10X单细胞（10X空间转录组）多种通讯软件联合分析之liana

hello，今天我们分享一个简单的，将最近流行的细胞通讯分析软件整合在一起，分析我们的单细胞数据，直接看看代码部分

注意一个名词， cell-cell communication (CCC)，细胞通讯的软件方法很多了，these methods and resources are usually in a fixed combination of a tool and its corresponding resource, but in principle any resource could be combined with any method.（随意结合）。

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加载包

require(liana)
require(tibble)
require(purrr)

首先看看数据库来源

# Resource currently included in OmniPathR (and hence `liana`) include:
get_resources()
#>  [1] "Reshuffled"       "Default"          "CellChatDB"       "CellPhoneDB"     
#>  [5] "Ramilowski2015"   "Baccin2019"       "LRdb"             "Kirouac2010"     
#>  [9] "ICELLNET"         "iTALK"            "EMBRACE"          "HPMR"            
#> [13] "Guide2Pharma"     "connectomeDB2020" "talklr"           "CellTalkDB"      
#> [17] "OmniPath"

# A list of resources can be obtained using the `select_resource()` function:
# See `?select_resource()` documentation for further information.
# select_resource(c('OmniPath')) %>% glimpse() 

来源是真地多。

CCC Methods

- CellPhoneDB algorithm (via Squidpy)
- CellChat
- NATMI
- Connectome
- SingleCellSignalR (SCA)
- iTALK

目前就支持这么多方法，但是后续本人会再往上添加分析方法。

调用包装函数 liana

liana_path <- system.file(package = "liana")
testdata <-
    readRDS(file.path(liana_path , "testdata", "input", "testdata.rds"))

testdata %>% glimpse()
#> Formal class 'Seurat' [package "Seurat"] with 13 slots
#>   ..@ assays      :List of 1
#>   .. ..$ RNA:Formal class 'Assay' [package "Seurat"] with 8 slots
#>   ..@ meta.data   :'data.frame': 90 obs. of  4 variables:
#>   .. ..$ orig.ident        : Factor w/ 1 level "pbmc3k": 1 1 1 1 1 1 1 1 1 1 ...
#>   .. ..$ nCount_RNA        : num [1:90] 4903 3914 4973 3281 2641 ...
#>   .. ..$ nFeature_RNA      : int [1:90] 1352 1112 1445 1015 928 937 899 1713 960 888 ...
#>   .. ..$ seurat_annotations: Factor w/ 3 levels "B","CD8 T","NK": 1 1 1 1 3 3 1 1 1 1 ...
#>   ..@ active.assay: chr "RNA"
#>   ..@ active.ident: Factor w/ 3 levels "