Strategies for cellular deconvolution in human brain RNA sequencing data

Sosina, Olukayode A.; Tran, Matthew N.; Maynard, Kristen R.; Tao, Ran; Taub, Margaret A.; Martinowich, Keri; Semick, Stephen A.; Quach, Bryan C.; Weinberger, Daniel R.; Hyde, Thomas M.; Hancock, Dana B.; Kleinman, Joel E.; Leek, Jeffrey T.; Jaffe, Andrew E.

doi:10.1101/2020.01.19.910976

Cited by 6 publications

(5 citation statements)

References 56 publications

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“…Firstly, current deconvolution method depends on feature selection and the choice of reference set. Since the adult cells in scRNA-seq data are unlikely to appear in fetal tissues (and vice versa), and very different cell populations between the substantia nigra and cortex region, using suitable reference scRNA-data for deconvolution analysis is very important (Sosina et al 2020). However, due to the minute amount of starting material, scRNA-seq data is prone to batch effects (Haghverdi et al 2018).…”

Section: Discussionmentioning

confidence: 99%

Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders

Pei¹,

Wang²,

Simon³

et al. 2020

Genome Res.

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As the most complex organ of the human body, the brain is composed of diverse regions, each consisting of distinct cell types and their respective cellular interactions. Human brain development involves a finely tuned cascade of interactive events. These include spatiotemporal gene expression changes and dynamic alterations in cell-type composition. However, our understanding of this process is still largely incomplete owing to the difficulty of brain spatiotemporal transcriptome collection. In this study, we developed a tensor-based approach to impute gene expression on a transcriptome-wide level. After rigorous computational benchmarking, we applied our approach to infer missing data points in the widely used BrainSpan resource and completed the entire grid of spatiotemporal transcriptomics. Next, we conducted deconvolutional analyses to comprehensively characterize major cell-type dynamics across the entire BrainSpan resource to estimate the cellular temporal changes and distinct neocortical areas across development. Moreover, integration of these results with GWAS summary statistics for 13 brain-associated traits revealed multiple novel trait–cell-type associations and trait-spatiotemporal relationships. In summary, our imputed BrainSpan transcriptomic data provide a valuable resource for the research community and our findings help further studies of the transcriptional and cellular dynamics of the human brain and related diseases.

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Section: Discussionmentioning

confidence: 99%

Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders

Pei¹,

Wang²,

Simon³

et al. 2020

Genome Res.

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“…Third, the estimated cell-type proportions are used to perform cell-type specific association studies with bulk data, This was done by fitting, for each transcript, the deconvolution model described elsewhere 13 (S1.3). These analyses were performed using the Bioconductor package RaMWAS 38 .…”

Section: Methodsmentioning

confidence: 99%

Fine-grained deconvolution of cell-type effects from human bulk brain data using a large single-nucleus RNA sequencing based reference panel

Oord

Åberg

2022

Preprint

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Brain disorders are leading causes of disability worldwide. Gene expression studies provide promising opportunities to better understand their etiology. When studying bulk tissue, cellular diversity may cause many genes that are differentially expressed in cases and controls to remain undetected. Furthermore, identifying the specific cell-types from which association signals originate is key to formulating refined hypotheses of disease etiology, designing proper follow-up experiments and, eventually, developing novel clinical interventions. Cell-type effects can be deconvoluted statistically from bulk expression data using cell-type proportions estimated with the help of a reference panel. To create a fine-grained reference panel for the human prefrontal cortex, we analyzed data from the seven largest single nucleus RNA-seq (snRNA-seq) studies. Seventeen cell-types were robustly detected across all seven studies. To estimate the cell-type proportions, we proposed an empirical Bayes estimator that is suitable for the new panel that involves multiple low abundant cell-types. Furthermore, to avoid the use of a very large reference panel and prevent challenges with public access of nuclei level data, our estimator uses a panel comprising mean expression levels rather than the nuclei level snRNA-seq data. Evaluations show that our empirical Bayes estimator produces highly accurate and unbiased cell-type proportion estimates. Transcriptome-wide association studies performed with permuted bulk RNA-seq data showed that it is possible to perform TWASs for even the rarest cell-types without an increased risk of false positives. Furthermore, we determined that for optimal statistical power the best approach is to analyze all cell-types in the panel as opposed to grouping or omitting (rare) cell-types.

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“…(Wald's test; adjusted P-value < 0.05 and fold-change > 1.5) (2). RNA-seq deconvolution also relies on cell-type specific expression (11,12,15,47 Figure 1).…”

Section: Generation Of Cell-type Signature Matrices From Publicly Avamentioning

confidence: 99%

“…Despite many advances, technical limitations (e.g., low gene detection per cell, cell dissociation optimization) and cost currently limit the use of scRNA-seq for hard-todissociate cell types and large study designs (5,6). Importantly, bioinformatics methods that integrate bulk RNA-seq and scRNA-seq demonstrate the highly complementary nature of these two technologies (7)(8)(9)(10)(11)(12)(13)(14)(15)(16).…”

Section: Introductionmentioning

confidence: 99%

“…This computational efficiency is important to scMappR because, for example, if there are 2,500 DEGs, the cell-type deconvolution is completed 2,501 times. Furthermore, scMappR's cwFold-changes rely on the average relative cell-type proportions across conditions and thus it is reasonable to sacrifice a small amount of sensitivity in cell-type proportion estimation for computational efficiency(11,12,14,47).scMappR leverages scRNA-seq data to characterize the cell-type specificity of a list of bulk DEGs while providing a cell-type marker database to test the over-representation of celltype markers in any gene list. Currently, single-cell genomic technologies are evolving and expanding to include new assays such as single cell open chromatin (single cell ATAC-seq) (61, 62) and single cell DNA methylation (DNAm) (63, 64), scRNA-seq across many biological conditions with replicates, and single-cell genomics with fewer technical limitations.…”

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confidence: 99%

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Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes

Sokolowski

Faykoo-Martinez

Erdman

et al. 2020

Preprint

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RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by integrating cell-type expression data generated by scRNA-seq and existing deconvolution methods. After benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. We found that scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small proportion of immune cells. While scMappR can work with any user supplied scRNA-seq data, we curated scRNA-seq expression matrices for ~100 human and mouse tissues to facilitate its use with bulk RNA-seq data alone. Overall, scMappR is a user-friendly R package that complements traditional differential expression analysis available at CRAN.

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Strategies for cellular deconvolution in human brain RNA sequencing data

Cited by 6 publications

References 56 publications

Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders

Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders

Fine-grained deconvolution of cell-type effects from human bulk brain data using a large single-nucleus RNA sequencing based reference panel

Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes

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