单细胞数据挖掘(4)-PCA分析可视化(生信技能树视频笔记)

本笔记来源于B站@生信技能树-jimmy；学习视频链接: 「生信技能树」单细胞数据挖掘

4、降维，PCA分析，可视化

library(Seurat)
library(ggplot2)
# 先进行归一化（正态分布）
load("../../2.3.Rdata")
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
?DimPlot
?RunPCA
# 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

Fig1.D.jpeg

DimPlot(scRNA, reduction = "pca",  split.by = 'Patient_ID')

SinglceCell4_1.jpeg

# 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_3
# 结果显示可挑选前20个pc

p2_1| p2_2 | p2_3  # Fig1中图

Fig1D,E.jpeg