7 易生信-数据可视化案例分享

从一套表达和通路数据入手,探索常见的绘图展示方式和报错处理。

7.1 加载需要的包

library(dplyr)
library(ggpubr)
library(tidyr)
library(ggplot2)
library(pheatmap)
library(ggstatsplot)
library(Hmisc)

7.2 读入数据

7.2.1 ’row.names’里不能有重复的名字 Duplicate row names

expr <- read.table("ehbio.simplier.DESeq2.normalized.symbol.txt", row.names = 1, header = T, sep = "\t")

7.2.2 行名唯一化处理

这里使用make.names转换行名为唯一,实际需要先弄清楚为什么会有重复名字

expr <- read.table("ehbio.simplier.DESeq2.normalized.symbol.txt", row.names = NULL, header = T, sep = "\t")
head(expr)
##       id untrt_N61311 untrt_N052611 untrt_N080611 untrt_N061011 trt_N61311
## 1    FN1    245667.66      427435.1     221687.51      371144.2  240187.24
## 2    DCN    212953.14      360796.2     258977.30      408573.1  210002.18
## 3  CEMIP     40996.34      137783.1      53813.92       91066.8   62301.12
## 4 CCDC80    137229.15      232772.2      86258.13      212237.3  136730.76
## 5 IGFBP5     77812.65      288609.2     210628.87      168067.4   96021.74
## 6 COL1A1    146450.41      127367.3     152281.50      140861.1   62358.64
##   trt_N052611 trt_N080611 trt_N061011
## 1   450103.21   280226.19   376518.23
## 2   316009.14   225547.39   393843.74
## 3   223111.85   212724.84   157919.47
## 4   226070.89   124634.56   236237.81
## 5   217439.21   162677.38   168387.36
## 6    53800.47    69160.97    51044.06

有哪些基因名是重复出现的?

expr$id[duplicated(expr$id)]
##   [1] "MATR3"      "PKD1P1"     "HSPA14"     "OR7E47P"    "POLR2J3"   
##   [6] "ATXN7"      "TMSB15B"    "LINC-PINT"  "TBCE"       "SNX29P2"   
##  [11] "SCO2"       "POLR2J4"    "CCDC39"     "RGS5"       "BMS1P21"   
##  [16] "RF00017"    "GOLGA8M"    "RF00017"    "DNAJC9-AS1" "CYB561D2"  
##  [21] "RF00017"    "IPO5P1"     "RF00017"    "RF00017"    "RF00017"   
##  [26] "SPATA13"    "RF00017"    "RF00017"    "RF00017"    "RF00017"   
##  [31] "RF00017"    "RF00017"    "RF00017"    "RF00017"    "RF00017"   
##  [36] "RF00017"    "RF00017"    "RF00017"    "RF00017"    "RF00017"   
##  [41] "RF00019"    "RF00019"    "RF00017"    "RF00017"    "RF00017"   
##  [46] "RF00019"    "BMS1P4"     "RF00019"    "RF00019"    "RF00017"   
##  [51] "RF00017"    "RF00017"    "RF00017"    "RF00017"    "RF00017"   
##  [56] "RF00017"    "RF00017"    "RF00017"    "RF00017"    "RF00017"   
##  [61] "RF00017"    "RF00017"    "RF00017"    "RF00019"    "RF00017"   
##  [66] "RF00017"    "RF00017"    "RF00019"    "RF00017"    "RF00017"   
##  [71] "LINC01238"  "RF00017"    "RF00017"    "RF00017"    "RF00017"   
##  [76] "RF00017"    "RF00017"    "RF00017"    "RF00017"    "RF00017"   
##  [81] "RF00017"    "RF00017"    "RF02271"    "RF00017"    "RF00017"   
##  [86] "RF00017"    "RF00017"    "RF00017"    "LINC01297"  "RF00019"   
##  [91] "RF00017"    "RF00012"    "RF00019"    "RF00017"    "RF00017"   
##  [96] "RF00019"    "RF00017"    "RF00017"    "RF00017"    "ZNF503"    
## [101] "RF00017"    "RF00017"    "RF00017"    "RF00017"    "RF00017"   
## [106] "RF00017"    "RF00017"    "RF00017"    "RF02271"    "RF00019"   
## [111] "RF00019"    "RF00017"    "RF00019"    "RF02271"    "RF00017"   
## [116] "RF00017"    "RF00017"    "RF00017"    "RF00019"    "RF00019"   
## [121] "RF00017"    "RF00019"    "ITFG2-AS1"  "RF00019"    "RF00019"   
## [126] "RF00017"    "RF00019"    "RF00017"    "RF00017"    "RF00017"   
## [131] "RF00019"    "RF00017"    "RF00012"    "RF00017"    "RF00017"   
## [136] "RAET1E-AS1" "RF00017"    "RF00017"    "RF00017"    "RF00017"   
## [141] "RF00017"    "RF00017"    "RF00017"    "RF00017"    "RF00012"   
## [146] "RF02271"    "RF00019"    "LINC01422"  "RF02271"    "RF00017"   
## [151] "RF00019"    "RF00019"    "RF00019"    "RF00019"    "RF00017"   
## [156] "LINC01481"  "RF00017"    "SNHG28"     "RF00019"    "RF00019"   
## [161] "RF00019"    "RF00019"    "LINC00484"  "LINC00941"  "ALG1L9P"   
## [166] "RF00017"    "DUXAP8"     "RF00017"    "RF00017"    "RF00017"   
## [171] "RF00017"    "RF00017"    "RF00017"    "RMRP"       "RF00017"   
## [176] "RF00017"    "RF00017"    "RF00017"    "DIABLO"

