增强火山图，试一下？

Kevin Blighe 协和医学院苑晓梅

2019-06-03

前言

最近道听途说EnhancedVolcano绘制火山图的方便性，所以本人就根据其说明文档进行操作。但在操作过程中发现，其shape功能并没有在help文档中找到，经过搜索在github上看到了以下的答复。。。（说明整个文档功能并没有完全开发，需进行选择）

1 Introduction

火山图是可视化差异表达分析结果的有效方法。这次更新的EnhancedVolcano目的就是两个（1）使转录本基因名称的显示更加的合理化，避免出现相互重叠的现象；（2）允许用户通过颜色，形状和阴影参数配置在同一绘图空间中识别多达3种不同类型的属性。

2 Installation

2.1 1. 下载安装包

# if (!requireNamespace('BiocManager', quietly = TRUE))
#    install.packages('BiocManager')
# BiocManager::install('EnhancedVolcano')
if (!requireNamespace('devtools', quietly = TRUE))install.packages('devtools')
devtools::install_github('kevinblighe/EnhancedVolcano')

2.2 2. 加载R包

 library(EnhancedVolcano)

3 开始

作者使用该流程： RNA-seq workflow: gene-level exploratory analysis and differential expression。具体来说，我们将加载airway数据，其中不同的气道平滑肌细胞用地塞米松治疗。

library(airway)
library(magrittr)data('airway')
# %<>%复合赋值操作符， 功能与 %>% 基本是一样的，但多了一项额外的操作，就是把结果写到左侧对象。
# 对dex列进行relevel，再把revel后的结果赋值到airway$dex。
airway$dex %<>% relevel('untrt')

使用DESeq2进行差异表达，以创建两组结果(DESeq2差异基因分析和批次效应移除)：

  library('DESeq2')dds <- DESeqDataSet(airway, design = ~ cell + dex)dds <- DESeq(dds, betaPrior=FALSE)# compare trt & untrtres1 <- results(dds,contrast = c('dex','trt','untrt'))# shrink log2 fold changeres1 <- lfcShrink(dds,contrast = c('dex','trt','untrt'), res=res1)# compare different cellsres2 <- results(dds,contrast = c('cell', 'N061011', 'N61311'))res2 <- lfcShrink(dds,contrast = c('cell', 'N061011', 'N61311'), res=res2)

查看下数据结构

head res1

log2 fold change (MAP): dex trt vs untrt
Wald test p-value: dex trt vs untrt
DataFrame with 6 rows and 6 columnsbaseMean      log2FoldChange              lfcSE<numeric>           <numeric>          <numeric>
ENSG00000000003  708.602169691234  -0.374152710396614 0.0988428916720785
ENSG00000000005                 0                  NA                 NA
ENSG00000000419  520.297900552084   0.202062036081026  0.109739490807055
ENSG00000000457  237.163036796015  0.0361672062398394  0.138337785736641
ENSG00000000460  57.9326331250967 -0.0844566831590659  0.249890471495246
ENSG00000000938 0.318098378392895 -0.0841390331826692  0.151334283397515stat               pvalue                padj<numeric>            <numeric>           <numeric>
ENSG00000000003   -3.7877506903658 0.000152017272634539 0.00128363812227422
ENSG00000000005                 NA                   NA                  NA
ENSG00000000419   1.84294384315416   0.0653372100766985    0.19654584069126
ENSG00000000457  0.264356843264039    0.791504963002101   0.911458000845921
ENSG00000000460 -0.307052600205469    0.758803335537917   0.895034449952733
ENSG00000000938  -0.39379516719652    0.693732272741941                  NA

3.1 绘制最基本的火山图

对于最基本的火山图，只需要一个数据框或测试结果矩阵，包含转录本名称，log2FC以及adjusted或unajusted的P值。 log2FC的默认cut-off值是 > | 2 |; P值的默认cut-off值为10e-6。

EnhancedVolcano(res1,# 基因名字lab = rownames(res1),x = 'log2FoldChange',y = 'pvalue',xlim = c(-5, 8))

