NGS系列文章包括NGS基礎(chǔ)淹仑、轉(zhuǎn)錄組分析 （Nature重磅綜述|關(guān)于RNA-seq你想知道的全在這）、ChIP-seq分析 （ChIP-seq基本分析流程）肺孵、單細(xì)胞測(cè)序分析 (重磅綜述：三萬字長(zhǎng)文讀懂單細(xì)胞RNA測(cè)序分析的最佳實(shí)踐教程 （原理匀借、代碼和評(píng)述）)、DNA甲基化分析平窘、重測(cè)序分析吓肋、GEO數(shù)據(jù)挖掘（典型醫(yī)學(xué)設(shè)計(jì)實(shí)驗(yàn)GEO數(shù)據(jù)分析 (step-by-step) - Limma差異分析、火山圖瑰艘、功能富集）等內(nèi)容是鬼。
單細(xì)胞轉(zhuǎn)錄組測(cè)序發(fā)展至今，我們發(fā)現(xiàn)許多文章的最后一部分都會(huì)落到配受體結(jié)合紫新，可是如何挑配受體均蜜，哪些基因可能是配受體，我一臉懵逼芒率。囤耳。。
于是偶芍，我一不小心發(fā)現(xiàn)了celltalker(https://arc85.github.io/celltalker/articles/celltalker.html#vignette-overview)充择，大家可以嘗試一下哦，嘻嘻腋寨，廢話不多說聪铺。
Introduction
對(duì)單細(xì)胞RNAseq數(shù)據(jù)可能進(jìn)行的多種分析之一是評(píng)估細(xì)胞間的交流 (cell-cell communication)化焕。celltalker通過尋找細(xì)胞群內(nèi)和細(xì)胞群之間已知的配體和受體對(duì)的表達(dá)來評(píng)估細(xì)胞之間的交流萄窜。 我們采用的配受體數(shù)據(jù)庫來自Ramilowski等人于2015年在Nature communication上發(fā)表的A draft network of ligand-receptor-mediated multicellular signalling in human描述的一組配體和受體。我們建議使用此數(shù)據(jù)集作為起點(diǎn)，并整理自己的已知配體和受體列表查刻。另外Tormo 2018年發(fā)表的Nature文章Single-cell reconstruction of the early maternal-fetal interface in humans擴(kuò)展了受體和配體對(duì)键兜，也會(huì)應(yīng)用于cellTalker的更新版中。
為了獲得可靠的結(jié)果穗泵，我們要求每個(gè)組中都有多個(gè)重復(fù)樣品普气，并且只對(duì)不同組間一致性表達(dá)的配體和受體感興趣（而僅在單個(gè)重復(fù)中發(fā)現(xiàn)的互作可信度低）。我們通過評(píng)估每組中各個(gè)重復(fù)的表達(dá)矩陣并僅對(duì)滿足一定閾值（這個(gè)閾值隨意性也比較強(qiáng)）的相互作用進(jìn)行提取佃延。
配體和受體相互作用的差異至少在三種方面具有生物學(xué)意義：
- 在一組細(xì)胞中獨(dú)特地存在现诀；
- 各個(gè)cluster間配體或受體的互作差異；
- 參與組間配體和受體相互作用的細(xì)胞網(wǎng)絡(luò)的差異履肃。
我們提供了評(píng)估每種潛在生物學(xué)差異的方法仔沿，并提供了具體示例。
在這個(gè)vignette中尺棋，我們展示了cellTalker在評(píng)估健康捐獻(xiàn)者外周血（N = 2）和扁桃體（N = 3）中鑒定配體/受體相互作用的基本應(yīng)用封锉。該數(shù)據(jù)可從我們最近發(fā)布的數(shù)據(jù)集GSE139324中獲得 (Cillo et al, Immunity 2020)。
Vignette overview
展示Celltalker應(yīng)用于10X Genomics數(shù)據(jù)的的標(biāo)準(zhǔn)用法膘螟。具體分為下面幾步：
- 使用標(biāo)準(zhǔn)的Seurat工作流程（v.3.1.1）對(duì)數(shù)據(jù)進(jìn)行聚類;
- 使用Celltalker建立樣品組中穩(wěn)定表達(dá)的配體和受體的列表;
- 確定配體/受體相互作用;
- 評(píng)估組之間特異表達(dá)的配體/受體對(duì);
- 識(shí)別和可視化組特異的配體/受體對(duì);
Installation
library(devtools)
install_github("arc85/celltalker")
library(celltalker)
Clustering data with Seurat
使用Seurat進(jìn)行標(biāo)準(zhǔn)的聚類分析和免疫譜系識(shí)別（假設(shè)已從GEO下載了raw matrix）成福。（重磅綜述：三萬字長(zhǎng)文讀懂單細(xì)胞RNA測(cè)序分析的最佳實(shí)踐教程 （原理、代碼和評(píng)述））
suppressMessages({
library(Seurat)
library(celltalker)
})
# 設(shè)置可重復(fù)性的種子數(shù)字
set.seed(02221989)
# 讀取raw data
# path.to.working：自行修改路徑
path.to.working <- “”
setwd(paste(path.to.working,"/data_matrices/",sep=""))
data.paths <- list.files()
# GRCh38 根據(jù)需要調(diào)整為其它基因組版本
specific.paths <- paste(path.to.working,"data_matrices",data.paths,"GRCh38",sep="/")