名字唯一化处理

# 该行命令是展示make.names的效果
make.names(c("a", "a", "b", "b", "b"), unique = T)
## [1] "a"   "a.1" "b"   "b.1" "b.2"

唯一化之后的名字作为行名字,并去掉原来的第一列

expr_names <- make.names(expr$id, unique = T)
rownames(expr) <- expr_names
expr <- expr[, -1]
head(expr)
##        untrt_N61311 untrt_N052611 untrt_N080611 untrt_N061011 trt_N61311
## FN1       245667.66      427435.1     221687.51      371144.2  240187.24
## DCN       212953.14      360796.2     258977.30      408573.1  210002.18
## CEMIP      40996.34      137783.1      53813.92       91066.8   62301.12
## CCDC80    137229.15      232772.2      86258.13      212237.3  136730.76
## IGFBP5     77812.65      288609.2     210628.87      168067.4   96021.74
## COL1A1    146450.41      127367.3     152281.50      140861.1   62358.64
##        trt_N052611 trt_N080611 trt_N061011
## FN1      450103.21   280226.19   376518.23
## DCN      316009.14   225547.39   393843.74
## CEMIP    223111.85   212724.84   157919.47
## CCDC80   226070.89   124634.56   236237.81
## IGFBP5   217439.21   162677.38   168387.36
## COL1A1    53800.47    69160.97    51044.06

7.3 热图绘制

library(pheatmap)
top6 <- head(expr)
pheatmap(top6)

### 提取差异基因绘制热图 {#visual6}

读入差异基因列表

de_gene <- read.table("ehbio.DESeq2.all.DE.symbol", row.names = NULL, header = F, sep = "\t")
head(de_gene)
##        V1                     V2
## 1 ARHGEF2 untrt._higherThan_.trt
## 2  KCTD12 untrt._higherThan_.trt
## 3  SLC6A9 untrt._higherThan_.trt
## 4  GXYLT2 untrt._higherThan_.trt
## 5   RAB7B untrt._higherThan_.trt
## 6   NEK10 untrt._higherThan_.trt

提取Top3 差异的基因

# library(dplyr)
top6_de_gene <- de_gene %>% group_by(V2) %>% dplyr::slice(1:3)
top6 <- expr[which(rownames(expr) %in% top6_de_gene$V1), ]
head(top6)
##         untrt_N61311 untrt_N052611 untrt_N080611 untrt_N061011 trt_N61311
## KCTD12    4700.79369     3978.0401    4416.15169    4792.34174  936.69481
## MAOA       438.54451      452.9934     516.63033     258.73279 4628.00860
## ARHGEF2   3025.62334     3105.7830    3094.51304    2909.99043 1395.39850
## SPARCL1     58.15705      102.5827      80.00997      82.59042 2220.50867
## PER1       170.61639      156.3692     194.97497     123.47689 1728.38117
## SLC6A9     360.66314      413.8797     365.47650     443.71982   63.90538
##         trt_N052611 trt_N080611 trt_N061011
## KCTD12     633.4462   979.77576   641.49582
## MAOA      4429.7201  4629.66529  3778.17351
## ARHGEF2   1441.9916  1464.59769  1501.51509
## SPARCL1   1750.9879  1374.90745  2194.58930
## PER1      1230.2575  1120.00650  1333.91208
## SLC6A9      56.8962    86.82929    95.33916

读入样品分组信息作为列注释

metadata <- read.table("sampleFile", header = T, row.names = 1)
pheatmap(top6, annotation_col = metadata)

按行标准化后展示

pheatmap(top6, annotation_col = metadata, scale = "row", cluster_cols = F)

7.4 箱线图和统计比较

head(top6)
##         untrt_N61311 untrt_N052611 untrt_N080611 untrt_N061011 trt_N61311
## KCTD12    4700.79369     3978.0401    4416.15169    4792.34174  936.69481
## MAOA       438.54451      452.9934     516.63033     258.73279 4628.00860
## ARHGEF2   3025.62334     3105.7830    3094.51304    2909.99043 1395.39850
## SPARCL1     58.15705      102.5827      80.00997      82.59042 2220.50867
## PER1       170.61639      156.3692     194.97497     123.47689 1728.38117
## SLC6A9     360.66314      413.8797     365.47650     443.71982   63.90538
##         trt_N052611 trt_N080611 trt_N061011
## KCTD12     633.4462   979.77576   641.49582
## MAOA      4429.7201  4629.66529  3778.17351
## ARHGEF2   1441.9916  1464.59769  1501.51509
## SPARCL1   1750.9879  1374.90745  2194.58930
## PER1      1230.2575  1120.00650  1333.91208
## SLC6A9      56.8962    86.82929    95.33916

矩阵转置

top6_t <- as.data.frame(t(top6))
top6_t
##                  KCTD12      MAOA  ARHGEF2    SPARCL1      PER1    SLC6A9
## untrt_N61311  4700.7937  438.5445 3025.623   58.15705  170.6164 360.66314
## untrt_N052611 3978.0401  452.9934 3105.783  102.58269  156.3692 413.87971
## untrt_N080611 4416.1517  516.6303 3094.513   80.00997  194.9750 365.47650
## untrt_N061011 4792.3417  258.7328 2909.990   82.59042  123.4769 443.71982
## trt_N61311     936.6948 4628.0086 1395.398 2220.50867 1728.3812  63.90538
## trt_N052611    633.4462 4429.7201 1441.992 1750.98786 1230.2575  56.89620
## trt_N080611    979.7758 4629.6653 1464.598 1374.90745 1120.0065  86.82929
## trt_N061011    641.4958 3778.1735 1501.515 2194.58930 1333.9121  95.33916

