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Hunt, Gregory J., Saskia Freytag, Melanie Bahlo, and Johann A. Gagnon-Bartsch. "dtangle: accurate and robust cell type deconvolution." Bioinformatics 35, no. 12 (2018): 2093–99. http://dx.doi.org/10.1093/bioinformatics/bty926.
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Abstract Motivation Cell type composition of tissues is important in many biological processes. To help understand cell type composition using gene expression data, methods of estimating (deconvolving) cell type proportions have been developed. Such estimates are often used to adjust for confounding effects of cell type in differential expression analysis (DEA). Results We propose dtangle, a new cell type deconvolution method. dtangle works on a range of DNA microarray and bulk RNA-seq platforms. It estimates cell type proportions using publicly available, often cross-platform, reference data. We evaluate dtangle on 11 benchmark datasets showing that dtangle is competitive with published deconvolution methods, is robust to outliers and selection of tuning parameters, and is fast. As a case study, we investigate the human immune response to Lyme disease. dtangle’s estimates reveal a temporal trend consistent with previous findings and are important covariates for DEA across disease status. Availability and implementation dtangle is on CRAN (cran.r-project.org/package=dtangle) or github (dtangle.github.io). Supplementary information Supplementary data are available at Bioinformatics online.
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Aguirre-Gamboa, Raúl, Klein Niek de, Tommaso Jennifer di, et al. "Deconvolution of bulk blood eQTL effects into immune cell subpopulations." BMC Bioinformatics 21, no. 1 (2020): 243. https://doi.org/10.1186/s12859-020-03576-5.
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<strong>Background: </strong>Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We introduce a new method, <i>Decon2,</i> as a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL).<strong>Results: </strong>The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell, we could predict the proportions of 34 circulating cell types for 3194 samples from a population-based cohort. Next, we identified 16,362 whole-blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with eQTL (≥ 96–100%) and chromatin mark QTL (≥87–92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect.<strong>Conclusions: </strong>Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs that is useful for pinpointing the most relevant cell type for a given complex disease. Decon2 is available as an R package and Java application ( https://github.com/molgenis/systemsgenetics/tree/master/Decon2) and as a web tool ( www.molgenis.org/deconvolution).
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Diaz, Michael, Jasmine Tran, Nicole Natarelli, Akash Sureshkumar, and Mahtab Forouzandeh. "Cellular Deconvolution Reveals Unique Findings in Several Cell Type Fractions Within the Basal Cell Carcinoma Tumor Microenvironment." SKIN The Journal of Cutaneous Medicine 7, no. 6 (2023): 1170–73. http://dx.doi.org/10.25251/skin.7.6.15.
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Introduction: Despite therapeutic advancements, locally advanced and metastatic basal cell carcinomas continue to carry poor prognoses and high recurrence rates. Current treatment options remain suboptimal due to limited efficacy and associated adverse events. The objectives of this study are to 1) characterize the basal cell carcinoma immune cell microenvironment and 2) identify novel therapeutic targets.
 Methods: Transcriptome data representing 25 basal cell carcinoma and 25 control tissue samples were obtained from the Gene Expression Omnibus. Cell type fraction estimates were derived by least-squares deconvolution. Population differences were determined by Mann-Whitney U test.
 Results: Most significantly, two deconvolution algorithms similarly observed greater B cell infiltration in tumor samples compared to normal tissue (P<0.0001).
 Conclusion: Importantly, the results of this study provide new insight into the basal cell carcinoma tumor microenvironment and nominate testable immune cell populations for future therapeutic discovery. Study limitations include sample size and applicable background prediction levels of bulk deconvolution tools.
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Qi, Zongtai, Yating Liu, Michael Mints, et al. "Single-Cell Deconvolution of Head and Neck Squamous Cell Carcinoma." Cancers 13, no. 6 (2021): 1230. http://dx.doi.org/10.3390/cancers13061230.
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Complexities in cell-type composition have rightfully led to skepticism and caution in the interpretation of bulk transcriptomic analyses. Recent studies have shown that deconvolution algorithms can be utilized to computationally estimate cell-type proportions from the gene expression data of bulk blood samples, but their performance when applied to tumor tissues, including those from head and neck, remains poorly characterized. Here, we use single-cell data (~6000 single cells) collected from 21 head and neck squamous cell carcinoma (HNSCC) samples to generate cell-type-specific gene expression signatures. We leverage bulk RNA-seq data from >500 HNSCC samples profiled by The Cancer Genome Atlas (TCGA), and using single-cell data as a reference, apply two newly developed deconvolution algorithms (CIBERSORTx and MuSiC) to the bulk transcriptome data to quantitatively estimate cell-type proportions for each tumor in TCGA. We show that these two algorithms produce similar estimates of constituent/major cell-type proportions and that a high T-cell fraction correlates with improved survival. By further characterizing T-cell subpopulations, we identify that regulatory T-cells (Tregs) were the major contributor to this improved survival. Lastly, we assessed gene expression, specifically in the Treg population, and found that TNFRSF4 (Tumor Necrosis Factor Receptor Superfamily Member 4) was differentially expressed in the core Treg subpopulation. Moreover, higher TNFRSF4 expression was associated with greater survival, suggesting that TNFRSF4 could play a key role in mechanisms underlying the contribution of Treg in HNSCC outcomes.
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Erdogan, Nuray Sogunmez, and Deniz Eroglu. "Sparse deconvolution of cell type medleys in spatial transcriptomics." PLOS Computational Biology 21, no. 6 (2025): e1013169. https://doi.org/10.1371/journal.pcbi.1013169.
