从一套表达和通路数据学习常见的绘图展示方式和报错处理
生物信息学习的正确姿势
NGS系列文章包括NGS基础、高颜值在线绘图和分析、转录组分析 (Nature重磅综述|关于RNA-seq你想知道的全在这)、ChIP-seq分析 (ChIP-seq基本分析流程)、单细胞测序分析 (重磅综述:三万字长文读懂单细胞RNA测序分析的最佳实践教程)、DNA甲基化分析、重测序分析、GEO数据挖掘(典型医学设计实验GEO数据分析 (step-by-step))、批次效应处理等内容。
加载需要的包
library(dplyr)
library(ggpubr)
library(tidyr)
library(ggplot2)
library(pheatmap)
library(ggstatsplot)
library(Hmisc)
读入数据
’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")
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 trt_N052611 trt_N080611 trt_N061011
## 1 FN1 245667.66 427435.1 221687.51 371144.2 240187.24 450103.21 280226.19 376518.23
## 2 DCN 212953.14 360796.2 258977.30 408573.1 210002.18 316009.14 225547.39 393843.74
## 3 CEMIP 40996.34 137783.1 53813.92 91066.8 62301.12 223111.85 212724.84 157919.47
## 4 CCDC80 137229.15 232772.2 86258.13 212237.3 136730.76 226070.89 124634.56 236237.81
## 5 IGFBP5 77812.65 288609.2 210628.87 168067.4 96021.74 217439.21 162677.38 168387.36
## 6 COL1A1 146450.41 127367.3 152281.50 140861.1 62358.64 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" "ATXN7" "TMSB15B" "LINC-PINT"
## [9] "TBCE" "SNX29P2" "SCO2" "POLR2J4" "CCDC39" "RGS5" "BMS1P21" "RF00017"
## [17] "GOLGA8M" "RF00017" "DNAJC9-AS1" "CYB561D2" "RF00017" "IPO5P1" "RF00017" "RF00017"
## [25] "RF00017" "SPATA13" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017"
## [33] "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017"
## [41] "RF00019" "RF00019" "RF00017" "RF00017" "RF00017" "RF00019" "BMS1P4" "RF00019"
## [49] "RF00019" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017"
## [57] "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00019"
## [65] "RF00017" "RF00017" "RF00017" "RF00019" "RF00017" "RF00017" "LINC01238" "RF00017"
## [73] "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017"
## [81] "RF00017" "RF00017" "RF02271" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017"
## [89] "LINC01297" "RF00019" "RF00017" "RF00012" "RF00019" "RF00017" "RF00017" "RF00019"
## [97] "RF00017" "RF00017" "RF00017" "ZNF503" "RF00017" "RF00017" "RF00017" "RF00017"
## [105] "RF00017" "RF00017" "RF00017" "RF00017" "RF02271" "RF00019" "RF00019" "RF00017"
## [113] "RF00019" "RF02271" "RF00017" "RF00017" "RF00017" "RF00017" "RF00019" "RF00019"
## [121] "RF00017" "RF00019" "ITFG2-AS1" "RF00019" "RF00019" "RF00017" "RF00019" "RF00017"
## [129] "RF00017" "RF00017" "RF00019" "RF00017" "RF00012" "RF00017" "RF00017" "RAET1E-AS1"
## [137] "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RF00017"
## [145] "RF00012" "RF02271" "RF00019" "LINC01422" "RF02271" "RF00017" "RF00019" "RF00019"
## [153] "RF00019" "RF00019" "RF00017" "LINC01481" "RF00017" "SNHG28" "RF00019" "RF00019"
## [161] "RF00019" "RF00019" "LINC00484" "LINC00941" "ALG1L9P" "RF00017" "DUXAP8" "RF00017"
## [169] "RF00017" "RF00017" "RF00017" "RF00017" "RF00017" "RMRP" "RF00017" "RF00017"
## [177] "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$id, unique = T)
rownames(expr) <- expr_names
expr <- expr[, -1]
head(expr)## untrt_N61311 untrt_N052611 untrt_N080611 untrt_N061011 trt_N61311 trt_N052611 trt_N080611 trt_N061011
