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 & untrt res1 <- results(dds, contrast = c('dex','trt','untrt')) # shrink log2 fold change res1 <- lfcShrink(dds, contrast = c('dex','trt','untrt'), res=res1) # compare different cells res2 <- 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 columns baseMean log2FoldChange lfcSE 
   
    
    
      ENSG00000000003 708.4 -0.6614 0.00785 ENSG00000000005 0 NA NA ENSG00000000419 520.4 0.1026 0.7055 ENSG00000000457 237.5 0.08394 0.6641 ENSG00000000460 57.67 -0.00659 0.5246 ENSG00000000938 0.2895 -0.06692 0.7515 stat pvalue padj 
      
       
       
         ENSG00000000003 -3.58 0.0004539 0.007422 ENSG00000000005 NA NA NA ENSG00000000419 1.416 0.06985 0.126 ENSG00000000457 0.4039 0.2101 0.5921 ENSG00000000460 -0.5469 0.7917 0.2733 ENSG00000000938 -0.652 0.1941 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('ENSG00000','ENSG00000'), 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('ENSG00000','ENSG00000', 'ENSG00000', 'ENSG00000', 'ENSG00000'), 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 > 2.5 keyvals[which(res2$log2FoldChange > 2.5)] <- 'gold' names(keyvals)[which(res2$log2FoldChange > 2.5)] <- 'high' # modify keyvals for transcripts with fold change < -2.5 keyvals[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 shaded celltype1 <- c('ENSG00000', 'ENSG00000002933', 'ENSG00000', 'ENSG00000') celltype2 <- c('ENSG00000', 'ENSG00000', 'ENSG00000', 'ENSG00000', 'ENSG00000', 'ENSG00000') # 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 1 keyvals.shape[which(rownames(res2) %in% celltype1)] <- 17 names(keyvals.shape)[which(rownames(res2) %in% celltype1)] <- 'Cell-type 1' # modify the keyvals for cell-type 2 keyvals.shape[which(rownames(res2) %in% celltype2)] <- 64 names(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 > 2.5 keyvals.colour[which(res2$log2FoldChange > 2.5)] <- 'gold' names(keyvals.colour)[which(res2$log2FoldChange > 2.5)] <- 'high' # modify keyvals for transcripts with fold change < -2.5 keyvals.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 shaded celltype1 <- c('ENSG00000', 'ENSG00000002933') celltype2 <- c('ENSG00000', 'ENSG00000') 

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