hi 各位颜曾，今天周五了纠拔，要開會的關(guān)系，沒有那么多時間準(zhǔn)備分享文獻了泛豪，但是我很想和大家分享這個方法稠诲，類似于多組學(xué)分析侦鹏，配受體與TF分析聯(lián)合起來的網(wǎng)絡(luò)分析結(jié)果，非常有價值（配體臀叙，受體种柑，靶基因，TF多層網(wǎng)絡(luò)結(jié)構(gòu)匹耕，非常贊）聚请，我們今天就不分享文獻了，多關(guān)注一下代碼,文章在Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19, 2020年底發(fā)表于Briefings in Bioinformatics稳其，影響因子9分驶赏。

Introduction

scMLnet是一個R軟件包，用于根據(jù)單細(xì)胞RNA-seq表達數(shù)據(jù)構(gòu)建細(xì)胞間/細(xì)胞內(nèi)多層信號交換網(wǎng)絡(luò)既鞠。 scMLnet通過基于細(xì)胞類型特異性基因表達煤傍，先驗網(wǎng)絡(luò)信息和統(tǒng)計推斷，整合細(xì)胞間途徑（配體-受體相互作用）和細(xì)胞內(nèi)子網(wǎng)絡(luò)（受體-TF途徑以及TF-靶基因相互作用）嘱蛋，構(gòu)建多層網(wǎng)絡(luò)蚯姆。 scMLnet還可以可視化中央細(xì)胞和鄰近細(xì)胞之間構(gòu)建的細(xì)胞間/細(xì)胞內(nèi)信號通路。

The main steps of the scMLnet algorithm include:

(1) Step1 Constructing Ligand-Receptor subnetwork:

通過獲取高度表達的基因（HEG）洒敏，從scRNA-Seq數(shù)據(jù)和配體受體數(shù)據(jù)庫定義潛在的配體受體子網(wǎng)龄恋。類型A（發(fā)送者細(xì)胞）中的HEG被視為潛在的配體，類型B（受體細(xì)胞）中的HEG被視為潛在的受體凶伙。

(2) Step2 Constructing TF-Target gene subnetwork

通過獲取HEG和Fisher的精確檢驗（這個可以參考文章Fisher 精確檢驗與卡方檢驗(10X單細(xì)胞和10X空間轉(zhuǎn)錄組的基礎(chǔ)知識)）郭毕，從scRNA-Seq數(shù)據(jù)和TF-Target基因數(shù)據(jù)庫中定義了有效的TF-Target基因子網(wǎng)絡(luò)。 B型HEG被認(rèn)為是潛在的靶基因函荣。可以從TF-Target基因子網(wǎng)絡(luò)中推斷出激活的TF显押。

(3) Step3 Constructing Receptor-TF subnetwork

通過Fisher的精確測試從激活的TF和Receptor-TF數(shù)據(jù)庫中定義了有效的Receptor-TF子網(wǎng)。激活的受體可以從TF-Target基因子網(wǎng)絡(luò)中推斷出來傻挂。

（4）Step4 constructing multi-layer signaling network

通過在受體與TF乘碑，TF和靶基因之間進行相關(guān)分析，然后根據(jù)共同的受體和TF重疊配體-受體金拒，TF-靶基因兽肤，受體-TF子網(wǎng)來定義多層信號網(wǎng)絡(luò)。（相當(dāng)贊）殖蚕。

圖片.png

輸入和輸出

For this tutorial, we will be using scMLnet to construct the multi-layer signaling network between B cells and Secretory cells from scRNA-Seq data of BALF in COVID-19 patients. The expression matrix and annotation of clstuers can be found in the /example folder (or be downloaded from Zenodo) and the prior information about interactions in the /database folder.

library(Seurat)
library(Matrix)
library(parallel)
library(scMLnet)

加載包轿衔，Seurat包用于normalizing the raw scRNA-Seq data，the Matrix package is used for transformation between matrix and sparse matrix and the parallel package is used for parallel computation of t.test for screening（后面兩步好像Seurat本身就可以完成睦疫，Seurat依賴于后兩個包，所以有這個功能）鞭呕。
輸入的是We then read a raw scRNA-Seq data with rows as genes (gene symbols) and columns as cells and the gene expression matrix is required as a sparse matrix（原始矩陣）蛤育。

 # import sample data
    GCMat <- readRDS("./example/data.Rdata")
    GCMat<- as(GCMat,"dgCMatrix")
    
    # import sample annotation
    BarCluFile <- "./example/barcodetype.txt"
    BarCluTable <- read.table(BarCluFile,sep = "\t",header = TRUE,stringsAsFactors = FALSE)

我們這里以配體為B細(xì)胞，受體為Secretory為例

types <- unique(BarCluTable$Cluster)
LigClu <- "B cells"       #types[4]
RecClu <- "Secretory"     #types[8]