## FN1 245667.66 427435.1 221687.51 371144.2 240187.24 450103.21 280226.19 376518.23
## DCN 212953.14 360796.2 258977.30 408573.1 210002.18 316009.14 225547.39 393843.74
## CEMIP 40996.34 137783.1 53813.92 91066.8 62301.12 223111.85 212724.84 157919.47
## CCDC80 137229.15 232772.2 86258.13 212237.3 136730.76 226070.89 124634.56 236237.81
## IGFBP5 77812.65 288609.2 210628.87 168067.4 96021.74 217439.21 162677.38 168387.36
## COL1A1 146450.41 127367.3 152281.50 140861.1 62358.64 53800.47 69160.97 51044.06
热图绘制
library(pheatmap)
top6 <- head(expr)
pheatmap(top6)
提取差异基因绘制热图
读入差异基因列表
de_gene <- read.table("ehbio.DESeq2.all.DE.symbol", row.names = NULL, header = F, sep = "\t")
head(de_gene)## V1 V2
## 1 ARHGEF2 untrt._higherThan_.trt
## 2 KCTD12 untrt._higherThan_.trt
## 3 SLC6A9 untrt._higherThan_.trt
## 4 GXYLT2 untrt._higherThan_.trt
## 5 RAB7B untrt._higherThan_.trt
## 6 NEK10 untrt._higherThan_.trt
提取Top3 差异的基因
# library(dplyr)
top6_de_gene <- de_gene %>% group_by(V2) %>% dplyr::slice(1:3)
top6 <- expr[which(rownames(expr) %in% top6_de_gene$V1), ]
head(top6)## untrt_N61311 untrt_N052611 untrt_N080611 untrt_N061011 trt_N61311 trt_N052611 trt_N080611 trt_N061011
## KCTD12 4700.79369 3978.0401 4416.15169 4792.34174 936.69481 633.4462 979.77576 641.49582
## MAOA 438.54451 452.9934 516.63033 258.73279 4628.00860 4429.7201 4629.66529 3778.17351
## ARHGEF2 3025.62334 3105.7830 3094.51304 2909.99043 1395.39850 1441.9916 1464.59769 1501.51509
## SPARCL1 58.15705 102.5827 80.00997 82.59042 2220.50867 1750.9879 1374.90745 2194.58930
## PER1 170.61639 156.3692 194.97497 123.47689 1728.38117 1230.2575 1120.00650 1333.91208
## SLC6A9 360.66314 413.8797 365.47650 443.71982 63.90538 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)
箱线图和统计比较
head(top6)## untrt_N61311 untrt_N052611 untrt_N080611 untrt_N061011 trt_N61311 trt_N052611 trt_N080611 trt_N061011
## KCTD12 4700.79369 3978.0401 4416.15169 4792.34174 936.69481 633.4462 979.77576 641.49582
## MAOA 438.54451 452.9934 516.63033 258.73279 4628.00860 4429.7201 4629.66529 3778.17351
## ARHGEF2 3025.62334 3105.7830 3094.51304 2909.99043 1395.39850 1441.9916 1464.59769 1501.51509
## SPARCL1 58.15705 102.5827 80.00997 82.59042 2220.50867 1750.9879 1374.90745 2194.58930
## PER1 170.61639 156.3692 194.97497 123.47689 1728.38117 1230.2575 1120.00650 1333.91208
## SLC6A9 360.66314 413.8797 365.47650 443.71982 63.90538 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 PER1 SLC6A9
## 1 trt_N052611 trt N052611 633.4462 4429.7201 1441.992 1750.98786 1230.2575 56.89620
## 2 trt_N061011 trt N061011 641.4958 3778.1735 1501.515 2194.58930 1333.9121 95.33916
## 3 trt_N080611 trt N080611 979.7758 4629.6653 1464.598 1374.90745 1120.0065 86.82929
## 4 trt_N61311 trt N61311 936.6948 4628.0086 1395.398 2220.50867 1728.3812 63.90538
## 5 untrt_N052611 untrt N052611 3978.0401 452.9934 3105.783 102.58269 156.3692 413.87971
## 6 untrt_N061011 untrt N061011 4792.3417 258.7328 2909.990 82.59042 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 PER1 SLC6A9
## 1 trt_N052611 trt N052611 633.4462 4429.7201 1441.992 1750.98786 1230.2575 56.89620
## 2 trt_N061011 trt N061011 641.4958 3778.1735 1501.515 2194.58930 1333.9121 95.33916
## 3 trt_N080611 trt N080611 979.7758 4629.6653 1464.598 1374.90745 1120.0065 86.82929
## 4 trt_N61311 trt N61311 936.6948 4628.0086 1395.398 2220.50867 1728.3812 63.90538
## 5 untrt_N052611 untrt N052611 3978.0401 452.9934 3105.783 102.58269 156.3692 413.87971
## 6 untrt_N061011 untrt N061011 4792.3417 258.7328 2909.990 82.59042 123.4769 443.71982
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,
