CoD: inferring immune-cell quantities related to disease states

Abstract

Motivation: The immune system comprises a complex network of genes, cells and tissues, coordinated through signaling pathways and cell-cell communications. However, the orchestrated role of the multiple immunological components in disease is still poorly understood. Classifications based on gene-expression data have revealed immune-related signaling pathways in various diseases, but how such pathways describe the immune cellular physiology remains largely unknown.

Results: We identify alterations in cell quantities discriminating between disease states using ' Cell type of Disease' (CoD), a classification-based approach that relies on computational immune-cell decomposition in gene-expression datasets. CoD attains significantly higher accuracy than alternative state-of-the-art methods. Our approach is shown to recapitulate and extend previous knowledge acquired with experimental cell-quantification technologies.

Conclusions: The results suggest that CoD can reveal disease-relevant cell types in an unbiased manner, potentially heralding improved diagnostics and treatment.

Availability and implementation: The software described in this article is available at http://www.csgi.tau.ac.il/CoD/.