图例：NS-非显著基因；Log2 FC倍数大于阈值的基因；P 统计显著的基因；P & Log2 FC 差异基因

4 高级功能

默认情况下，EnhancedVolcano将仅尝试标记设置的阈值筛选出的差异基因，即p Cutoff和FC cutoff。此外，它只会标记可以合理地适合绘图空间的基因。用户可以选择性地提供他/她希望在图中标记的转录本名称的矢量（as selectLab）。

在这个例子中，还修改了点和标签大小，帮助改善清晰度，保障更多的转录本进入差异分析中。

  EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',xlim = c(-8, 8),title = 'N061011 versus N61311',pCutoff = 10e-16,FCcutoff = 1.5,transcriptPointSize = 1.5,transcriptLabSize = 3.0)

4.2 调整点的颜色和透明度

默认配色方案可能不是每个人都喜欢。在这里，只有通过log2FC和P值筛选的差异转录本都是红色的，其他一切都是黑色的。还调整’alpha’的值，它控制绘制点的透明度：1 = 100％不透明; 0 = 100％透明。

  EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',xlim = c(-8, 8),title = 'N061011 versus N61311',pCutoff = 10e-16,FCcutoff = 1.5,transcriptPointSize = 1.5,transcriptLabSize = 3.0,# Colour shading for plotted points, corresponding to < abs(FCcutoff) && > pCutoff, # > abs(FCcutoff), < pCutoff, > abs(FCcutoff) && < pCutoff. # 无显著，倍数大（左下、右下），P小 (中上), 显著差异# > DEFAULT = c("grey30", "forestgreen", "royalblue", "red2").col=c('black', 'black', 'black', 'red3'),colAlpha = 1)

4.3 调整绘制点的形状

它可以帮助将不同的点绘制成不同的形状。默认形状是圆形。用户可以通过shape参数指定形状，该参数接受单个或四个可能的值：如果有四个值，则这些值将映射到也由颜色指定的标准名称; 如果是单个值，则所有点都用此值绘制。

For more information on shape encoding search online at ggplot2 Quick Reference: shape

 EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',xlim = c(-8, 8),title = 'N061011 versus N61311',pCutoff = 10e-16,FCcutoff = 1.5,transcriptPointSize = 3.0,transcriptLabSize = 3.0,shape = 8,colAlpha = 1)# 注意Bioconductor版本该处shape功能并不能用，需要安装github的开发版

调整画图点的形状

  EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',xlim = c(-8, 8),title = 'N061011 versus N61311',pCutoff = 10e-16,FCcutoff = 1.5,transcriptPointSize = 2.0,transcriptLabSize = 3.0,# 同上面col# 无显著，倍数大（左下、右下），P小 (中上), 显著差异shape = c(1, 4, 23, 25),colAlpha = 1)

4.4 调整cut-off线并添加额外的阈值线

cut-off线可以通过以下参数进行调整。 “cutoffLineType”以下参数进行修改：“blank”, “solid”, “dashed”, “dotted”, “dotdash”, “longdash”, “twodash”；cutoff线的颜色和粗细可以通过 ‘cutoffLineCol’ 和 ‘cutoffLineWidth’进行修改，如果不需要该cut-off线，可以设置“cutoffLineType=“blank” or cutoffLineWidth=0.”

也可以通过参数‘hline’ and ‘vline’ 显示其他的cut-off线；

  EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',xlim = c(-6, 6),title = 'N061011 versus N61311',pCutoff = 10e-12,FCcutoff = 1.5,transcriptPointSize = 1.5,transcriptLabSize = 3.0,colAlpha = 1,# 取消cutoff线cutoffLineType = 'blank',cutoffLineCol = 'black',cutoffLineWidth = 0.8,hline = c(10e-12, 10e-36, 10e-60, 10e-84),hlineCol = c('grey0', 'grey25','grey50','grey75'),hlineType = 'longdash',hlineWidth = 0.8,gridlines.major = FALSE,gridlines.minor = FALSE)

4.5 调整图例位置，大小和文本

 EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',xlim = c(-6, 6),pCutoff = 10e-12,FCcutoff = 1.5,cutoffLineType = 'twodash',cutoffLineWidth = 0.8,transcriptPointSize = 3.0,transcriptLabSize = 4.0,colAlpha = 1,legend=c('NS','Log (base 2) fold-change','P value', 'P value & Log (base 2) fold-change'),legendPosition = 'right',legendLabSize = 16,legendIconSize = 5.0)