与样本属性信息合并

top6_t_with_group <- merge(metadata, top6_t, by = 0)
head(top6_t_with_group)
##       Row.names conditions individual    KCTD12      MAOA  ARHGEF2    SPARCL1
## 1   trt_N052611        trt    N052611  633.4462 4429.7201 1441.992 1750.98786
## 2   trt_N061011        trt    N061011  641.4958 3778.1735 1501.515 2194.58930
## 3   trt_N080611        trt    N080611  979.7758 4629.6653 1464.598 1374.90745
## 4    trt_N61311        trt     N61311  936.6948 4628.0086 1395.398 2220.50867
## 5 untrt_N052611      untrt    N052611 3978.0401  452.9934 3105.783  102.58269
## 6 untrt_N061011      untrt    N061011 4792.3417  258.7328 2909.990   82.59042
##        PER1    SLC6A9
## 1 1230.2575  56.89620
## 2 1333.9121  95.33916
## 3 1120.0065  86.82929
## 4 1728.3812  63.90538
## 5  156.3692 413.87971
## 6  123.4769 443.71982

修改第一列的列名字

colnames(top6_t_with_group)[1] = "Sample"
head(top6_t_with_group)
##          Sample conditions individual    KCTD12      MAOA  ARHGEF2    SPARCL1
## 1   trt_N052611        trt    N052611  633.4462 4429.7201 1441.992 1750.98786
## 2   trt_N061011        trt    N061011  641.4958 3778.1735 1501.515 2194.58930
## 3   trt_N080611        trt    N080611  979.7758 4629.6653 1464.598 1374.90745
## 4    trt_N61311        trt     N61311  936.6948 4628.0086 1395.398 2220.50867
## 5 untrt_N052611      untrt    N052611 3978.0401  452.9934 3105.783  102.58269
## 6 untrt_N061011      untrt    N061011 4792.3417  258.7328 2909.990   82.59042
##        PER1    SLC6A9
## 1 1230.2575  56.89620
## 2 1333.9121  95.33916
## 3 1120.0065  86.82929
## 4 1728.3812  63.90538
## 5  156.3692 413.87971
## 6  123.4769 443.71982

7.4.1 单基因箱线图

library(ggpubr)

ggboxplot(top6_t_with_group, x = "conditions", y = "KCTD12", title = "KCTD12", ylab = "Expression", color = "conditions", 
    palette = "jco")
# palette npg, lancet,

7.4.2 多基因箱线图 (combine)

ggboxplot(top6_t_with_group, x = "conditions", y = c("KCTD12", "MAOA", "PER1", "SLC6A9"), ylab = "Expression", combine = T, 
    color = "conditions", palette = "jco")

7.4.3 多基因箱线图 (merge)

ggboxplot(top6_t_with_group, x = "conditions", y = c("KCTD12", "MAOA", "PER1", "SLC6A9"), ylab = "Expression", merge = "flip", 
    color = "conditions", palette = "nature")

7.4.4 数据对数转换后绘制箱线图

top6_t_with_group_log = top6_t_with_group %>% purrr::map_if(is.numeric, log1p) %>% as.data.frame
head(top6_t_with_group_log)
##          Sample conditions individual   KCTD12     MAOA  ARHGEF2  SPARCL1
## 1   trt_N052611        trt    N052611 6.452752 8.396317 7.274474 7.468506
## 2   trt_N061011        trt    N061011 6.465360 8.237261 7.314896 7.694206
## 3   trt_N080611        trt    N080611 6.888344 8.440456 7.290018 7.226869
## 4    trt_N61311        trt     N61311 6.843425 8.440098 7.241652 7.705942
## 5 untrt_N052611      untrt    N052611 8.288796 6.118083 8.041343 4.640370
## 6 untrt_N061011      untrt    N061011 8.474983 5.559653 7.976249 4.425929
##       PER1   SLC6A9
## 1 7.115791 4.058652
## 2 7.196621 4.567875
## 3 7.021982 4.475395
## 4 7.455519 4.172930
## 5 5.058595 6.027989
## 6 4.824120 6.097444
ggboxplot(top6_t_with_group_log, x = "conditions", y = c("KCTD12", "MAOA", "PER1", "SLC6A9"), ylab = "Expression", merge = "flip", 
    fill = "conditions", palette = "Set3")

7.4.5 用ggplot2实现ggpubr

head(top6_t_with_group)
##          Sample conditions individual    KCTD12      MAOA  ARHGEF2    SPARCL1
## 1   trt_N052611        trt    N052611  633.4462 4429.7201 1441.992 1750.98786
## 2   trt_N061011        trt    N061011  641.4958 3778.1735 1501.515 2194.58930
## 3   trt_N080611        trt    N080611  979.7758 4629.6653 1464.598 1374.90745
## 4    trt_N61311        trt     N61311  936.6948 4628.0086 1395.398 2220.50867
## 5 untrt_N052611      untrt    N052611 3978.0401  452.9934 3105.783  102.58269
## 6 untrt_N061011      untrt    N061011 4792.3417  258.7328 2909.990   82.59042
##        PER1    SLC6A9
## 1 1230.2575  56.89620
## 2 1333.9121  95.33916
## 3 1120.0065  86.82929
## 4 1728.3812  63.90538
## 5  156.3692 413.87971
## 6  123.4769 443.71982

