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Abstract To understand phenotypic variations and key factors which affect disease susceptibility of complex traits, it is important to decipher cell‐type tissue compositions. To study cellular compositions of bulk tissue samples, one can evaluate cellular abundances and cell‐type‐specific gene expression patterns from the tissue transcriptome profiles. We develop both fixed and mixed models to reconstruct cellular expression fractions for bulk‐profiled samples by using reference single‐cell (sc) RNA‐sequencing (RNA‐seq) reference data. In benchmark evaluations of estimating cellular expression fractions, the mixed‐effect models provide similar results as an elegant machine learning algorithm named cell‐type identification by estimating relative subsets of RNA transcripts (CIBERSORTx), which is a well‐known and reliable procedure to reconstruct cell‐type abundances and cell‐type‐specific gene expression profiles. In real data analysis, the mixed‐effect models outperform or perform similarly as CIBERSORTx. The mixed models perform better than the fixed models in both benchmark evaluations and data analysis. In simulation studies, we show that if the heterogeneity exists in scRNA‐seq data, it is better to use mixed models with heterogeneous mean and variance–covariance. As a byproduct, the mixed models provide fractions of covariance between subject‐specific gene expression and cell types to measure their correlations. The proposed mixed models provide a complementary tool to dissect bulk tissues using scRNA‐seq data.
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DWEN: A novel method for accurate estimation of cell type compositions from bulk data samples
Advances in single-cell RNA sequencing (scRNAseq) technologies have allowed us to study the heterogeneity of cell populations. The cell compositions of tissues from different hosts may vary greatly, indicating the condition of the hosts, from which the samples are collected. However, the high sequencing cost and the lack of fresh tissues make single-cell approaches less appealing. In many cases, it is practically impossible to generate single-cell data in a large number of subjects, making it challenging to monitor changes in cell type compositions in various diseases. Here we introduce a novel approach, named Deconvolution using Weighted Elastic Net (DWEN), that allows researchers to accurately estimate the cell type compositions from bulk data samples without the need of generating single-cell data. It also allows for the re-analysis of bulk data collected from rare conditions to extract more in-depth cell-type level insights. The approach consists of two modules. The first module constructs the cell type signature matrix from single-cell data while the second module estimates the cell type compositions of input bulk samples. In an extensive analysis using 20 datasets generated from scRNA-seq data of different human tissues, we demonstrate that DWEN outperforms current state-of-the-arts in estimating cell type compositions of bulk samples.
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- PAR ID:
- 10409686
- Date Published:
- Journal Name:
- 2022 14th International Conference on Knowledge and Systems Engineering (KSE)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/KSE56063.2022.9953757