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Mapping cell distributions across spatial locations with whole-genome coverage is essential for understanding cellular responses and signaling However, current deconvolution models aim to estimate the proportions of distinct cell types in each spatial transcriptomics spot by integrating reference single-cell data. These models often assume strong overlap between the reference and spatial datasets, neglecting biology-grounded constraints such as sparsity and cell-type variations, as well as technical sparsity. As a result, these methods rely on over-permissive algorithms that ignore given constraints leading to inaccurate predictions, particularly in heterogeneous or unmatched datasets. We introduce Weight-Induced Sparse Regression (WISpR), a machine learning algorithm that integrates spot-specific hyperparameters and sparsity-driven modeling. Unlike conventional approaches that neglect biology-grounded constraints, WISpR accurately predicts cell-type distributions while preserving biological coherence, i.e., spatially and functionally consistent cell-type localization, even in unmatched datasets. Benchmarking against five alternative methods across ten datasets, WISpR consistently outperformed competitors and predicted cellular landscapes in both normal and cancerous tissues. By leveraging sparse cell-type arrangements, WISpR provides biologically informed, high-resolution cellular maps. Its ability to decode tissue organization in both healthy and diseased states highlights WISpR’s practical utility for spatial transcriptomics, particularly in challenging settings involving noise, sparsity, or reference mismatches.
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Gloudemans, Michael, Elena Zotenko, Tam Banh, et al. "Abstract 1951: Inferring immune and tissue cell type contributions to cell-free DNA (cfDNA) with a DNA methylation assay." Cancer Research 85, no. 8_Supplement_1 (2025): 1951. https://doi.org/10.1158/1538-7445.am2025-1951.
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Background: In tissue-localized disease, dying cells release DNA into the bloodstream, enabling disease detection and characterization via blood draws. Circulating tumor DNA (ctDNA) is a key biomarker for cancer screening and monitoring. Quantification of immune cell- and tissue-derived DNA in plasma can expand the range of conditions identifiable through liquid biopsies. Here, we demonstrate the ability to infer cell type contributions to plasma cell-free DNA (cfDNA) from a clinically validated differential methylation-based cfDNA assay. Methods: We first implemented and validated a non-negative least squares cell type deconvolution method that uses single-site methylation (SSM) data. Accuracy was assessed by comparisons to gold-standard CyTOF and RNA-seq-based deconvolution on blood samples, as well as in silico simulations in blood and plasma. Next, we developed a model to estimate immune and tissue cell types contributing to plasma based on data derived from a clinically validated differential methylation-based cfDNA assay. Using molecule counts from &gt;2700 regions, we trained a model to optimize alignment with our validated SSM deconvolution method run on the same samples. Training and evaluation was done using leave-one-out cross validation. Results: Within the cellular blood compartment, SSM deconvolution accurately estimated T cell, B cell, and neutrophil proportions compared to CyTOF (r ≥ 0.82; N=13) and RNA-seq-based deconvolution (r ≥ 0.78; N=70). Within the plasma compartment, we used SSM deconvolution to infer, in 106 healthy plasma samples, the frequencies of cell types commonly contributing to cfDNA: T cells, B cells, neutrophils, monocytes, megakaryocytes, erythroid cells, hepatocytes, and endothelial cells. In silico simulations showed the limit of detection (LOD90) for these cell types in plasma to be &lt;=1.1% (median=0.28%). Inferred abundances aligned with distributions in published literature (Loyfer et al., Nature 2023). We compared the hypermethylation assay-derived cell type estimates to our validated SSM deconvolution approach on 106 healthy plasma samples run on both assays. We observed Pearson correlations of 0.66-0.98 for all evaluated cell types except for monocytes (r = 0.3; P&lt;0.003), indicating accurate estimation of the main cell types contributing to cfDNA in plasma. Conclusions: We developed an algorithm using a clinically validated cfDNA assay to accurately infer immune and tissue cell type contributions to plasma cfDNA. This technology has the potential to improve disease detection and management by identifying biological processes involving immune and/or tissue shedding into plasma. Further development of this technology will include application and evaluation on additional sample cohorts, including various disease states. Citation Format: Michael Gloudemans, Elena Zotenko, Tam Banh, Kimberly Zhu, Gleb Martovetsky, Sara Wienke, Min Woo Sun, Alan Selewa, Samantha Liang, John Connolly, Stefanie Mortimer, Noam Vardi, Emily Tsang, Drew Kennedy, Meromit Singer. Inferring immune and tissue cell type contributions to cell-free DNA (cfDNA) with a DNA methylation assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1951.
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An, Lingling. "Comparing Microbial Source Tracking Methods for Precision and Reliability." International Journal of Forensic Sciences 9, no. 1 (2024): 1–8. http://dx.doi.org/10.23880/ijfsc-16000369.
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Microbial source tracking is a valuable tool in forensic science, specifically in the analysis of trace evidence. Numerous tools have been developed to estimate the proportion of different contamination sources within a mixture. In this study, we evaluate the accuracy of various source tracking methods using datasets from microbiome studies. In addition to assessing source tracking methods, we also incorporate two widely used cell type deconvolution methods, namely EPIC and PREDE, which are designed to identify missing cell types in a given dataset. Furthermore, we investigate the effectiveness of combined methods by integrating RAD, a source tracking method aimed at filtering out unimportant sources, with either EPIC or PREDE for enhanced accuracy in both source tracking and cell type deconvolution. This research represents a pioneering effort to examine the application of cell type deconvolution methods in source tracking and vice versa. Particularly noteworthy is our focus on scenarios involving missing sources or cell types in the reference data, shedding light on the intricate interplay between these two analytical domains.