setwd(path.to.working)
raw.data <- Read10X(specific.paths)
# 設(shè)置metadata （這一部分是這個(gè)數(shù)據(jù)特異的荆残，實(shí)際還是自己整理個(gè)metadata文件更為方便）
sample.data <- data.frame(matrix(data=NA,nrow=ncol(raw.data),ncol=2))
rownames(sample.data) <- colnames(raw.data)
colnames(sample.data) <- c("sample.id","sample.type")
sample.data[grep("^[A-z]",rownames(sample.data)),"sample.id"] <- "pbmc_1"
sample.data[grep("^2",rownames(sample.data)),"sample.id"] <- "tonsil_1"
sample.data[grep("^3",rownames(sample.data)),"sample.id"] <- "pbmc_2"
sample.data[grep("^4",rownames(sample.data)),"sample.id"] <- "tonsil_2"
sample.data[grep("^5",rownames(sample.data)),"sample.id"] <- "pbmc_3"
sample.data[grep("^6",rownames(sample.data)),"sample.id"] <- "tonsil_3"
sample.data[,"sample.type"] <- sapply(strsplit(sample.data$sample.id,split="_"),function(x) x[1])
## 使用原始數(shù)據(jù)創(chuàng)建Seurat對(duì)象奴艾，并添加sample-specific metadata
ser.obj <- CreateSeuratObject(counts=raw.data,meta.data=sample.data)
Seurat三部曲
#標(biāo)準(zhǔn)Seurat工作流程
ser.obj <- NormalizeData(ser.obj)
ser.obj <- FindVariableFeatures(ser.obj)
ser.obj <- ScaleData(ser.obj)
ser.obj <- RunPCA(ser.obj)
獲得對(duì)各個(gè)主成分貢獻(xiàn)比較大的基因 (用了這么多年的PCA可視化竟然是錯(cuò)的！Ｄ谒埂握侧！)
## PC_ 1
## Positive: S100A6, IL32, S100A4, ANXA1, VIM, FTL, TRBC1, SRGN, S100A9, S100A8
## TYROBP, LYZ, CTSW, XIST, NEAT1, VCAN, S100A12, FCER1G, S100A11, FCN1
## PLAC8, ID2, CCL5, NKG7, CST3, CSTA, ZFP36, IL1B, MT2A, KLRB1
## Negative: RGS13, KIAA0101, NUSAP1, AURKB, MKI67, BIRC5, TYMS, TOP2A, TK1, CDKN3
## UBE2C, PTTG1, CDK1, STMN1, CCNB2, GTSE1, BIK, RRM2, TCL1A, SHCBP1
## CDCA3, CDC20, TPX2, LRMP, CCNA2, MND1, CCNB1, PBK, ZWINT, RMI2
## PC_ 2
## Positive: CST3, LYZ, FCN1, CSTA, S100A9, S100A8, TYROBP, LST1, FGL2, VCAN
## S100A12, SERPINA1, MNDA, FCER1G, CLEC7A, MS4A6A, CD14, CFD, IL1B, TYMP
## LGALS1, RP11-1143G9.4, AIF1, CTSS, NAMPT, CFP, TNFSF13B, CSF3R, MPEG1, TMEM176B
## Negative: IL32, NPM1, CD69, TRBC1, ISG20, ITM2A, IGKC, IGHA1, HSP90AB1, DDIT4
## HIST1H4C, PSIP1, AQP3, MYC, PIM2, HMGN1, PASK, NUCB2, HSPA1B, HSPB1
## CD79A, SUSD3, KLRB1, SYNE2, CHI3L2, IGHG3, IGLC2, FKBP11, IGHG1, SH2D1A
## PC_ 3
## Positive: IL32, NKG7, CTSW, TRBC1, GZMA, CST7, GNLY, MKI67, ANXA1, TOP2A
## CCL5, PRF1, BIRC5, S100A4, KLRB1, CCNA2, AURKB, CENPF, GTSE1, CDKN3
## KLRD1, UBE2C, CDK1, TYMS, TPX2, RRM2, ID2, S100A6, FGFBP2, CDC20
## Negative: HLA-DRA, HLA-DQA1, HLA-DQB1, CD79A, HLA-DRB1, MS4A1, CD74, HLA-DPA1, HLA-DPB1, CD79B
## HLA-DMA, HLA-DMB, BANK1, VPREB3, IGKC, HLA-DRB5, MEF2C, CD22, IRF8, CD19
## SMIM14, FCRLA, HLA-DOB, CD24, CD40, FCER2, BLK, HLA-DQA2, IGHD, CTSH
