deconvSeq: deconvolution of cell mixture distribution in sequencing data.

Abstract

Although single-cell sequencing is becoming more widely available, many tissue samples such as intracranial aneurysms are both fibrous and minute, and therefore not easily dissociated into single cells. To account for the cell type heterogeneity in such tissues therefore requires a computational method. We present a computational deconvolution method, deconvSeq, for sequencing data (RNA and bisulfite) obtained from bulk tissue. This method can also be applied to single-cell RNA sequencing data. DeconvSeq utilizes a generalized linear model to model effects of tissue type on feature quantification, which is specific to the data structure of the sequencing type used. Estimated model coefficients can then be used to predict the cell type mixture within a tissue. Predicted cell type mixtures were validated against actual cell counts in whole blood samples. Using this method, we obtained a mean correlation of 0.998 (95% CI 0.995-0.999) from the RNA sequencing data of 35 whole blood samples and 0.95 (95% CI 0.91-0.98) from the reduced representation bisulfite sequencing data from 35 whole blood samples. Using symmetric balances to obtain the correlation between compositional parts, we found that the lowest correlation occurred for monocytes for both RNA and bisulfite sequencing. Comparison with other methods of decomposition such as deconRNAseq, CIBERSORT, MuSiC and EpiDISH showed that deconvSeq is able to achieve good prediction using mean correlation with far fewer genes or CpG sites in the signature set. Software implementing deconvSeq is available at https://github.com/rosedu1/deconvSeq. Supplementary data are available at Bioinformatics online.