Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

Dai, R.; Chu, T.; Zhang, M.; Wang, X.; Jourdon, A.; Wu, F.; Mariani, J.; Vaccarino, F. M.; Lee, D.; Fullard, J. F.; Hoffman, G. E.; Roussos, P.; Wang, Y.; Pinto, D.; Wang, S.; Zhang, C.; Chen, C.; LIU, C.

doi:10.1101/2023.03.13.532468

bioRxiv · 10.1101/2023.03.13.532468

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

Dai, R.·Chu, T.·Zhang, M.·Wang, X.·Jourdon, A.·Wu, F.·Mariani, J.·Vaccarino, F. M.·Lee, D.·Fullard, J. F.·Hoffman, G. E.·Roussos, P.·Wang, Y.·Pinto, D.·Wang, S.·Zhang, C.·Chen, C.·LIU, C.

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Abstract

Sample-wise deconvolution methods have been developed to estimate cell-type proportions and gene expressions in bulk-tissue samples. However, the performance of these methods and their biological applications has not been evaluated, particularly on human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk-tissue RNAseq, single-cell/nuclei (sc/sn) RNAseq, and immunohistochemistry. A total of 1,130,767 nuclei/cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expression. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk-tissue or single-cell eQTLs alone. Differential gene expression associated with multiple phenotypes were also examined using the deconvoluted data. Our findings, which were replicated in bulk-tissue RNAseq and sc/snRNAseq data, provided new insights into the biological applications of deconvoluted data.

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Dai, R., Chu, T., Zhang, M., Wang, X., Jourdon, A., Wu, F., Mariani, J., Vaccarino, F. M., Lee, D., Fullard, J. F., Hoffman, G. E., Roussos, P., Wang, Y., Pinto, D., Wang, S., Zhang, C., Chen, C., LIU, C.. 2023-03-15. Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data. https://doi.org/10.1101/2023.03.13.532468

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