<i>SimBu</i>: bias-aware simulation of bulk RNA-seq data with variable cell-type composition

Dietrich, Alexander; Sturm, Gregor; Merotto, Lorenzo; Marini, Fédérico; Finotello, Francesca; List, Markus

doi:10.1093/bioinformatics/btac499

Cited by 14 publications

(15 citation statements)

References 43 publications

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“…We excluded sample 2428 due to an insufficient number of cells. We used SimBu [59] to create four datasets of 50 pseudo-bulk samples each, spanning a range of potential scenarios that a deconvolution method should be able to accurately characterize (Figure 5A). Each pseudo-bulk sample consisted of count data from 2000 single cells, sampled according to the scenario parameters.…”

Section: Deconvolution Methods Have Cell Type Bias In Real and Pseudo...mentioning

confidence: 99%

Performance of computational algorithms to deconvolve heterogeneous bulk tumor tissue depends on experimental factors

Hippen

Omran

Weber

et al. 2022

Preprint

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Background: Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay. Results: We generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-cell sequencing and subsequent demultiplexing has a minimal effect. We identify dissociation-induced differences that affect cell composition, leading to changes that may compromise the assumptions underlying some deconvolution algorithms. We also observe differences across mRNA enrichment methods that introduce additional discrepancies between the two data types. We also find that experimental factors change cell composition estimates and that the impact differs by method. Conclusions: Previous benchmarks of deconvolution methods have largely ignored experimental factors. We find that methods vary in their robustness to experimental factors. We provide recommendations for methods developers seeking to produce the next generation of deconvolution approaches and for scientists designing experiments using deconvolution to study tumor heterogeneity.

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Section: Deconvolution Methods Have Cell Type Bias In Real and Pseudo...mentioning

confidence: 99%

Performance of computational algorithms to deconvolve heterogeneous bulk tumor tissue depends on experimental factors

Hippen

Omran

Weber

et al. 2022

Preprint

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“…We have demonstrated the utility of this type of inference in biomedical applications by reanalyzing published data of pediatric ependymal tumors and have discovered a previously unreported implication of microglial neurodegenerative gene expression programs in the mesenchymal transformation of these tumors. We expect that the development of advanced methods for simulating realistic bulk gene expression data from single-cell RNA-seq data 54 will improve the predictive power of ConDecon. In addition, we have shown that the approach of ConDecon can be adapted to other omics data modalities, such as spatial transcriptomics and chromatin accessibility data, for which there is currently a scarcity of deconvolution approaches.…”

Section: Discussionmentioning

confidence: 99%

Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data

Aubin

Montelongo

et al. 2023

Preprint

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Single-cell RNA-sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes like cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in functionality and accuracy with respect to current deconvolution methods. Using ConDecon, we discover the implication of neurodegenerative microglial inflammatory pathways in the mesenchymal transformation of ependymoma, recapitulate spatial patterns of cell differentiation during zebrafish embryogenesis, and make temporal inferences from bulk ATAC-seq data. Overall, ConDecon significantly enhances our understanding of dynamic cellular processes within bulk tissue samples.

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“…One main challenge has been limited availability of “ground truth” or “gold/silver standard” cell type proportions against which deconvolution methods can be benchmarked. In the absence of cell type proportion standards, it has been common to pseudobulk sc/snRNA-seq data or use mixture simulations to generate pseudobulk RNA-seq data, use the same sc/snRNA-seq data as the reference, and compare the deconvolution results against the cell type proportions observed in the sc/snRNA-seq data [18–20, 23, 24]. However, sc/snRNA-seq library preparation protocols have filtering steps that can introduce biases in the estimated cell type proportions [21], limiting their use as “ground truth” references.…”

Section: Introductionmentioning

confidence: 99%

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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SimBu: bias-aware simulation of bulk RNA-seq data with variable cell-type composition

Cited by 14 publications

References 43 publications

Performance of computational algorithms to deconvolve heterogeneous bulk tumor tissue depends on experimental factors

Performance of computational algorithms to deconvolve heterogeneous bulk tumor tissue depends on experimental factors

Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data

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

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