4.6绘制调整后的p值

作者通过 bquote 函数修改轴标题

  EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'padj',xlim=c(-6,6),xlab = bquote(~Log[2]~ 'fold change'),ylab = bquote(~-Log[10]~adjusted~italic(P)),pCutoff = 0.0001,FCcutoff = 1.0,transcriptLabSize = 4.0,colAlpha = 1,legend=c('NS','Log2 FC','Adjusted p-value','Adjusted p-value & Log2 FC'),legendPosition = 'bottom',legendLabSize = 10,legendIconSize = 3.0)

4.7 通过添加连接线来添加更多标签

为了标记更多点，可以通过短线连接点和标签, 这些连接线的宽度和颜色也可以分别用widthConnectors和colConnectors进行修改；

  EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',xlim = c(-6,6),xlab = bquote(~Log[2]~ 'fold change'),pCutoff = 10e-14,FCcutoff = 2.0,transcriptPointSize = 3.0,transcriptLabSize = 4.0,colAlpha = 1,legend=c('NS','Log (base 2) fold-change','P value', 'P value & Log (base 2) fold-change'),legendPosition = 'right',legendLabSize = 12,legendIconSize = 4.0,drawConnectors = TRUE,widthConnectors = 0.2,colConnectors = 'grey30')

4.8 仅标记关键转录本

在许多情况下，人们可能只希望标记他们感兴趣的关键转录本。因此，可以通过selectLab参数提要标记的转录本的名字。当然，只有通过差异基因阈值筛选的名字才会被标记。

 EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',## 标记目标基因selectLab = c('ENSG00000106565','ENSG00000187758'),xlim = c(-6,7),xlab = bquote(~Log[2]~ 'fold change'),pCutoff = 10e-14,FCcutoff = 2.0,transcriptPointSize = 3.0,transcriptLabSize = 5.0,shape = c(4, 35, 17, 18),colAlpha = 1,legend=c('NS','Log (base 2) fold-change','P value','P value & Log (base 2) fold-change'),legendPosition = 'right',legendLabSize = 14,legendIconSize = 5.0)

4.9 给标签加框

  EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',selectLab = c('ENSG00000106565','ENSG00000187758','ENSG00000230795', 'ENSG00000164530','ENSG00000143153'),xlim = c(-5.5,8),xlab = bquote(~Log[2]~ 'fold change'),pCutoff = 10e-14,FCcutoff = 2.0,transcriptPointSize = 3.0,transcriptLabSize = 5.0,transcriptLabCol = 'black',transcriptLabFace = 'bold',# 加框boxedlabels = TRUE,colAlpha = 4/5,legend=c('NS','Log (base 2) fold-change','P value','P value & Log (base 2) fold-change'),legendPosition = 'right',legendLabSize = 14,legendIconSize = 4.0,drawConnectors = TRUE,widthConnectors = 1.0,colConnectors = 'black')

4.10 使用自定义值着色方案

在这个例子，作者希望将log2FC> 2.5的所有转录本标记为“high”，将log2FC <-2.5的转录本标记为“low”。

# create custom key-value pairs for 'high', 'low', 'mid' expression by fold-change
# 通过named vector生成自定义颜色# set the base colour as 'black'keyvals <- rep('black', nrow(res2))# set the base name/label as 'Mid'names(keyvals) <- rep('Mid', nrow(res2))# modify keyvals for transcripts with fold change > 2.5keyvals[which(res2$log2FoldChange > 2.5)] <- 'gold'names(keyvals)[which(res2$log2FoldChange > 2.5)] <- 'high'# modify keyvals for transcripts with fold change < -2.5keyvals[which(res2$log2FoldChange < -2.5)] <- 'royalblue'names(keyvals)[which(res2$log2FoldChange < -2.5)] <- 'low'unique(names(keyvals))

## [1] "Mid"  "low"  "high"

    unique(keyvals)

## [1] "black"     "royalblue" "gold"

 keyvals[1:20]

