> Jour 5 | Analyse des données qPCR > qPCR data

qPCR data

Data availability

The data tables retrieved from the RT-qPCR analysis are now available in the csv and xlsx formats in the “download section” :

Graphs

You can perform graphs for these data, either using R (the better solution) or using Excel.

If you choose to use R, here is the script I used to perform the graph :

1 - First, let’s load the required library and load the dataset (available above and located in a “data” folder inside your working directory) :


# For this graph, you will need to install the tidyverse library
library(tidyverse)

# Set the working directory on your computer
setwd("/Users/fredericbouche/Desktop/Résultats_TP/R/")

# Load the results (here, located in a folder "data", located inside the WD you set above)
results <- read.csv2('data/final_results_qPCR_all_groups.csv')
head(results)

# Releveling the factors so that WT genotypes are first, then frd3 
results$Genotype <- factor(results$Genotype, levels=c("WT","frd3"))

Now, lets build a graph with all the data :

#### Graph common to all groups 

# First, let's create a ggplot object
p <- ggplot(results, aes(x=Genotype, y=RQ, fill=Genotype))
# Now, let's set the type of graph
p <- p + geom_bar(stat="identity")  # "Identity" is reauired when you have only 1 value for the barplot
# Then, we make subgraphs according to two variables : Gene and replicates
p <- p + facet_wrap(Gene ~ Replicate, scales="free") # Free makes sure the Y axis is adapted to each dataset
# Now, we add the error bar to the results 
p <- p + geom_errorbar(aes(ymin=RQ.Min, ymax=RQ.Max), colour="grey30", width=.5, linewidth=.5, alpha=(0.7))  
print(p)

# If you want to save your image to a "figure" folder, run the following command line: 
ggsave("figures/all_graphs.png", width=10, height=6)

Now, let’s try to draw a graph specifically for 1 of the gene (e.g. IRT1)


### Analyse des données de IRT1 

### Replace the name below by the name of your gene
gene_int <- "IRT1"

# This line will filter the table to only keep the ones
results_sub <- results[which(results$Gene==gene_int),]

p <- ggplot(results_sub, aes(x=Genotype, y=RQ, fill=Genotype))
p <- p + geom_bar(stat="identity") 
p <- p + geom_errorbar(aes(ymin=RQ.Min, ymax=RQ.Max), colour="grey30", width=.5, linewidth=.5, alpha=(0.7))
p <- p + facet_wrap(. ~ Replicate, scales= "free" )
p <- p + ggtitle(paste("Relative expression of",gene_int))
print(p)

ggsave(paste("figures/",gene_int,".png"), width=10, height=6)

Now, let’s compare this with the DESEQ2 analysis to see whether your results confirm the results of the RNA-seq analysis.

Primer table

Usually, in publication, the primers are written in a table, which is organized this way:

Primer Table