Predicting host immune cell dynamics using tissue gene expression

Immune cell dynamics are critical to disease pathology. Deconvolution algorithms attempt to predict cell quantities based on gene expression profiles derived from diseased tissue and are evaluated using artificial or simple samples. We tested their capability of estimating cell counts given data from influenza-infected lung sampled over time post infection. Our findings suggest that current methods must be improved in order to characterize disease-associated immune cell dynamics.