Effective methods for bulk RNA-Seq deconvolution using scnRNA-Seq transcriptomes

Cobos, Francisco Avila; Panah, Mohammad Javad Najaf; Epps, Jessica; Long, Xiaochen; Man, Tsz-Kwong; Chiu, Hua-Sheng; Chomsky, Elad; Kiner, Evgeny; Krueger, Michael; Bernardo, Diego di; Voloch, Luis F.; Molenaar, Jan J.; Hooff, Sander R. van; Westermann, Frank; Jansky, Selina; Redell, Michele S.; Mestdagh, Pieter; Sumazin, Pavel

doi:10.1101/2022.12.13.520241

Cited by 5 publications

(28 citation statements)

References 43 publications

(42 reference statements)

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“…In the application to complex retina samples from patients with AMD, DeMixSC identifies subtle yet critical cell-type proportion changes, highlighting its ability to reflect cellular dynamics during disease progressions and facilitate the cell-type-specific gene expression analysis. Most existing single-cell-based deconvolution methods [10][11][12][13][14][15][16][17][18] are adept at discerning between 7 to 13 cell types from bulk RNA-seq data, DeMixSC aligns with these capabilities as demonstrated in our study.…”

Section: Discussionmentioning

confidence: 66%

“…This work addresses technological discrepancies between bulk and sc/sn RNA-seq data in order to improve the deconvolution accuracy of bulk RNA-seq data. We construct a specialized benchmark dataset of healthy retina samples and thoroughly evaluate the impact of technological discrepancies on existing single-cellbased deconvolution methods [10][11][12][13][14][15][16][17][18] . Utilizing this benchmark dataset, we introduce the DeMixSC deconvolution method that makes innovative improvements on the wNNLS framework to address the consistently observed technological discrepancy at a gene level.…”

Section: Discussionmentioning

confidence: 99%

“…The baseline constant constrains the weight range. Previous weight functions contain either the first or the second part only 10,[14][15][16][17] . We reason that both parts are needed to comprehensively account for variations across cells and across samples, hence introducing this new weight function.…”

Section: Overview Of Demixscmentioning

confidence: 99%

“…Using our benchmark data, we compare the performance of DeMixSC to eight existing deconvolution methods [10][11][12][13][14][15]17,18 , including AutoGeneS, BayesPrism, CIBERSORTx, DWLS, MuSiC, RNAseive, SCDC, and SQUID (see Methods, Fig. 3A).…”

Section: Compare the Estimation Accuracy Of Demixsc To Existing Decon...mentioning

confidence: 99%

“…They are typically constrained by low-resolution estimates, limited to identifying only two or three cellular components within the bulk samples. The progress of sc/snRNA-seq techniques opens the door to the emergence of single-cell-based deconvolution methods [10][11][12][13][14][15][16][17][18] , which tap into the granularity of even a modest set of single-cell data to provide far superior resolution in estimating cell-type proportions in complex tissues, thereby offering a cost-effective alternative.…”

Section: Introductionmentioning

confidence: 99%

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DeMixSC: a deconvolution framework that uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples

Guo,

Liu,

Cheng

et al. 2023

Preprint

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We introduce a novel deconvolution framework, DeMixSC, to resolve technological discrepancies between bulk and single-cell/nucleus RNA-seq data, a critical issue unaddressed by existing single-cell-based deconvolution methods. Built upon the weighted non-negative least squares framework, DeMixSC introduces two key improvements: it leverages a small benchmark dataset to identify and rescale genes affected by technological discrepancies; it employs a novel weight function to account for variations across subjects and cells. The advanced utility of DeMixSC is demonstrated by its superior deconvolution accuracy on a benchmark dataset of healthy retinas and its broad applicability to a large aged-macular degeneration (AMD) cohort. Our work is the first to systematically evaluate the impact of technological discrepancies on deconvolution performance and underscores the importance of using a benchmark dataset to counteract these discrepancies. Our study positions DeMixSC as a transferable tool for accurate deconvolution of large bulk RNA-seq cohorts, necessitating only a tissue-type match between the benchmark and targeted datasets.

