In jonathandesmedt92/RtqpcR: RtqpcR facilitates analysis and QC of RT-qPCR data.

knitr::opts_chunk$set(echo = TRUE)

RtqpcR: Analysis of RT-qPCR expression data.

This package facilitates the analysis of RT-qPCR expression data (reverse transcriptase quantitative polymerase chain reaction). Here below we provide a tutorial for a standard qPCR analysis.

Dependencies

Before starting any qPCR analysis, please install the following packages:

BiocManager::install("readxl")
BiocManager::install("dplyr")
BiocManager::install("ggplot2")

Installation

Install the RTqpcR package by running:

devtools::install_github("jonathandesmedt92/RtqpcR")
library(RtqpcR)

Tutorial

Reading the raw expression data.

To date, RtqpcR is only compatible with qPCR data from a Viaa7 platform (384-well plates). The first step is to read in the expression data. At this point, the user has to decide how to deal with undetected mRNA expression values. We generally recommend setting these expression values to 40 (i.e. maximal CT value). These values will be plotted in graphs, however they will not be included in statistical analyses.

# Initialise an RtqpcR object
obj<-qpcr()

# Read in the expression data
obj<-read_qpcr(qpcr = obj, files = "data/test.xlsx")

Adding a sample annotation

We recommend to always write a sample annotation file. This allows one to... - keep track of all experimental data in the long run, - have a detailed and full experimental description for each sample, - easily include and use experimental variables in plots, - use a minimal labelling (i.e. numbering) in each step of the wet lab part of qPCR.

The sample annotation file should have at least columnn (named 'Sample'). Each biological replicate should have a different sample identifier. Technical replicates will have the same identifier and their expression values will be aggregated accordingly.

As as example for this tutorial, our annotation file looks like this:

annot<-readxl::read_excel("data/annotation.xlsx", sheet=1)
head(data.frame(annot,stringsAsFactors = F))

# Read in the sample annotation
obj<-add_annot(qpcr = obj, file = "data/annotation.xlsx")

Performing the core qPCR analysis

In this step the main analysis will be done. This includes aggregating values of the technical replicates, aggregating values of the reference genes, and calculating delta CT values.

obj<-analyse_qpcr(qpcr = obj, reference_genes = c("GENE4","GENE5","GENE6"), aggregation ="geomean")

Creating plots

Several plotting functions are available to make bar charts and line graphs.

# We create a barplot with linear or logarithmic scale
bar_plot(qpcr=obj, 
        xvar="Concentration", 
        baseline_samples=c(1,2,3), 
        genes=c("GENE1","GENE2","GENE3"), 
        ND_y_nudge=0.5, 
        comparisons = list(c("0","20"),
                           c("0","40")),
        xlabels = c("0" = "Concentration 0", "20" = "Concentration 20","40" = "Concentration 40"),
        linear = T,
        y_breaks = NULL,
        map_signif_level = T,
        legend = T,
        step_increase=0.1,
        tip_length=0.05,
        bar_fill = "Concentration",
        colors = c("#000000","#bfbfbf","#666666"))

bar_plot(qpcr=obj, 
        xvar="Concentration", 
        baseline_samples=c(1,2,3), 
        genes=c("GENE1","GENE2","GENE3"), 
        ND_y_nudge=0.5, 
        comparisons = list(c("0","20"),
                           c("0","40")),
        xlabels = c("0" = "Concentration 0", "20" = "Concentration 20","40" = "Concentration 40"),
        linear = F,
        y_breaks = c(1,10,100,1000,10000),
        map_signif_level = T,
        legend = T,
        step_increase=0.1,
        tip_length=0.05,
        bar_fill = "Concentration",
        colors = c("#000000","#bfbfbf","#666666"))