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")
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")
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 PER1 SLC6A9
## 1 trt_N052611 trt N052611 6.452752 8.396317 7.274474 7.468506 7.115791 4.058652
## 2 trt_N061011 trt N061011 6.465360 8.237261 7.314896 7.694206 7.196621 4.567875
## 3 trt_N080611 trt N080611 6.888344 8.440456 7.290018 7.226869 7.021982 4.475395
## 4 trt_N61311 trt N61311 6.843425 8.440098 7.241652 7.705942 7.455519 4.172930
## 5 untrt_N052611 untrt N052611 8.288796 6.118083 8.041343 4.640370 5.058595 6.027989
## 6 untrt_N061011 untrt N061011 8.474983 5.559653 7.976249 4.425929 4.824120 6.097444ggboxplot(top6_t_with_group_log, x = "conditions", y = c("KCTD12", "MAOA", "PER1", "SLC6A9"), ylab = "Expression", merge = "flip", fill = "conditions", palette = "Set3")
用ggplot2实现ggpubr
head(top6_t_with_group)## Sample conditions individual KCTD12 MAOA ARHGEF2 SPARCL1 PER1 SLC6A9
## 1 trt_N052611 trt N052611 633.4462 4429.7201 1441.992 1750.98786 1230.2575 56.89620
## 2 trt_N061011 trt N061011 641.4958 3778.1735 1501.515 2194.58930 1333.9121 95.33916
## 3 trt_N080611 trt N080611 979.7758 4629.6653 1464.598 1374.90745 1120.0065 86.82929
## 4 trt_N61311 trt N61311 936.6948 4628.0086 1395.398 2220.50867 1728.3812 63.90538
## 5 untrt_N052611 untrt N052611 3978.0401 452.9934 3105.783 102.58269 156.3692 413.87971
## 6 untrt_N061011 untrt N061011 4792.3417 258.7328 2909.990 82.59042 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 360.66314library(ggplot2)
ggplot(top6_t_with_group_melt, aes(x = Gene, y = Expr)) + geom_boxplot(aes(color = conditions)) + theme_classic()
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")
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 pointsadd.params = list(size = 0.1, jitter = 0.2), # Point size and the amount of jitteringlabel = "Sample", # column containing point labelslabel.select = list(top.up = 2, top.down = 2),# Select some labels to displayfont.label = list(size = 9, face = "italic"), # label fontrepel = TRUE, # Avoid label text overplottingfill = "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 pointsadd.params = list(size = 0.1, jitter = 0.2), # Point size and the amount of jitteringlabel = "Sample", # column containing point labelslabel.select = list(top.up = 2, top.down = 2),# Select some labels to displayfont.label = list(size = 9, face = "italic"), # label fontrepel = TRUE, # Avoid label text overplottingfill = "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")
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_PATHWAYtarget_pathway <- pathway[pathway$ont %in% c("HALLMARK_HYPOXIA", "HALLMARK_DNA_REPAIR", "HALLMARK_P53_PATHWAY"), ]target_pathway <- droplevels.data.frame(target_pathway)summary(target_pathway)## ont gene
## Length:550 Length:550
## Class :character Class :character
## Mode :character Mode :characterhead(target_pathway)## ont gene
## 201 HALLMARK_HYPOXIA PGK1
## 202 HALLMARK_HYPOXIA PDK1
## 203 HALLMARK_HYPOXIA GBE1
## 204 HALLMARK_HYPOXIA PFKL
## 205 HALLMARK_HYPOXIA ALDOA
## 206 HALLMARK_HYPOXIA ENO2
表达矩阵提取
