Volcano Plot from DEseq2
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4.2 years ago
krushnach80 ★ 1.0k

Im using this code to make based on log2foldchange and padj value ,im getting the plot but i want those value for my reference how do i extract the same .

alpha <- 0.05 # Threshold on the adjusted p-value
cols <- densCols(res$log2FoldChange, -log10(res$pvalue))
plot(res$log2FoldChange, -log10(res$padj), col=cols, panel.first=grid(),
     main="Volcano plot", xlab="Effect size: log2(fold-change)", ylab="-log10(adjusted p-value)",
     pch=20, cex=0.6)
abline(v=0)
abline(v=c(-1,1), col="brown")
abline(h=-log10(alpha), col="brown")

gn.selected <- abs(res$log2FoldChange) > 2.5 & res$padj < alpha 
text(res$log2FoldChange[gn.selected],
     -log10(res$padj)[gn.selected],
     lab=rownames(res)[gn.selected ], cex=0.4)

when i view gn.selected i get only logical value that is true or false

Any help or suggestion would be highly appreciated

Update I'm doing this

> DF <- DF[DF$log2FoldChange > 1.5 & DF$padj < 0.05,]

is that suffice and am i doing it correctly ?

R • 36k views
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4.2 years ago

Edit (October 24, 2018):

This is now a Bioconductor package: EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling

---------------------------------------------

Your code appears to run fine on my DESeq2 results objects: yours


I normally do these simple volcano plots a different way:

par(mar=c(5,5,5,5), cex=1.0, cex.main=1.4, cex.axis=1.4, cex.lab=1.4)

topT <- as.data.frame(resultsObject)

#Adjusted P values (FDR Q values)
with(topT, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot", cex=1.0, xlab=bquote(~Log[2]~fold~change), ylab=bquote(~-log[10]~Q~value)))

with(subset(topT, padj<0.05 & abs(log2FoldChange)>2), points(log2FoldChange, -log10(padj), pch=20, col="red", cex=0.5))

#with(subset(topT, padj<0.05 & abs(log2FoldChange)>2), text(log2FoldChange, -log10(padj), labels=subset(rownames(topT), topT$padj<0.05 & abs(topT$log2FoldChange)>2), cex=0.8, pos=3))

#Add lines for absolute FC>2 and P-value cut-off at FDR Q<0.05
abline(v=0, col="black", lty=3, lwd=1.0)
abline(v=-2, col="black", lty=4, lwd=2.0)
abline(v=2, col="black", lty=4, lwd=2.0)
abline(h=-log10(max(topT$pvalue[topT$padj<0.05], na.rm=TRUE)), col="black", lty=4, lwd=2.0)

volcano


There is an even better solution that I and colleagues developed using ggplot2, which allows you to easily fit labels into your plot using ggrepel().

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@Kevin wow the author himself ...yes i took the code from this site code

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Glad to have helped. Note the particular use of the bquote() function in order to get super- and sub-script. These are small modifications but can make a plot feel more professional. bquote() also works with ggplot2 (here I've used it with base functions).

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@Kevin i have some issues Im not able to get the following with the code which you have given as im trying to include

so this is my condition

Up-regulated: =/> 0.58 (Green color)
Down-regulated: =/> -0.59 (Red color)
Significant: =/>1.3 (-Log10 p-Value)

its like all the data points are getting overlapped with same color no distinction

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Can you paste your code so that I can put this in context?

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okay here is the code im using

par(mar=c(5,5,5,5), cex=1.0, cex.main=1.4, cex.axis=1.4, cex.lab=1.4)
res <- read.csv("volcano.txt", header=TRUE,sep = '\t')

topT <- as.data.frame(res)
head(topT)

with(topT, plot(lfc, -log10(padj), pch=20, main="Volcano plot", cex=1.0, xlab=bquote(~Log[2]~fold~change), ylab=bquote(~-log[10]~P~value)))

with(subset(topT, padj<0.05 & abs(lfc)>=0.58), points(lfc, -log10(padj), pch=20, col="red", cex=0.5))

with(subset(topT, padj<0.05 & abs(lfc)>=-0.59), points(lfc, -log10(padj), pch=20, col="green", cex=0.5))
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Yes, the last two lines contradict each other, specifically padj<0.05 & abs(lfc)>=0.58) and padj<0.05 & abs(lfc)>=-0.59).

