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Created September 24, 2016 21:45
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How to use R and Plot.ly to create a Volcano Plot from DiffBind data
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DiffBind_Volcano_Plotly.md

Plot.ly Volcano Plot Example

Stephen Kelly
9/24/2016

# install.packages("plotly")
# install.packages("ggplot2")
# install.packages("gridExtra")
suppressPackageStartupMessages(library("plotly"))
# https://plot.ly/r/offline/
# https://plot.ly/r/
# https://plot.ly/r/text-and-annotations/

# path to the DiffBind table with genes
input_file <- "/Users/steve/Bioinformatics/DiffBind_scripts_reports/DiffBind_Volcano_Plot_report/input/diff_bind.Treatment4-ChIPSeq-vs-Control-ChIPSeq.p100.csv"

# read the file into a dataframe
diff_df <- read.delim(file = input_file,header = TRUE,sep = ',')

# previes some of the data
colnames(diff_df)
##  [1] "seqnames"                   "start"                     
##  [3] "end"                        "width"                     
##  [5] "strand"                     "Conc"                      
##  [7] "Conc_Treatment4.ChIPSeq"    "Conc_Control.ChIPSeq"      
##  [9] "Fold"                       "p.value"                   
## [11] "FDR"                        "Sample1.Treatment4.ChIPSeq"
## [13] "Sample2.Treatment4.ChIPSeq" "Sample3.Treatment4.ChIPSeq"
## [15] "Sample7.Control.ChIPSeq"    "Sample8.Control.ChIPSeq"   
## [17] "Sample9.Control.ChIPSeq"    "feature"                   
## [19] "external_gene_name"         "gene_biotype"              
## [21] "start_position"             "end_position"              
## [23] "insideFeature"              "distancetoFeature"         
## [25] "shortestDistance"           "fromOverlappingOrNearest"

dim(diff_df)
## [1] 3192   26

head(diff_df, 3)
##   seqnames    start      end width strand Conc Conc_Treatment4.ChIPSeq
## 1     chr1  8158035  8158269   235      * 2.90                   -0.31
## 2     chr1 10239463 10239824   362      * 3.56                    4.31
## 3     chr1 11346504 11346677   174      * 2.97                    3.90
##   Conc_Control.ChIPSeq  Fold  p.value      FDR Sample1.Treatment4.ChIPSeq
## 1                 3.82 -4.13 4.86e-06 1.04e-05                          1
## 2                 1.90  2.42 3.91e-06 8.71e-06                         18
## 3                -0.37  4.27 2.26e-06 5.50e-06                         13
##   Sample2.Treatment4.ChIPSeq Sample3.Treatment4.ChIPSeq
## 1                       0.73                       0.69
## 2                      20.42                      21.29
## 3                      13.13                      18.54
##   Sample7.Control.ChIPSeq Sample8.Control.ChIPSeq Sample9.Control.ChIPSeq
## 1                    9.39                   14.53                   18.49
## 2                    2.56                    5.54                    3.08
## 3                    0.85                    0.69                    0.77
##           feature external_gene_name   gene_biotype start_position
## 1 ENSG00000238290      RP11-431K24.1        lincRNA        8086798
## 2 ENSG00000130939              UBE4B protein_coding       10092890
## 3 ENSG00000120942             UBIAD1 protein_coding       11333263
##   end_position insideFeature distancetoFeature shortestDistance
## 1      8182762        inside             71354            24493
## 2     10241297        inside            146754             1473
## 3     11356106        inside             13327             9429
##   fromOverlappingOrNearest
## 1         shortestDistance
## 2         shortestDistance
## 3         shortestDistance

# keep only the fields needed for the plot
diff_df <- diff_df[c("external_gene_name", "Fold", "p.value")]

# add a grouping column; default value is "not significant"
diff_df["group"] <- "NotSignificant"

# for our plot, we want to highlight 
# p value < 0.05
# Fold Change > 1

# change the grouping for the entries with significance but not a large enough Fold change
diff_df[which(diff_df['p.value'] < 0.05 & abs(diff_df['Fold']) < 1 ),"group"] <- "Significant"

# change the grouping for the entries a large enough Fold change but not a low enough p value
diff_df[which(diff_df['p.value'] > 0.05 & abs(diff_df['Fold']) > 1 ),"group"] <- "FoldChange"

# change the grouping for the entries with both significance and large enough fold change
diff_df[which(diff_df['p.value'] < 0.05 & abs(diff_df['Fold']) > 1 ),"group"] <- "Significant&FoldChange"

# make the Plot.ly plot
p <- plot_ly(data = diff_df, x = Fold, y = -log10(p.value), text = external_gene_name, mode = "markers", color = group) %>% 
  layout(title ="DiffBind Volcano Plot")
p
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Volcano Plot","xaxis":{"title":"Fold"},"yaxis":{"title":"-log10(p.value)"},"hovermode":"closest","margin":{"b":40,"l":60,"t":25,"r":10}},"url":null,"width":null,"height":null,"source":"A","config":{"modeBarButtonsToRemove":["sendDataToCloud"]},"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script> 
# to save plot to a HTML file:
htmlwidgets::saveWidget(as.widget(p), "graph.html")
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