Number of assays vs. number of samples: Shows the effect of replication number on design size and compression. Larger replication numbers provide less compression and require more samples.
Assay compression vs prevalence: Shows the efficient frontier of optimized designs. In general, larger pool sizes allow greater compression but at a cost of reduced prevalence.
Compare two designs: Shows side by side comparisons of some interesting designs.
Pool size vs. Prevalence: Performs a head to head comparison of all of the models in this database against a Dorfman type screen to show which design yields the greatest compression when limited by pool size and prevalence.
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Name
Total assays
Total samples
Compression
Replicates
Max prevalence
Rate (bits)*
Pool size
* Note: Rate is defined as the number of bits of information learned per test. The maximum is 1.0 bits for a binary test. Rate is defined as log2(n!/(k!*(n-k)!))/m.