转换为长矩阵

top6_t_with_group_melt <- gather(top6_t_with_group, key = "Gene", value = "Expr", -conditions, -Sample, -individual)
top6_t_with_group_melt
##           Sample conditions individual    Gene       Expr
## 1    trt_N052611        trt    N052611  KCTD12  633.44616
## 2    trt_N061011        trt    N061011  KCTD12  641.49582
## 3    trt_N080611        trt    N080611  KCTD12  979.77576
## 4     trt_N61311        trt     N61311  KCTD12  936.69481
## 5  untrt_N052611      untrt    N052611  KCTD12 3978.04011
## 6  untrt_N061011      untrt    N061011  KCTD12 4792.34174
## 7  untrt_N080611      untrt    N080611  KCTD12 4416.15169
## 8   untrt_N61311      untrt     N61311  KCTD12 4700.79369
## 9    trt_N052611        trt    N052611    MAOA 4429.72011
## 10   trt_N061011        trt    N061011    MAOA 3778.17351
## 11   trt_N080611        trt    N080611    MAOA 4629.66529
## 12    trt_N61311        trt     N61311    MAOA 4628.00860
## 13 untrt_N052611      untrt    N052611    MAOA  452.99337
## 14 untrt_N061011      untrt    N061011    MAOA  258.73279
## 15 untrt_N080611      untrt    N080611    MAOA  516.63033
## 16  untrt_N61311      untrt     N61311    MAOA  438.54451
## 17   trt_N052611        trt    N052611 ARHGEF2 1441.99162
## 18   trt_N061011        trt    N061011 ARHGEF2 1501.51509
## 19   trt_N080611        trt    N080611 ARHGEF2 1464.59769
## 20    trt_N61311        trt     N61311 ARHGEF2 1395.39850
## 21 untrt_N052611      untrt    N052611 ARHGEF2 3105.78299
## 22 untrt_N061011      untrt    N061011 ARHGEF2 2909.99043
## 23 untrt_N080611      untrt    N080611 ARHGEF2 3094.51304
## 24  untrt_N61311      untrt     N61311 ARHGEF2 3025.62334
## 25   trt_N052611        trt    N052611 SPARCL1 1750.98786
## 26   trt_N061011        trt    N061011 SPARCL1 2194.58930
## 27   trt_N080611        trt    N080611 SPARCL1 1374.90745
## 28    trt_N61311        trt     N61311 SPARCL1 2220.50867
## 29 untrt_N052611      untrt    N052611 SPARCL1  102.58269
## 30 untrt_N061011      untrt    N061011 SPARCL1   82.59042
## 31 untrt_N080611      untrt    N080611 SPARCL1   80.00997
## 32  untrt_N61311      untrt     N61311 SPARCL1   58.15705
## 33   trt_N052611        trt    N052611    PER1 1230.25755
## 34   trt_N061011        trt    N061011    PER1 1333.91208
## 35   trt_N080611        trt    N080611    PER1 1120.00650
## 36    trt_N61311        trt     N61311    PER1 1728.38117
## 37 untrt_N052611      untrt    N052611    PER1  156.36920
## 38 untrt_N061011      untrt    N061011    PER1  123.47689
## 39 untrt_N080611      untrt    N080611    PER1  194.97497
## 40  untrt_N61311      untrt     N61311    PER1  170.61639
## 41   trt_N052611        trt    N052611  SLC6A9   56.89620
## 42   trt_N061011        trt    N061011  SLC6A9   95.33916
## 43   trt_N080611        trt    N080611  SLC6A9   86.82929
## 44    trt_N61311        trt     N61311  SLC6A9   63.90538
## 45 untrt_N052611      untrt    N052611  SLC6A9  413.87971
## 46 untrt_N061011      untrt    N061011  SLC6A9  443.71982
## 47 untrt_N080611      untrt    N080611  SLC6A9  365.47650
## 48  untrt_N61311      untrt     N61311  SLC6A9  360.66314
library(ggplot2)
ggplot(top6_t_with_group_melt, aes(x = Gene, y = Expr)) + geom_boxplot(aes(color = conditions)) + theme_classic()

7.4.6 配色

序列型颜色板适用于从低到高排序明显的数据,浅色数字小,深色数字大。

library(RColorBrewer)
display.brewer.all(type = "seq")

离散型颜色板适合带“正、负”的,对极值和中间值比较注重的数据。

display.brewer.all(type = "div")

分类型颜色板比较适合区分分类型的数据。

display.brewer.all(type = "qual")

7.4.7 箱线图加统计分析

my_comparisons <- list(c("trt", "untrt"))
ggboxplot(top6_t_with_group, x = "conditions", y = "PER1",
          title = "PER1", ylab = "Expression",
          add = "jitter",                               # Add jittered points
          #add = "dotplot",
          fill = "conditions", palette = "Paired") +
  stat_compare_means(comparisons = my_comparisons)

标记点来源的样本

my_comparisons <- list(c("trt", "untrt"))
ggboxplot(top6_t_with_group, x = "conditions", y = "PER1",
          title = "PER1", ylab = "Expression",
          add = "jitter",                               # Add jittered points
          add.params = list(size = 0.1, jitter = 0.2),  # Point size and the amount of jittering
          label = "Sample",                # column containing point labels
          label.select = list(top.up = 2, top.down = 2),# Select some labels to display
          font.label = list(size = 9, face = "italic"), # label font
          repel = TRUE,                                 # Avoid label text overplotting
          fill = "conditions", palette = "Paired") +
  stat_compare_means(comparisons = my_comparisons)

修改统计检验方法

my_comparisons <- list(c("trt", "untrt"))
ggboxplot(top6_t_with_group_log, x = "conditions", y = "PER1",
          title = "PER1", ylab = "Expression",
          add = "jitter",                               # Add jittered points
          add.params = list(size = 0.1, jitter = 0.2),  # Point size and the amount of jittering
          label = "Sample",                # column containing point labels
          label.select = list(top.up = 2, top.down = 2),# Select some labels to display
          font.label = list(size = 9, face = "italic"), # label font
          repel = TRUE,                                 # Avoid label text overplotting
          fill = "conditions", palette = "Paired") +
  stat_compare_means(comparisons = my_comparisons, method = "t.test", paired = T)

小提琴图

ggviolin(top6_t_with_group, x = "conditions", y = c("KCTD12","MAOA"),
          ylab = "Expression", merge="flip",
          color = "conditions", palette = "jco", 
          add = "boxplot"
          # add = "median_iqr"
         )

点带图(适合数据比较多时)

ggstripchart(top6_t_with_group, x = "conditions", y = c("KCTD12","MAOA"),
          ylab = "Expression", combine=T,
          color = "conditions", palette = "jco", 
          size = 0.1, jitter = 0.2,
          add.params = list(color = "gray"),
          # add = "boxplot"
          add = "median_iqr")

