Real-Time Quantitative PCR Data Analysis

Transcription

1 Real-Time Quantitative PCR Data Analysis Josep Lluís Mosquera UNITAT D ESTADÍSTICA I BIOINFORMÀTICA

2 OUTLINE Recapitulation Normalization Absolute Quantification Relative Quantification Data Analysis Pipeline Software

3 RECAPITULATION (1): Basic Concepts RT-qPCR is a method for determining the amount of nucleic acid present in a sample. Rn: increment of fluorescent signal at each time point. Baseline: cycles in which a signal is accumulating but is beneath the limits of detection. Threshold: arbitrary level of fluorescence chosen on the basis of the baseline variability. Ct: the fractional PCR cycle number at which the fluorescence is greater than the threshold.

4 RECAPITULATION (2): Basic Equations Target Reporter Fluorescence is determined by R + Ct o = 1 R ( ) E exp Ct Amplification Efficiency (at threshold) E exp ( ) =10 1 s 1 Fluorescence increase id proportional to the amount of target DNA I = k R Ct

5 PIPELINE OF RT-QPCR DATA ANALYSIS 1. Quality assessment 2. Normalisation 3. Data visualisation 4. Testing for statistical significance 5. Anotation/Mapping features

6 QUALITY ASSESSMENT

7 NORMALIZATION When analyzing results of RT-qPCR assays you are faced with several uncontrolled variables, which can lead to misinterpretation of the results. Uncontrolled variation: The amount of starting material Enzymatic efficiencies Differences between: tissues, individuals, experimental conditions To correct systematic variation BUT NOT biological variation NORMALIZATION

8 NORMALIZATION: Methods The most commonly known and used methods of normalization: Normalization to the original number of cells Normalization to the total RNA mass Normalization to one or more housekeeping genes Normalization to an internal or external calibrator

9 NORMALIZATION: Quantification Methods

10 NORMALIZATION: Biological Meaning and Quantification Methods BIOLOGICAL QUESTIONS: ANALYSIS METHODS: If I d like to know what can I do? 1) the number of viral particles in a given amount of blood, or 2) the fold change of p53 mrna in an equivalent amount of cancerous vs. normal tissue 1) Absolute Quantification, or 2) Relative Quantification are commonly used to address with these two scenarios

11 ABSOLUTE QUANTIFICATION Absolute quantification requires a standard curve of known copy numbers It can be constructed using several standards Most frequently used quantification standards. From Nucleic Acid Research Group, (NARG) survey 2007,

12 DATA ANALYSIS: Absolute Quantification. Standard Curve Absolute quantification is achieved by comparing C T values of each sample to a standard curve Standard curve is obtained by Using different known concentrations, for which C T are calculated and plotted vs the (log) (known) quantity

13 DATA ANALYSIS: Absolute Quantification. Standard Calibration Curve EXAMPLE: Determining Absolute Copy Number from Absolute Quantification SAMPLE REPLICATE Ct COPIES A A A Average ± B B B Average ± The standard curve is used only for interpolation but not for extrapolation (relation may not be linear outside the limits tested)

14 RELATIVE QUANTIFICATION (1) Relative quantification is the most widely used technique. Gene expression levels are calculated by the ratio between the amount of target gene and an endogenous reference gene, which is present in all samples. The reference gene has to be chosen so that its expression does not change under the experimental conditions or between different tissues (Cook NL et al., 2008). There are simple and more complex methods for relative quantification, depending on the PCR efficiency, and the number of reference genes used.

15 RELATIVE QUANTIFICATION (2) Most common approaches are Livak or Ct method Pfafl method Relative Standard Curve Method

16 RELATIVE QUANTIFICATION: Delta delta Ct ( Ct) method The simplest one: a direct comparison of Ct values, target gene vs reference gene. PCR efficiencies of both should be close to 100 % and not differ by more than 10 %. Involves the choice of a calibrator sample the untreated sample, the time = 0 sample, or Any sample you want to compare your unknown to.