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Xue, Yuanqing (Bianca), and Verena Friedl. "135. Tumor deconvolution using comprehensive single-cell RNA sequencing cell type signatures." Cancer Genetics 268-269 (November 2022): 43. http://dx.doi.org/10.1016/j.cancergen.2022.10.138.
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Sturm, Gregor, Francesca Finotello, Florent Petitprez, et al. "Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology." Bioinformatics 35, no. 14 (2019): i436—i445. http://dx.doi.org/10.1093/bioinformatics/btz363.
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Abstract Motivation The composition and density of immune cells in the tumor microenvironment (TME) profoundly influence tumor progression and success of anti-cancer therapies. Flow cytometry, immunohistochemistry staining or single-cell sequencing are often unavailable such that we rely on computational methods to estimate the immune-cell composition from bulk RNA-sequencing (RNA-seq) data. Various methods have been proposed recently, yet their capabilities and limitations have not been evaluated systematically. A general guideline leading the research community through cell type deconvolution is missing. Results We developed a systematic approach for benchmarking such computational methods and assessed the accuracy of tools at estimating nine different immune- and stromal cells from bulk RNA-seq samples. We used a single-cell RNA-seq dataset of ∼11 000 cells from the TME to simulate bulk samples of known cell type proportions, and validated the results using independent, publicly available gold-standard estimates. This allowed us to analyze and condense the results of more than a hundred thousand predictions to provide an exhaustive evaluation across seven computational methods over nine cell types and ∼1800 samples from five simulated and real-world datasets. We demonstrate that computational deconvolution performs at high accuracy for well-defined cell-type signatures and propose how fuzzy cell-type signatures can be improved. We suggest that future efforts should be dedicated to refining cell population definitions and finding reliable signatures. Availability and implementation A snakemake pipeline to reproduce the benchmark is available at https://github.com/grst/immune_deconvolution_benchmark. An R package allows the community to perform integrated deconvolution using different methods (https://grst.github.io/immunedeconv). Supplementary information Supplementary data are available at Bioinformatics online.
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Shen-Orr, Shai S., and Renaud Gaujoux. "Computational deconvolution: extracting cell type-specific information from heterogeneous samples." Current Opinion in Immunology 25, no. 5 (2013): 571–78. http://dx.doi.org/10.1016/j.coi.2013.09.015.
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Rowland, Bryce, Ruth Huh, Zoey Hou, et al. "THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data." PLOS Genetics 18, no. 3 (2022): e1010102. http://dx.doi.org/10.1371/journal.pgen.1010102.
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Hi-C data provide population averaged estimates of three-dimensional chromatin contacts across cell types and states in bulk samples. Effective analysis of Hi-C data entails controlling for the potential confounding factor of differential cell type proportions across heterogeneous bulk samples. We propose a novel unsupervised deconvolution method for inferring cell type composition from bulk Hi-C data, the Two-step Hi-c UNsupervised DEconvolution appRoach (THUNDER). We conducted extensive simulations to test THUNDER based on combining two published single-cell Hi-C (scHi-C) datasets. THUNDER more accurately estimates the underlying cell type proportions compared to reference-free methods (e.g., TOAST, and NMF) and is more robust than reference-dependent methods (e.g. MuSiC). We further demonstrate the practical utility of THUNDER to estimate cell type proportions and identify cell-type-specific interactions in Hi-C data from adult human cortex tissue samples. THUNDER will be a useful tool in adjusting for varying cell type composition in population samples, facilitating valid and more powerful downstream analysis such as differential chromatin organization studies. Additionally, THUNDER estimated contact profiles provide a useful exploratory framework to investigate cell-type-specificity of the chromatin interactome while experimental data is still rare.
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Guo, Shuai, Xuesen Cheng, Andrew Koval, et al. "Abstract 4273: Integration with benchmark data of paired bulk and single-cell RNA sequencing data substantially improves the accuracy of bulk tissue deconvolution." Cancer Research 83, no. 7_Supplement (2023): 4273. http://dx.doi.org/10.1158/1538-7445.am2023-4273.
