Digital assays relate the fraction of positive measurement units to the number of target molecules distributed among partitions. Because some units may receive no molecules while others receive one or more, Poisson statistics correct for this random occupancy pattern. The resulting estimate expresses absolute target concentration from positive-unit counts rather than from a calibration curve.
Limiting dilution distributes target molecules across many isolated reactions, increasing the chance that rare molecules become distinguishable as individual occupied units. Each unit produces a discrete positive or negative result, reducing dependence on a continuous bulk signal. This partition-level measurement supports sensitive detection when the target occurs at very low abundance in the original sample.
Digital assays determine concentration from the proportion of positive partitions and the statistical distribution of molecules across those partitions. Calibration-based measurements instead interpret signal intensity by comparison with reference standards. Because the digital approach can estimate absolute concentration without a calibration curve, it offers a distinct strategy for quantitative biological analysis and improves comparability across measurements.
A positive partition indicates that the target-specific reaction generated a detectable signal in that isolated measurement unit, while a negative partition indicates that no such signal was recorded. Counting both outcomes provides the fraction of positive units, which is the central measurement used to infer target abundance and distinguish rare targets from background units.
A typical workflow partitions the sample into many isolated reaction or measurement units, applies a target-specific reaction, records each unit as positive or negative, and counts the resulting signals. The positive fraction is then interpreted with an occupancy model, commonly using Poisson statistics, to estimate the absolute amount or concentration of the target.
Their value is greatest when researchers need precise measurement of targets present at low abundance or when absolute quantification is important. Supported applications include digital PCR for nucleic-acid quantification, rare-variant detection, pathogen measurement, and gene-expression assessment. The same analytical advantages also support diagnostic studies, environmental monitoring, and broader molecular research.
Digital PCR applies partitioning and discrete signal counting to nucleic-acid analysis. Researchers can use it to quantify nucleic acids, identify rare sequence variants, measure pathogens, or assess gene expression. By estimating target abundance from positive and negative reaction units, the method provides a biological measurement suited to sensitive molecular studies and diagnostic investigations.