17 RELATIVE QUANTIFICATION: Delta delta Ct ( Ct) method 1) Normalize Ct of the target gene to the reference gene is calculated for each sample Ct = Ct target Ct reference 2) Normalize the Ct of the test sample to the Ct of the calibrator Ct = (Ct target Ct reference ) test (Ct target Ct reference ) calibrator 3) Calculate the fold difference in expression 2 - Ct = normalized expression ratio

18 RELATIVE QUANTIFICATION: Delta delta Ct ( Ct) method EXAMPLE: SAMPLE Control (calibrator) Tumor (test) Ct p53 (target) GENE Ct GAPDH (reference) ) Ct calibrator = = -1.5 and Ct test = = ) Ct = Ct test ct calibrator = -3.9 (-1.5) = ) 2 - Ct = 2 -(-2.4) = 5.3 Tumor cells express p53 at a 5.3-fold higher level than control cells

19 RELATIVE QUANTIFICATION: Pfaffl Methods If difference in PCR efficiencies > 10%, between the reference gene and the target gene Ct method is inaccurate The value used is calculated with Pfaffl method Ct ( calibrator test ) target target RQ = Ct ( calibrator test ) reference E E reference where E gene : is the efficieny of the target, gene =target or refence Ct target ( calibrator test) = Ct ( calibrator) target Ct target ( test)

20 RELATIVE QUANTIFICATION: Relative Standard Curve Method It is used to determine changes in amount of a given sample relative to another, internal, control sample Does NOT require standards with known concentrations 1) Normalize the target gene to the reference gene Qty SampleTest Qty target reference ( test) ( test) Calibrator = Qty Qty target reference ( calibrator) ( calibrator) 2) Normalize the sample test to the calibrator RQ = Qty Qty target target ( test) ( calibrator) Qty Qty reference reference ( test) ( calibrator)

21 DATA ANALYSIS: Relative Standard Curve Method Example:

22 NORMALIZATION: Other Methods There are many different normalization methods among others Geometric mean calculates the average Ct value for each sample, and scales all Ct values according to the ratio of these mean Ct values across samples. Scale rank invariant computes the pairwise rank-invariant features, but then takes only the features found in a certain number of samples, and used the average Ct value of those as a scaling factor for correcting all Ct values. Normal rank invariant computes all rank-invariant sets of features between pairwise comparisons of each sample against a reference, such as a pseudo-mean. The rankinvariant features are used as a reference for generating a smoothing curve, which is then applied to the entire sample. Quantile makes the distribution of Ct values more or less identical across samples.

23 STATISTICAL ANALYSIS Two main types of analyses Comparative analyses Relatively rigorous Check a predefined hypotheses Relies on statistical testing Expression profiling: Search for trends and patterns in the data Exploratory, hypothesis generating approach Less rigorous Cluster analysis or PCA

24 STATISTICAL ANALYSIS : Basic Premises Statistical analyses of RT-qPCR data relies on three assumptions One gene-at-a-time We are sampling from two different (unknown) independent populations There exist unknown mechanisms that contribute to variability

25 STATISTICAL ANALYSIS: From Assumptions to Strategies Use random sampling and randomization to obtain independent and representative samples Apply experimental design principles to minimize confounding variability Perform statistical testing DO NOT FORGET about multiple testing adjustments Standard statistical approach: Confirmatory study Reject or Accept predefined hypothesis

26 STATISTICAL ANALYSIS: Comparing Two Groups

27 STATISTICAL ANALYSIS: Comparing More Than Groups

28 SOFTWARE SOURCE ABI Biogazelle Bioconductor Integromics biomcc SOFTWARE DataAssist GeneExpression REST Relative Expression Software Tool HTqPCR, ddct, StatMiner GenEx

29 UEB CAN HELP YOU

30 REMEMBER!!!! 1. Father of modern Mathematical Statistics and Developer of Experimental Design and ANOVA. To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of. Sir Ronald A.Fisher 1