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Abstract The accuracy of current deconvolution methods largely relies on the quality of cell-type expression references. However, single-cell (sc) and single-nuclei (sn) RNA-seq data used for building the reference are usually generated from independent studies that are distinct from the bulk RNA-seq data to be deconvolved. This study design inherently introduces technical confounding factors as unwanted variations, which is not fully addressed by current methods. To evaluate the impact of this variation on deconvolution accuracy, we generated a benchmark dataset where bulk and snRNA-seq profiling were performed from the same aliquot of single-nuclei that were extracted from 24 healthy retina samples. All donor eye samples were collected within six hours post-mortem and were absent of any disease. This study design guarantees the matched sequencing data to present the same cell-type compositions, so that cross-platform technical artifacts become the remaining confounding factor. We used the benchmark dataset to evaluate the performance of seven current deconvolution methods and found they performed much worse in matched real-bulk data than in matched pseudo-bulks that were summations of the single-cell data. This finding suggests that none of these methods have fully addressed the major technical artifacts between bulk and single-cell sequencing platforms. We therefore propose DeMix.SC, a new deconvolution framework that optimizes deconvolution parameters using a small set of matched bulk and sc/snRNA-seq data from the same tissue type. DeMix.SC includes two major steps. First, we measure the technical variations across genes and across platforms using the benchmark data. Second, we introduce a new weight function for each gene that produces a ranking order that accounts for both the platform-specific technical variations and cell-type specific expressions at gene level. Using the benchmark data for retina, we applied DeMix.SC to previously published human retinal RNA-seq data from 523 individuals with different stages of age-related macular degeneration (AMD). We observed that DeMix.SC can accurately capture the cell-type composition shifts in the AMD retina. DeMix.SC revealed a significant drop of rod cells as well as increased astrocytes, bipolar cells, and Müller cells in the AMD retina compared to the non-AMD group. The proportion changes of the later three minor cell types were not identified by other methods, while DeMix.SC could reveal such tendency. In summary, DeMix.SC integrates benchmark data to improve the deconvolution accuracy in retina samples. Our method is generic and can be applied to other disease conditions, such as deciphering the cell-type heterogeneity in cancer. We expect DeMix.SC will help revolutionize the downstream cell-type specific analysis of bulk RNA-seq data and identify cellular targets of human diseases. Citation Format: Shuai Guo, Xuesen Cheng, Andrew Koval, Shuangxi Ji, Qingnan Liang, Yumei Li, Leah A. Owen, Ivana K. Kim, John Weinstein, Scott Kopetz, John Paul Shen, Margaret M. DeAngelis, Rui Chen, Wenyi Wang. Integration with benchmark data of paired bulk and single-cell RNA sequencing data substantially improves the accuracy of bulk tissue deconvolution. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4273.
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Du, Rose, Vince Carey, and Scott T. Weiss. "deconvSeq: deconvolution of cell mixture distribution in sequencing data." Bioinformatics 35, no. 24 (2019): 5095–102. http://dx.doi.org/10.1093/bioinformatics/btz444.
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Abstract Motivation Although single-cell sequencing is becoming more widely available, many tissue samples such as intracranial aneurysms are both fibrous and minute, and therefore not easily dissociated into single cells. To account for the cell type heterogeneity in such tissues therefore requires a computational method. We present a computational deconvolution method, deconvSeq, for sequencing data (RNA and bisulfite) obtained from bulk tissue. This method can also be applied to single-cell RNA sequencing data. Results DeconvSeq utilizes a generalized linear model to model effects of tissue type on feature quantification, which is specific to the data structure of the sequencing type used. Estimated model coefficients can then be used to predict the cell type mixture within a tissue. Predicted cell type mixtures were validated against actual cell counts in whole blood samples. Using this method, we obtained a mean correlation of 0.998 (95% CI 0.995–0.999) from the RNA sequencing data of 35 whole blood samples and 0.95 (95% CI 0.91–0.98) from the reduced representation bisulfite sequencing data from 35 whole blood samples. Using symmetric balances to obtain the correlation between compositional parts, we found that the lowest correlation occurred for monocytes for both RNA and bisulfite sequencing. Comparison with other methods of decomposition such as deconRNAseq, CIBERSORT, MuSiC and EpiDISH showed that deconvSeq is able to achieve good prediction using mean correlation with far fewer genes or CpG sites in the signature set. Availability and implementation Software implementing deconvSeq is available at https://github.com/rosedu1/deconvSeq. Supplementary information Supplementary data are available at Bioinformatics online.
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Lin, Wei-Yu, Melissa Kartawinata, Bethany R. Jebson, et al. "Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods." PLOS Computational Biology 21, no. 3 (2025): e1012859. https://doi.org/10.1371/journal.pcbi.1012859.
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Gene expression studies often use bulk RNA sequencing of mixed cell populations because single cell or sorted cell sequencing may be prohibitively expensive. However, mixed cell studies may miss expression patterns that are restricted to specific cell populations. Computational deconvolution can be used to estimate cell fractions from bulk expression data and infer average cell-type expression in a set of samples (e.g., cases or controls), but imputing sample-level cell-type expression is required for more detailed analyses, such as relating expression to quantitative traits, and is less commonly addressed. Here, we assessed the accuracy of imputing sample-level cell-type expression using a real dataset where mixed peripheral blood mononuclear cells (PBMC) and sorted (CD4, CD8, CD14, CD19) RNA sequencing data were generated from the same subjects (N=158), and pseudobulk datasets synthesised from eQTLgen single cell RNA-seq data. We compared three domain-specific methods, CIBERSORTx, bMIND and debCAM/swCAM, and two cross-domain machine learning methods, multiple response LASSO and ridge, that had not been used for this task before. We also assessed the methods according to their ability to recover differential gene expression (DGE) results. LASSO/ridge showed higher sensitivity but lower specificity for recovering DGE signals seen in observed data compared to deconvolution methods, although LASSO/ridge had higher area under curves than deconvolution methods. Machine learning methods have the potential to outperform domain-specific methods when suitable training data are available.
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Tang, Fanying, Dean Lee, Lexiang Ji, and Jincheng Wu. "Abstract 6283: Performance evaluation of tumor microenvironment deconvolution and gene prediction methods in lung cancer." Cancer Research 85, no. 8_Supplement_1 (2025): 6283. https://doi.org/10.1158/1538-7445.am2025-6283.
