Abstract
Motivation: Immune cell dynamics is a critical factor of disease-associated pathology (immunopathology) that also impacts the levels of mRNAs in diseased tissue. Deconvolution algorithms attempt to infer cell quantities in a tissue/organ sample based on gene expression profiles and are often evaluated using artificial, non-complex samples. Their accuracy on estimating cell counts given temporal tissue gene expression data remains not well characterized and has never been characterized when using diseased lung. Further, how to remove the effects of cell migration on transcript counts to improve discovery of disease factors is an open question. Results: Four cell count inference (i.e., deconvolution) tools are evaluated using microarray data from influenza-infected lung sampled at several time points post-infection. The analysis finds that inferred cell quantities are accurate only for select cell types and there is a tendency for algorithms to have a good relative fit (R 2 ) but a poor absolute fit (normalized mean squared error; NMSE), which suggests systemic biases exist. Nonetheless, using cell fraction estimates to adjust gene expression data, we show that genes associated with influenza virus replication and increased infection pathology are more likely to be identified as significant than when applying traditional statistical tests.
Highlights
Identifying and quantifying the immune cells is critical to understanding both how the body manages disease and how immune mismanagement may increase the overall disease pathology
To characterize the accuracy of predictions from the set of deconvolution algorithms, the number of immune cells in mouse lung at five timepoints after influenza virus infection were measured by FACS
B cell counts did not increase significantly from the counts observed in mock animals until day 7 for the sample cohorts of H1N1 and pH1N1, while for H5N1 the quantity of B cells shows a significant decrease at both day 2 and day 7
Summary
Identifying and quantifying the immune cells is critical to understanding both how the body manages disease and how immune mismanagement may increase the overall disease pathology (e.g., immunopathology). A specific example is lethal influenza infections, which are often characterized by extremely high quantities of macrophages or neutrophils that infiltrate into lungs [12,13,14,15]. It has been shown in influenza infection studies that modulating inflammatory immune cell counts by interfering with immune cell trafficking or activation can significantly improve infection outcomes [16,17,18]. An accurate quantification of immune cells is essential to identifying the mechanisms of disease pathology and can provide insights in innovating treatments
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