大秀直播v p

作家提议：We recommend all users to consider using [LIANA+](https://liana-py.readthedocs.io/en/latest/index.html), instead of LIANA, due to it's increased efficiency and completeness. We plan to eventually port all LIANA+ functionalities and backend to R, but in the meantime [LIANA+ in Python](https://liana-py.readthedocs.io/en/latest/index.html) is recommended. 也便是说在2024年发布的LIANA+在功能完竣性和后果上是优化的，比如，之前的cpdb用的是v2数据库，咱们也知说念现在cpdb是v5数据库，是以这些可能齐进了更新。关联词LIANA+现在（2024年11-15）惟一python版，后续也会出R版的分析，敬请期待吧！

装配现在的R包试试吧！这里咱们强调R版，因为大多半东说念主的使用原因！

setwd("D:\\KS口头\\公众号著述\\liana单细胞通信分析框架先容")#装配和加载一些包library(Seurat)library(dplyr)library(tidyverse)remotes::install_github('saezlab/liana')library(liana)#需要提防的是，我腹地电脑用remotes::install_github很汉典且莫得告捷#是以下载装配包腹地装配，然后可能需要一些依赖包，相同的下载腹地装配即可packageVersion('liana')[1] '0.1.14’

望望资源和分析措施，泰国按摩群齐挺全，常见的不常见的齐在（这里的不常见仅指我的默契）。从两个层面来说，一些python的措施历程了R的转动，不错说能在R内部出手cellphonedb了，关于不庄重python的小伙伴是很友好的！

liana::show_resources()# [1] "Default"          "Consensus"        "Baccin2019"       "CellCall"        # [5] "CellChatDB"       "Cellinker"        "CellPhoneDB"      "CellTalkDB"      # [9] "connectomeDB2020" "EMBRACE"          "Guide2Pharma"     "HPMR"            # [13] "ICELLNET"         "iTALK"            "Kirouac2010"      "LRdb"            # [17] "Ramilowski2015"   "OmniPath"         "MouseConsensus"  liana::show_methods()# [1] "connectome"      "logfc"           "natmi"           "sca"             "cellphonedb"    # [6] "cytotalk"        "call_squidpy"    "call_cellchat"   "call_connectome" "call_sca"       # [11] "call_italk"      "call_natmi"

出手下分析，提供单细胞seurat obj简略SingleCellExperiment obj即可，不错指定措施和resource。

##load dataload("C:/Users/tq199/Desktop/fsdownload/scRNA_Y16.Rdata")Idents(scRNA_Y16) <- 'celltype'##run analysisliana_test <- liana_wrap(scRNA_Y16, idents_col = 'celltype')# Expression from the `RNA` assay will be used# Running LIANA with `celltype` as labels!#   LIANA: LR summary stats calculated!#   Now Running: Natmi# Now Running: Connectome# Now Running: Logfc# Now Running: Sca# Now Running: Cellphonedb# |============================================================== |100% ~0 s remaining liana_test <- liana_test %>%liana_aggregate(aggregate_how = "magnitude")#====================================================================================liana_cpdb <- liana_wrap(scRNA_Y16, idents_col = 'celltype', method = "cellphonedb",resource = c('CellPhoneDB'))liana_cpdb_sig <- liana_cpdb[liana_cpdb$pvalue <= 0.05,]liana_cpdb_sig$inter <- paste0(liana_cpdb_sig$source,"_",liana_cpdb_sig$target)table(liana_cpdb_sig$inter)

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可视化就很通俗了：

#可视化library(ggplot2)library(RColorBrewer)ggplot(liana_cpdb_sig,aes(inter,LRpairs))+  geom_point(aes(size= -log10(pvalue+0.00001), color=lr.mean))+  geom_point(aes(size= -log10(pvalue+0.00001)), shape=21,stroke=1)+  theme(axis.text.x = element_text(angle = 90, hjust = 1))+  scale_color_gradientn(colours = RColorBrewer::brewer.pal(n = 10, name = "Spectral"))

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• 大秀直播v p