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

Rujia Dai; Tianyao Chu; Ming Zhang; Xuan Wang; Alexandre Jourdon; Feinan Wu; Jessica Mariani; Flora M Vaccarino; Donghoon Lee; John F Fullard; Gabriel E Hoffman; Panos Roussos; Yue Wang; Xusheng Wang; Dalila Pinto; Sidney H Wang; Chunling Zhang; PsychENCODE consortium; Chao Chen; Chunyu Liu

doi:10.1101/2023.03.13.532468

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

bioRxiv [Preprint]. 2023 Mar 15:2023.03.13.532468. doi: 10.1101/2023.03.13.532468.

Authors

Rujia Dai¹, Tianyao Chu², Ming Zhang², Xuan Wang², Alexandre Jourdon³, Feinan Wu³, Jessica Mariani³, Flora M Vaccarino^{3

4}, Donghoon Lee⁵, John F Fullard⁵, Gabriel E Hoffman⁵, Panos Roussos⁵, Yue Wang⁶, Xusheng Wang⁷, Dalila Pinto⁸, Sidney H Wang⁹, Chunling Zhang¹⁰; PsychENCODE consortium; Chao Chen², Chunyu Liu^{1

2

10}

Affiliations

¹ Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA.
² Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China.
³ Child Study Center, Yale University, New Haven, CT, USA.
⁴ Department of Neuroscience, Yale University, New Haven, CT, USA.
⁵ Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁶ Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, VA, USA.
⁷ Department of Biology, University of North Dakota, Grand Forks, ND, USA.
⁸ Department of Psychiatry, Department of Genetics and Genomic Sciences, Mindich Child Health and Development Institute, and Icahn Genomics Institute for Data Science and Genomic Technology, Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁹ Center for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA.
¹⁰ Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA.

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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Abstract

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