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Abstract The tumor microenvironment (TME) is complex and plays a key role in cancer development, progression and metastasis. Two layers of information are critical for understanding tumor compositions (1) the proportion of each cell type (2) the level of gene expression in each cell type. Many single-cell profile-assisted algorithms have been developed to infer cell type proportions from bulk RNA-seq data, including CIBERSORTx, MuSic and Scaden, and there are several benchmark studies published to compare the methods. However, most of the deconvolution methods do not support prediction of gene expression in a heterogenous population of tumor cells, and there’s no published study systemically comparing the gene expression prediction performance of the methods. To overcome the limitations, we simulate large-scale of pseudobulk data using scRNA-seq profiling from lung tumors, representing various levels of tumor purities and cell lineages, to compare the cell fraction and gene expression prediction performance of recently published TME deconvolution methods. Our results indicate BayesPrism is the best at predicting gene expression in each of the cell types, and differential gene expression between two groups across cell types, especially in tumor epithelial cells, with higher balanced accuracy and correlation with ground truth. The inclusion of normal epithelial cells in the reference could increase additional risks for tumor deconvolution. Altogether we evaluate both cell type proportion and gene expression prediction performance of recently published methods and offer a guideline for future benchmarking work. Currently we're working on the downstream application of inferring cellular composition and gene expression in large cohorts of bulk RNA-seq data, allowing for more insights into tumor-intrinsic expression and tumor-microenvironment interactions. Citation Format: Fanying Tang, Dean Lee, Lexiang Ji, Jincheng Wu. Performance evaluation of tumor microenvironment deconvolution and gene prediction methods in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6283.
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Sosina, Olukayode A., Matthew N. Tran, Kristen R. Maynard, et al. "Strategies for cellular deconvolution in human brain RNA sequencing data." F1000Research 10 (August 4, 2021): 750. http://dx.doi.org/10.12688/f1000research.50858.1.
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Background: Statistical deconvolution strategies have emerged over the past decade to estimate the proportion of various cell populations in homogenate tissue sources like brain using gene expression data. However, no study has been undertaken to assess the extent to which expression-based and DNAm-based cell type composition estimates agree. Results: Using estimated neuronal fractions from DNAm data, from the same brain region (i.e., matched) as our bulk RNA-Seq dataset, as proxies for the true unobserved cell-type fractions (i.e., as the gold standard), we assessed the accuracy (RMSE) and concordance (R2) of four reference-based deconvolution algorithms: Houseman, CIBERSORT, non-negative least squares (NNLS)/MIND, and MuSiC. We did this for two cell-type populations - neurons and non-neurons/glia - using matched single nuclei RNA-Seq and mismatched single cell RNA-Seq reference datasets. With the mismatched single cell RNA-Seq reference dataset, Houseman, MuSiC, and NNLS produced concordant (high correlation; Houseman R2 = 0.51, 95% CI [0.39, 0.65]; MuSiC R2 = 0.56, 95% CI [0.43, 0.69]; NNLS R2 = 0.54, 95% CI [0.32, 0.68]) but biased (high RMSE, >0.35) neuronal fraction estimates. CIBERSORT produced more discordant (moderate correlation; R2 = 0.25, 95% CI [0.15, 0.38]) neuronal fraction estimates, but with less bias (low RSME, 0.09). Using the matched single nuclei RNA-Seq reference dataset did not eliminate bias (MuSiC RMSE = 0.17). Conclusions: Our results together suggest that many existing RNA deconvolution algorithms estimate the RNA composition of homogenate tissue, e.g. the amount of RNA attributable to each cell type, and not the cellular composition, which relates to the underlying fraction of cells.
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Larsen, Jannik Hjortshøj, Iben Skov Jensen, and Per Svenningsen. "Benchmarking transcriptome deconvolution methods for estimating tissue‐ and cell‐type‐specific extracellular vesicle abundances." Journal of Extracellular Vesicles 13, no. 9 (2024). http://dx.doi.org/10.1002/jev2.12511.
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AbstractExtracellular vesicles (EVs) contain cell‐derived lipids, proteins and RNAs; however, determining the tissue‐ and cell‐type‐specific EV abundances in body fluids remains a significant hurdle for our understanding of EV biology. While tissue‐ and cell‐type‐specific EV abundances can be estimated by matching the EV's transcriptome to a tissue's/cell type's expression signature using deconvolutional methods, a comparative assessment of deconvolution methods' performance on EV transcriptome data is currently lacking. We benchmarked 11 deconvolution methods using data from four cell lines and their EVs, in silico mixtures, 118 human plasma and 88 urine EVs. We identified deconvolution methods that estimated cell type‐specific abundances of pure and in silico mixed cell line‐derived EV samples with high accuracy. Using data from two urine EV cohorts with different EV isolation procedures, four deconvolution methods produced highly similar results. The three methods were also concordant in their tissue‐ and cell‐type‐specific plasma EV abundance estimates. We identified driving factors for deconvolution accuracy and highlighted the importance of implementing biological knowledge in creating the tissue/cell type signature. Overall, our analyses demonstrate that the deconvolution algorithms DWLS and CIBERSORTx produce highly similar and accurate estimates of tissue‐ and cell‐type‐specific EV abundances in biological fluids.
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Deng, Wenxuan, Bolun Li, Jiawei Wang, et al. "A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy." Briefings in Bioinformatics, January 11, 2023. http://dx.doi.org/10.1093/bib/bbac616.
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Abstract Computational cell type deconvolution on bulk transcriptomics data can reveal cell type proportion heterogeneity across samples. One critical factor for accurate deconvolution is the reference signature matrix for different cell types. Compared with inferring reference signature matrices from cell lines, rapidly accumulating single-cell RNA-sequencing (scRNA-seq) data provide a richer and less biased resource. However, deriving cell type signature from scRNA-seq data is challenging due to high biological and technical noises. In this article, we introduce a novel Bayesian framework, tranSig, to improve signature matrix inference from scRNA-seq by leveraging shared cell type-specific expression patterns across different tissues and studies. Our simulations show that tranSig is robust to the number of signature genes and tissues specified in the model. Applications of tranSig to bulk RNA sequencing data from peripheral blood, bronchoalveolar lavage and aorta demonstrate its accuracy and power to characterize biological heterogeneity across groups. In summary, tranSig offers an accurate and robust approach to defining gene expression signatures of different cell types, facilitating improved in silico cell type deconvolutions.