7.5 通路内基因的比较

pathway <- read.table("h.all.v6.2.symbols.gmt.forGO", sep = "\t", row.names = NULL, header = T)
head(pathway)
##                                ont    gene
## 1 HALLMARK_TNFA_SIGNALING_VIA_NFKB    JUNB
## 2 HALLMARK_TNFA_SIGNALING_VIA_NFKB   CXCL2
## 3 HALLMARK_TNFA_SIGNALING_VIA_NFKB    ATF3
## 4 HALLMARK_TNFA_SIGNALING_VIA_NFKB  NFKBIA
## 5 HALLMARK_TNFA_SIGNALING_VIA_NFKB TNFAIP3
## 6 HALLMARK_TNFA_SIGNALING_VIA_NFKB   PTGS2

通路提取

# HALLMARK_HYPOXIA, HALLMARK_DNA_REPAIR, HALLMARK_P53_PATHWAY

target_pathway <- pathway[pathway$ont %in% c("HALLMARK_HYPOXIA", "HALLMARK_DNA_REPAIR", "HALLMARK_P53_PATHWAY"), ]

target_pathway <- droplevels.data.frame(target_pathway)

summary(target_pathway)
##      ont                gene          
##  Length:550         Length:550        
##  Class :character   Class :character  
##  Mode  :character   Mode  :character
head(target_pathway)
##                  ont  gene
## 201 HALLMARK_HYPOXIA  PGK1
## 202 HALLMARK_HYPOXIA  PDK1
## 203 HALLMARK_HYPOXIA  GBE1
## 204 HALLMARK_HYPOXIA  PFKL
## 205 HALLMARK_HYPOXIA ALDOA
## 206 HALLMARK_HYPOXIA  ENO2

表达矩阵提取

expr_with_gene <- expr
expr_with_gene$gene <- rownames(expr_with_gene)
target_pathway_with_expr <- left_join(target_pathway, expr_with_gene)
summary(target_pathway_with_expr)
##      ont                gene            untrt_N61311      untrt_N052611     
##  Length:550         Length:550         Min.   :     0.0   Min.   :     0.0  
##  Class :character   Class :character   1st Qu.:   254.2   1st Qu.:   240.8  
##  Mode  :character   Mode  :character   Median :   781.3   Median :   784.1  
##                                        Mean   :  2528.6   Mean   :  2895.1  
##                                        3rd Qu.:  1852.4   3rd Qu.:  1727.2  
##                                        Max.   :212953.1   Max.   :360796.2  
##                                        NA's   :36         NA's   :36        
##  untrt_N080611      untrt_N061011        trt_N61311        trt_N052611      
##  Min.   :     0.0   Min.   :     0.0   Min.   :     0.0   Min.   :     0.0  
##  1st Qu.:   235.0   1st Qu.:   237.9   1st Qu.:   248.2   1st Qu.:   211.0  
##  Median :   734.9   Median :   764.2   Median :   766.6   Median :   723.2  
##  Mean   :  2549.2   Mean   :  2864.9   Mean   :  2531.8   Mean   :  2783.3  
##  3rd Qu.:  1932.4   3rd Qu.:  1870.0   3rd Qu.:  1872.4   3rd Qu.:  1832.2  
##  Max.   :258977.3   Max.   :408573.1   Max.   :210002.2   Max.   :316009.1  
##  NA's   :36         NA's   :36         NA's   :36         NA's   :36        
##   trt_N080611        trt_N061011      
##  Min.   :     0.0   Min.   :     0.0  
##  1st Qu.:   250.6   1st Qu.:   227.9  
##  Median :   739.3   Median :   746.0  
##  Mean   :  2840.3   Mean   :  3043.6  
##  3rd Qu.:  1825.8   3rd Qu.:  1925.1  
##  Max.   :225547.4   Max.   :393843.7  
##  NA's   :36         NA's   :36

移除通路中未检测到表达的基因

target_pathway_with_expr <- na.omit(target_pathway_with_expr)
summary(target_pathway_with_expr)
##      ont                gene            untrt_N61311      untrt_N052611     
##  Length:514         Length:514         Min.   :     0.0   Min.   :     0.0  
##  Class :character   Class :character   1st Qu.:   254.2   1st Qu.:   240.8  
##  Mode  :character   Mode  :character   Median :   781.3   Median :   784.1  
##                                        Mean   :  2528.6   Mean   :  2895.1  
##                                        3rd Qu.:  1852.4   3rd Qu.:  1727.2  
##                                        Max.   :212953.1   Max.   :360796.2  
##  untrt_N080611      untrt_N061011        trt_N61311        trt_N052611      
##  Min.   :     0.0   Min.   :     0.0   Min.   :     0.0   Min.   :     0.0  
##  1st Qu.:   235.0   1st Qu.:   237.9   1st Qu.:   248.2   1st Qu.:   211.0  
##  Median :   734.9   Median :   764.2   Median :   766.6   Median :   723.2  
##  Mean   :  2549.2   Mean   :  2864.9   Mean   :  2531.8   Mean   :  2783.3  
##  3rd Qu.:  1932.4   3rd Qu.:  1870.0   3rd Qu.:  1872.4   3rd Qu.:  1832.2  
##  Max.   :258977.3   Max.   :408573.1   Max.   :210002.2   Max.   :316009.1  
##   trt_N080611        trt_N061011      
##  Min.   :     0.0   Min.   :     0.0  
##  1st Qu.:   250.6   1st Qu.:   227.9  
##  Median :   739.3   Median :   746.0  
##  Mean   :  2840.3   Mean   :  3043.6  
##  3rd Qu.:  1825.8   3rd Qu.:  1925.1  
##  Max.   :225547.4   Max.   :393843.7
head(target_pathway_with_expr)
##                ont  gene untrt_N61311 untrt_N052611 untrt_N080611 untrt_N061011
## 1 HALLMARK_HYPOXIA  PGK1     7567.398     7893.2150     6254.5945      5529.122
## 2 HALLMARK_HYPOXIA  PDK1     1009.850     1042.4868      735.9359       673.208
## 3 HALLMARK_HYPOXIA  GBE1     3859.557     1494.4120     3803.5627      3295.191
## 4 HALLMARK_HYPOXIA  PFKL     3581.499     3018.0675     2789.4430      3084.570
## 5 HALLMARK_HYPOXIA ALDOA    19139.085    19587.3216    18089.5116     15519.899
## 6 HALLMARK_HYPOXIA  ENO2     1964.796      979.5255     1041.4660      1288.837
##   trt_N61311 trt_N052611 trt_N080611 trt_N061011
## 1  7595.0408   6969.6128   15011.823   6076.4392
## 2   419.6273    365.0062    1056.622    383.6163
## 3  4769.5464   2359.7150    4759.809   4296.5471
## 4  2867.2464   2599.5095    4399.403   3090.6701
## 5 16388.1123  13949.5659   22630.701  14374.3437
## 6  1303.5671    766.9436    1473.336    892.4621

