diff --git a/RNA-arry.R b/RNA-arry.R
index 5ae2a15..3d99ac8 100644
--- a/RNA-arry.R
+++ b/RNA-arry.R
@@ -1,5 +1,5 @@
 ## Written By LiShang
-## Notice：适用于基因芯片平台
+## Notice: 适用于基因芯片平台
 
 #加载依赖包
 library(GEOquery)
@@ -19,11 +19,11 @@ exp <- exprs(gse)
 grp <- pData(gse)
 
 ###表达矩阵数据前处理
-#将表达矩阵的行名（探针ID）转换为Gene Symbol
-#方法一：直接从GEO拿数据，好处是方便快捷，通用性高
+#将表达矩阵的行名(探针ID)转换为Gene Symbol
+#方法一: 直接从GEO拿数据, 好处是方便快捷, 通用性高
 gene_symbols <- fData(gse)[,c("ID","Gene Symbol")]
 gene_symbols <- setNames(gene_symbols$`Gene Symbol`,gene_symbols$ID)[rownames(exp)]
-#方法二：通过芯片提供的R包拿数据，数据不如GEO的全，好处是基因名短
+#方法二: 通过芯片提供的R包拿数据, 数据不如GEO的全, 好处是基因名短
 #install.packages("hgu133a.db")
 library(hgu133a.db)
 gene_symbols <- toTable(hgu133aSYMBOL)[,c("probe_id","symbol")]
@@ -35,7 +35,7 @@ rownames(exp) <- exp$Group.1
 exp <- exp[, -1]
 
 ###样本分组数据前处理
-#将样本按pData$title分为normal组和cancer组，并转换为factor
+#将样本按pData$title分为normal组和cancer组, 并转换为factor
 grp <- grp[colnames(exp),]
 grp <- ifelse(str_detect(grp$title,"Normal"),"normal","cancer") %>%
   factor(c("normal","cancer"))
@@ -67,4 +67,4 @@ pca_plot1 + theme(legend.position = "none") + pca_plot2
 
 fit <- lmFit(exp, model.matrix(~grp))
 fit <- eBayes(fit)
-deg <- topTable(fit, coef="grpcancer", adjust.method="fdr", number=Inf)
\ No newline at end of file
+deg <- topTable(fit, coef="grpcancer", adjust.method="fdr", number=Inf)