Deconvolving the contributions of cell-type heterogeneity on cortical gene expression

Patrick, Ellis; Taga, Mariko; Ergün, Ayla; Ng, Bernard; Casazza, William; Cimpean, Maria; Yung, Christina J.; Schneider, Julie A.; Bennett, David A.; Gaiteri, Chris; Jager, Philip L. De; Bradshaw, Elizabeth M.; Mostafavi, Sara

doi:10.1101/566307

Cited by 39 publications

(74 citation statements)

References 22 publications

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“…2e). Moreover, immunohistochemistry (IHC) analyses on a 70 of these samples found similar proportions of major cell populations 28 , confirming the relative accuracy of snRNA-seq-based estimates of cell proportions.…”

Section: Resultsmentioning

confidence: 55%

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

et al. 2020

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We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. We further propose an additional mode of operation that merely requires a set of known marker genes.

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Section: Resultsmentioning

confidence: 55%

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

et al. 2020

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“…We next wanted to test how the algorithm performs on postmortem human brain tissue of a subsample from the Religious Orders Study and Memory and Aging Project (ROSMAP) study (22), for which ground-truth cell composition information was recently measured by immunohistochemistry (41 samples with all cell types given) (23). The data provided by this study consist of bulk RNA-seq data from the dorsolateral prefrontal cortex and pose a special challenge due to the complexity of its cell type composition, which is further complicated by the fact that the data originate from brains of healthy individuals as well as patients with Alzheimer's disease (AD) at various stages of neuronal loss.…”

Section: Downloaded Frommentioning

confidence: 99%

“…The ROSMAP RNA-seq data were downloaded from www.synapse.org/. The cell composition values were provided by the authors of the study (23).…”

Section: Tissue Datasets For Benchmarkingmentioning

confidence: 99%

Deep learning–based cell composition analysis from tissue expression profiles

et al. 2020

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We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single-cell RNA sequencing (RNA-seq) data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple datasets. Because of this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden’s software package and web application are easy to use on new as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.

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“…Finally, the actual cellular composition of the bulk tissue samples we used is not known. While the approach of using cell-type markers to infer composition has been validated many times (Newman et al, 2015;Patrick et al, 2019;Mancarci et al, 2017) , we do not claim it is a perfect substitute for accurate direct counts. It remains formally possible that some of the variation we attribute to cellular composition is instead due to complex patterns of gene regulation that mimic compositional effects, but we feel the most parsimonious interpretation of the data is that cellular composition is a major contributor.…”

Section: Discussionmentioning

confidence: 82%

“…Recently it has been proposed that variation in cell type composition between individual samples explains a substantial degree of variation in gene expression in human brain (Kelley et al, 2018). In general, celltype "deconvolution" methods rely on the idea that cell-type markers can be used to infer cellular composition (Newman et al, 2015;Patrick et al, 2019). Inferred cellular composition is also used for adjusting statistical models, as in some expression quantitative trait locus (eQTL) analyses (Westra et al, 2015;Ng et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Untangling the effects of cellular composition on coexpression analysis

Farahbod

Pavlidis

2019

Preprint

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Background: Coexpression analysis is one of the most widely used methods in genomics, with applications to inferring regulatory networks, predicting gene function, and interpretation of transcriptome profiling studies. Most studies use data collected from bulk tissue, where the effects of cellular composition present a potential confound. However, the impact of composition on coexpression analysis have not been studied in detail. Here we examine this issue for the case of human brain RNA analysis.Results: We found that for most genes, differences in expression levels across cell types account for a large fraction of the variance of their measured RNA levels in brain (median R 2 = 0.64). We then show that genes that have similar expression patterns across cell types will have correlated RNA levels in bulk tissue, due to the effect of variation in cellular composition. We demonstrate that much of the coexpression in the bulk tissue can be attributed to this effect. We further show how this compositioninduced coexpression masks underlying intra-cell-type coexpression observed in single-cell data.Attempt to correct for composition yielded mixed results. Conclusions:The dominant coexpression signal in brain can be attributed to cellular compositional effects, rather than intra-cell-type regulatory relationships, and this is likely to be true for other tissues.These results have important implications for the relevance and interpretation of coexpression in many applications.

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Deconvolving the contributions of cell-type heterogeneity on cortical gene expression

Cited by 39 publications

References 22 publications

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

Deep learning–based cell composition analysis from tissue expression profiles

Untangling the effects of cellular composition on coexpression analysis

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