转换宽矩阵为长矩阵

target_pathway_with_expr_long <- target_pathway_with_expr %>% gather(key = "Sample", value = "Expr", -ont, -gene)

head(target_pathway_with_expr_long)
##                ont  gene       Sample      Expr
## 1 HALLMARK_HYPOXIA  PGK1 untrt_N61311  7567.398
## 2 HALLMARK_HYPOXIA  PDK1 untrt_N61311  1009.850
## 3 HALLMARK_HYPOXIA  GBE1 untrt_N61311  3859.557
## 4 HALLMARK_HYPOXIA  PFKL untrt_N61311  3581.499
## 5 HALLMARK_HYPOXIA ALDOA untrt_N61311 19139.085
## 6 HALLMARK_HYPOXIA  ENO2 untrt_N61311  1964.796

合并样本信息

metadata$Sample <- rownames(metadata)
target_pathway_with_expr_conditions_long <- target_pathway_with_expr_long %>% left_join(metadata, by = "Sample")

head(target_pathway_with_expr_conditions_long)
##                ont  gene       Sample      Expr conditions individual
## 1 HALLMARK_HYPOXIA  PGK1 untrt_N61311  7567.398      untrt     N61311
## 2 HALLMARK_HYPOXIA  PDK1 untrt_N61311  1009.850      untrt     N61311
## 3 HALLMARK_HYPOXIA  GBE1 untrt_N61311  3859.557      untrt     N61311
## 4 HALLMARK_HYPOXIA  PFKL untrt_N61311  3581.499      untrt     N61311
## 5 HALLMARK_HYPOXIA ALDOA untrt_N61311 19139.085      untrt     N61311
## 6 HALLMARK_HYPOXIA  ENO2 untrt_N61311  1964.796      untrt     N61311

再次画点带图 (也不太好看)

ggstripchart(target_pathway_with_expr_conditions_long, x = "conditions", y = "Expr",
          ylab = "Expression", combine=F,
          color = "conditions", palette = "jco", 
          size = 0.1, jitter = 0.2,
          facet.by = "ont",
          add.params = list(color = "gray"),
          # add = "boxplot"
          add = "median_iqr")

表达数据log转换(减小高表达基因的影响)

target_pathway_with_expr_conditions_long$logExpr <- log2(target_pathway_with_expr_conditions_long$Expr + 1)
ggstripchart(target_pathway_with_expr_conditions_long, x = "conditions", y = "logExpr",
          ylab = "Expression", combine=F,
          color = "conditions", palette = "jco", 
          size = 0.1, jitter = 0.2,
          facet.by = "ont",
          add.params = list(color = "gray"),
          # add = "boxplot"
          add = "median_iqr")

head(target_pathway_with_expr_conditions_long)
##                ont  gene       Sample      Expr conditions individual   logExpr
## 1 HALLMARK_HYPOXIA  PGK1 untrt_N61311  7567.398      untrt     N61311 12.885772
## 2 HALLMARK_HYPOXIA  PDK1 untrt_N61311  1009.850      untrt     N61311  9.981353
## 3 HALLMARK_HYPOXIA  GBE1 untrt_N61311  3859.557      untrt     N61311 11.914593
## 4 HALLMARK_HYPOXIA  PFKL untrt_N61311  3581.499      untrt     N61311 11.806750
## 5 HALLMARK_HYPOXIA ALDOA untrt_N61311 19139.085      untrt     N61311 14.224310
## 6 HALLMARK_HYPOXIA  ENO2 untrt_N61311  1964.796      untrt     N61311 10.940898

提取P53通路进行后续分析

HALLMARK_P53_PATHWAY = target_pathway_with_expr_conditions_long[target_pathway_with_expr_conditions_long$ont=="HALLMARK_P53_PATHWAY",]
ggstripchart(HALLMARK_P53_PATHWAY, x = "conditions", y = "logExpr",
             title = "HALLMARK_P53_PATHWAY",
          ylab = "Expression",
          color = "conditions", palette = "jco", 
          size = 0.1, jitter = 0.2,
          add.params = list(color = "gray"),
          # add = "boxplot"
          add = "median_iqr")

ggdotplot(HALLMARK_P53_PATHWAY, x = "conditions", y = "logExpr",
             title = "HALLMARK_P53_PATHWAY",
          ylab = "Expression",
          color = "conditions", palette = "jco", 
          fill = "white",
          binwidth = 0.1,
          add.params = list(size = 0.9),
          # add = "boxplot"
          add = "median_iqr")