I think that you want to colour positive fold-change genes as red, and negative as green? Try this:

par(mar=c(5,5,5,5), cex=1.0, cex.main=1.4, cex.axis=1.4, cex.lab=1.4)
res <- read.csv("volcano.txt", header=TRUE,sep = '\t')
topT <- as.data.frame(res)
head(topT)
with(topT, plot(lfc, -log10(padj), pch=20, main="Volcano plot", cex=1.0, xlab=bquote(~Log[2]~fold~change), ylab=bquote(~-log[10]~P~value)))
with(subset(topT, padj<0.05 & lfc>=0.58), points(lfc, -log10(padj), pch=20, col="red", cex=0.5))
with(subset(topT, padj<0.05 & lfc<=-0.59), points(lfc, -log10(padj), pch=20, col="green", cex=0.5))
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"I think that you want to colour positive fold-change genes as red, and negative as green? Try this:" yes thats what i need to show

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Yep, take a look at the code that I pasted (above). You just needed to remove the abs() function call, and switch the 'greater than' sign to a 'less than' sign on the last line

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Hi Kevin,

I am wondering if you have rscript showing the upreguted and down regulated? Can you share them?

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Yes, please take a look here: https://github.com/kevinblighe/EnhancedVolcano

In which program did you conduct differential expression?

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Thanks for the quick reply.

I used edgeR in getting my DE. Once I got them, I pre-filtered my DE list in excel. I only extract FDR<1e-5 which is my significant. Then from those list, my up-reg is log2FC>0 while my down is log2FC<0. I wanted to plot my FDR against log2FC.

Can you embed the script here? Thank you very much!

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@Kevin

I have this data not row data to do DESeq2 myself

Gene Name   q value A+B: OAC vs normal Log2FC   Minus log10 (q-value) 
PRDM1   0.000113142 -1.18   3.95
TRABD   0.000688272 0.61    3.16
WWC3    0.038536606 -0.46   1.41
LYNX1   0.022322605 -1.54   1.65
ALOX15B 3.97666E-06 -2.02   5.40
RAP2C   0.030696226 -0.35   1.51
FBP2    0.001542994 1.80    2.81
GBP6    1.44846E-08 -2.54   7.84

I don't know how to feed this into your tutorial for volcano plot instead of a top table you have from DESeq2

Can you help me please?

Thank you

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Hey, you seem to have all columns that you need

EnhancedVolcano(res1,
    lab = df[,'Gene Name'],
    x = 'Log2FC',
    y = 'q value A+B: OAC vs normal')

Using Q values is not the typical approach for volcanos, but it is no major issue.

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Thanks a lot worked well

Sorry @Kevin I am wondering if I want to put a horizontal line for the threshold used for the q-value (0.05) and two vertical lines (0.6 and -0.6 log2 fold change) for the thresholds used for fold change. The points that represent proteins with q<0.05 and log2FC>0.6 (up-regulated proteins) be red whereas the points with q<0.05 and log2FC<-0.6t (down-regulated proteins) be blue. In your tutorial I am seeing vertical line but how would be with horizontal line?

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Oh, sure, you can add any number of lines with the following parameters:

EnhancedVolcano(
  ...
  hline = c(10e-12, 10e-36, 10e-60, 10e-84),
  hlineCol = c('grey0', 'grey25','grey50','grey75'),
  hlineType = 'longdash',
  hlineWidth = 0.8,
  ...)

For vertical lines, they are:

vline
vlineCol
vlineType
vlineWidth

You can add any number of lines.

