Differential Correlation Across Subpopulations of Single Cells in Subtypes of Acute Myeloid Leukemia

Mass cytometers can record 40-50 parameters per single cell for millions of cells in a sample. Many methods have been developed to cluster phenotypically similar cells within cytometry data, but there are fewer methods to visualize activity and interactions of pairs of proteins across these populations. We have developed a workflow for analyzing correlations associated with predefined populations. By clustering blood samples from acute myeloid leukemia (AML) patients and normal controls using an established algorithm, we obtained a minimum spanning tree of clusters of single cells. Using surface marker expression, we identified clusters on the tree that belonged to phenotypes of interest. Next, we computed correlations between pairs of proteins in each cluster. We developed a novel, coherent, probability-based statistic to test differences between vectors of correlation coefficients. By comparing all combinations of the normal controls under the statistic, we created an empirical distribution that could provide a conservative threshold of differential correlation. Using this empirically derived distribution to define significance, we compared pooled samples from AML subtypes and normal controls to detect differential correlations. Given the structure present within this cytometry dataset, we found it natural to consider correlations in this manner versus aggregating all data and computing a single correlation. Our approach has the advantage that we can localize the statistical measure to determine contributions from particular phenotypic populations. Differentially correlated pairs of proteins can be further explored as possible testable hypotheses by considering a population's distribution of correlation coefficients or biaxially plotting protein expressions within individual cells in a given population.