## PC_ 4
## Positive: TOP2A, UBE2C, MKI67, GTSE1, CENPF, AURKB, PLK1, CCNA2, CDK1, CDCA8
## HMMR, CDCA3, CDC20, TPX2, CDKN3, DLGAP5, CENPE, BIRC5, CCNB2, CENPA
## KIF2C, CKAP2L, PBK, NUSAP1, KIFC1, AURKA, SPC25, NUF2, KIF23, ASPM
## Negative: NKG7, GNLY, CST7, GZMB, GZMA, PRF1, KLRD1, FGFBP2, CCL5, KLRF1
## HOPX, CTSW, GZMH, TRDC, FCGR3A, SPON2, CLIC3, MATK, ADGRG1, S1PR5
## CCL4, CMC1, XCL2, PFN1, CD160, FCRL6, IL2RB, TRGC1, KLRC1, C12orf75
## PC_ 5
## Positive: ICA1, PDCD1, TBC1D4, ITM2A, ICOS, MAF, TOX2, IL32, TNFRSF4, PASK
## PKM, SMCO4, ACTG1, CORO1B, CTLA4, NPM1, TRBC1, PCAT29, TIGIT, AC133644.2
## TOX, ANP32B, ENO1, GBP2, COTL1, GAPDH, SUSD3, PIM2, AQP3, SERPINA9
## Negative: NKG7, GNLY, KLRD1, FGFBP2, GZMB, GZMA, KLRF1, CCL5, PRF1, TRDC
## GZMH, CST7, CTSW, BANK1, MATK, PLK1, HMMR, HLA-DPB1, CENPA, CLIC3
## GTSE1, CENPE, CCL4, SPON2, PDLIM1, HLA-DPA1, CDCA8, DLGAP5, TPX2, IGHD
拐點(diǎn)法尋找top可用的主成分用于后續(xù)分析 (具體選擇方式見：（重磅綜述：三萬字長(zhǎng)文讀懂單細(xì)胞RNA測(cè)序分析的最佳實(shí)踐教程 （原理、代碼和評(píng)述））)
ElbowPlot(ser.obj)
降維可視化
＃我們選擇top 15 PCs用于后續(xù)分析
ser.obj <- RunUMAP(ser.obj,reduction="pca", dims=1:15)
## Computing nearest neighbor graph
ser.obj <- FindNeighbors(ser.obj,reduction="pca",dims=1:15)
## Computing SNN
ser.obj <- FindClusters(ser.obj,resolution=0.5)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 15524
## Number of edges: 543084
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9185
## Number of communities: 17
## Elapsed time: 2 seconds
畫圖看一看嘿期！ggplot2高效實(shí)用指南 (可視化腳本品擎、工具、套路备徐、配色)
p1 <- DimPlot(ser.obj,reduction="umap",group.by="sample.id")
p2 <- DimPlot(ser.obj,reduction="umap",group.by="sample.type")
p3 <- DimPlot(ser.obj,reduction="umap",group.by="RNA_snn_res.0.5",label=T) + NoLegend()
cowplot::plot_grid(p1,p2,p3)
讓我們看看部分基因的表達(dá)情況萄传！
FeaturePlot(ser.obj,reduction="umap",features=c("CD3D","CD8A","CD4","CD14","MS4A1","FCGR3A","IL3RA"))
命名細(xì)胞簇并移除紅細(xì)胞 (cellassign：用于腫瘤微環(huán)境分析的單細(xì)胞注釋工具（9月Nature）)
# 為細(xì)胞類型增加metadata
cell.types <- vector("logical",length=ncol(ser.obj))
names(cell.types) <- colnames(ser.obj)
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="0"] <- "cd4.tconv"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="1"] <- "cd4.tconv"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="2"] <- "b.cells"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="3"] <- "b.cells"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="4"] <- "cd14.monocytes"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="5"] <- "cd8.cells"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="6"] <- "cd4.tconv"