(or use the buttons below)

Daniel I S Rosenbloom, Oliver Elliott, Alison L Hill, Timothy J Henrich, Janet M Siliciano, Robert F Siliciano. Designing and interpreting limiting dilution assays: general principles and applications to the latent reservoir for HIV-1.

To upload data for a *single* patient, create a text file where each row has three numbers: the well size (# cells), the number of replicates for this size, and the number of positive outcomes for this size. The values may be delimited by either white space or commas. For example, your file could look like this:

`
900,9,1 `

400,5,2

100,10,2

For*multiple* patients, simply add rows with a greater-than sign followed by the patient name (in homage to fasta format). For example,

`
>patient1 `

900,9,0

100,10,2

>patient2

400,5,2

500,10,1

>patient3

600,10,1

500,4,3

300,5,2

In this mode, the app will produce a graph showing the infection frequencies (IUPM) across the set of patients.

400,5,2

100,10,2

For

900,9,0

100,10,2

>patient2

400,5,2

500,10,1

>patient3

600,10,1

500,4,3

300,5,2

In this mode, the app will produce a graph showing the infection frequencies (IUPM) across the set of patients.

- The
**maximum likelihood estimate**and**confidence interval**provide a good estimate of the infection frequency, assuming that (1) many cells (>100s) are used in the assay, and (2) each cell in each well has the same probability of being infected. - The
**p-value**can be used to detect very strange, or unlikely, results in the limiting dilution assay. For instance, if several large wells are negative but several small wells are positive, the resulting p-value will be very low, indicating that there may have been cross-contamination, improper labeling of wells, insufficiently mixed cells, or another failure of the assay. (Frequent users of this calculator should note: Every 100 assays that you run, you should expect to see a p-value below 0.01, just by chance!) **For all-negative well outcomes**, the maximum likelihood estimate is formally zero. To provide more useful information, a**posterior median estimate**is given instead. This value reports the median of the Bayesian posterior distribution, using a uniform prior. If you had a reasonable prior belief that your assay could turn up at least one positive well, then this is a decent rough estimate. A higher bound is also provided, at the**95th percentile of the posterior**distribution; this is a good upper bound. The p-value is not a meaningful output in this case, so it is not shown.**If you have all-positive assay results**, then close this app and use smaller wells!

Daniel I S Rosenbloom, Oliver Elliott, Alison L Hill, Timothy J Henrich, Janet M Siliciano, Robert F Siliciano. Designing and interpreting limiting dilution assays: general principles and applications to the latent reservoir for HIV-1.