Understanding cytokine profiles of disease states has provided researchers with great insight into immunologic signaling associated with disease onset and progression, affording opportunities for advancement in diagnostics and therapeutic intervention. Multiparameter flow cytometric assays support identification of specific cytokine secreting subpopulations. Bead-based assays provide simultaneous measurement for the production of ever-growing numbers of cytokines. These technologies demand appropriate analytical techniques to extract relevant information efficiently. We illustrate the power of an analytical workflow to reveal significant alterations in T-cell cytokine expression patterns in type 1 diabetes (T1D) and breast cancer. This workflow consists of population-level analysis, followed by donor-level analysis, data transformation such as stratification or normalization, and a return to population-level analysis. In the T1D study, T-cell cytokine production was measured with a cytokine bead array. In the breast cancer study, intracellular cytokine staining measured T cell responses to stimulation with a variety of antigens. Summary statistics from each study were loaded into a relational database, together with associated experimental metadata and clinical parameters. Visual and statistical results were generated with custom Java software. In the T1D study, donor-level analysis led to the stratification of donors based on unstimulated cytokine expression. The resulting cohorts showed statistically significant differences in poststimulation production of IL-10, IL-1 beta, IL-8, and TNF beta. In the breast cancer study, the differing magnitude of cytokine responses required data normalization to support statistical comparisons. Once normalized, data showed a statistically significant decrease in the expression of IFN gamma on CD4+ and CD8+ T cells when stimulated with tumor-associated antigens (TAAs) when compared with an infectious disease antigen stimulus, and a statistically significant increase in expression of IL-2 on CD8+ T cells. In conclusion, the analytical workflow described herein yielded statistically supported and biologically relevant findings that were otherwise unapparent.