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Song, Junyan, and Pei-Fen Kuan. "A systematic assessment of cell type deconvolution algorithms for DNA methylation data." Briefings in Bioinformatics, October 14, 2022. http://dx.doi.org/10.1093/bib/bbac449.
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Abstract We performed systematic assessment of computational deconvolution methods that play an important role in the estimation of cell type proportions from bulk methylation data. The proposed framework methylDeConv (available as an R package) integrates several deconvolution methods for methylation profiles (Illumina HumanMethylation450 and MethylationEPIC arrays) and offers different cell-type-specific CpG selection to construct the extended reference library which incorporates the main immune cell subsets, epithelial cells and cell-free DNAs. We compared the performance of different deconvolution algorithms via simulations and benchmark datasets and further investigated the associations of the estimated cell type proportions to cancer therapy in breast cancer and subtypes in melanoma methylation case studies. Our results indicated that the deconvolution based on the extended reference library is critical to obtain accurate estimates of cell proportions in non-blood tissues.
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Tu, Jia-Juan, Hui-Sheng Li, Hong Yan, and Xiao-Fei Zhang. "EnDecon: cell type deconvolution of spatially resolved transcriptomics data via ensemble learning." Bioinformatics, December 22, 2022. http://dx.doi.org/10.1093/bioinformatics/btac825.
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Abstract Motivation Spatially resolved gene expression profiles are the key to exploring the cell type spatial distributions and understanding the architecture of tissues. Many spatially resolved transcriptomics (SRT) techniques do not provide single-cell resolutions, but they measure gene expression profiles on captured locations (spots) instead, which are mixtures of potentially heterogeneous cell types. Currently, several cell type deconvolution methods have been proposed to deconvolute SRT data. Due to the different model strategies of these methods, their deconvolution results also vary. Results Leveraging the strengths of multiple deconvolution methods, we introduce a new weighted ensemble learning deconvolution method, EnDecon, to predict cell type compositions on SRT data in this work. EnDecon integrates multiple base deconvolution results using a weighted optimization model to generate a more accurate result. Simulation studies demonstrate that EnDecon outperforms the competing methods and the learned weights assigned to base deconvolution methods have high positive correlations with the performances of these base methods. Applied to real datasets from different spatial techniques, EnDecon identifies multiple cell types on spots, localizes these cell types to specific spatial regions, and distinguishes distinct spatial colocalization and enrichment patterns, providing valuable insights into spatial heterogeneity and regionalization of tissues. Availability The source code is available at https://github.com/Zhangxf-ccnu/EnDecon. Supplementary information Supplementary data are available at Bioinformatics online.
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Khatri, Robin, Pierre Machart, and Stefan Bonn. "DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation." Genome Biology 25, no. 1 (2024). http://dx.doi.org/10.1186/s13059-024-03251-5.
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AbstractCell deconvolution is the estimation of cell type fractions and cell type-specific gene expression from mixed data. An unmet challenge in cell deconvolution is the scarcity of realistic training data and the domain shift often observed in synthetic training data. Here, we show that two novel deep neural networks with simultaneous consistency regularization of the target and training domains significantly improve deconvolution performance. Our algorithm, DISSECT, outperforms competing algorithms in cell fraction and gene expression estimation by up to 14 percentage points. DISSECT can be easily adapted to other biomedical data types, as exemplified by our proteomic deconvolution experiments.
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Chen, Yunlu, Feng Ruan, and Ji-Ping Wang. "NLSDeconv: an efficient cell-type deconvolution method for spatial transcriptomics data." Bioinformatics, December 20, 2024. https://doi.org/10.1093/bioinformatics/btae747.
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Abstract Summary Spatial transcriptomics (ST) allows gene expression profiling within intact tissue samples but lacks single-cell resolution. This necessitates computational deconvolution methods to estimate the contributions of distinct cell types. This paper introduces NLSDeconv, a novel cell-type deconvolution method based on non-negative least squares, along with an accompanying Python package. Benchmarking against 18 existing deconvolution methods on various ST datasets demonstrates NLSDeconv’s competitive statistical performance and superior computational efficiency. Availability and Implementation NLSDeconv is freely available at https://github.com/tinachentc/NLSDeconv as a Python package.
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O’Neill, Nicholas K., Thor D. Stein, Junming Hu, et al. "Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM." BMC Bioinformatics 24, no. 1 (2023). http://dx.doi.org/10.1186/s12859-023-05476-w.
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Abstract Background Quantifying cell-type abundance in bulk tissue RNA-sequencing enables researchers to better understand complex systems. Newer deconvolution methodologies, such as MuSiC, use cell-type signatures derived from single-cell RNA-sequencing (scRNA-seq) data to make these calculations. Single-nuclei RNA-sequencing (snRNA-seq) reference data can be used instead of scRNA-seq data for tissues such as human brain where single-cell data are difficult to obtain, but accuracy suffers due to sequencing differences between the technologies. Results We propose a modification to MuSiC entitled ‘DeTREM’ which compensates for sequencing differences between the cell-type signature and bulk RNA-seq datasets in order to better predict cell-type fractions. We show DeTREM to be more accurate than MuSiC in simulated and real human brain bulk RNA-sequencing datasets with various cell-type abundance estimates. We also compare DeTREM to SCDC and CIBERSORTx, two recent deconvolution methods that use scRNA-seq cell-type signatures. We find that they perform well in simulated data but produce less accurate results than DeTREM when used to deconvolute human brain data. Conclusion DeTREM improves the deconvolution accuracy of MuSiC and outperforms other deconvolution methods when applied to snRNA-seq data. DeTREM enables accurate cell-type deconvolution in situations where scRNA-seq data are not available. This modification improves characterization cell-type specific effects in brain tissue and identification of cell-type abundance differences under various conditions.