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Section: Discussionmentioning

confidence: 66%

Section: Discussionmentioning

confidence: 99%

Section: Overview Of Demixscmentioning

confidence: 99%

Section: Compare the Estimation Accuracy Of Demixsc To Existing Decon...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

DeMixSC: a deconvolution framework that uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples

Guo,

Liu,

Cheng

et al. 2023

Preprint

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CATD: A reproducible pipeline for selecting cell-type deconvolution methods across tissues

Pournara

Miao

Beker

et al. 2023

Preprint

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Cell-type deconvolution methods aim to infer cell-type heterogeneity and the cell abundances from bulk transcriptomic data. The large number of currently developed methods (>50) and the vast inconsistency in their results in many cases, create an urgent need for guidance on method selection. Although previously suggested benchmarks have paved the way to better understand the performance of deconvolution methods, the proposed tests focus on simulated data and have only been applied to a handful of datasets. Besides, there is pressing interest to achieve the decomposition of database-level transcriptomic data of different tissues, conditions and species, scenarios in which deconvolution methods have not been tested thoroughly. Here, we propose a large-scale, multi-level assessment of 29 available deconvolution methods, leveraging single-cell RNA-sequencing (scRNA-seq) data from different organs and tissues. We extend previous benchmarks, suggesting a comprehensive simulation framework to evaluate deconvolution across a wide range of scenarios and we provide guidelines on the preprocessing of input matrices. We show that single-cell regression-based deconvolution methods such as DWLS are performing well but their performance is highly dependent on the reference selection and the tissue type. Our study also explores the vast impact of various batch effects in deconvolution including sample, study and the technology effect which have been overlooked. We validate deconvolution results on a golden standard bulk Polymorphonuclear Peripheral/Blood Mononuclear cell (PMN/PBMC) dataset with known cell-type proportions. Importantly, we suggest a novel methodology for consensus prediction of cell-type proportions for cases when ground truth is not available. The large-scale assessment of cell-type prediction methods is provided in a modularised pipeline for reproducibility (https://github.com/Functional-Genomics/CATD_snakemake). Last but not least, we envision that CATD (Critical Assessment of Transcriptomic Deconvolution) pipeline can be used for fast, simultaneous deconvolution of hundreds of real bulk samples with the use of different references. We also envision it to be used for speeding up the evaluation of newly published methods in the future.

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Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex

Huuki-Myers,

Montgomery,

Kwon

et al. 2024

Preprint

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Background: Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Several deconvolution methods have been developed and they have been previously benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or cell type proportions derived from immunohistochemistry reference data. A major limitation preventing the improvement of deconvolution algorithms has been the lack of highly integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue block. The performance of existing deconvolution algorithms has not yet been explored across different RNA extraction methods (e.g. cytosolic, nuclear, or whole cell RNA), different library preparation types (e.g. mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets. Results: A rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq across six RNA extraction and RNA-seq library combinations, and orthogonal cell type measurements via RNAScope/Immunofluorescence (RNAScope/IF). The Mean Ratio method was developed for selecting cell type marker genes for deconvolution and is implemented in the DeconvoBuddies R package. Five extensively benchmarked computational deconvolution algorithms were evaluated in DLPFC across six RNA-seq combinations and predicted cell type proportions were compared to those measured by RNAScope/IF. Conclusions: We show that Bisque and hspe are the top performing methods with performance dependent on the RNA-seq library preparation conditions. We provide a multi-assay resource for the development and evaluation of deconvolution algorithms.

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Effective methods for bulk RNA-Seq deconvolution using scnRNA-Seq transcriptomes

Cited by 5 publications

References 43 publications

DeMixSC: a deconvolution framework that uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples

DeMixSC: a deconvolution framework that uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples

CATD: A reproducible pipeline for selecting cell-type deconvolution methods across tissues

Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex

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