單細(xì)胞| Celltalker搭建細(xì)胞互作通訊橋梁

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單細(xì)胞分析之細(xì)胞間通訊分析能夠幫助我們解讀細(xì)胞與細(xì)胞之間的相互作用。目前單細(xì)胞通訊分析的工具有很多，小編在這里主要介紹 celltalker。celltalker根據(jù) ligand 和receptor 的表達(dá)，細(xì)胞 cluster 以及樣本分組來(lái)評(píng)估cell-cell communication。

celltalker主要功能函數(shù)介紹

reshape_matrices()：Reshape matrices，重構(gòu)數(shù)據(jù)，為每個(gè)樣本分配表達(dá)矩陣

create_lig_rec_tib()：Create a tibble of consistently expressed ligands and receptors，為每個(gè)分組創(chuàng)建一致的配體-受體表達(dá)

putative_interactions()：Create a tibble of consistently expressed ligands and receptors，推斷 ligand-receptor pairs

unique_interactions()：Unique interactions between two groups，識(shí)別分組中唯一的互作

circos_plot()：Creates a circos plot from the list of ligands and receptors，繪圖

讀入單細(xì)胞數(shù)據(jù)

數(shù)據(jù)來(lái)源：來(lái)自 GSM4138110 的6個(gè)樣本：分別是：HNC_1_PBMC、HNC_2_PBMC、HNC_3_PBMC、HNC_1_TIL、HNC_2_TIL、HNC_3_TIL

sce.ob <- readRDS(“F:/single_seruat.Rds”)

DimPlot(sce, group.by = “cellType”)

# 設(shè)置分組

sce.ob$sample[grep(“^pbmc[0-9]”, sce.ob$sample)] <- “HNC_PBMC”

sce.ob$sample[grep(“^TIL[0-9]”, sce.ob$sample)] <- “HNC_TIL”

選擇配體和受體

注：ramilowski_pairs來(lái)源于軟件自帶的人的配體受體數(shù)據(jù)

ramilowski_pairs %>% head

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

# 提取表達(dá)的配體和受體

ligs <- as.character(unique(ramilowski_pairs$ligand))

recs <- as.character(unique(ramilowski_pairs$receptor))

ligs.present <- rownames(sce.ob)[rownames(sce.ob) %in% ligs]

recs.present <- rownames(sce.ob)[rownames(sce.ob) %in% recs]

genes.to.use <- union(ligs.present,recs.present)

分組間差異分析，得到差異的配體受體

Idents(sce.ob) <- “sample”

markers <- FindAllMarkers(sce.ob, assay=”RNA”, features=genes.to.use, only.pos=TRUE)

ligs.recs.use <- unique(markers$gene)

##保留差異表達(dá)的配體-受體列表

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] 758 3

準(zhǔn)備celltalker的輸入文件

expr.mat 基因表達(dá)數(shù)據(jù)

defined.clusters 細(xì)胞亞群信息

defined.groups樣本分組信息，例如：HSC_PBMC，HSC_TIL

defined.replicates 樣本信息，例如：pbmc1，pbmc2，pbmc3，TIL1，TIL2，TIL3

##準(zhǔn)備celltalker的輸入文件

expr.mat <- GetAssayData(sce.ob, slot=”counts”)

rownames(expr.mat)<-rownames(expr.mat)

defined.clusters <- sce.ob@meta.data$cellType

names(defined.clusters)<-colnames(sce.ob)

defined.groups <- sce.ob@meta.data$sample

names(defined.groups)<-colnames(sce.ob)

defined.replicates <- sce.ob@meta.data$orig.ident

names(defined.replicates)<-colnames(sce.ob)

reshape_matrices()構(gòu)建 celltalker 輸入數(shù)據(jù)

##重構(gòu)數(shù)據(jù)，為每個(gè)樣本分配相應(yīng)的表達(dá)矩陣

reshaped.matrices <- reshape_matrices(count.matrix = expr.mat,

clusters = defined.clusters,

groups = defined.groups,

replicates = defined.replicates,

ligands.and.receptors = interact.for)

reshaped.matrices

# A tibble: 2 x 2

group samples

1 HNC_PBMC

2 HNC_TIL

unnest(reshaped.matrices,cols=”samples”)

# A tibble: 6 x 3

group sample expr.matrices

1 HNC_PBMC pbmc1

2 HNC_PBMC pbmc2

3 HNC_PBMC pbmc3

4 HNC_TIL TIL1

5 HNC_TIL TIL2

6 HNC_TIL TIL3

names(pull(unnest(reshaped.matrices,cols=”samples”))[[1]])

[1] “B cells” “Monocytes” “NK cells” “T cells, CD4+”

create_lig_rec_tib()為每個(gè)分組創(chuàng)建一致的配體-受體表達(dá)

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

1 HNC_PBMC

2 HNC_TIL

unnest(consistent.lig.recs[1,2], cols = “lig.rec.exp”)

# A tibble: 4 x 2

cluster.id ligands.and.receptors

1 B cells

2 Monocytes

3 NK cells

4 T cells, CD4+

putative_interactions（）在給定的分組的cluster之間推斷表達(dá)的ligand-receptor之間的相互作用

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)

put.int

# A tibble: 2 x 2

group lig_rec_list

1 HNC_PBMC

2 HNC_TIL

識(shí)別唯一的interactions

#Identify unique ligand/receptor interactions present in each sample

unique.ints <- unique_interactions(put.int, group1 = “HNC_PBMC”, group2 = “HNC_TIL”, interact.for)

unique.ints

# A tibble: 3 x 2

comparison ligands.and.receptors

1 unique1v2

2 unique2v1

3 common

circos_plot()可視化展示

#Get data to plot circos for HNC_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)

#Get data to plot circos for HNC_TIL

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)

Celltalker搭建細(xì)胞互作通訊橋梁，為細(xì)胞互作研究提供了基礎(chǔ)和方法，基于基因表達(dá)數(shù)據(jù)和配體-受體數(shù)據(jù)庫(kù)信息可以評(píng)估細(xì)胞之間的交流。小編今天為你簡(jiǎn)答介紹單細(xì)胞數(shù)據(jù)分析之Celltalker應(yīng)用，后期會(huì)為大家呈現(xiàn)更多的單細(xì)胞系列課程學(xué)習(xí)，歡迎大家一起學(xué)習(xí)更多關(guān)于單細(xì)胞數(shù)據(jù)處理方法和高階分析內(nèi)容。