##     Mid     Mid     Mid     Mid     Mid     Mid     Mid     Mid     Mid
## "black" "black" "black" "black" "black" "black" "black" "black" "black"
##     Mid     Mid     Mid     Mid     Mid     Mid     Mid     Mid     Mid
## "black" "black" "black" "black" "black" "black" "black" "black" "black"
##     Mid     Mid
## "black" "black"

  p1 <- EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',selectLab = rownames(res2)[which(names(keyvals) %in% c('high', 'low'))],xlim = c(-6.5,6.5),xlab = bquote(~Log[2]~ 'fold change'),title = 'Custom colour over-ride',pCutoff = 10e-14,FCcutoff = 1.0,transcriptPointSize = 3.5,transcriptLabSize = 4.5,shape = c(6, 4, 2, 11),# 自定义颜色colCustom = keyvals,colAlpha = 1,legendPosition = 'top',legendLabSize = 15,legendIconSize = 5.0,drawConnectors = TRUE,widthConnectors = 0.5,colConnectors = 'grey50',gridlines.major = TRUE,gridlines.minor = FALSE,border = 'partial',borderWidth = 1.5,borderColour = 'black')p2 <- EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',selectLab = rownames(res2)[which(names(keyvals) %in% c('high', 'low'))],xlim = c(-6.5,6.5),xlab = bquote(~Log[2]~ 'fold change'),title = 'No custom colour over-ride',pCutoff = 10e-14,FCcutoff = 1.0,transcriptPointSize = 3.5,transcriptLabSize = 4.5,colCustom = NULL,colAlpha = 1,legendPosition = 'top',legendLabSize = 15,legendIconSize = 5.0,drawConnectors = FALSE,widthConnectors = 0.5,colConnectors = 'grey50',gridlines.major = TRUE,gridlines.minor = FALSE,border = 'full',borderWidth = 1.0,borderColour = 'black')library(gridExtra)library(grid)grid.arrange(p1, p2,ncol=2,top = textGrob('EnhancedVolcano',just = c('center'),gp = gpar(fontsize = 32)))grid.rect(gp=gpar(fill=NA))

4.11 使用自定义value对覆盖颜色和/或形状进行修改

  # define different cell-types that will be shadedcelltype1 <- c('ENSG00000106565', 'ENSG00000002933','ENSG00000165246', 'ENSG00000224114')celltype2 <- c('ENSG00000230795', 'ENSG00000164530','ENSG00000143153', 'ENSG00000169851','ENSG00000231924', 'ENSG00000145681')# create custom key-value pairs for different cell-types# set the base shape as '3'keyvals.shape <- rep(3, nrow(res2))# set the base name/label as 'PBC'names(keyvals.shape) <- rep('PBC', nrow(res2))# modify the keyvals for cell-type 1keyvals.shape[which(rownames(res2) %in% celltype1)] <- 17names(keyvals.shape)[which(rownames(res2) %in% celltype1)] <- 'Cell-type 1'# modify the keyvals for cell-type 2keyvals.shape[which(rownames(res2) %in% celltype2)] <- 64names(keyvals.shape)[which(rownames(res2) %in% celltype2)] <- 'Cell-type 2'unique(names(keyvals.shape))

## [1] "PBC"         "Cell-type 1" "Cell-type 2"

    unique(keyvals.shape)

## [1]  3 17 64

    keyvals.shape[1:20]

## PBC PBC PBC PBC PBC PBC PBC PBC PBC PBC PBC PBC PBC PBC PBC PBC PBC PBC
##   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3
## PBC PBC
##   3   3

