R语言利用转录组基因表达矩阵做基因共表达分析的学习资料推荐
2023-06-13 09:16:32 时间
参考资料链接
https://github.com/cxli233/SimpleTidy_GeneCoEx/tree/v1.0.1
提供完整的示例数据和代码,非常好的学习材料
做基因共表达比较常用的是WGCNA那个R包,这个链接里提供的代码不是用WGCNA这个R包实现的,而是利用表达量数据计算不同基因之间的相关性,这种方法也挺常用的在论文里见过
表达量数据是来源于论文
High-resolution spatiotemporal transcriptome mapping of tomato fruit development and ripening
https://www.nature.com/articles/s41467-017-02782-9
数据是不同发育阶段的转录数据,表达量数据的下载链接是 https://zenodo.org/record/7117357#.Y0WB13ZBzic
关于样本的一些分组信息在链接里提供了,大家如果感兴趣可以自己下载数据然后跟着这个链接完全重复一下
接下来的内容我重复一下资料中利用表达量数据做PCA的内容
代码
setwd("data/20221012/")
list.files()
#library(data.table)
library(readr)
Exp_table <- read_csv("Shinozaki_tpm_all.csv", col_types = cols())
head(Exp_table)
dim(Exp_table)
library(readxl)
Metadata <- read_excel("SimpleTidy_GeneCoEx-1.0.1/Data/Shinozaki_datasets_SRA_info.xlsx")
head(Metadata)
dim(Metadata)
library(tidyverse)
Exp_table_long <- Exp_table %>%
rename(gene_ID = `...1`) %>%
pivot_longer(cols = !gene_ID, names_to = "library", values_to = "tpm") %>%
mutate(logTPM = log10(tpm + 1))
head(Exp_table_long)
Exp_table_log_wide <- Exp_table_long %>%
select(gene_ID, library, logTPM) %>%
pivot_wider(names_from = library, values_from = logTPM)
head(Exp_table_log_wide)
my_pca <- prcomp(t(Exp_table_log_wide[, -1]))
pc_importance <- as.data.frame(t(summary(my_pca)$importance))
head(pc_importance, 20)
PCA_coord <- my_pca$x[, 1:10] %>%
as.data.frame() %>%
mutate(Run = row.names(.)) %>%
full_join(Metadata %>%
select(Run, tissue, dev_stage, `Library Name`, `Sample Name`), by = "Run")
head(PCA_coord)
PCA_coord <- PCA_coord %>%
mutate(stage = case_when(
str_detect(dev_stage, "MG|Br|Pk") ~ str_sub(dev_stage, start = 1, end = 2),
T ~ dev_stage
)) %>%
mutate(stage = factor(stage, levels = c(
"Anthesis",
"5 DPA",
"10 DPA",
"20 DPA",
"30 DPA",
"MG",
"Br",
"Pk",
"LR",
"RR"
))) %>%
mutate(dissection_method = case_when(
str_detect(tissue, "epidermis") ~ "LM",
str_detect(tissue, "Collenchyma") ~ "LM",
str_detect(tissue, "Parenchyma") ~ "LM",
str_detect(tissue, "Vascular") ~ "LM",
str_detect(dev_stage, "Anthesis") ~ "LM",
str_detect(dev_stage, "5 DPA") &
str_detect(tissue, "Locular tissue|Placenta|Seeds") ~ "LM",
T ~ "Hand"
))
head(PCA_coord)
library(viridis)
library(RColorBrewer)
PCA_by_method <- PCA_coord %>%
ggplot(aes(x = PC1, y = PC2)) +
geom_point(aes(fill = dissection_method), color = "grey20", shape = 21, size = 3, alpha = 0.8) +
scale_fill_manual(values = brewer.pal(n = 3, "Accent")) +
labs(x = paste("PC1 (", pc_importance[1, 2] %>% signif(3)*100, "% of Variance)", sep = ""),
y = paste("PC2 (", pc_importance[2, 2] %>% signif(3)*100, "% of Variance)", " ", sep = ""),
fill = NULL) +
theme_bw() +
theme(
text = element_text(size= 14),
axis.text = element_text(color = "black")
)
PCA_by_method
PCA_by_tissue <- PCA_coord %>%
ggplot(aes(x = PC1, y = PC2)) +
geom_point(aes(fill = tissue), color = "grey20", shape = 21, size = 3, alpha = 0.8) +
scale_fill_manual(values = brewer.pal(11, "Set3")) +
labs(x = paste("PC2 (", pc_importance[2, 2] %>% signif(3)*100, "% of Variance)", sep = ""),
y = paste("PC3 (", pc_importance[3, 2] %>% signif(3)*100, "% of Variance)", " ", sep = ""),
fill = "tissue") +
theme_bw() +
theme(
text = element_text(size= 14),
axis.text = element_text(color = "black")
)
PCA_by_tissue
PCA_by_stage <- PCA_coord %>%
ggplot(aes(x = PC2, y = PC3)) +
geom_point(aes(fill = stage), color = "grey20", shape = 21, size = 3, alpha = 0.8) +
scale_fill_manual(values = viridis(10, option = "D")) +
labs(x = paste("PC2 (", pc_importance[2, 2] %>% signif(3)*100, "% of Variance)", sep = ""),
y = paste("PC3 (", pc_importance[3, 2] %>% signif(3)*100, "% of Variance)", " ", sep = ""),
fill = "stage") +
theme_bw() +
theme(
text = element_text(size= 14),
axis.text = element_text(color = "black")
)
PCA_by_stage
library(patchwork)
PCA_by_method+PCA_by_tissue+PCA_by_tissue
image.png
以上用到的代码和示例数据都可以在推文开头提到链接里找到
上面的代码有一步是对TPM值 加1然后取log10,他的实现方式是先将宽格式数据转换为长格式,然后把取log10后的长格式再转换为宽格式,这里我没能还可以借助mutate_at()
函数
Exp_table %>% select(1,2,3) %>%
rename("gene_id"="...1") %>%
mutate_at(vars(starts_with("SRR")),
function(x){log10(x+1)})
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