In the Accuracy study, we will:

• Set the acceptable working range
• Set the cutoffs for calling a sample positive or negative
• Assess how well our assay agrees with an alternative assay

Clinical samples can  be FFPE or fresh frozen. FFPE is usually poorer quality than fresh frozen, so the two sample types are treated separately.

Section 1. Setting the Acceptable Working Range

The “acceptable working range” is based on the observed values from clinical samples. Once the acceptable working range is set, samples that have values outside the working range will be rejected.

To calculate the acceptable working range, we look at the observed values of Texas Red for the wild-type samples and calculate prediction intervals. In R, alpha = (1 – confidence_interval) and P is coverage.

To calculate prediction intervals, we can use either normtol.int or nptol.int:

normtol.int (df$Cq, alpha=alpha_num, P= coverage_level, side=2) # for normally distributed data

nptol.int (df$Cq, alpha=alpha_num, P= coverage_level, side=2) #for data that's not normally distributed


The Shapiro test for normality:

shapiro.test (df$Cq)

showed that the Ct values were non-normal, so the non-parametric nptol.int was used to obtain the table below:

Based on the table above, we decide on the acceptable working range. We usually go for the broadest range of values because we don’t want to reject too many samples.

Section 2. Set the cutoffs for calling a sample positive or negative

A sample will be called positive or negative based on its ΔCt value. Based on the plot of the ΔCt values with both positive and negative samples, we pick a cutoff that separates the negative samples from the positive samples.

Based on the figure above, we chose a ΔCt cutoff of 5.  Samples with ΔCt <= 5 will be mutation-positive and samples with ΔCt > 5 will be mutation-negative.

Section 3. Setting the Acceptable Working Range

This is fairly easy and doesn’t need complicated statistics.