expr_with_gene <- expr
expr_with_gene$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 untrt_N080611
## Length:550 Length:550 Min. : 0.0 Min. : 0.0 Min. : 0.0
## Class :character Class :character 1st Qu.: 254.2 1st Qu.: 240.8 1st Qu.: 235.0
## Mode :character Mode :character Median : 781.3 Median : 784.1 Median : 734.9
## Mean : 2528.6 Mean : 2895.1 Mean : 2549.2
## 3rd Qu.: 1852.4 3rd Qu.: 1727.2 3rd Qu.: 1932.4
## Max. :212953.1 Max. :360796.2 Max. :258977.3
## NA's :36 NA's :36 NA's :36
## untrt_N061011 trt_N61311 trt_N052611 trt_N080611 trt_N061011
## Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 237.9 1st Qu.: 248.2 1st Qu.: 211.0 1st Qu.: 250.6 1st Qu.: 227.9
## Median : 764.2 Median : 766.6 Median : 723.2 Median : 739.3 Median : 746.0
## Mean : 2864.9 Mean : 2531.8 Mean : 2783.3 Mean : 2840.3 Mean : 3043.6
## 3rd Qu.: 1870.0 3rd Qu.: 1872.4 3rd Qu.: 1832.2 3rd Qu.: 1825.8 3rd Qu.: 1925.1
## Max. :408573.1 Max. :210002.2 Max. :316009.1 Max. :225547.4 Max. :393843.7
## NA's :36 NA's :36 NA's :36 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 untrt_N080611
## Length:514 Length:514 Min. : 0.0 Min. : 0.0 Min. : 0.0
## Class :character Class :character 1st Qu.: 254.2 1st Qu.: 240.8 1st Qu.: 235.0
## Mode :character Mode :character Median : 781.3 Median : 784.1 Median : 734.9
## Mean : 2528.6 Mean : 2895.1 Mean : 2549.2
## 3rd Qu.: 1852.4 3rd Qu.: 1727.2 3rd Qu.: 1932.4
## Max. :212953.1 Max. :360796.2 Max. :258977.3
## untrt_N061011 trt_N61311 trt_N052611 trt_N080611 trt_N061011
## Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 237.9 1st Qu.: 248.2 1st Qu.: 211.0 1st Qu.: 250.6 1st Qu.: 227.9
## Median : 764.2 Median : 766.6 Median : 723.2 Median : 739.3 Median : 746.0
## Mean : 2864.9 Mean : 2531.8 Mean : 2783.3 Mean : 2840.3 Mean : 3043.6
## 3rd Qu.: 1870.0 3rd Qu.: 1872.4 3rd Qu.: 1832.2 3rd Qu.: 1825.8 3rd Qu.: 1925.1
## Max. :408573.1 Max. :210002.2 Max. :316009.1 Max. :225547.4 Max. :393843.7head(target_pathway_with_expr)## ont gene untrt_N61311 untrt_N052611 untrt_N080611 untrt_N061011 trt_N61311 trt_N052611
## 1 HALLMARK_HYPOXIA PGK1 7567.398 7893.2150 6254.5945 5529.122 7595.0408 6969.6128
## 2 HALLMARK_HYPOXIA PDK1 1009.850 1042.4868 735.9359 673.208 419.6273 365.0062
## 3 HALLMARK_HYPOXIA GBE1 3859.557 1494.4120 3803.5627 3295.191 4769.5464 2359.7150
## 4 HALLMARK_HYPOXIA PFKL 3581.499 3018.0675 2789.4430 3084.570 2867.2464 2599.5095
## 5 HALLMARK_HYPOXIA ALDOA 19139.085 19587.3216 18089.5116 15519.899 16388.1123 13949.5659
## 6 HALLMARK_HYPOXIA ENO2 1964.796 979.5255 1041.4660 1288.837 1303.5671 766.9436
## trt_N080611 trt_N061011
## 1 15011.823 6076.4392
## 2 1056.622 383.6163
## 3 4759.809 4296.5471
## 4 4399.403 3090.6701
## 5 22630.701 14374.3437
## 6 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")
密度图
ggdensity(HALLMARK_P53_PATHWAY,x="logExpr",y = "..density..",combine = TRUE, # Combine the 3 plotsxlab = "Expression", add = "median", # Add median line. rug = TRUE, # Add marginal rugcolor = "conditions", fill = "conditions",palette = "jco"
)
head(top6_t_with_group)## Sample conditions individual KCTD12 MAOA ARHGEF2 SPARCL1 PER1 SLC6A9