Then, there are also the main cut-off lines:

cutoffLineType
cutoffLineCol
cutoffLineWidth

...and the standard ggplot2 engine lines:

gridlines.major = FALSE
gridlines.minor = FALSE

[source: https://github.com/kevinblighe/EnhancedVolcano#adjust-cut-off-lines-and-add-extra-threshold-lines]

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Sorry Kevin

With

> EnhancedVolcano(volcano,
+                 lab = volcano[,'Gene.Name'],
+                 x = 'A.B..OAC.vs.normal.Log2FC',
+                 y = 'q.value',
+                 xlim = c(-8, 8),
+                 title = 'Normal oesophagus versus Tumour',
+                 pCutoff = 0.05,
+                 FCcutoff = 0.6,
+                 transcriptPointSize = 1.5,
+                 transcriptLabSize = 3.0,    col=c('blue', 'blue', 'blue', 'red3'),
+                 colAlpha = 1,    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)
Warning message:
Removed 3 rows containing missing values (geom_hline). 
>

I finished with

enter image description here

But this is far from what I need to put a horizontal line for q-value < 0.05 and two vertical lines for 0.6 and -0.6 log2 fold change

I don't know how to do that can you help please?

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Hey F, as you are plotting Q values, you will have to change the y-axis label by adding: ylab = bquote(~-Log[10] ~ italic(Q))

Also, the cut-off lines are not appearing because you have cutoffLineType = 'blank'

Take a look (using a different dataset)

cutoffLineType = 'blank'

EnhancedVolcano(volcano,
  lab = volcano[,'Gene.Name'],
  x = 'A.B..OAC.vs.normal.Log2FC',
  y = 'q.value',
  ylab = bquote(~-Log[10] ~ italic(Q)),
  xlim = c(-8, 8),
  title = 'Normal oesophagus versus Tumour',
  pCutoff = 0.05,
  FCcutoff = 0.6,
  transcriptPointSize = 1.5,
  transcriptLabSize = 3.0,    col=c('blue', 'blue', 'blue', 'red3'),
  colAlpha = 1,
  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)

gg

cutoffLineType = 'solid'

EnhancedVolcano(volcano,
  lab = volcano[,'Gene.Name'],
  x = 'A.B..OAC.vs.normal.Log2FC',
  y = 'q.value',
  ylab = bquote(~-Log[10] ~ italic(Q)),
  xlim = c(-8, 8),
  title = 'Normal oesophagus versus Tumour',
  pCutoff = 0.05,
  FCcutoff = 0.6,
  transcriptPointSize = 1.5,
  transcriptLabSize = 3.0,    col=c('blue', 'blue', 'blue', 'red3'),
  colAlpha = 1,
  cutoffLineType = 'solid',
  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)

2

Another issue in your code is that the values passed to hline are too low. You could try hline = c(0.10, 0.15, 0.2, 0.25), i.e., in this case, extra cut-offs for Q-values 0.1, 0.15, 0.2, and 0.25

To avail of most recent functionality, you should also install the current dev version:

devtools::install_github('kevinblighe/EnhancedVolcano')
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Thank you so much

I finished with this

enter image description here

Again vertical line is far from and also plot seems a bit crowded that would be nice to show a list of genes instead of all

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There are a few ways to further manage the gene labels:

  • use drawConnectors = TRUE
  • use selectLab = c('TGM1', 'EPCAM', 'CLDN3')
  • increase labSize to, e.g., labSize = 5.0
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Sorry Kevin I want

For the non-significant (NS)  light grey 
For the Log2FC  darker grey
For the -Log10Q  darker grey
For the -Log10Q & Log2FC>0  red
For the -Log10Q & Log2FC<0 blue

But this

> EnhancedVolcano(res2,
+                 lab = rownames(res2),
+                 x = 'log2FoldChange',
+                 y = 'pvalue',
+                 xlim = c(-8, 8),
+                 title = 'Tumour versus Normal oesophagus',
+                 pCutoff = 0.05,
+                 FCcutoff = 0.6,col=c('grey75', 'grey50', 'grey25', 'red','blue'),
+                 colAlpha = 1)
>

Gives this enter image description here How I can get blue in left side and red in right side while here I am getting red in both side

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i see. In the default colour scheme, there can only be 4 colours. If you need to use 5 colours, you will have to customise it. There is more information here: https://github.com/kevinblighe/EnhancedVolcano#over-ride-colouring-scheme-with-custom-key-value-pairs

Do you think that you can follow that?