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="7"] <- "cd4.tconv"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="8"] <- "b.cells"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="9"] <- "b.cells"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="10"] <- "nk.cells"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="11"] <- "cd8.cells"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="12"] <- "plasma.cells"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="13"] <- "cd14.monocytes"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="14"] <- "cd16.monocytes"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="15"] <- "pdc"
cell.types[ser.obj@meta.data$RNA_snn_res.0.5=="16"] <- "RBCs"
ser.obj[["cell.types"]] <- cell.types
#去除紅細(xì)胞
rbc.cell.names <- names(cell.types)[ser.obj@meta.data$RNA_snn_res.0.5=="16"]
ser.obj <- ser.obj[,!colnames(ser.obj) %in% rbc.cell.names]
Consistently expressed ligands and receptors
現(xiàn)在，我們已經(jīng)在數(shù)據(jù)中識(shí)別并命名了cluster蜜猾，我們將繼續(xù)進(jìn)行celltalker分析秀菱。隨該軟件包一起提供的有一個(gè)ramilowski_pairs，它是一個(gè)由配體蹭睡、受體和推測(cè)的配體受體對(duì)組成的data.frame衍菱。
首先，根據(jù)通用型配體和受體從我們的數(shù)據(jù)集中選出對(duì)應(yīng)的基因肩豁，然后進(jìn)行差異基因分析脊串，只保留在樣品組之間差異表達(dá)的配體受體辫呻。
然后，我們將為每個(gè)重復(fù)樣本創(chuàng)建單獨(dú)的數(shù)據(jù)矩陣琼锋，存儲(chǔ)為tibble格式放闺，以便于使用tidyverse進(jìn)行后續(xù)處理。
(生信寶典注：如果我們自己有受體配體對(duì)缕坎，也可以整理成這樣一個(gè)三列的格式怖侦，導(dǎo)入進(jìn)來，替換掉數(shù)據(jù)包原有的配體受體對(duì)信息谜叹，實(shí)現(xiàn)更加定制的分析匾寝。)
#查閱 ramilowski_pairs 數(shù)據(jù)集
head(ramilowski_pairs)
## ligand receptor pair
## 1 A2M LRP1 A2M_LRP1
## 2 AANAT MTNR1A AANAT_MTNR1A
## 3 AANAT MTNR1B AANAT_MTNR1B
## 4 ACE AGTR2 ACE_AGTR2
## 5 ACE BDKRB2 ACE_BDKRB2
## 6 ADAM10 AXL ADAM10_AXL
dim(ramilowski_pairs)
## [1] 2557 3
#該數(shù)據(jù)集中有2,557個(gè)特異的配體/受體對(duì)
#鑒定差異表達(dá)的配體和受體
#在我們的數(shù)據(jù)集中識(shí)別配體和受體
ligs <- as.character(unique(ramilowski_pairs$ligand))
recs <- as.character(unique(ramilowski_pairs$receptor))
ligs.present <- rownames(ser.obj)[rownames(ser.obj) %in% ligs]
recs.present <- rownames(ser.obj)[rownames(ser.obj) %in% recs]
genes.to.use <- union(ligs.present,recs.present) # union用于合并子集
# 使用FindAllMarkers區(qū)分組之間差異表達(dá)的配體和受體
Idents(ser.obj) <- "sample.type"
markers <- FindAllMarkers(ser.obj, assay="RNA",features=genes.to.use,only.pos=TRUE)
ligs.recs.use <- unique(markers$gene)
length(ligs.recs.use)
## [1] 61
#產(chǎn)生61個(gè)配體和受體以進(jìn)行評(píng)估
#過濾ramilowski配受對(duì)
interactions.forward1 <- ramilowski_pairs[as.character(ramilowski_pairs$ligand) %in% ligs.recs.use,]
interactions.forward2 <- ramilowski_pairs[as.character(ramilowski_pairs$receptor) %in% ligs.recs.use,]