7.5.1 密度图

ggdensity(HALLMARK_P53_PATHWAY,
       x="logExpr",
       y = "..density..",
       combine = TRUE,                  # Combine the 3 plots
       xlab = "Expression", 
       add = "median",                  # Add median line. 
       rug = TRUE,                      # Add marginal rug
       color = "conditions", 
       fill = "conditions",
       palette = "jco"
)

head(top6_t_with_group)
##          Sample conditions individual    KCTD12      MAOA  ARHGEF2    SPARCL1
## 1   trt_N052611        trt    N052611  633.4462 4429.7201 1441.992 1750.98786
## 2   trt_N061011        trt    N061011  641.4958 3778.1735 1501.515 2194.58930
## 3   trt_N080611        trt    N080611  979.7758 4629.6653 1464.598 1374.90745
## 4    trt_N61311        trt     N61311  936.6948 4628.0086 1395.398 2220.50867
## 5 untrt_N052611      untrt    N052611 3978.0401  452.9934 3105.783  102.58269
## 6 untrt_N061011      untrt    N061011 4792.3417  258.7328 2909.990   82.59042
##        PER1    SLC6A9
## 1 1230.2575  56.89620
## 2 1333.9121  95.33916
## 3 1120.0065  86.82929
## 4 1728.3812  63.90538
## 5  156.3692 413.87971
## 6  123.4769 443.71982
top6_t_with_group_long = top6_t_with_group %>% gather(key = "Gene", value = "Expr", -conditions, -Sample, -individual)
top6_t_with_group_long$conditions <- as.factor(top6_t_with_group_long$conditions)
head(top6_t_with_group_long)
##          Sample conditions individual   Gene      Expr
## 1   trt_N052611        trt    N052611 KCTD12  633.4462
## 2   trt_N061011        trt    N061011 KCTD12  641.4958
## 3   trt_N080611        trt    N080611 KCTD12  979.7758
## 4    trt_N61311        trt     N61311 KCTD12  936.6948
## 5 untrt_N052611      untrt    N052611 KCTD12 3978.0401
## 6 untrt_N061011      untrt    N061011 KCTD12 4792.3417

7.6 ggstatsplot绘图和统计分析

箱线图

library(ggstatsplot)
ggstatsplot::ggwithinstats(
  data = top6_t_with_group,
  x = conditions,
  y = PER1,
  sort = "descending", # ordering groups along the x-axis based on
  sort.fun = median, # values of `y` variable
  pairwise.comparisons = TRUE,
  pairwise.display = "s",
  pairwise.annotation = "p",
  title = "PER1",
  caption = "PER1 compare",
  ggstatsplot.layer = FALSE,
  messages = FALSE
)

head(target_pathway_with_expr_conditions_long)
##                ont  gene       Sample      Expr conditions individual   logExpr
## 1 HALLMARK_HYPOXIA  PGK1 untrt_N61311  7567.398      untrt     N61311 12.885772
## 2 HALLMARK_HYPOXIA  PDK1 untrt_N61311  1009.850      untrt     N61311  9.981353
## 3 HALLMARK_HYPOXIA  GBE1 untrt_N61311  3859.557      untrt     N61311 11.914593
## 4 HALLMARK_HYPOXIA  PFKL untrt_N61311  3581.499      untrt     N61311 11.806750
## 5 HALLMARK_HYPOXIA ALDOA untrt_N61311 19139.085      untrt     N61311 14.224310
## 6 HALLMARK_HYPOXIA  ENO2 untrt_N61311  1964.796      untrt     N61311 10.940898
head(HALLMARK_P53_PATHWAY)
##                      ont   gene       Sample       Expr conditions individual
## 322 HALLMARK_P53_PATHWAY CDKN1A untrt_N61311 14406.1316      untrt     N61311
## 323 HALLMARK_P53_PATHWAY   BTG2 untrt_N61311  1163.7198      untrt     N61311
## 324 HALLMARK_P53_PATHWAY   MDM2 untrt_N61311  3614.5324      untrt     N61311
## 325 HALLMARK_P53_PATHWAY  CCNG1 untrt_N61311  5749.1367      untrt     N61311
## 326 HALLMARK_P53_PATHWAY    FAS untrt_N61311  1029.4007      untrt     N61311
## 327 HALLMARK_P53_PATHWAY   TOB1 untrt_N61311   829.7721      untrt     N61311
##       logExpr
## 322 13.814496
## 323 10.185767
## 324 11.819992
## 325 12.489381
## 326 10.008990
## 327  9.698309
library(ggstatsplot)
ggstatsplot::ggwithinstats(
  data = HALLMARK_P53_PATHWAY,
  x = conditions,
  y = logExpr,
  sort = "descending", # ordering groups along the x-axis based on
  sort.fun = median, # values of `y` variable
  pairwise.comparisons = TRUE,
  pairwise.display = "s",
  pairwise.annotation = "p",
  title = "HALLMARK_P53_PATHWAY",
  path.point = F,
  ggtheme = ggthemes::theme_fivethirtyeight(),
  ggstatsplot.layer = FALSE,
  messages = FALSE
)

library(ggstatsplot)

ggstatsplot::grouped_ggwithinstats(
  data = target_pathway_with_expr_conditions_long,
  x = conditions,
  y = logExpr,
  grouping.var = ont,
  xlab = "Condition",
  ylab = "CEMIP expression",
  path.point = F,
  palette = "Set1", # R color brewer
  ggstatsplot.layer = FALSE,
  messages = FALSE
)

ggstatsplot::grouped_ggwithinstats(data = top6_t_with_group_long, x = conditions, y = Expr, xlab = "Condition", ylab = "CEMIP expression", 
    grouping.var = Gene, ggstatsplot.layer = FALSE, messages = FALSE)

head(expr)
##        untrt_N61311 untrt_N052611 untrt_N080611 untrt_N061011 trt_N61311
## FN1       245667.66      427435.1     221687.51      371144.2  240187.24
## DCN       212953.14      360796.2     258977.30      408573.1  210002.18
## CEMIP      40996.34      137783.1      53813.92       91066.8   62301.12
## CCDC80    137229.15      232772.2      86258.13      212237.3  136730.76
## IGFBP5     77812.65      288609.2     210628.87      168067.4   96021.74
## COL1A1    146450.41      127367.3     152281.50      140861.1   62358.64
##        trt_N052611 trt_N080611 trt_N061011
## FN1      450103.21   280226.19   376518.23
## DCN      316009.14   225547.39   393843.74
## CEMIP    223111.85   212724.84   157919.47
## CCDC80   226070.89   124634.56   236237.81
## IGFBP5   217439.21   162677.38   168387.36
## COL1A1    53800.47    69160.97    51044.06