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Thank you so much

I did like below but after running R never finished with that I think I am doing something wrong

keyvals <- rep('black', nrow(res2))
 names(keyvals) <- rep('Mid', nrow(res2))
 keyvals[which(res2$log2FoldChange > 0.6 & res2$pvalue < 0.05)] <- 'red'
 names(keyvals)[which(res2$log2FoldChange > 0.6 & res2$pvalue < 0.05)] <- 'high'
 keyvals[which(res2$log2FoldChange < -0.6 & res2$pvalue < 0.05)] <- 'blue'
 names(keyvals)[which(res2$log2FoldChange < -0.6 & res2$pvalue < 0.05)] <- 'low'
 unique(names(keyvals))
[1] "low"  "high" "Mid" 
 unique(keyvals)
[1] "blue"  "red"   "black"
 keyvals[which(abs(res2$log2FoldChange) < 0.6 & res2$pvalue  0.05)] <- 'grey75'
 names(keyvals)[which(abs(res2$log2FoldChange) < 0.6 & res2$pvalue  0.05)] <- 'NS'
 unique(keyvals)
[1] "blue"   "red"    "black"  "grey75"
 keyvals[which(abs(res2$log2FoldChange) > 0.6 & res2$pvalue  0.05)] <- 'grey50'
 names(keyvals)[which(abs(res2$log2FoldChange) > 0.6 & res2$pvalue  0.05)] <- 'log2FoldChange'
 keyvals[which(abs(res2$log2FoldChange) < 0.6 & res2$pvalue < 0.05)] <- 'grey25'
 names(keyvals)[which(abs(res2$log2FoldChange)  < 0.6 & res2$pvalue < 0.05)] <- '-Log10Q'
 unique(keyvals)
[1] "blue"   "red"    "grey25" "grey75" "grey50" "black" 
 unique(names(keyvals))
[1] "low"            "high"           "-Log10Q"       
[4] "NS"             "log2FoldChange" "Mid"           
 EnhancedVolcano(res2,
                                   lab = rownames(res2),
                                   x = 'log2FoldChange',
                                   y = 'pvalue',
                                   selectLab = rownames(res2)[which(names(keyvals) %in% c('NS','log2FoldChange','-Log10Q','low','high'))],
                                   xlim = c(-6.5,6.5),
                                   xlab = bquote(~Log[2]~ 'fold change'),
                                   title = 'Custom colour over-ride',
                                   pCutoff = 0.05,
                                   FCcutoff = 0.6,
                                   colCustom = keyvals,
                                   colAlpha = 1,
                                   legendPosition = 'right',
                                   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')
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Hey, here is a reproducible example using the data mentioned on the vignette.

Remember, and assuming that you are using the same data as before, you need to change the ylab parameter to ylab = bquote(~-Log[10] ~ italic(Q)), as you are plotting q-values (adjusted p-values). Also, when deriving the key-value pairs, you need to specify the correct columns, so, for example:

keyvals[which(abs(res2$A.B..OAC.vs.normal.Log2FC) > FC & res2$q.value > p)] <- 'grey50'
names(keyvals)[which(abs(res2$A.B..OAC.vs.normal.Log2FC) > FC & res2$q.value > p)] <- 'log2FoldChange'

Please adapt (modify) the code to suit your data.

library(EnhancedVolcano)
library(airway)
library(magrittr)
data('airway')
airway$dex %<>% relevel('untrt')

library('DESeq2')
dds <- DESeqDataSet(airway, design = ~ cell + dex)
dds <- DESeq(dds, betaPrior=FALSE)
res2 <- results(dds,
  contrast = c('cell', 'N061011', 'N61311'))
res2 <- lfcShrink(dds,
  contrast = c('cell', 'N061011', 'N61311'), res=res2)


