Statistical Estimation & Inference of Cell Counts from ELISPOT Limiting Dilution Assays

Abstract

The ELISPOT assay is often used for cell count determination in immunological studies. Automated methods are needed to estimate cell concentrations from spot counts obtained from the assay. Three major distributions are assumed for observational cell counts. For each assumed distribution, individual least squares (LS)/ maximum likelihood and/or individual robust least squares (RLS) are applied for parameter estimation. Distributions of study endpoints (derived variables), defined as percentages of antigen-specific memory cell per total immunoglobulin G (IgG), are investigated to provide a basis for hypothesis testing. We show, under some weak conditions, that the distribution of endpoint estimates across subjects is approximately the same within a group. Thus, the t -test or the Wilcoxon Rank Sum test can be applied to detect group differences. These methods are compared through simulations and application to real data.

Key Words:

• Limiting dilution assay
• Robust least squares
• Wilcoxon rank sum test

ACKNOWLEDGMENTS

This work is partially supported by the National Institute of Allergy and Infectious Diseases (grant N01-AI-50020 and HSN272201000055C) and partially supported by the National Institute of Allergy and Infectious Diseases (grant HHSN266200700008C). The authors thank the referees and the editors for their detailed comments and valuable suggestions that greatly improved the presentation and contents of this article.

Notes

Note. ARE, average relative error; PSAE, proportion of yielding estimates with smallest absolute error among 500 runs; MD, median approach; ME, mean approach; LS, least squares; RLS, robust least squares; POI, poisson approach. RPOI, robust least squares when measurement errors depend on dilution levels.

*Method applied after probable staining errors on first two dilutions removed.

Note. MD, median approach; ME, mean approach; LS, least squares; RLS, robust least squares; POI, poisson approach; RPOI, robust least squares when measurement errors depend on dilution levels.

*Method applied after probable staining errors on first two dilutions removed.