7.6.1 散点图

ggstatsplot::ggscatterstats(data = expr, x = untrt_N61311, y = untrt_N052611, xlab = "untrt_N61311", ylab = "untrt_N052611", 
    title = "Sample correlation", messages = FALSE)

ggstatsplot::ggscatterstats(
  data = log2(expr+1),
  x = untrt_N61311,
  y = trt_N61311,
  xlab = "untrt_N61311",
  ylab = "trt_N61311",
  title = "Sample correlation",
  #marginal.type = "density", # type of marginal distribution to be displayed
  messages = FALSE
)

7.6.2 相关性图

7.6.2.1 基因共表达

gene_cor <- cor(t(top6))

head(gene_cor)
##             KCTD12       MAOA    ARHGEF2    SPARCL1       PER1     SLC6A9
## KCTD12   1.0000000 -0.9792624  0.9799663 -0.9619660 -0.9529732  0.9772852
## MAOA    -0.9792624  1.0000000 -0.9897706  0.9406196  0.9614877 -0.9871408
## ARHGEF2  0.9799663 -0.9897706  1.0000000 -0.9628750 -0.9660416  0.9791535
## SPARCL1 -0.9619660  0.9406196 -0.9628750  1.0000000  0.9853858 -0.9510121
## PER1    -0.9529732  0.9614877 -0.9660416  0.9853858  1.0000000 -0.9615253
## SLC6A9   0.9772852 -0.9871408  0.9791535 -0.9510121 -0.9615253  1.0000000
pheatmap(gene_cor)

Hmisc::rcorr(as.matrix(top6_t))
##         KCTD12  MAOA ARHGEF2 SPARCL1  PER1 SLC6A9
## KCTD12    1.00 -0.98    0.98   -0.96 -0.95   0.98
## MAOA     -0.98  1.00   -0.99    0.94  0.96  -0.99
## ARHGEF2   0.98 -0.99    1.00   -0.96 -0.97   0.98
## SPARCL1  -0.96  0.94   -0.96    1.00  0.99  -0.95
## PER1     -0.95  0.96   -0.97    0.99  1.00  -0.96
## SLC6A9    0.98 -0.99    0.98   -0.95 -0.96   1.00
## 
## n= 8 
## 
## 
## P
##         KCTD12 MAOA  ARHGEF2 SPARCL1 PER1  SLC6A9
## KCTD12         0e+00 0e+00   1e-04   3e-04 0e+00 
## MAOA    0e+00        0e+00   5e-04   1e-04 0e+00 
## ARHGEF2 0e+00  0e+00         1e-04   0e+00 0e+00 
## SPARCL1 1e-04  5e-04 1e-04           0e+00 3e-04 
## PER1    3e-04  1e-04 0e+00   0e+00         1e-04 
## SLC6A9  0e+00  0e+00 0e+00   3e-04   1e-04
head(top6_t)
##                  KCTD12      MAOA  ARHGEF2    SPARCL1      PER1    SLC6A9
## untrt_N61311  4700.7937  438.5445 3025.623   58.15705  170.6164 360.66314
## untrt_N052611 3978.0401  452.9934 3105.783  102.58269  156.3692 413.87971
## untrt_N080611 4416.1517  516.6303 3094.513   80.00997  194.9750 365.47650
## untrt_N061011 4792.3417  258.7328 2909.990   82.59042  123.4769 443.71982
## trt_N61311     936.6948 4628.0086 1395.398 2220.50867 1728.3812  63.90538
## trt_N052611    633.4462 4429.7201 1441.992 1750.98786 1230.2575  56.89620
ggstatsplot::ggcorrmat(
  data = top6_t,
  corr.method = "robust", # correlation method
  sig.level = 0.0001, # threshold of significance
  p.adjust.method = "holm", # p-value adjustment method for multiple comparisons
  # cor.vars = c(sleep_rem, awake:bodywt), # a range of variables can be selected
  # cor.vars.names = c(
  #   "REM sleep", # variable names
  #   "time awake",
  #   "brain weight",
  #   "body weight"
  # ),
  matrix.type = "upper", # type of visualization matrix
  palette = "Set2",
  #colors = c("#B2182B", "white", "#4D4D4D"),
  title = "Correlalogram for mammals sleep dataset",
  subtitle = "sleep units: hours; weight units: kilograms"
)

7.6.2.2 样品相关性

top100 <- head(expr,100)
ggstatsplot::ggcorrmat(
  data = top100,
  corr.method = "robust", # correlation method
  sig.level = 0.05, # threshold of significance
  p.adjust.method = "holm", # p-value adjustment method for multiple comparisons
  # cor.vars = c(sleep_rem, awake:bodywt), # a range of variables can be selected
  # cor.vars.names = c(
  #   "REM sleep", # variable names
  #   "time awake",
  #   "brain weight",
  #   "body weight"
  # ),
  matrix.type = "upper", # type of visualization matrix
  palette = "Set2"
  #colors = c("#B2182B", "white", "#4D4D4D"),

)