FC <- 0.6
p <- 10e-3

keyvals <- rep('grey75', nrow(res2))
names(keyvals) <- rep('NS', nrow(res2))

keyvals[which(abs(res2$log2FoldChange) > FC & res2$pvalue > p)] <- 'grey50'
names(keyvals)[which(abs(res2$log2FoldChange) > FC & res2$pvalue > p)] <- 'log2FoldChange'

keyvals[which(abs(res2$log2FoldChange) < FC & res2$pvalue < p)] <- 'grey25'
names(keyvals)[which(abs(res2$log2FoldChange)  < FC & res2$pvalue < p)] <- '-Log10Q'

keyvals[which(res2$log2FoldChange < -FC & res2$pvalue < p)] <- 'blue2'
names(keyvals)[which(res2$log2FoldChange  < -FC & res2$pvalue < p)] <- 'Signif. down-regulated'

keyvals[which(res2$log2FoldChange > FC & res2$pvalue < p)] <- 'red2'
names(keyvals)[which(res2$log2FoldChange > FC & res2$pvalue < p)] <- 'Signif. up-regulated'

unique(keyvals)
unique(names(keyvals))

EnhancedVolcano(res2,
  lab = rownames(res2),
  x = 'log2FoldChange',
  y = 'pvalue',
  #selectLab = rownames(res2)[which(names(keyvals) %in% c('NS','log2FoldChange','-Log10Q','low','high'))],
  xlim = c(-6.5,6.5),
  xlab = bquote(~Log[2]~ 'fold change'),
  ylab = bquote(~-Log[10] ~ italic(P)),
  title = 'Custom colour over-ride',
  pCutoff = 10e-3,
  FCcutoff = 0.6,
  pointSize = 2.5,
  labSize = 4.5,
  #shape = c(6, 4, 2, 11, 15),
  colCustom = keyvals,
  colAlpha = 0.75,
  legendPosition = 'right',
  legendLabSize = 15,
  legendIconSize = 5.0,
  drawConnectors = FALSE,
  widthConnectors = 0.5,
  colConnectors = 'grey50',
  gridlines.major = TRUE,
  gridlines.minor = FALSE,
  border = 'partial',
  borderWidth = 1.5,
  borderColour = 'black')

aaa

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Thank you so much to be this much helpful

I used

  > EnhancedVolcano(res2,
+                 lab = rownames(res2),
+                 x = 'log2FoldChange',
+                 y = 'pvalue',
+                 selectLab = c('DSG2','CALML4','MLEC','PKP2','VIL1','EPCAM','KRT8','FUT8','MUC13','TSPAN8','TBXAS1','GBP6','CLIC3','TACC2','TGM1','CRABP2','MAST4','FASN','ALDH3A1','ACPP','ANXA8L2'),
+                 xlim = c(-6.5,6.5),
+                 xlab = bquote(~Log[2]~ 'fold change'),
+                 ylab = bquote(~-Log[10] ~ italic(Q)),
+                 title = 'Tumour versus normal oesophagus',
+                 pCutoff = 0.05,
+                 FCcutoff = 0.6,
+                 #shape = c(6, 4, 2, 11, 15),
+                 colCustom = keyvals,
+                 colAlpha = 4/5,
+                 legend=c('NS','Log (base 2) fold-change','P value',
+                          'P value & Log (base 2) fold-change'),
+                 legendPosition = 'right',
+                 legendLabSize = 20,
+                 legendIconSize = 10,
+                 drawConnectors = TRUE,
+                 widthConnectors = 1.2,
+                 colConnectors = 'black',boxedLabels=TRUE,labSize =10)

Worked well, but when I want a box around the genes I got error

enter image description here

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boxedLabels was only added in a recent version. You can update to the very latest version with:

devtools::install_github('kevinblighe/EnhancedVolcano')
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Sorry I updated that right now but the same error

Error in EnhancedVolcano(res2, lab = rownames(res2), x = "log2FoldChange",  : 
  unused arguments (labFace = "bold", boxedLabels = TRUE)

Also today I am getting error about labSize argument too

Error in EnhancedVolcano(res2, lab = rownames(res2), x = "log2FoldChange",  : 
  unused arguments (pointSize = 3, labSize = 3)

Is this because of colCustom = keyvals?