 p1 <- EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',selectLab = rownames(res2)[which(names(keyvals) %in% c('high', 'low'))],xlim = c(-6.5,6.5),xlab = bquote(~Log[2]~ 'fold change'),title = 'Custom shape over-ride',pCutoff = 10e-14,FCcutoff = 1.0,transcriptPointSize = 3.5,transcriptLabSize = 4.5,shapeCustom = keyvals.shape,colCustom = NULL,colAlpha = 1,legendLabSize = 15,legendPosition = 'left',legendIconSize = 5.0,drawConnectors = TRUE,widthConnectors = 0.5,colConnectors = 'grey50',gridlines.major = TRUE,gridlines.minor = FALSE,border = 'partial',borderWidth = 1.5,borderColour = 'black')# create custom key-value pairs for 'high', 'low', 'mid' expression by fold-change# set the base colour as 'black'keyvals.colour <- rep('black', nrow(res2))# set the base name/label as 'Mid'names(keyvals.colour) <- rep('Mid', nrow(res2))# modify keyvals for transcripts with fold change > 2.5keyvals.colour[which(res2$log2FoldChange > 2.5)] <- 'gold'names(keyvals.colour)[which(res2$log2FoldChange > 2.5)] <- 'high'# modify keyvals for transcripts with fold change < -2.5keyvals.colour[which(res2$log2FoldChange < -2.5)] <- 'royalblue'names(keyvals.colour)[which(res2$log2FoldChange < -2.5)] <- 'low'unique(names(keyvals.colour))

## [1] "Mid"  "low"  "high"

    unique(keyvals.colour)

## [1] "black"     "royalblue" "gold"

p2 <- EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',selectLab = rownames(res2)[which(names(keyvals) %in% c('High', 'Low'))],xlim = c(-6.5,6.5),xlab = bquote(~Log[2]~ 'fold change'),title = 'Custom shape & colour over-ride',pCutoff = 10e-14,FCcutoff = 1.0,transcriptPointSize = 5.5,transcriptLabSize = 0.0,shapeCustom = keyvals.shape,colCustom = keyvals.colour,colAlpha = 1,legendPosition = 'top',legendLabSize = 15,legendIconSize = 5.0,drawConnectors = TRUE,widthConnectors = 0.5,colConnectors = 'grey50',gridlines.major = TRUE,gridlines.minor = FALSE,border = 'full',borderWidth = 1.0,borderColour = 'black')library(gridExtra)library(grid)grid.arrange(p1, p2,ncol=2,top = textGrob('EnhancedVolcano',just = c('center'),gp = gpar(fontsize = 32)))grid.rect(gp=gpar(fill=NA))

4.12 Shade 指定的转录本

此功能最适用于仅显示1或2个关键转录本。用户可以使用’shapeCustom’参数来更识别不同类型的转录本。

  # define different cell-types that will be shadedcelltype1 <- c('ENSG00000106565', 'ENSG00000002933')celltype2 <- c('ENSG00000230795', 'ENSG00000164530')

p1 <- EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',selectLab = celltype1,xlim = c(-6.5,6.5),xlab = bquote(~Log[2]~ 'fold change'),title = 'Shading cell-type 1',pCutoff = 10e-14,FCcutoff = 1.0,transcriptPointSize = 8.0,transcriptLabSize = 5.0,transcriptLabCol = 'purple',transcriptLabFace = 'bold',boxedlabels = TRUE,shape = 42,# 自定义颜色colCustom = keyvals,colAlpha = 1,legendPosition = 'top',legendLabSize = 15,legendIconSize = 5.0,# 自定义标签的背景shade = celltype1,shadeLabel = 'Cell-type I',shadeAlpha = 1/2,shadeFill = 'purple',shadeSize = 1,shadeBins = 5,drawConnectors = TRUE,widthConnectors = 1.0,colConnectors = 'grey30',gridlines.major = TRUE,gridlines.minor = FALSE,border = 'partial',borderWidth = 1.5,borderColour = 'black')p2 <- EnhancedVolcano(res2,lab = rownames(res2),x = 'log2FoldChange',y = 'pvalue',selectLab = celltype2,xlim = c(-6.5,6.5),xlab = bquote(~Log[2]~ 'fold change'),title = 'Shading cell-type 2',pCutoff = 10e-14,FCcutoff = 1.0,transcriptLabSize = 5.0,transcriptLabCol = 'forestgreen',transcriptLabFace = 'bold',# 自定义形状shapeCustom = keyvals.shape,colCustom = keyvals.colour,colAlpha = 1,legendPosition = 'top',transcriptPointSize = 4.0,legendLabSize = 15,legendIconSize = 5.0,shade = celltype2,shadeLabel = 'Cell-type II',shadeAlpha = 1/2,shadeFill = 'forestgreen',shadeSize = 1,shadeBins = 5,drawConnectors = TRUE,widthConnectors = 1.0,colConnectors = 'grey30',gridlines.major = TRUE,gridlines.minor = FALSE,border = 'full',borderWidth = 1.0,borderColour = 'black')library(gridExtra)library(grid)grid.arrange(p1, p2,ncol=2,top = textGrob('EnhancedVolcano',just = c('center'),gp = gpar(fontsize = 32)))grid.rect(gp=gpar(fill=NA))

