CommPath（二）：鉴定关键细胞互作信号介导的功能通路

前言

Immugent在前一期推文：CommPath（一）：鉴定特定细胞的互作关系中，已经介绍了如何利用CommPath包鉴定差异的信号通路，特别是找出自己感兴趣细胞与其它细胞之间的互作关系。那么在本期推文中，Immugent将会继续介绍CommPath包不同于其它细胞通讯分析软件的新功能。

CommPath包基于功能性LR相互作用更有可能触发受体细胞中的特定分子途径，因此这些途径传递信号以介导更多的下游反应的假设，率先将细胞自身的功能通路信号和该细胞与其它细胞互作信号相关联。

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代码流程

Pathway enrichment analysis

# To find pathways of which the genesets show overlap with the marker ligands and receptors
# CommPath provides pathway annotations from KEGG pathways, WikiPathways, reactome pathways, and GO terms
# Here we take the KEGG pathways as an example
tumor.obj <- findLRpath(object = tumor.obj, category = "kegg")

Then we score the pathways to measure the activation levels for each pathway in each cell.

# To compute pathway activation score by the gsva algorithm or in an average manner
# For more information about the gsva algorithm, see the GSVA package (PMID23323831)
tumor.obj <- scorePath(object = tumor.obj, method = "gsva", min.size = 10, parallel.sz = 4)

# To get significantly up-regulated pathways in each cluster
acti.path.dat <- diffAllPath(object = tumor.obj, only.posi = TRUE, only.sig = TRUE)
head(acti.path.dat)

pdf('pathHeatmap.pdf',height=10,width=7)
pathHeatmap(object = tumor.obj,
       acti.path.dat = acti.path.dat,
       top.n.pathway = 10,
       cell.aver = TRUE)
dev.off()

image.png

# To find upstream clusters and ligands for the selected cluster and receptor 
select.ident = "Endothelial"
select.receptor = "ACKR1"

ident.up.dat <- findLigand(object = tumor.obj, 
    select.ident = select.ident, 
    select.receptor = select.receptor)
head(ident.up.dat)

# To find downstream clusters and receptors for the selected cluster and ligand 
select.ident = "Endothelial"
select.ligand = "CXCL12"

ident.down.dat <- findReceptor(object = tumor.obj, 
    select.ident = select.ident, 
    select.ligand = select.ligand)
head(ident.down.dat)

Then CommPath provides network graph tools to visualize the pathways and associated functional LR interactions:

# First to integrate the statistics of filtered activated pathways and their associated LR interactions 
tumor.obj <- pathNet(object = tumor.obj, acti.path.filtered.dat = acti.path.filtered.dat)
# To visualize the pathways and the associated upstream LR interactions in a network plot
pdf('pathNet.upstream.Endothelial.1.pdf',width=6,heigh=6)
set.seed(1234)
pathNetPlot(object = tumor.obj, select.ident =  select.ident, plot = "upstream",
    layout = 'layout.davidson.harel',
    vert.size.LR = 3, vert.size.path.adj = 10, 
    LR.label = 'R', vertex.label.cex.LR=0.25, vertex.label.cex.path=0.3)
dev.off()

image.png

To select the top 5 significantly upregulated pathways in Endothelial cells
pdf('pathNet.upstream.Endothelial.top5.pdf',width=6,heigh=6)
set.seed(1234)
pathNetPlot(object = tumor.obj, select.ident =  select.ident, plot = "upstream",
    top.n.path=5,
    layout = 'layout.davidson.harel',
    vert.size.LR = 3, vert.size.path.adj = 10, 
    LR.label = 'R', vertex.label.cex.LR=0.25, vertex.label.cex.path=0.3)
dev.off()

image.png

To select and show pathways of interest in the diagram by names of pathways directly
pdf('pathNet.upstream.Endothelial.selectpathway.pdf',width=6,heigh=6)
set.seed(1234)
pathNetPlot(object = tumor.obj, select.ident =  select.ident, plot = "upstream",
    select.path = c("JAK-STAT signaling pathway", "MAPK signaling pathway", "Ras signaling pathway", "PI3K-Akt signaling pathway", "AMPK signaling pathway"),
    layout = 'layout.davidson.harel',
    vert.size.LR = 3, vert.size.path.adj = 10, 
    LR.label = 'R', vertex.label.cex.LR=0.25, vertex.label.cex.path=0.3)
dev.off()

image.png

# Also to visualize the pathways and the associated downstream LR interactions in a network plot
pdf('pathNet.downstream.Endothelial.pdf',width=6,heigh=6)
set.seed(1234)
pathNetPlot(object = tumor.obj, select.ident =  select.ident, plot = "downstream",
    layout = 'layout.davidson.harel',
    vert.size.LR = 3, vert.size.path.adj = 10, 
    LR.label = 'L', vertex.label.cex.LR=0.25, vertex.label.cex.path=0.3)
dev.off()

image.png

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说在最后

事实上，细胞互作的信号会极大的影响下游功能通路的调节，因此我们可以利用通讯信号和下游事件的关系反推上游的调控机制；同时，也能根据上游信号推导下游发生的生物学事件。CommPath包全面还原了最真实的细胞信号调控关系，连接了作为上游的细胞通讯信号和下游的细胞功能信号，并且对每一种细胞之间的联系进行归纳汇总，极大的方便了我们研究每一种感兴趣的细胞类型。

好啦，本期分享到这里就结束了，我们下期再会~~