ddCt Method for qRT-PCR Data Analysis: Implementation in the ddCt Package, Study notes of Data Analysis & Statistical Methods

Download ddCt Method for qRT-PCR Data Analysis: Implementation in the ddCt Package and more Study notes Data Analysis & Statistical Methods in PDF only on Docsity! ddCt method for qRT–PCR data analysis Jitao David Zhang, Markus Ruschhaupt and Rudolf Biczok April 26, 2022 Abstract Here we describe the 2−∆∆CT algorithm implemented in the ddCt package. The package is designed for the data analysis of quantitative real–time PCR (qRT–PCR) experiemtns in Bioconductor. With the ddCt package, one can acquire the relative expression of the target gene in different samples. This vignette mainly dicusses the principles of the ddCt algorithm and demonstrates the functionality of the package with a compact example. Another vignette in the package, rtPCR-usage, gives instructions to call the script for end–to–end analysis. Both absolute and relative quantification have been used to analyse the data from the quantita- tive real–time PCR (qRT–PCR, or RT–PCR for short) experiments. The 2−∆∆CT algorithm, also known as the the delta-delta-Ct or ddCt algorithm, is a convenient method to analyze the relative changes in gene expression [2]. It requires the assignment of one or more housekeeping genes, which are assumed to be uniformly and constantly expressed in all samples, as well as one or more reference samples. The expression of other samples is then compared to that in the reference sample1. 1 RT–PCR Rich background knowledge about RT–PCR can be found at [3], [1] and [5]. There are many variations in the experimental processes. Here we shortly summarize the general key steps in the TaqMan®assay to help the understanding of following discussions. 1. RNA preparation: total or specific type of RNA are extracted from cell lines, tissues, biop- sies, etc. 2. RNA is Reversed Transcribed into DNA, which is also known as the RT-reaction. 3. qPCR probes (sometimes also known as ’primers’) are added to the transcribed cDNA sam- ple and the polymerase chain reaction takes place. This probe is an oligonucleotide with a 1The qpcrNorm package in the Bioconductor repository introduces the data–driven normalization method for high– throughput qPCR data, which does not depend on the house–keeping genes but makes extra assumptions. See the help pages and the vignette of the qpcrNorm package for further information 1 reporter dye attached to the 5’ end and a quencher dye attached to the 3’ end. Till the time the probe is not hydrolized, the quencher and the fluorophore remain in proximity to each other, which does not completely quench the flourescence of the reporter dye and therefore only a background flourescence is observed. 4. During PCR, the probe anneals specifically between the forward and reverse primer to an internal region of the PCR product. The polymerase then carries out the extension of the primer and replicates the template to which the TaqMan®is bound. The 5’ exonuclease activity of the polymerase cleaves the probe, releasing the reporter molecule away from the close vicinity of the quencher. The fluorescence intensity of the reporter dye, as a result increases. This process repeats in every cycle. 5. As the cycle number increases, the detected fluorescence also increases. And when the fluorescence crosses an arbitrary line, the device recodes the cycle number until then, which is known as the CT value. In princple one could also report the CT values of the housekeeping gene and the sample gene(s) in the form of barplots to show their relative relation. However, this has two main drawbacks: • This is only applicable in cases where more than one genes are compared in the same sam- ple. In case of mutilple samples one has to calculate the relative expression to a specified reference sample • CT value is exponential. In case of a ideal amplification efficiency of 1, increase of the CT value by 1 indicates a two–fold expression. Therefore, it maybe misleading to illustrate the expression with the raw CT value. 2 The ddCt Algorithm The ddCt method was one of the first methods used to to calculate real–time PCR results. Different the standard curve [3] and the Pfaffl method [4], ddCt is an approximation method and makes various assumptions. However, it reduces lot of experiment effort by making these assumptions and is easy to implement, and in many cases they return results similarly to other non-approximation methods [2]. 2.1 Deviation The exponential amplification of the polymerase chain reaction (PCR) can be described by the equation 1. Xn = X0 × (1 + EX)n (1) where Xn is the number of target molecules at cycle n of the reaction, and X0 is the number of target molecules initially. Ex is the amplification efficiency of target amplification, and n is the 2 3.3 Read in data SDMFrame function is called to read in experiment data.Optionally one could also read in the sample annotation, which is the sampleInformation object in the example. > library(Biobase) > CtData <- SDMFrame(file.names) > sampleInformation <- read.AnnotatedDataFrame(info,header=TRUE, row.names=NULL) Note that SDMFrame is able to accept one or more files as input. 3.4 Apply the ddCt method Next step we call ddCtExpression to perform ddCt method on the data. > result <- ddCtExpression(CtData, + calibrationSample=name.reference.sample, + housekeepingGene=name.reference.gene, + sampleInformation=sampleInformation, + warningStream=warningFile) Please refer to the help page of ddCtExpression-class for the methods to access all the values calculated by the ddCt method. For example the error (either standard deviation or median absolute deviation) of all replicates are accessible through > CtErr(result) Sample Detector Sample1 Sample2 Sample3 Gene1 0.72107613 0.1657458 0.45246203 Gene2 0.06299529 0.0954406 0.03288257 Gene3 0.44017062 0.6452858 0.17610413 Sample Detector Sample4 Gene1 0.05064879 Gene2 0.03057597 Gene3 NA 3.5 Visualization errBarchart provides a simple way to visualize the experiment results with the modified barchart from the lattice package, as seen in the figure 1 5 > br <- errBarchart(result) > print(br) Sample E xp re ss io n fo ld c ha ng e 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Sam ple 1 Sam ple 2 Sam ple 3 Sam ple 4 8 ± 2 Gene1 Sam ple 1 Sam ple 2 Sam ple 3 Sam ple 4 Gene2 0.0 0.5 1.0 1.5 2.0 2.5 3.0 ND Gene3 Figure 1: Barchart with error bars to visualize the expression fold change of target genes in samples. Each panel represents one target gene (Gene 1, 2 and 3 in this case), and the expression level in each sample is indicated by the height of bars. If one gene is not detected in one sample, a ’NA’ symbol in grey will appear in the position of the bar (Gene3 in Sample4), which helps to differ from the situations where the expression is very low but not yet undetectable (for instance Gene3 in Sample3). See the help page of errBarchart for more details. 6 3.6 Write result to text file Finally we can save the results as tab-delimited text files. > elistWrite(result,file=savedir("allValues.txt")) 4 Acknowledgement We thank Florian Hahne, Wolfgang Huber, Andreas Buness and Stefan Wiemann for their sug- gestion and comments during the development of the package. The example data has been kindly provided by Ute Ernst and was produced in the Division of Molecular Genome Analysis, DKFZ Heidelberg, Germany. 7