一、加载包

library(ggThemeAssist)
library(ggplot2)
library(PCAtools)
library(tidyverse)

二、读入数据

注意： PCA和聚类树图一般用原始的表达矩阵数据

de_matrix<-read.table("GSE11200_series_matrix.txt",sep="\t",header=T, row.names = 1)
sample_info <- read.csv(file = "GSE11200_sampleinfo.csv", header = T, row.names = 1)
# > de_matrix[1:4, 1:4]
#         GSM1 GSM2 GSM3 GSM4
# 7892501   2.070292   2.124219   2.291860   2.405174
# 7892502   3.823558   2.690961   2.725019   3.900261
# 7892503   3.387269   2.678249   3.054946   2.953741
# 7892504   7.631480   6.559667   7.422657   6.576941
# > head(sample_info)
#            group Sample_title sub_group
# GSM1   CON    control_1       CON
# GSM2   CON    control_2       CON
# GSM3   CON    control_3       CON
# GSM4   CON    control_4       CON
# GSM5   Treat   Treat_1  Treat_POST
# GSM6   Treat   Treat_2  Treat_high

三、pca分析

pca <- pca(de_matrix, metadata = sample_info)
pca
pca$variance
screeplot(pca) ##根据pca$variance画的解释度
biplot(pca, x = 'PC1', y = 'PC2')

四、将pca数据和分组信息整合到一起

pca_rotated_plus <- rownames_to_column(pca$rotated, 
                                       var = 'sample_name') %>%
  left_join(rownames_to_column(sample_info, var = 'sample_name'), 
            by = 'sample_name')

head(pca_rotated_plus)

五、ggplot绘图

可使用ggThemeAssist对图像进行进一步美化

ggplot(pca_rotated_plus, aes(x = PC1, y = PC2)) +
  geom_point(size = 8, aes(shape = group, color = sub_group)) +
  theme_test()