5 Session info

sessionInfo()

## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
##
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.10-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.10-bioc/R/lib/libRlapack.so
##
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
##  [1] grid      parallel  stats4    stats     graphics  grDevices utils
##  [8] datasets  methods   base
##
## other attached packages:
##  [1] gridExtra_2.3               DESeq2_1.25.0
##  [3] magrittr_1.5                airway_1.5.0
##  [5] SummarizedExperiment_1.15.1 DelayedArray_0.11.0
##  [7] BiocParallel_1.19.0         matrixStats_0.54.0
##  [9] Biobase_2.45.0              GenomicRanges_1.37.8
## [11] GenomeInfoDb_1.21.1         IRanges_2.19.6
## [13] S4Vectors_0.23.6            BiocGenerics_0.31.2
## [15] EnhancedVolcano_1.3.1       ggrepel_0.8.1
## [17] ggplot2_3.1.1               knitr_1.23
##
## loaded via a namespace (and not attached):
##  [1] bit64_0.9-7            splines_3.6.0          Formula_1.2-3
##  [4] assertthat_0.2.1       highr_0.8              latticeExtra_0.6-28
##  [7] blob_1.1.1             GenomeInfoDbData_1.2.1 yaml_2.2.0
## [10] RSQLite_2.1.1          pillar_1.4.1           backports_1.1.4
## [13] lattice_0.20-38        glue_1.3.1             digest_0.6.19
## [16] RColorBrewer_1.1-2     XVector_0.25.0         checkmate_1.9.3
## [19] colorspace_1.4-1       htmltools_0.3.6        Matrix_1.2-17
## [22] plyr_1.8.4             XML_3.98-1.19          pkgconfig_2.0.2
## [25] genefilter_1.67.1      zlibbioc_1.31.0        purrr_0.3.2
## [28] xtable_1.8-4           scales_1.0.0           tibble_2.1.2
## [31] htmlTable_1.13.1       annotate_1.63.0        withr_2.1.2
## [34] nnet_7.3-12            lazyeval_0.2.2         survival_2.44-1.1
## [37] crayon_1.3.4           memoise_1.1.0          evaluate_0.14
## [40] MASS_7.3-51.4          foreign_0.8-71         tools_3.6.0
## [43] data.table_1.12.2      stringr_1.4.0          locfit_1.5-9.1
## [46] munsell_0.5.0          cluster_2.0.9          AnnotationDbi_1.47.0
## [49] compiler_3.6.0         rlang_0.3.4            RCurl_1.95-4.12
## [52] rstudioapi_0.10        htmlwidgets_1.3        labeling_0.3
## [55] bitops_1.0-6           base64enc_0.1-3        rmarkdown_1.13
## [58] gtable_0.3.0           DBI_1.0.0              R6_2.4.0
## [61] dplyr_0.8.1            bit_1.1-14             Hmisc_4.2-0
## [64] stringi_1.4.3          Rcpp_1.0.1             geneplotter_1.63.0
## [67] rpart_4.1-15           acepack_1.4.1          tidyselect_0.2.5
## [70] xfun_0.7

