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Cell-type-aware analysis of RNA-seq data

Nature Computational Science volume 1, pages 253–261 (2021)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

Most tissue samples are composed of different cell types. Differential expression analysis without accounting for cell-type composition cannot separate the changes due to cell-type composition or cell type-specific expression. We propose a computational framework to address these limitations: CARseq (cell-type-aware analysis of RNA-seq). CARseq employs a negative binomial distribution that appropriately models the count data from RNA-seq experiments. Simulation studies show that CARseq has substantially higher power than a linear model-based approach and it also provides more accurate estimate of the rankings of differentially expressed genes. We have applied CARseq to compare gene expression of schizophrenia/autism subjects versus controls, and identified the cell types underlying the difference and similarities of these two neuron-developmental diseases. Our results are consistent with the results from differential expression analysis using single-cell RNA-seq data.

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Fig. 1: Illustration of CT-specific expression.
Fig. 2: Simulation results.
Fig. 3: Reproducibility of effect size estimation.
Fig. 4: CT-specific DE results for SCZ versus controls.
Fig. 5: Microglia-specific DE signals.

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Data availability

The data used in this study are available in the following public repositories.

snRNA-seq data for CT-specific expression reference were generated by the Allen Institute for Brain Science. File ‘http://human_mtg_gene_expression_matrices_2018-06-14.zip/’ was downloaded from http://celltypes.brain-map.org/api/v2/well_known_file_download/694416044.

The gene expression and clinical data of schizophrenia patients and healthy controls were generated by Common Mind Consortium (CMC) and the relevant data were obtained from the following links. Data access is governed by the NIMH Repository and Genomics Resources. CMC gene expression data, https://www.synapse.org/#!Synapse:syn3346749; CMC gene expression meta data, https://www.synapse.org/#!Synapse:syn18103174; CMC clinical data, https://www.synapse.org/#!Synapse:syn3275213.

The gene expression and clinical data of autism patients and healthy controls were part of The PsychENCODE (PEC) Capstone Collection https://www.synapse.org/#!Synapse:syn12080241 and the relevant data were obtained from the following links. Data access is governed by the NIMH Repository and Genomics Resources. UCLA-ASD gene expression data, https://www.synapse.org/#!Synapse:syn8365527; UCLA-ASD gene expression meta data, https://www.synapse.org/#!Synapse:syn5602933; UCLA-ASD clinical data, https://www.synapse.org/#!Synapse:syn5602932. The list of SFARI ASD risk genes was downloaded from https://gene.sfari.org/database/human-gene/.

Source Data for Figures 2–5 are available with this manuscript.

Code availability

The codes for generating the CT-specific gene expression reference panel are included in GitHub repository scRNAseq_pipelines (https://github.com/Sun-lab/scRNAseq_pipelines). We analyzed three scRNA-seq datasets: MTG, dronc and psychENCODE, and the codes were saved in corresponding folders. The codes to compare different references and generate final references were saved in ‘_brain_cell_type’ folder. The codes for CARseq analyses (including simulation and analyses of SCZ and ASD datasets) were included in GitHub repository CARseq_pipelines (https://github.com/Sun-lab/CARseq_pipelines). The file ‘reproducible_figures.html’ has the code to generate most figures in this paper. The R package CARseq was deposited at GitHub repository CARseq (https://github.com/Sun-lab/CARseq). All codes were also deposited in a Zendo repository36.

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Acknowledgements

We acknowledge the following grants: NIH R01GM105785 to W.S. and C.J., NIH R21CA224026 to W.S., NIH R01GM126550 to W.S., NIH R01HG009974 to D.-Y.L., NIH P01CA142538 to D.-Y.L., NIH R01GM126553 to M.C., NSF 2016307 to M.C. and a Sloan Foundation Fellowship to M.C. We also appreciate helpful discussions with P. Little.

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Authors and Affiliations

  1. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Chong Jin, Dan-Yu Lin & Wei Sun

  2. Genetic Medicine, University of Chicago, Chicago, IL, USA

    Mengjie Chen

  3. Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Wei Sun

  4. Department of Biostatistics, University of Washington, Seattle, WA, USA

    Wei Sun

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Contributions

W.S. and C.J. conceived the approach. C.J. implemented the methods and performed analysis, with input from W.S., M.C. and D.-Y.L.; W.S. and C.J. wrote the paper, with input from M.C. and D.-Y.L.

Corresponding author

Correspondence to Wei Sun.

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The authors declare no competing interests.

Additional information

Peer review information Nature Computational Science thanks Ruibin Xi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Ananya Rastogi was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary Information

Supplementary notes, results, Tables 1–6 and Figs. 1–60.

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Jin, C., Chen, M., Lin, DY. et al. Cell-type-aware analysis of RNA-seq data. Nat Comput Sci 1, 253–261 (2021). https://doi.org/10.1038/s43588-021-00055-6

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