## 1 trt_N052611 trt N052611 633.4462 4429.7201 1441.992 1750.98786 1230.2575 56.89620
## 2 trt_N061011 trt N061011 641.4958 3778.1735 1501.515 2194.58930 1333.9121 95.33916
## 3 trt_N080611 trt N080611 979.7758 4629.6653 1464.598 1374.90745 1120.0065 86.82929
## 4 trt_N61311 trt N61311 936.6948 4628.0086 1395.398 2220.50867 1728.3812 63.90538
## 5 untrt_N052611 untrt N052611 3978.0401 452.9934 3105.783 102.58269 156.3692 413.87971
## 6 untrt_N061011 untrt N061011 4792.3417 258.7328 2909.990 82.59042 123.4769 443.71982top6_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
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 onsort.fun = median, # values of `y` variablepairwise.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 10.940898head(HALLMARK_P53_PATHWAY)## ont gene Sample Expr conditions individual logExpr
## 322 HALLMARK_P53_PATHWAY CDKN1A untrt_N61311 14406.1316 untrt N61311 13.814496
## 323 HALLMARK_P53_PATHWAY BTG2 untrt_N61311 1163.7198 untrt N61311 10.185767
## 324 HALLMARK_P53_PATHWAY MDM2 untrt_N61311 3614.5324 untrt N61311 11.819992
## 325 HALLMARK_P53_PATHWAY CCNG1 untrt_N61311 5749.1367 untrt N61311 12.489381
## 326 HALLMARK_P53_PATHWAY FAS untrt_N61311 1029.4007 untrt N61311 10.008990
## 327 HALLMARK_P53_PATHWAY TOB1 untrt_N61311 829.7721 untrt N61311 9.698309library(ggstatsplot)
ggstatsplot::ggwithinstats(data = HALLMARK_P53_PATHWAY,x = conditions,y = logExpr,sort = "descending", # ordering groups along the x-axis based onsort.fun = median, # values of `y` variablepairwise.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 brewerggstatsplot.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 trt_N052611 trt_N080611 trt_N061011
## FN1 245667.66 427435.1 221687.51 371144.2 240187.24 450103.21 280226.19 376518.23
## DCN 212953.14 360796.2 258977.30 408573.1 210002.18 316009.14 225547.39 393843.74
## CEMIP 40996.34 137783.1 53813.92 91066.8 62301.12 223111.85 212724.84 157919.47
## CCDC80 137229.15 232772.2 86258.13 212237.3 136730.76 226070.89 124634.56 236237.81
## IGFBP5 77812.65 288609.2 210628.87 168067.4 96021.74 217439.21 162677.38 168387.36
## COL1A1 146450.41 127367.3 152281.50 140861.1 62358.64 53800.47 69160.97 51044.06
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 displayedmessages = FALSE
)
相关性图
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 1.0000000pheatmap(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-04head(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 56.89620ggstatsplot::ggcorrmat(data = top6_t,corr.method = "robust", # correlation methodsig.level = 0.0001, # threshold of significancep.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 matrixpalette = "Set2",#colors = c("#B2182B", "white", "#4D4D4D"),title = "Correlalogram for mammals sleep dataset",subtitle = "sleep units: hours; weight units: kilograms"
)
样品相关性
top100 <- head(expr,100)
ggstatsplot::ggcorrmat(data = top100,corr.method = "robust", # correlation methodsig.level = 0.05, # threshold of significancep.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 matrixpalette = "Set2"#colors = c("#B2182B", "white", "#4D4D4D"),)
节选自:这个为生信学习和生信作图打造的开源R教程真香!!!
大部分图都可在 高颜值免费在线绘图工具升级版来了~~~制作了
从一套表达和通路数据学习常见的绘图展示方式和报错处理相关推荐
- 今日发现导出dbf类型为dBASE IV的文件,数据列不能超过40个,否则报错“字段将不能置于记录中”
今日发现导出dbf类型为dBASE IV的文件,数据列不能超过40个,否则报错"字段将不能置于记录中" 一开始还以为其他问题呢,结果逐步排除,中英文列名,均可,各种定义列方式,最后 ...