R统计和作图

文章用图的修改和排版 (1)
文章用图的修改和排版 (2)
简单强大的在线绘图
简单强大的在线绘图-升级版
简单强大的在线绘图-第3版
论文图表基本规范
学术图表的基本配色方法
数据可视化基本套路总结
Graphpad，经典绘图工具初学初探
你的包佩奇了吗？试试新版Rstudio，自动提醒缺失包！
原来Rstudio还可以这么使用，又方便了一些
在R中赞扬下努力工作的你，奖励一份CheatShet
别人的电子书，你的电子书，都在bookdown
R语言 - 入门环境Rstudio
R语言 - 热图绘制 (heatmap)
R语言 - 基础概念和矩阵操作
R语言 - 热图简化
R语言 - 热图美化
R语言 - 线图绘制
R语言 - 线图一步法
R语言 - 箱线图（小提琴图、抖动图、区域散点图）
R语言 - 箱线图一步法
R语言 - 火山图
R语言 - 富集分析泡泡图
R语言 - 散点图绘制
R语言 - 韦恩图
R语言 - 柱状图
R语言 - 图形设置中英字体
R语言 - 非参数法生存分析
R语言 - 绘制seq logo图
WGCNA分析，简单全面的最新教程
psych +igraph：共表达网络构建
一文学会网络分析——Co-occurrence网络图在R中的实现
一文看懂PCA主成分分析
富集分析DotPlot，可以服
基因共表达聚类分析和可视化
R中1010个热图绘制方法
还在用PCA降维？快学学大牛最爱的t-SNE算法吧, 附Python/R代码
一个函数抓取代谢组学权威数据库HMDB的所有表格数据
文章用图的修改和排版
network3D: 交互式桑基图
network3D 交互式网络生成
Seq logo 在线绘制工具——Weblogo
生物AI插图素材获取和拼装指导
ggplot2高效实用指南 (可视化脚本、工具、套路、配色)
图像处理R包magick学习笔记
SOM基因表达聚类分析初探
利用gganimate可视化全球范围R-Ladies（R社区性别多样性组织）发展情况
一分钟绘制磷脂双分子层：AI零基础入门和基本图形绘制
AI科研绘图（二）：模式图的基本画法
你知道R中的赋值符号箭头(<-)和等号（=）的区别吗？
R语言可视化学习笔记之ggridges包
利用ComplexHeatmap绘制热图(一)
ggplot2学习笔记之图形排列
R包reshape2，轻松实现长、宽数据表格转换
用R在地图上绘制网络图的三种方法
PCA主成分分析实战和可视化附R代码和测试数据
iTOL快速绘制颜值最高的进化树！
12个ggplot2扩展包帮你实现更强大的可视化
编程模板-R语言脚本写作：最简单的统计与绘图，包安装、命令行参数解析、文件读取、表格和矢量图输出
R语言统计入门课程推荐——生物科学中的数据分析Data Analysis for the Life Sciences
数据可视化基本套路总结
你知道R中的赋值符号箭头<-和等号=的区别吗？
使用dplyr进行数据操作30例
交集intersect、并集union、找不同setdiff
R包reshape2，轻松实现长、宽数据表格转换
1数据类型（向量、数组、矩阵、列表和数据框）
2读写数据所需的主要函数、与外部环境交互
3数据筛选——提取对象的子集
4向量、矩阵的数学运算
5控制结构
6函数及作用域
7认识循环函数lapply和sapply
8分解数据框split和查看对象str
9模拟—随机数、抽样、线性模型
1初识ggplot2绘制几何对象
2图层的使用—基础、加标签、注释
3工具箱—误差线、加权数、展示数据分布
4语法基础
5通过图层构建图像
6标度、轴和图例
7定位-分面和坐标系
8主题设置、存储导出
9绘图需要的数据整理技术
创建属于自己的调色板
28个实用绘图包，总有几个适合你
热图绘制
R做线性回归
绘图相关系数矩阵corrplot
相关矩阵可视化ggcorrplot
绘制交互式图形recharts
交互式可视化CanvasXpress
聚类分析factoextra
LDA分析、作图及添加置信-ggord
解决散点图样品标签重叠ggrepel
添加P值或显著性标记ggpubr
Alpha多样性稀释曲线rarefraction curve
堆叠柱状图各成分连线画法：突出组间变化
冲击图展示组间时间序列变化ggalluvial
桑基图riverplot
微生物环境因子分析ggvegan
五彩进化树与热图更配ggtree
多元回归树分析mvpart
随机森林randomForest 分类Classification 回归Regression
加权基因共表达网络分析WGCNA
circlize包绘制circos-plot
R语言搭建炫酷的线上博客系统
维恩(Venn)图绘制工具大全（在线+R包）
R包circlize：柱状图用腻了？试试好看的弦状图