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I have generated a volcano plot with a differential expression file.

Code for inputing file:

macrophage_list <- read.table("differential_expression_macrophage.csv", header = T, sep = ",")

library(tidyr)
final_df <- df %>% pivot_longer(., -c(Feature.ID, Feature.Name), names_to = c("set",".value"), names_pattern = "(.+)_(.+)")

# A tibble: 80 x 6
   Feature.ID Feature.Name set       Mean.Counts Log2.fold.change Adjusted.p.value
   <fct>      <fct>        <chr>           <dbl>            <dbl>            <dbl>
 1 a          A            Cluster.1    0.000961            0.292           1     
 2 a          A            Cluster.2    0.000902            0.793           1     
 3 a          A            Cluster.3    0.00181             1.46            0.758 
 4 a          A            Cluster.4    0.000642            0.269           1     
 5 b          B            Cluster.1    0.000320            1.95            0.910 
 6 b          B            Cluster.2    0.00180             4.77            0.154 
 7 b          B            Cluster.3    0                   2.19            1     
 8 b          B            Cluster.4    0                   1.66            1     
 9 c          C            Cluster.1    0.00128            -2.01            0.0467
10 c          C            Cluster.2    0.00632             0.352           1     
# … with 70 more rows

output of head(final_tumor)

# A tibble: 6 x 6
  Feature.ID        Feature.Name set       Mean.Counts Log2.fold.change Adjusted.p.value
  <fct>             <fct>        <chr>           <dbl>            <dbl>            <dbl>
1 ENSG00000227232.5 WASH7P       Cluster.1     0                   1.50            1    
2 ENSG00000227232.5 WASH7P       Cluster.2     0                   1.73            1    
3 ENSG00000227232.5 WASH7P       Cluster.3     0                   1.77            1    
4 ENSG00000227232.5 WASH7P       Cluster.4     0.00114             4.30            0.293
5 ENSG00000227232.5 WASH7P       Cluster.5     0                   2.15            1    
6 ENSG00000227232.5 WASH7P       Cluster.6     0                   1.22            1 

output of tail(final_tumor)

# A tibble: 6 x 6
  Feature.ID        Feature.Name set        Mean.Counts Log2.fold.change Adjusted.p.value
  <fct>             <fct>        <chr>            <dbl>            <dbl>            <dbl>
1 ENSG00000210196.2 MT-TP        Cluster.6       0.0699          -0.202           0.790  
2 ENSG00000210196.2 MT-TP        Cluster.7       0.0801           0.0386          1      
3 ENSG00000210196.2 MT-TP        Cluster.8       0.0711           0.0875          1      
4 ENSG00000210196.2 MT-TP        Cluster.9       0.0152          -2.31            0.00127
5 ENSG00000210196.2 MT-TP        Cluster.10      0.0147          -2.30            0.00612
6 ENSG00000210196.2 MT-TP        Cluster.11      0.122            0.762           1  

Code for generating volcano plot:

library(ggplot2)
library(ggrepel)
ggplot(final_tumor, aes(x = Log2.fold.change,y = -log10(Adjusted.p.value), label = Feature.Name))+ 
  geom_point()+
  geom_text_repel(data = subset(final_tumor, Adjusted.p.value < 0.05), 
                  aes(label = Feature.Name))

Now, I want to pull out a certain gene, Casp14, from the list and box it on the plot. How do I do that?

ggplot of rep_final

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As you did for labeling genes with an adjusted p value below 0.05, you can subset your dataset for keeping only rows corresponding to "Casp14":

library(ggplot2)
library(ggrepel)
ggplot(final_tumor, aes(x = Log2.fold.change,y = -log10(Adjusted.p.value), label = Feature.Name))+ 
  geom_point()+
  geom_text_repel(data = subset(final_tumor, Adjusted.p.value < 0.05), 
                  aes(label = Feature.Name))+
  geom_text_repel(data = subset(final_tumor, Feature.Name == "Casp14"), 
                  aes(label = Feature.Name), color = "red")

With this code, you should see now the labeling of the gene of interest (Casp14) in red on your volcano plot.

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  • $\begingroup$ How about for Enhanced volcano? I have this code EnhancedVolcano(final_tumor, lab = as.character(final_tumor$FeatureName), x = 'Log2.fold.change', y = 'Adjusted.p.value', xlim = c(-8,8), title = 'Tumor', pCutoff = 10e-5, FCcutoff = 1.5, pointSize = 3.0, labSize = 3.0) $\endgroup$ – mmpp Jan 25 at 22:08
  • $\begingroup$ ggplot2 is taking forever to generate. $\endgroup$ – mmpp Jan 25 at 23:24
  • $\begingroup$ How long is your dataset ? Maybe it is not relevant to plot all genes. Maybe, you can plot a representative part of your dataset (e.g. 2000 randomly selected genes). Regarding EnhancedVolcano, I do not have experience with it. Sorry. $\endgroup$ – dc37 Jan 26 at 2:52
  • $\begingroup$ How do I go about just plotting a representation of my dataset? $\endgroup$ – mmpp Jan 26 at 3:07
  • $\begingroup$ you can use sample function to select random rows of your dataframe: rep_final = final_tumor[sample(1:nrow(final_tumor), size = 1000), ] $\endgroup$ – dc37 Jan 26 at 3:11

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