- OpenCV学习笔记(一):OpenCV报错“libpng error: IDAT: invalid distance too far back”
这一阵子在学习用OpenCV的SVM+HOG进行行人捕捉,在学习网上源码的时候使用了OpenCV官方的训练数据集,cv2的库在read训练数据的时候,有那么几个文件夹里面的照片是没办法直接读取,会报错 ...
- 学习笔记二:IBIS模型编辑报错纠正
新建IBIS文件后,写好了相应的IBIS模型文件. 写好文件后,最好要检查下是否符合ibis语法. 注意: 第一点:IBIS头文件的[File name],此处名称需要和文件名称相同,同时名称字符数需 ...
- Sampler 在数据下沉模式超时; 不同Sampler策略,在非数据下沉模式下,模型训练失败 报错Segmentation fault(core dumped)
环境版本 mindspore-ascend 1.7.1 背景 我在使用自定义Sampler策略,对imagenet1K数据集进行采样,进行ResNet50训练. Sampler代码如下: class ...
- 大数据——那些年走过的坑(异常报错解决方案,持续更新)
1. Sqoop将mysql中表导入到hive遇到 ERROR hive.HiveConfig: Could not load org.apache.hadoop.hive.conf.HiveConf ...
- 【原创】VBA学习笔记(7)VBA各种报错和原因备忘
0 报错经验的查询 报错,发生在报错的那一行 报错,也经常发生在报错的前一行 所以debug 要重点看,报错行和报错行的下一行! 报错,也经常发生在报错的前一行的例子 下面这个报错,其实是fn2=di ...
- 大数据学习——hive阶段:启动HiveServer2启动报TezConfiguration类NoClassDefFoundError错误
背景说明:在hive-site.xm配置metastore之前启动了一把HiveServer2,报错: WARN jdbc.HiveConnection: Failed to connect to h ...
- vue-axios下载文件流blob,ie下载报传递给系统调用的数据区域太小.ie文件流下载报错;文件下载失败将blob的错误信息转换成json格式
本次下载是后台文件流传输,前端下载,前端将拿到的下载id和名称downloadName传递给下载方法:如果是多个下载,可以采用数组for循环 情景描述: 1.如果符合导出条件, 后端直接返回数据流,如 ...
- Java Json数据中有双引号未转义的解析报错
例如Json数据中有双引号,解析时会报Json格式错误或者转义有问题 {"content": "重要任务提醒","matterName": ...
最新文章
- 【DirectX12】4.用FBX_SDK读取网格数据
- python合并两个数据框_python 学习的第五天 数据框合并
- Maven,Eclipse和Java 9
- c/c++ 前置声明 -- typedef问题
- 【Flink】Flink RecordWriterOutput pushToRecordWriter InterruptedException
- 敏捷开发日常跟进系列之一:燃尽图(上)
- 「图与推荐指南针」: 顶级学者/研究组有哪些?
- 服务器 虚拟声卡,虚拟声卡,小编教你怎么安装虚拟声卡
- 半年时间破获电信诈骗大案:我是如何利用数据分析建模,从零基础做到的
- 移动硬盘删除的文件如何恢复呢?
- 重启计算机连线的标志是,win10右下角总出现一个小地球图标怎么办_win10电脑网络连接图标变成地球如何解决...
- 项目经理的主要工作内容职责有哪些?
- 测试用例优先级划分_测试用例的重要级别如何划分?
- 蓝桥杯JAVA-7.集合(容器)在竞赛中的使用
- i7 7700hq安装Linux,翻出了自己吃灰的IfunkS i7-7700HQ笔记本 装了个ubuntu跑个分
- 031:山寨版istream_iterator
- linus开启snmp_Linux开通snmp
- c++实现代码执行时间计时器(精确到秒)
- Jenkins 搭建C++持续集成环境
- ✠OpenGL-5-纹理贴图
热门文章
- 填涂颜色(洛谷P1162题题解,Java语言描述)
- 无人机内嵌计算机Manifold 2可部署容器化应用
- 拥抱变化——从Atlas到ASP.NET AJAX(4):大大简化的了的Extender扩展器控件
- 《团队作业》五小福团队--UNO的博客链接汇总
- centos7安装redmine3,并升级redmine1.8到3
- 42.Linux应用调试-初步制作系统调用(用户态-内核态)
- idea历史版本下载
- Windows下安装python2和python3双版本
- CentOS FTP服务(vsftpd)配置
- 程序员经典面试题,高并发系统,一般需要怎么做