生信技能树单细胞数据挖掘笔记(3)

生信技能树单细胞数据挖掘笔记(1)：数据读入

生信技能树单细胞数据挖掘笔记(2)：创建Seurat对象并进行质控、筛选高变基因并可视化

生信技能树单细胞数据挖掘笔记(3)：降维与聚类

生信技能树单细胞数据挖掘笔记(4)：其他分析（周期判断、double诊断、细胞类型注释）

生信技能树单细胞数据挖掘笔记(5)：轨迹分析

降维，PCA分析

load("../2.3.Rdata")
### 4、降维，PCA分析，可视化----
#先进行归一化（正态分布）
scRNA <- ScaleData(scRNA, features = (rownames(scRNA)))
#储存到"scale.data"的slot里
GetAssayData(scRNA,slot="scale.data",assay="RNA")[1:8,1:4]
#对比下原来的count矩阵
GetAssayData(scRNA,slot="counts",assay="RNA")[1:8,1:4]
#scRNA@assays$RNA@
#PCA降维，利用之前挑选的hvg，可提高效率
scRNA <- RunPCA(scRNA, features = VariableFeatures(scRNA)) 
#挑选第一，第二主成分对cell可视化
DimPlot(scRNA, reduction = "pca", group.by="Patient_ID")
#发现与原文献中颠倒了
#seed.use   :Set a random seed. By default, sets the seed to 42. 
#Setting NULL will not set a seed.
scRNA <- RunPCA(scRNA, features = VariableFeatures(scRNA),seed.use=3)
#尝试了seed.use的不同取值发现图形只有四种变化（四个拐角），其中以seed.use=3为代表的一类与原文文献一致
DimPlot(scRNA, reduction = "pca", group.by="Patient_ID")
#与文献一致了。个人觉得颠倒与否如果只是随机种子的差别的话，对后续分析应该没影响
p2_1 <- DimPlot(scRNA, reduction = "pca", group.by="Patient_ID")+
  labs(tag = "D")
p2_1

原图 seed.use=3后的PCA图

#挑选主成分，RunPCA默认保留了前50个
scRNA <- JackStraw(scRNA,reduction = "pca", dims=20)
scRNA <- ScoreJackStraw(scRNA,dims = 1:20)

p2_2 <- JackStrawPlot(scRNA,dims = 1:20, reduction = "pca") +
  theme(legend.position="bottom") +
  labs(tag = "E")
p2_2
p2_3 <- ElbowPlot(scRNA, ndims=20, reduction="pca") 
p2_2 | p2_3

E图 D图与E图

聚类、筛选maker基因并可视化

聚类

pc.num=1:20
#基于PCA数据
scRNA <- FindNeighbors(scRNA, dims = pc.num) 
# dims参数，需要指定哪些pc轴用于分析；这里利用上面的分析，选择20
scRNA <- FindClusters(scRNA, resolution = 0.5)
table(scRNA@meta.data$seurat_clusters)

scRNA = RunTSNE(scRNA, dims = pc.num)
DimPlot(scRNA, reduction = "tsne",label=T)
p3_1 <- DimPlot(scRNA, reduction = "tsne",label=T) +
  labs(tag = "E")
p3_1

F图

筛选maker基因

#5.2 marker gene
#进行差异分析，一般使用标准化数据
scRNA <- NormalizeData(scRNA, normalization.method = "LogNormalize")
#结果储存在"data"slot里
GetAssayData(scRNA,slot="data",assay="RNA")[1:8,1:4]

#if test.use is "negbinom", "poisson", or "DESeq2", slot will be set to "counts
diff.wilcox = FindAllMarkers(scRNA)##默认使用wilcox方法挑选差异基因，大概4-5min
load("../diff.wilcox.Rdata")
head(diff.wilcox)
dim(diff.wilcox)
library(tidyverse)
all.markers = diff.wilcox %>% select(gene, everything()) %>%
  subset(p_val<0.05 & abs(diff.wilcox$avg_logFC) > 0.5)
#An adjusted P value < 0.05and | log 2 [fold change (FC)] | > 0.5 
#were considered the 2 cutoff criteria for identifying marker genes.
dim(all.markers)
summary(all.markers)
save(all.markers,file = "../markergene.Rdata")

top10 = all.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
top10
top10 = CaseMatch(search = as.vector(top10$gene), match = rownames(scRNA)) 
top10
length(top10)
length(unique(sort(top10)))

p3_2 <- DoHeatmap(scRNA, features = top10, group.by = "seurat_clusters")
p3_2
p3_1 | p3_2 #下图 

G图 图片合并

目前，图一的图片已经全部完成，接下来将进行拼图

拼图

### 6、拼图，比较----
p <- (p1_1 | p1_2 | p1_3 ) /
  ((p2_1| p2_2 | p2_3) /
     (p3_1 | p3_2))
ggsave("../.my_try.pdf", plot = p, width = 15, height = 18) 

save(scRNA,file = "scRNA.Rdata")

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