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# 加载必要的包
library(DESeq2)
# 读取转录组数据
header=TRUE，row.names=1)countData <- read.csv("transcriptome.csv
# 建立 DESeq2 对象
dds <- DESeqDataSetFromMatrix(countData, colData=sampleTable, design=wcondition)
# 运行差异表达分析dds <- DESeq(dds)
# 提取差异表达基因
res <- results(dds )
# 根据 p-value 和 log2FoldChange 过滤基因res <- res[which(res$padj<0.05 & abs(res$log2FoldChange)>1),]
# 输出结果
write.csv(res，file="diffExpressedGenes .csv")

# Load libraries
library(Seurat)
library(dplyr)
# Read in data
data <- read.csv("data.csv")
# Create Seurat object
seurat_obj <- CreateSeuratObject(counts = data, project = "Cell Annotation")
# Normalize data
seurat_obj <- NormalizeData( seurat obj)
# Find variable genes
seurat_obj<- Findariableees(surat bj, mean.function = Explen, dispersion.function = logWR,x.lo.cutoff = 0.0125, x.high.cutof = 3, y.cutoff = 0.5
# Scale data
seurat_obj <- ScaleData(seurat obj, vars.to.regress = c("nUMI", "percent.mito"))
# Run PCA
seurat_obj <- RunPCA(seurat obj, pc,genes = seurat objvar,genes, do.print = TRUE, pcs.print = 1:5, genes,print = 5)
# Run clustering
seurat_obj <- FindClusters(seurat obj, resolution = 0.5, print.output = 0, save.SNN = TRUE)
# Annotate clusters
seurat_obj <- Annotate(lusters(seurat obj, annotation = "cluster annotation.csy")

#加载必要的包
import scanpy as sc
# 读取数据
adata = sc.read 10x mtx("./data/')
# 进行标准化
sc.pp.normalize_per_cell(adata, counts_per_cell after=1e4)
# 进行降维sc.tl.pca(adata, svd solver='arpack')
# 进行聚类
sc.pp.neighbors(adata, n neighbors=10,n pcs=40)sc.tl.louvain(adata)
#进行细胞通讯分析sc.tl.cell communication(adata, type='all')
# 输出结果
adata.uns['cell communication']