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Gaspard-Boulinc, Lucie C., Luca Gortana, Thomas Walter, Emmanuel Barillot, and Florence M. G. Cavalli. "Cell-type deconvolution methods for spatial transcriptomics." Nature Reviews Genetics, May 14, 2025. https://doi.org/10.1038/s41576-025-00845-y.
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Zhang, Ze, John K. Wiencke, Karl T. Kelsey, Devin C. Koestler, Brock C. Christensen, and Lucas A. Salas. "HiTIMED: hierarchical tumor immune microenvironment epigenetic deconvolution for accurate cell type resolution in the tumor microenvironment using tumor-type-specific DNA methylation data." Journal of Translational Medicine 20, no. 1 (2022). http://dx.doi.org/10.1186/s12967-022-03736-6.
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Abstract Background Cellular compositions of solid tumor microenvironments are heterogeneous, varying across patients and tumor types. High-resolution profiling of the tumor microenvironment cell composition is crucial to understanding its biological and clinical implications. Previously, tumor microenvironment gene expression and DNA methylation-based deconvolution approaches have been shown to deconvolve major cell types. However, existing methods lack accuracy and specificity to tumor type and include limited identification of individual cell types. Results We employed a novel tumor-type-specific hierarchical model using DNA methylation data to deconvolve the tumor microenvironment with high resolution, accuracy, and specificity. The deconvolution algorithm is named HiTIMED. Seventeen cell types from three major tumor microenvironment components can be profiled (tumor, immune, angiogenic) by HiTIMED, and it provides tumor-type-specific models for twenty carcinoma types. We demonstrate the prognostic significance of cell types that other tumor microenvironment deconvolution methods do not capture. Conclusion We developed HiTIMED, a DNA methylation-based algorithm, to estimate cell proportions in the tumor microenvironment with high resolution and accuracy. HiTIMED deconvolution is amenable to archival biospecimens providing high-resolution profiles enabling to study of clinical and biological implications of variation and composition of the tumor microenvironment.
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Xu, Xintian, Rui Li, Ouyang Mo, Kai Liu, Justin Li, and Pei Hao. "Cell-type deconvolution for bulk RNA-seq data using single-cell reference: a comparative analysis and recommendation guideline." Briefings in Bioinformatics 26, no. 1 (2024). https://doi.org/10.1093/bib/bbaf031.
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Abstract The accurate estimation of cell type proportions in tissues is crucial for various downstream analyses. With the increasing availability of single-cell sequencing data, numerous deconvolution methods that use single-cell RNA sequencing data as a reference have been developed. However, a unified understanding of how these deconvolution approaches perform in practical applications is still lacking. To address this, we systematically assessed the accuracy and robustness of nine deconvolution methods that use single-cell RNA sequencing data as a reference, evaluating them on real bulk data with cell proportions verified through flow cytometry, as well as simulated bulk data generated from five single-cell RNA sequencing datasets. Our study highlights the importance of several factors—including reference dataset construction strategies, dataset size, cell type subdivision, and cell type inconsistency—on the accuracy and robustness of deconvolution results. We also propose a set of recommended guidelines for software users in diverse scenarios.
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Dong, Qishi, Yi Yang, Ziye Luo, Haipeng Shen, Xingjie Shi, and Jin Liu. "Robust Spatial Cell‐Type Deconvolution with Qualitative Reference for Spatial Transcriptomics." Small Methods, March 9, 2025. https://doi.org/10.1002/smtd.202401145.
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AbstractMany spatially resolved transcriptomic technologies have been developed to provide gene expression profiles for spots that may contain heterogeneous mixtures of cells. To decompose cellular composition and expression levels, various deconvolution methods have been developed using single‐cell RNA sequencing (scRNA‐seq) data with known cell‐type labels as a reference. However, in the absence of a reliable reference dataset or in the presence of heterogeneous batch effects, these methods may introduce bias. Here, a Qualitative‐Reference‐based Spatially‐Informed Deconvolution method (QR‐SIDE) is developed for multi‐cellular spatial transcriptomic data. Uniquely, QR‐SIDE provides a detailed map of spatial heterogeneity for individual marker genes and performs robust deconvolution by adaptively adjusting the contributions of each marker gene. Simultaneously, QR‐SIDE unifies cell‐type deconvolution with spatial clustering and incorporates spatial information via a Potts model to promote spatial continuity. The identified spatial domains represent a meaningful biological effect in potential tissue segments. Using simulated data and three real spatial transcriptomic datasets from the 10x Visium and ST platforms, QR‐SIDE demonstrates improved accuracy and robustness in cell‐type deconvolution and its superiority over established methods in recognizing and delineating spatial structures within a given context. These results can facilitate a range of downstream analyses and provide a refined understanding of cellular heterogeneity.
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Cai, Manqi, Jingtian Zhou, Chris McKennan, and Jiebiao Wang. "scMD facilitates cell type deconvolution using single-cell DNA methylation references." Communications Biology 7, no. 1 (2024). http://dx.doi.org/10.1038/s42003-023-05690-5.
