參考https://mp.weixin.qq.com/s/76hSRtF7m3V9AXonrCNc8w
2021發(fā)表在 Cell Reports 雜志的文章：《TREM2-independent oligodendrocyte, astrocyte, and T cell responses to tau and amyloid pathology in mouse models of Alzheimer disease》
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作者是把在兩次差異分析至少有一次是統(tǒng)計學(xué)顯著的基因拿過去繪圖，英文的描述是Only genes called as DEGs (FDR < 0.05, fold change >2 or < 2) for either comparison are shown.
兩次單細(xì)胞差異分析后的結(jié)果進(jìn)行相關(guān)性散點圖繪制
#total=c(rownames(deg_eCRSvsPB[abs(deg_eCRSvsPB$avg_log2FC)>1,]),
# rownames(deg_nCRSvsPB[abs(deg_nCRSvsPB$avg_log2FC)>1,]))
#markers_nCRSvsPB <- subset(deg_nCRSvsPB,p_val_adj<0.05&abs(avg_log2FC)>1)
#markers_eCRSvsPB <- subset(deg_eCRSvsPB,p_val_adj<0.05&abs(avg_log2FC)>1)
#markers_inter <-intersect(rownames(markers_nCRSvsPB),rownames(markers_eCRSvsPB))
ids <- total #變量
df= data.frame(
deg_eCRSvsPB = deg_eCRSvsPB[ids,'avg_log2FC'],
deg_nCRSvsPB = deg_nCRSvsPB[ids,'avg_log2FC']
)
library(ggpubr)
ggscatter(df, x = "deg_nCRSvsPB", y = "deg_eCRSvsPB",
color = "magenta", shape = 16, size =1.5, # Points color, shape and size
add = "reg.line", # Add regressin line
ylim = c(-4, 6), xlim = c(-4, 6),
add.params = list(color = "gray25", fill = "lightgray", linetype="dashed"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "pearson", label.sep = "\n")
)
ggsave("eCRSvsPB_nCRSvsPB_total_ggscatter.pdf")
image.png
