented by spatial gene associations based on spatial transcriptomic data
- Host: GitHub
- URL: https://github.com/Zhu-JC/IGAN
- Owner: Zhu-JC
- Created: 2024-03-15T08:58:29.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-08-19T11:46:43.000Z (3 months ago)
- Last Synced: 2024-08-20T06:57:56.361Z (3 months ago)
- Language: R
- Homepage:
- Size: 19 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-cell-cell-communication - IGAN - [R]- IGAN is a method for inferring cell-cell communication pathways represented by spatial gene associations based on spatial transcriptomic data. (Uncategorized / Uncategorized)
README
# IGAN
A method for Inferring cell-cell communication pathways represented by spatial gene associations based on spatial transcriptomic data
## Installation
IGAN can be installed in R by devtools:
``devtools::install_github('Zhu-JC/IGAN')``
## Tutorials
``library(IGAN2)``
``library(clusterProfiler)``
``library(org.Hs.eg.db)``
``library(org.Mm.eg.db)``
``library(ggplot2)``
``library(ggsankey)``
``library(networkD3)``
``library(KEGGREST)``
``library(tidyverse)``
``library(plotly)``
``library(magick)``
``library(CellChat)``
``library(patchwork)``
``library(NMF)``
``library(ggalluvial)``
``library(ComplexHeatmap)``
``library(gridExtra)``
__#loading input datas.__
__##spatial is coordinate information, a dataframe which rows represent spots and 2 cols represent coordinate.__
``spatial = read.csv('spatial.csv')``
__##ident is celltype information, a 1 column dataframe which rows represent spots.__
``ident = read.csv('ident.csv')``
__##M is gene expression matrix, a dataframe which cols represent genes and rows represent spots.__
``M = read.csv('M')``
__##gene is genes' name of the dataset, a 1 column dataframe which rows represent genes.__
``gene = read.csv('gene.csv')``
__##genelist1 is gene list selected in sending spots, a 1 column dataframe which rows represent genes.__
``genelist1 = read.csv('genelist1')``
__##genelist2 is gene list selected in receiving spots, a 1 column dataframe which rows represent genes.__
``genelist2 = read.csv('genelist2')``
``gene2 = toupper(gene[,1])``
``gene_list1 = toupper(gene_list1[,1])``
``gene_list2 = toupper(gene_list2[,1])``
``match_list1 = na.omit(match(gene_list1, gene2))``
``match_list2 = na.omit(match(gene_list2, gene2))``
__#Grouping spots pairs into groups.__
``celltype = as.data.frame(c('CAF','HSC','Kupffer','LSEC','T','hepatocyte 1','hepatocyte 2','hepatocyte 3','interferon response','monocyte_DC','vascular smooth mc','tumor I','tumor II'))``
``cells_group = cells_to_group(spatial, ident, 5000, celltype)``
__#Compute gene-gene associations in every single spots pair.__
``result = IGAN(match_list1, match_list2, gene, cells_group, M, 0.01)``
result is a list of n lists, n is the number of groups get in ``cells_to_group``. Each list in result containing m dataframes, m is the number of genes corresponding ``gene_list1``. In each dataframe, rows represent target genes' order which corresponding ``gene_list2`` and cols represent the order of cell pairs within the corresponding groups. 0 represents no association and 1 represents having association.
__#Loading the precomputed tumor data.__
load('tumor.Rdata')
__#Get the matrix contain every spots' CCC feature.__
``cccM = get_cccM(result, cells_group, spatial, gene, match_list1, match_list2)``
cccM is described in network analysis of IGAN part in our paper.
``Gene_corM = getGene_corM(result, cells_group, gene, match_list1, match_list2)``
__#Get the graph of CCC activity of every spot.__
``a = plot_cor(result, cells_group, spatial)``
``a + scale_color_distiller(palette = "Set1")``
__#Get the graph of SPP1's CCC activity of every spot.__
``a = plot_Genecor('SPP1', result, cells_group, spatial, match_list1, gene)``
``a + scale_color_distiller(palette = "Set1")``
``plot_ligand_target(result, spatial, cells_group, gene, match_list1, match_list2, 'Plg')``
__#Get the cellchat's network circle graph.__
``par(mfrow = c(1,1), xpd=TRUE)``
``plot_cellchat('SPP1',result,cells_group,gene,match_list1,ident)``
``dev.off()``
__#Get the sankey graph of ligand's GO-ligand-recepter-downstream pathway.__
``OrgDb = org.Mm.eg.db``
``sankey_graph = plot_sankey(result,cells_group,gene,match_list1,match_list2,0.05,5,OrgDb = OrgDb,send_cell = 'monocyte_DC',rec_cell = 'CAF',threshold = 10)``
``sankeyNetwork(Links = sankey_graph[[1]], Nodes =sankey_graph[[2]], Source = 'source',
Target = 'target', Value = 'value', NodeID = 'name',LinkGroup = 'color',
units = 'TWh', fontSize = 20, nodeWidth = 30, margin = 1, height = NULL,
sinksRight = FALSE)``
``dev.off()``
__#Get the graph of CCC pattern.__
``pattern_graph = plot_pattern(result,cells_group,gene,match_list1,match_list2,ident,'outgoing',4)``
``pushViewport(viewport(x = 0.1, y = 0.1, width = 0.2, height = 0.8, just = c("left", "bottom")))``
``grid.draw(pattern_graph[[1]])``
``popViewport()``
``pushViewport(viewport(x = 0.6, y = 0.1, width = 0.2, height = 0.8, just = c("left", "bottom")))``
``grid.draw(pattern_graph[[2]])``
``popViewport()``