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I am sorry, but you are therefore not using the latest version. I have just confirmed on my computer that boxedLabels and labSize are functioning correctly.

Please restart your R session, install the development version of EnhancedVolcano, and then confirm (via sessionInfo()) that you are using v1.3.4

Please do not post again until you debug these problems some more.

Thank you! :)

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Sorry you were right, problem was the update

Sorry to be too silly

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No problem, Angel.

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Sorry Kevin

By these lines I plotted the volcano

FC <-0.5
p <- 0.05
keyvals <- rep('grey75', nrow(res_responders))
names(keyvals) <- rep('NS', nrow(res_responders))
keyvals[which(abs(res_responders$log2FoldChange) > FC & res_responders$pvalue > p)] <- 'grey50'
names(keyvals)[which(abs(res_responders$log2FoldChange) > FC & res_responders$pvalue > p)] <- 'log2FoldChange'
keyvals[which(abs(res_responders$log2FoldChange) < FC & res_responders$pvalue < p)] <- 'grey25'
names(keyvals)[which(abs(res_responders$log2FoldChange)  < FC & res_responders$pvalue < p)] <- 'pvalue'
keyvals[which(res_responders$log2FoldChange < -FC & res_responders$pvalue < p)] <- 'blue2'
names(keyvals)[which(res_responders$log2FoldChange  < -FC & res_responders$pvalue < p)] <- 'Signif. down-regulated'
keyvals[which(res_responders$log2FoldChange > FC & res_responders$pvalue < p)] <- 'red2'
names(keyvals)[which(res_responders$log2FoldChange > FC & res_responders$pvalue < p)] <- 'Signif. up-regulated'
unique(keyvals)
unique(names(keyvals))



EnhancedVolcano(res_responders,
                lab = rownames(res_responders),
                x = 'log2FoldChange',
                y = 'pvalue',
                selectLab = c("KRT8","KRT18","EPCAM","AGR2","TSPAN8"),
                xlim = c(-8,8),
                xlab = bquote(~Log[2]~ 'fold change'),
                ylab = bquote(~-Log[10] ~ italic(P)),
                title = 'Responders',
                pCutoff = 0.05,
                FCcutoff = 0.5,
                pointSize = 10,
                labSize = 15,
                #shape = c(6, 4, 2, 11, 15),
                colCustom = keyvals,
                colAlpha = 0.75,
                legendPosition = 'right',
                legendLabSize = 40,
                legendIconSize = 30,
                drawConnectors = FALSE,
                widthConnectors = 0.5,
                colConnectors = 'grey50',
                gridlines.major = TRUE,
                gridlines.minor = FALSE,border = 'partial',
                borderWidth = 3,
                borderColour = 'black',boxedLabels=TRUE,titleLabSize = 50,
                subtitleLabSize = 1,
                captionLabSize = 40,axisLabSize = 40)

enter image description here

But I don't know how to reduce the length of x and particularly y axis

I mean I am seeking for a more shrunk plot

Can you help me please?

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Hi all, who did you go from ensemble names to gene names?

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but I got an dds or res list from DeSeq2

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......and what is the problem? - you implied that you have Ensembl IDs. These will be either rownames or the first column of your results object.

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the code for using the biomart in the posted link to change the ensemble ID o gene names as far as I understand is for matrix. enchancedVolcano uses the dds from Deseq2 OR there is something that I do not understand? Sorry for my ignorance edit: will this fit better A: ensembl id to gene symbol

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I developed EnhancedVolcano - it will accept any data coming from any source. At minimum, you just need 2 vectors:

  1. p-values (y-axis)
  2. fold-changes (x-axis)

There does not exist any annotation conversion tool that will accept an entire matrix. They will all accept a vector of gene names that you want to convert. The other tasks (editing the old names and replacing them in your matrix) is your role.

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Got it, thought it was only taking a list of sorts. thank you for the clarification. and love the script. saves time from manually writing the code.

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3.1 years ago
Renesh ★ 2.0k

Easy Volcano plot for gene expression data in Python https://reneshbedre.github.io/blog/volcano.html

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looks pretty cool will give it a try

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