Seurat整合刺激性和对照性，以学习细胞类型特异性反应

Identify conserved cell type markers

寻找cluster 7的marker

DefaultAssay(immune.combined) <- "RNA"
nk.markers <- FindConservedMarkers(immune.combined, ident.1 = 7, grouping.var = "stim", verbose = FALSE)

grouping.var = "stim"什么意思？

head(nk.markers)
write.table(nk.markers,file="C:\\Users\\wlx\\Desktop\\1226\\nk.markers.xls", sep = "\t")
        XGD_p_val XGD_avg_logFC XGD_pct.1 XGD_pct.2 XGD_p_val_adj

MT-CO3 2.048573e-134 1.439299 1 1 3.639495e-130
MT-CO1 3.520456e-134 1.464769 1 1 6.254441e-130
MT-ND1 1.409064e-124 1.257897 1 1 2.503343e-120
MT-ATP6 1.355165e-131 1.306249 1 1 2.407586e-127
MT-CO2 1.882075e-134 1.432689 1 1 3.343694e-130
MT-ND2 5.793576e-120 1.358689 1 1 1.029287e-115
XPBS_p_val XPBS_avg_logFC XPBS_pct.1 XPBS_pct.2 XPBS_p_val_adj
MT-CO3 6.452291e-195 1.335722 1 1 1.146314e-190
MT-CO1 6.965532e-191 1.373682 1 1 1.237496e-186
MT-ND1 2.715760e-190 1.201851 1 1 4.824819e-186
MT-ATP6 2.837825e-189 1.196921 1 1 5.041679e-185
MT-CO2 1.931908e-188 1.254372 1 1 3.432228e-184
MT-ND2 4.483493e-188 1.269303 1 1 7.965374e-184
max_pval minimump_p_val
MT-CO3 2.048573e-134 1.290458e-194
MT-CO1 3.520456e-134 1.393106e-190
MT-ND1 1.409064e-124 5.431520e-190
MT-ATP6 1.355165e-131 5.675649e-189
MT-CO2 1.882075e-134 3.863817e-188
MT-ND2 5.793576e-120 8.966987e-188

可以看出某个基因表达在二者中呈现相同的趋势