interact.for <- rbind(interactions.forward1, interactions.forward2)
dim(interact.for)
## [1] 241 3
生成celltalker的輸入數(shù)據(jù)
#產(chǎn)生241個(gè)配體和受體相互作用
#Create data for celltalker
expr.mat <- GetAssayData(ser.obj,slot="counts")
defined.clusters <- ser.obj@meta.data$cell.types
defined.groups <- ser.obj@meta.data$sample.type
defined.replicates <- ser.obj@meta.data$sample.id
reshaped.matrices <- reshape_matrices(count.matrix=expr.mat,clusters=defined.clusters,groups=defined.groups,replicates=defined.replicates,ligands.and.receptors=interact.for)
#查看tibble的層次結(jié)構(gòu)
reshaped.matrices
## # A tibble: 2 x 2
## group samples
## <chr> <list>
## 1 pbmc <tibble [3 × 2]>
## 2 tonsil <tibble [3 × 2]>
數(shù)據(jù)展開為單個(gè)樣品展示
unnest(reshaped.matrices,cols="samples")
## # A tibble: 6 x 3
## group sample expr.matrices
## <chr> <chr> <list>
## 1 pbmc pbmc_1 <named list [8]>
## 2 pbmc pbmc_2 <named list [8]>
## 3 pbmc pbmc_3 <named list [8]>
## 4 tonsil tonsil_1 <named list [8]>
## 5 tonsil tonsil_2 <named list [8]>
## 6 tonsil tonsil_3 <named list [8]>
names(pull(unnest(reshaped.matrices,cols="samples"))[[1]])
## [1] "b.cells" "cd14.monocytes" "cd16.monocytes" "cd4.tconv"
## [5] "cd8.cells" "nk.cells" "pdc" "plasma.cells"
在此初始步驟中，我們要做的是將我們的整體表達(dá)矩陣中給每個(gè)樣本中分離出單獨(dú)的表達(dá)矩陣荷腊。
接下來旗吁，使用create_lig_rec_tib函數(shù)為每個(gè)組創(chuàng)建一組一致表達(dá)的配體和受體。
# cells.reqd=10：每個(gè)cluster中至少有10個(gè)細(xì)胞表達(dá)了配體/受體
# freq.pos.reqd=0.5：至少有50％重復(fù)個(gè)體中表達(dá)的配體/受體
consistent.lig.recs <- create_lig_rec_tib(exp.tib=reshaped.matrices,clusters=defined.clusters,groups=defined.groups,replicates=defined.replicates,cells.reqd=10,freq.pos.reqd=0.5,ligands.and.receptors=interact.for)
consistent.lig.recs
## # A tibble: 2 x 2
## group lig.rec.exp
## <chr> <list>
## 1 pbmc <tibble [8 × 2]>
## 2 tonsil <tibble [8 × 2]>
unnest(consistent.lig.recs[1,2],cols="lig.rec.exp")
## # A tibble: 8 x 2
## cluster.id ligands.and.receptors
## <chr> <named list>
## 1 b.cells <named list [2]>
## 2 cd14.monocytes <named list [2]>
## 3 cd16.monocytes <named list [2]>
## 4 cd4.tconv <named list [2]>
## 5 cd8.cells <named list [2]>
## 6 nk.cells <named list [2]>
## 7 pdc <named list [2]>
## 8 plasma.cells <named list [2]>
pull(unnest(consistent.lig.recs[1,2],cols="lig.rec.exp")[1,2])[[1]]
根據(jù)上面指定的標(biāo)準(zhǔn)停局，我們現(xiàn)在已經(jīng)從每個(gè)實(shí)驗(yàn)組的每個(gè)cluster中獲得了一致表達(dá)的配體和受體的列表很钓。