參數(shù)設(shè)置

默認(rèn)參數(shù)設(shè)置如下。用戶可以提供自己的數(shù)據(jù)庫瓦糕，其中必須包括三個列：分子A底洗，分子B和密鑰（用下劃線將A與B連接起來）。分子A和B需要具有清晰的身份（即配體咕娄，受體亥揖，TF和靶標(biāo)基因），并且它們之間應(yīng)存在相互作用（這里我們用本身數(shù)據(jù)庫的就好）圣勒。
根據(jù)sender細(xì)胞和receiver細(xì)胞之間基因表達百分比的差異（受pct參數(shù)影響）和比率（受logfc參數(shù)影響）费变，我們首先在兩種細(xì)胞中分別定義特定的高表達基因。（這里差異基因的選擇我們需要注意）圣贸。
sender細(xì)胞中高表達的基因被認(rèn)為是潛在的配體挚歧，而receiver細(xì)胞中高表達的基因被認(rèn)為是潛在的受體和靶基因。我們通過在數(shù)據(jù)庫中搜索配體-受體對來篩選潛在的配體-受體相互作用吁峻。然后滑负，我們通過Fisher精確檢驗（受pval參數(shù)限制）篩選潛在的受體-TF，TF-Target基因相互作用用含。（構(gòu)建這個網(wǎng)絡(luò)信息矮慕，很重要）。

 pval <- 0.05
 logfc <- 0.15
 LigRecLib <- "./database/LigRec.txt"
 TFTarLib <- "./database/TFTargetGene.txt"
 RecTFLib <- "./database/RecTF.txt"

Construction of Multi-layer Signaling Networks

獲得高表達基因的運行時間取決于兩種類型細(xì)胞中基因表達的t檢驗啄骇。并行執(zhí)行t檢驗可以提高scMLnet的性能（前提：已安裝并行軟件包）凡傅。

netList <- RunMLnet(GCMat, BarCluFile, RecClu, LigClu, 
                        pval, logfc, 
                        LigRecLib, TFTarLib, RecTFLib)
###這個函數(shù)沒有其他的參數(shù)

Save and Visualization of Multi-layer Signaling Networks

輸出netList是由連接每個上游層和下游層（即Ligand_Receptor，Receptor_TF和TF_Gene子網(wǎng)絡(luò)）的基因?qū)M成的列表肠缔。信令子網(wǎng)作為數(shù)據(jù)幀對象返回夏跷，其結(jié)構(gòu)與示例數(shù)據(jù)庫相同。

workdir <- "sample"
DrawMLnet(netList,LigClu,RecClu,workdir,PyHome,plotMLnet = T)

圖片.png

Construction of Multi-cellular Multi-layer Signaling Networks

import data

GCMat <- readRDS("./example/data.Rdata")
GCMat<- as(GCMat,"dgCMatrix")

# import annotation
BarCluFile <- "./example/barcodetype.txt"
BarCluTable <- read.table(BarCluFile,sep = "\t",header = TRUE,stringsAsFactors = FALSE)

## get LigClu
LigClus <- unique(BarCluTable$Cluster)
LigClus <- LigClus[-grep("Secretory|Doublets",LigClus)]

## creat MLnet
netList <- list()
for(ligclu in LigClus){
  
  #sender cell and receiver cell
  LigClu <- ligclu
  RecClu <- "Secretory"
  
  name <- paste(strsplit(LigClu,split = "\\W")[[1]][1],RecClu,sep = "_")
  
  #main
  netList[[name]] <- RunMLnet(GCMat,BarCluFile,RecClu,LigClu)
  
}

## save output and plot MLnet
workdir <- "multi-cellular"
for (name in names(netList)) {
  
  #scMLnet output
  MLnetList <- netList[[name]]
  print(paste0(name,":"))
  
  #sender cell and receiver cell
  LigClu <- strsplit(name,"_")[[1]][1]
  RecClu <- strsplit(name,"_")[[1]][2]
  
  #main
  PyHome <- "D:/Miniconda3/envs/R36/python.exe" #for Window
  DrawMLnet(MLnetList,LigClu,RecClu,workdir,PyHome,plotMLnet = T)
  
}

圖片.png

分析方法相當(dāng)不錯

生活很好明未，有你更好

10X單細(xì)胞通訊分析之scMLnet（配受體與TF再菊，差異基因（靶基因）網(wǎng)絡(luò)通訊分析）

10X單細(xì)胞通訊分析之scMLnet（配受體與TF，差異基因（靶基因）網(wǎng)絡(luò)通訊分析）

Introduction

The main steps of the scMLnet algorithm include:

(1) Step1 Constructing Ligand-Receptor subnetwork:

(2) Step2 Constructing TF-Target gene subnetwork

(3) Step3 Constructing Receptor-TF subnetwork

（4）Step4 constructing multi-layer signaling network

輸入和輸出

參數(shù)設(shè)置

Construction of Multi-layer Signaling Networks

Save and Visualization of Multi-layer Signaling Networks

Construction of Multi-cellular Multi-layer Signaling Networks

import data