Search Thermo Fisher Scientific
Search Thermo Fisher Scientific
The comparative CT method (ΔΔCT) for relative quantitation is a commonly used method for measuring siRNA-induced silencing or knockdown of a particular gene when using TaqMan Gene Expression Assays. In this method, data are normalized using a control transcript (e.g., 18S rRNA), and the normalized expression value (ΔCT) for the gene of interest in the experimental sample is compared to the equivalent ΔCT for the negative control siRNA-treated sample (NC).
The formula for converting ΔΔCT to gene expression levels (percent remaining gene expression or percent knockdown) distorts the appearance of the variability of samples. When the ΔΔCT is small, there is a larger incremental change in percent remaining gene expression or percent knockdown compared to when the ΔΔCT is large, because one CT value represents a 2-fold change. Samples can have similar precision at the original CT levels, the ΔCT-NC, ΔCT-Sample, or the final ΔΔCT level, yet display wide variability in the percent remaining gene expression or percent knockdown (Figure 1). See sidebar on page 10 for a brief discussion of precision and accuracy.
Figure 1. Data Variability Translates to Large Differences in the Ability to Identify Targets. The variability disappears as the percent remaining gene expression decreases and the percent knockdown increases.
The comparison of variability in ΔΔCT, percent remaining gene expression, and percent knockdown is shown graphically in Figures 1B and 1C.
The precision of the original real-time assay is the same at low or high ΔΔCT values. However, when ΔΔCT values are low, even small variability results in large changes in percent knockdown, essentially making target genes harder to identify. As ΔΔCT values increase, the variability appears diminished by the calculation, making the target genes easier to identify. This is important when viewing graphs and error bars for percent remaining gene expression or percent knockdown of different samples. The error bars of the samples with higher knockdown will appear smaller, even when the raw data used in the knockdown calculation was as variable as other samples with low knockdown and error bars that appear larger.
Figure 2. Examples of Accuracy and Precision for Normally Distributed Data. The light brown line indicates the “true” answer (zero) for a test. The black line represents an accurate and precise result (0 ± 1). The blue curve shows data that is less precise (has increased variability) but still accurately defines the average (0 ± 10). The green curve (10 ± 10) represents data that are both more variable and less accurate than data graphed by the black or blue curves, but it is correct a small percentage of the time. The orange curve represents very precise data (10 ± 1); however, it is inaccurate, because it does not encompass the correct answer.
For Research Use Only. Not for use in diagnostic procedures.