## $ligands
## [1] "S100A9" "S100A8" "IL1B" "FN1" "BTLA" "SPON2"
## [7] "LRPAP1" "VCAN" "CD14" "LY86" "HLA-G" "HLA-A"
## [13] "HLA-E" "HLA-B" "LTB" "HSPA1A" "CD24" "NAMPT"
## [19] "TIMP1" "CD40LG" "ADAM28" "PNOC" "IL7" "ANXA1"
## [25] "SEMA4D" "VIM" "PSAP" "LYZ" "SELPLG" "HMGB1"
## [31] "TNFSF13B" "GZMB" "CALM1" "SERPINA1" "HSP90AA1" "B2M"
## [37] "PKM" "IL16" "CCL5" "CCL3" "ICAM2" "CD70"
## [43] "ICAM1" "ICAM3" "TGFB1" "FLT3LG" "APP"
##
## $receptors
## [1] "CSF3R" "TGFBR3" "KCNA3" "CD53" "CD1A" "CD247"
## [7] "SELL" "CXCR4" "ITGA4" "GRM7" "TGFBR2" "CCR5"
## [13] "TFRC" "TLR1" "IL7R" "CD180" "ADRB2" "CD74"
## [19] "HMMR" "HLA-F" "KCNQ5" "IGF2R" "CCR6" "CD36"
## [25] "CXCR3" "PGRMC1" "CD72" "TGFBR1" "ABCA1" "IFITM1"
## [31] "CD81" "KCNQ1" "CD44" "CD82" "IL10RA" "CD3D"
## [37] "CD3G" "CXCR5" "SORL1" "APLP2" "ITGB1" "FAS"
## [43] "CD27" "CD4" "KLRG1" "CLEC2B" "KLRD1" "KLRC1"
## [49] "PDE1B" "CD63" "TNFRSF17" "CD19" "ITGAL" "ITGAM"
## [55] "TNFRSF13B" "ERBB2" "PTPRA" "CD40" "OPRL1" "INSR"
## [61] "TYROBP" "CD79A" "KCNN4" "FPR2" "LILRB2" "LILRB1"
## [67] "KIR2DL3" "KIR2DL1" "KIR3DL2" "TNFRSF13C" "CELSR1" "ITGB2"
Determine putative ligand/receptor pairs
獲得穩(wěn)定表達(dá)的受體-配體后，鑒定給定樣品組細(xì)胞簇內(nèi)和細(xì)胞簇間可能存在的互作 (基于ramilowski_pairs$pair數(shù)據(jù))董栽。獲得的Tibble數(shù)據(jù) (put.int)中包含樣本組和每個(gè)組的配體/受體對(duì)列表码倦，以及參與配體/受體相互作用的cluster。
# freq.group.in.cluster: 只對(duì)包含細(xì)胞數(shù)大于總細(xì)胞數(shù)5%的簇進(jìn)行互作分析
put.int <- putative_interactions(ligand.receptor.tibble=consistent.lig.recs,clusters=defined.clusters,groups=defined.groups,freq.group.in.cluster=0.05,ligands.and.receptors=interact.for)
## Warning: `cols` is now required.
## Please use `cols = c(lig.rec.exp)`
## Warning: `cols` is now required.
## Please use `cols = c(lig.rec.exp)`
Identifying and visualizing unique ligand/receptor pairs in a group
現(xiàn)在我們有了配體/受體相互作用的列表锭碳，我們可以使用unique_interactions函數(shù)鑒定組特異的互作袁稽，并使用circos_plot函數(shù)可視化組之間的差異。
#Identify unique ligand/receptor interactions present in each sample
unique.ints <- unique_interactions(put.int,group1="pbmc",group2="tonsil",interact.for)
#Get data to plot circos for PBMC
pbmc.to.plot <- pull(unique.ints[1,2])[[1]]
for.circos.pbmc <- pull(put.int[1,2])[[1]][pbmc.to.plot]
circos_plot(interactions=for.circos.pbmc,clusters=defined.clusters)
PBMC組特有的受體-配體互作
從以上圖中我們可以看出研究人員用黃色表示配體基因擒抛，綠色表示受體基因推汽，然后將可以相互配對(duì)的基因連在一起構(gòu)成簇之間的互作關(guān)系。
Tonsil組特有的受體-配體互作
#Get data to plot circos for tonsil
tonsil.to.plot <- pull(unique.ints[2,2])[[1]]
for.circos.tonsil <- pull(put.int[2,2])[[1]][tonsil.to.plot]
circos_plot(interactions=for.circos.tonsil,clusters=defined.clusters)
Tonsil組特有的受體-配體互作
- CIRCOS圈圖繪制 - circos安裝
- CIRCOS圈圖繪制 - 最簡(jiǎn)單繪圖和解釋
- CIRCOS圈圖繪制 - 染色體信息展示和調(diào)整
- CIRCOS增加熱圖歧沪、點(diǎn)圖歹撒、線圖和區(qū)塊屬性
作者：May（協(xié)和醫(yī)院）
編輯：生信寶典
