RQdeltaCT - Relative Quantification of Gene Expression using Delta Ct Methods

Table of contents

• Introduction

  • The summary of standard workflow

• Data import

  • Reading long-format data using the read_Ct_long() function
  • Reading wide-format data using the read_Ct_wide() function
  • Other methods

• Part A: The workflow for analysis of independent groups of samples

• Quality control of raw Ct data

• Filtering of raw Ct data

• Collapsing technical replicates and imputation of missing data - make_Ct_ready() function

• Reference gene selection

• Data normalization using reference gene

• Quality control and filtering of transformed Ct data

  • Analysis of data distribution
  • Hierarchical clustering
  • PCA analysis
  • Data filtering after quality control

• Relative quantification: 2^-dCt method

• Relative quantification: 2^-ddCt method

• Final visualisations

  • The FCh_plot() function
  • The results_volcano() function
  • The results_boxplot() function
  • The results_barplot() function
  • The results_heatmap() function

• Further analyses

  • PCA and k means clustering
  • Correlation analysis
  • Simple linear regression analysis
  • Receiver Operating Characteristic (ROC) analysis
  • Simple logistic regression

• Part B: A pairwise analysis

• Quality control of raw Ct data (a pairwise approach)

• Filtering of raw Ct data (a pairwise approach)

• Collapsing technical replicates and imputation of missing data - make_Ct_ready() function (a pairwise approach)

• Reference gene selection (a pairwise approach)

• Data normalization using reference gene (a pairwise approach)

• Quality control and filtering of transformed Ct data (a pairwise approach)

  • Analysis of data distribution (a pairwise approach)
  • Hierarchical clustering (a pairwise approach)
  • PCA analysis (a pairwise approach)
  • Data filtering after quality control (a pairwise approach)

• Relative quantification: 2^-dCt method (a pairwise approach)

• Relative quantification: 2^-ddCt method (a pairwise approach)

• Final visualisations (a pairwise approach)

  • The FCh_plot() function (a pairwise approach)
  • The results_volcano() function (a pairwise approach)
  • The results_boxplot() function (a pairwise approach)
  • The results_barplot() function (a pairwise approach)
  • The results_heatmap() function (a pairwise approach)
  • The parallel_plot() function (a pairwise approach)

• Further analyses (a pairwise approach)

  • PCA and k means clustering (a pairwise approach)
  • Correlation analysis (a pairwise approach)
  • Simple linear regression analysis (a pairwise approach)
  • Receiver Operating Characteristic (ROC) analysis (a pairwise approach)
  • Simple logistic regression (a pairwise approach)

• Session info

Introduction

RQdeltaCT is an R package developed to perform relative quantification of gene expression using delta Ct methods, proposed by Kenneth J. Livak and Thomas D. Schmittgen in Article1 and Article2.

These methods were designed to analyse gene expression data (Ct values) obtained from real-time PCR experiments. The main idea is to normalise gene expression values using endogenous control gene, present gene expression levels in linear form by using the 2^-(value) transformation, and calculate differences in gene expression levels between groups of samples (or technical replicates of a single sample).

There are two main delta Ct methods used for relative quantification. The choice of the best method depends on the study design. A short description of these methods is provided below; for more details, refer to articles in links provided above.

1. 2^-dCt method. In this method, Ct values are normalised by the endogenous control gene (often GAPDH, beta-actin, or other) by subtracting the Ct value of the endogenous control in each sample from the Ct value of the gene of interest in the same samples, obtaining delta Ct (dCt) values. Subsequently, the dCt values are transformed using the 2^-dCt formula, summarised by means in the compared study groups, and a ratio of means (fold change) is calculated for a study group. This method is useful in scenarios where samples should be analysed as individual data points, e.g., in comparison between patients and healthy subjects. See example no 5. in Article2.

2. 2^-ddCt method. Similarly to the 2^-dCt method, Ct values are normalised by endogenous control gene, but the obtained delta Ct (dCt) values are not exponentially transformed, but summarised by means in the compared study groups, and the mean dCt in a control group is subtracted from the mean dCt in a study group, giving the delta delta Ct (ddCt) value. Subsequently, the ddCt values are transformed using the 2^-ddCt formula to obtain the fold change value (also called the RQ value). This method is useful where a compared groups contain rather technical than biological replicates, e.g. where samples of cell line before adding stimulant are compared to samples of the same cell line after stimulation. See examples no. 1 and 2 in Article2.

Presented RQdeltaCT package includes functions that encompass both of these methods either for comparison of independent groups of samples or groups with a paired samples (pairwise analysis). The selection of a suitable method for analysis is up to the user.

To install and load RQdeltaCT package simply run:

remotes::install_github("Donadelnal/RQdeltaCT")
library(RQdeltaCT)

The functions developed within the RQdeltaCT package are designed to be maximally easy to use, even for the users who are beginners to R. The parameters of functions were prepared to sufficiently range all essential tasks and options, and no additional, extensive coding steps are necessary in standard workflow. The package was developed with the intention to be user-friendly and provide an opportunity to perform relative quantification analysis of gene expression using RQdeltaCT package by non-experts in R programming (only a very basic programming skills are required).