LOB and LOD Definition
Before routinely using your digital PCR assay, you should estimate the Limit Of Blank (LOB) and the Limit Of Detection (LOD).
Starting with the LOB, the estimation of the LOD will be then derived from this estimated LOB.
How would you define each parameter?
When the number of positive partitions found in a sample is:
- smaller than the LOB, then the target is said not detected
- larger than the LOB, then the target is said detected
Figure A : Illustration of LOB and LOD
Here are the mathematical definitions of LOB and LOD :
- The Limit of Blank \(LOB\) with a confidence level \((1 – \alpha)\) is defined (in number of partitions) as the maximum number of positive partitions expected in a well with a probability of \(1 – \alpha\) in a sample containing no target sequence.
In other words, it is the maximum number of false positives that are plausible with a \(1 – \alpha\) probability (typically 95% for \(\alpha = 5\%\) of false positives).
To go further on a method to calculate the LOB of a given digital PCR experiment, do not hesitate to have a look at the memo LOB calculation Method.
- The Limit of Detection \(LOD\) with a confidence level \((1 – \beta)\) is defined as the minimum concentration for which detecting the target sequence in a well is possible with a probability of \(1 – \beta\).
In other words, this is the minimum concentration that can be said to be non-zero and statistically higher than the limit of blank \(LOB\) with a \(1 – \beta\) probability (typically 95% for \(\beta = 5\%\) of false negatives).
Knowing the LOB, if you need a method to calculate the LOD of a given digital PCR experiment, do not hesitate to have a look at the memo provided LOD calculation Method.
If you need to automatically compute the LOB and the LOD of a target gene from a set of negative control replicates, an online tool is provided for automated LOB and LOD estimation, let’s try it !