包作者：Kevin Blighe撰文：協和醫學院苑曉梅編輯：生信寶典時間：2019-06-03

前言

最近道聽途說EnhancedVolcano繪製火山圖的方便性，所以本人就根據其說明文檔進行操作。但在操作過程中發現，其shape功能並沒有在help文檔中找到，經過搜索在github上看到了以下的答覆。。。（說明整個文檔功能並沒有完全開發，需進行選擇）

1 Introduction

火山圖是可視化差異表達分析結果的有效方法。這次更新的EnhancedVolcano目的就是兩個（1）使轉錄本基因名稱的顯示更加的合理化，避免出現相互重疊的現象；（2）允許用戶通過顏色，形狀和陰影參數配置在同一繪圖空間中識別多達3種不同類型的屬性。

2 Installation

2.1 1. 下載安裝包

# 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. 加載R包

 library(EnhancedVolcano)

3 開始

作者使用該流程： RNA-seq workflow: gene-level exploratory analysis and differential expression。具體來說，我們將加載airway數據，其中不同的氣道平滑肌細胞用地塞米松治療。

library(airway)library(magrittr)data('airway')# %<>%複合賦值操作符， 功能與 %>% 基本是一樣的，但多了一項額外的操作，就是把結果寫到左側對象。# 對dex列進行relevel，再把revel後的結果賦值到airway$dex。airway$dex %<>% relevel('untrt')

使用DESeq2進行差異表達，以創建兩組結果(DESeq2差異基因分析和批次效應移除)：

 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)

查看下數據結構

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 <numeric> <numeric> <numeric>ENSG00000000003 708.602169691234 -0.374152710396614 0.0988428916720785ENSG00000000005 0 NA NAENSG00000000419 520.297900552084 0.202062036081026 0.109739490807055ENSG00000000457 237.163036796015 0.0361672062398394 0.138337785736641ENSG00000000460 57.9326331250967 -0.0844566831590659 0.249890471495246ENSG00000000938 0.318098378392895 -0.0841390331826692 0.151334283397515 stat pvalue padj <numeric> <numeric> <numeric>ENSG00000000003 -3.7877506903658 0.000152017272634539 0.00128363812227422ENSG00000000005 NA NA NAENSG00000000419 1.84294384315416 0.0653372100766985 0.19654584069126ENSG00000000457 0.264356843264039 0.791504963002101 0.911458000845921ENSG00000000460 -0.307052600205469 0.758803335537917 0.895034449952733ENSG00000000938 -0.39379516719652 0.693732272741941 NA

3.1 繪製最基本的火山圖

對於最基本的火山圖，只需要一個數據框或測試結果矩陣，包含轉錄本名稱，log2FC以及adjusted或unajusted的P值。 log2FC的默認cut-off值是 > | 2 |; P值的默認cut-off值為10e-6。

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

圖例：NS-非顯著基因；Log2 FC倍數大於閾值的基因；P 統計顯著的基因；P & Log2 FC 差異基因

4 高級功能

默認情況下，EnhancedVolcano將僅嘗試標記設置的閾值篩選出的差異基因，即p Cutoff和FC cutoff。此外，它只會標記可以合理地適合繪圖空間的基因。用戶可以選擇性地提供他/她希望在圖中標記的轉錄本名稱的矢量（as selectLab）。

在這個例子中，還修改了點和標籤大小，幫助改善清晰度，保障更多的轉錄本進入差異分析中。

 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 調整點的顏色和透明度

默認配色方案可能不是每個人都喜歡。在這裡，只有通過log2FC和P值篩選的差異轉錄本都是紅色的，其他一切都是黑色的。還調整』alpha』的值，它控制繪製點的透明度：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. # 無顯著，倍數大（左下、右下），P小 (中上), 顯著差異 # > DEFAULT = c("grey30", "forestgreen", "royalblue", "red2"). col=c('black', 'black', 'black', 'red3'), colAlpha = 1)

4.3 調整繪製點的形狀

它可以幫助將不同的點繪製成不同的形狀。默認形狀是圓形。用戶可以通過shape參數指定形狀，該參數接受單個或四個可能的值：如果有四個值，則這些值將映射到也由顏色指定的標準名稱; 如果是單個值，則所有點都用此值繪製。

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版本該處shape功能並不能用，需要安裝github的開發版

調整畫圖點的形狀

 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 # 無顯著，倍數大（左下、右下），P小 (中上), 顯著差異 shape = c(1, 4, 23, 25), colAlpha = 1)

4.4 調整cut-off線並添加額外的閾值線

cut-off線可以通過以下參數進行調整。「cutoffLineType」以下參數進行修改：「blank」, 「solid」, 「dashed」, 「dotted」, 「dotdash」, 「longdash」, 「twodash」；cutoff線的顏色和粗細可以通過『cutoffLineCol』和『cutoffLineWidth』進行修改，如果不需要該cut-off線，可以設置「cutoffLineType=「blank」 or cutoffLineWidth=0.」

也可以通過參數『hline』 and 『vline』顯示其他的cut-off線；

 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線 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 調整圖例位置，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 繪製調整後的p值

作者通過 bquote 函數修改軸標題

 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 通過添加連接線來添加更多標籤

為了標記更多點，可以通過短線連接點和標籤, 這些連接線的寬度和顏色也可以分別用widthConnectors和colConnectors進行修改；

 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 僅標記關鍵轉錄本

在許多情況下，人們可能只希望標記他們感興趣的關鍵轉錄本。因此，可以通過selectLab參數提要標記的轉錄本的名字。當然，只有通過差異基因閾值篩選的名字才會被標記。

 EnhancedVolcano(res2, lab = rownames(res2), x = 'log2FoldChange', y = 'pvalue', ## 標記目標基因 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 給標籤加框

 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 使用自定義值著色方案

在這個例子，作者希望將log2FC> 2.5的所有轉錄本標記為「high」，將log2FC <-2.5的轉錄本標記為「low」。

# create custom key-value pairs for 'high', 'low', 'mid' expression by fold-change# 通過named vector生成自定義顏色 # 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), # 自定義顏色 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 使用自定義value對覆蓋顏色和/或形狀進行修改

 # define different cell-types that will be shaded celltype1 <- 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 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 指定的轉錄本

此功能最適用於僅顯示1或2個關鍵轉錄本。用戶可以使用’shapeCustom』參數來更識別不同類型的轉錄本。

 # define different cell-types that will be shaded celltype1 <- 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, # 自定義顏色 colCustom = keyvals, colAlpha = 1, legendPosition = 'top', legendLabSize = 15, legendIconSize = 5.0, # 自定義標籤的背景 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', # 自定義形狀 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

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前言