Computational deconvolution of gene expression by individual host cellular subsets from microarray analyses of complex, parasite-infected whole tissues

Analyses of whole organs from parasite-infected animals can reveal the entirety of the host tissue transcriptome, but conventional approaches make it difficult to dissect out the contributions of individual cellular subsets to observed gene expression. Computational deconvolution of gene expression data may be one solution to this problem. We tested this potential solution by deconvoluting whole bladder gene expression microarray data derived from a model of experimental urogenital schistosomiasis. A supervised technique was used to group B-cell and T-cell related genes based on their cell types, with a semi-supervised technique to calculate the proportions of urothelial cells. We demonstrate that the deconvolution technique was able to group genes into their correct cell types with good accuracy. A clustering-based methodology was also used to improve prediction. However, incorrectly predicted genes could not be discriminated using this methodology. The incorrect predictions were primarily IgH- and IgK-related genes. To our knowledge, this is the first application of computational deconvolution to complex, parasite-infected whole tissues. Other computational techniques such as neural networks may need to be used to improve prediction.