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AbstractThe proliferation of single-cell RNA-sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell-type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent developments in single-cell DNA methylation (scDNAm), there are emerging opportunities for deconvolving bulk DNAm data, particularly for solid tissues like brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create precise cell-type signature matrixes that surpass state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD’s superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer’s disease.
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Fan, Jiaxin, Yafei Lyu, Qihuang Zhang, Xuran Wang, Mingyao Li, and Rui Xiao. "MuSiC2: cell-type deconvolution for multi-condition bulk RNA-seq data." Briefings in Bioinformatics, October 7, 2022. http://dx.doi.org/10.1093/bib/bbac430.
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Abstract Cell-type composition of intact bulk tissues can vary across samples. Deciphering cell-type composition and its changes during disease progression is an important step toward understanding disease pathogenesis. To infer cell-type composition, existing cell-type deconvolution methods for bulk RNA sequencing (RNA-seq) data often require matched single-cell RNA-seq (scRNA-seq) data, generated from samples with similar clinical conditions, as reference. However, due to the difficulty of obtaining scRNA-seq data in diseased samples, only limited scRNA-seq data in matched disease conditions are available. Using scRNA-seq reference to deconvolve bulk RNA-seq data from samples with different disease conditions may lead to a biased estimation of cell-type proportions. To overcome this limitation, we propose an iterative estimation procedure, MuSiC2, which is an extension of MuSiC, to perform deconvolution analysis of bulk RNA-seq data generated from samples with multiple clinical conditions where at least one condition is different from that of the scRNA-seq reference. Extensive benchmark evaluations indicated that MuSiC2 improved the accuracy of cell-type proportion estimates of bulk RNA-seq samples under different conditions as compared with the traditional MuSiC deconvolution. MuSiC2 was applied to two bulk RNA-seq datasets for deconvolution analysis, including one from human pancreatic islets and the other from human retina. We show that MuSiC2 improves current deconvolution methods and provides more accurate cell-type proportion estimates when the bulk and single-cell reference differ in clinical conditions. We believe the condition-specific cell-type composition estimates from MuSiC2 will facilitate the downstream analysis and help identify cellular targets of human diseases.
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Swapna, Lakshmipuram Seshadri, Michael Huang, and Yue Li. "Source package and associated scripts for GTM-decon: Guided-topic modelling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes." August 2, 2023. https://doi.org/10.5281/zenodo.8200316.
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This package contains the source code for GTM-decon, a method for sub-cell-type deconvolution and disease-sub-type deconvolution from bulk transcriptomes, described in "GTM-decon: guided-topic modelling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes", as well as the associated scripts for generating the main figures in the paper. The method uses a Guided Topic Model to automatically infer cell-type-specific gene topic distributions from single-cell RNA-seq data for deconvolving bulk transcriptomes. GTM-decon performs competitively on deconvolving simulated and real bulk data compared with the state-of-the-art methods. Moreover, as demonstrated in deconvolving disease transcriptomes, GTM-decon can infer multiple cell-type-specific gene topic distributions per cell type, which captures sub-cell-type variations. GTM-decon can also use phenotype labels from single-cell or bulk data to infer phenotype-specific gene distributions. In a nested-guided design, GTM-decon identified cell-type-specific differentially expressed genes from bulk breast cancer transcriptomes.
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Avila Cobos, Francisco, José Alquicira-Hernandez, Joseph E. Powell, Pieter Mestdagh, and Katleen De Preter. "Benchmarking of cell type deconvolution pipelines for transcriptomics data." Nature Communications 11, no. 1 (2020). http://dx.doi.org/10.1038/s41467-020-19015-1.
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AbstractMany computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance.
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Cobos, F., J. Alquicira-Hernandez, J. Powell, P. Mestdagh, and K. Peter. "Benchmarking of cell type deconvolution pipelines for transcriptomics data." Nature Communications Article number: 5650 (2020), no. 11 (2020). https://doi.org/10.1038/s41467-020-20288-9.
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Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, preprocessing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semisupervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance.
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Sutton, Gavin J., Daniel Poppe, Rebecca K. Simmons, et al. "Comprehensive evaluation of deconvolution methods for human brain gene expression." Nature Communications 13, no. 1 (2022). http://dx.doi.org/10.1038/s41467-022-28655-4.
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AbstractTranscriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-types, including transcriptionally similar subtypes of neurons. Here, we carry out a comprehensive evaluation of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with human pancreas and heart. We evaluate eight transcriptome deconvolution approaches and nine cell-type signatures, testing the accuracy of deconvolution using in silico mixtures of single-cell RNA-seq data, RNA mixtures, as well as nearly 2000 human brain samples. Our results identify the main factors that drive deconvolution accuracy for brain data, and highlight the importance of biological factors influencing cell-type signatures, such as brain region and in vitro cell culturing.
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Chen, Chixiang, Yuk Yee Leung, Matei Ionita, Li-San Wang, and Mingyao Li. "Omnibus and Robust Deconvolution Scheme for Bulk RNA Sequencing Data Integrating Multiple Single-Cell Reference Sets and Prior Biological Knowledge." Bioinformatics, August 18, 2022. http://dx.doi.org/10.1093/bioinformatics/btac563.
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Abstract Motivation Cell-type deconvolution of bulk tissue RNA sequencing (RNA-seq) data is an important step towards understanding the variations in cell-type composition among disease conditions. Owing to recent advances in single-cell RNA sequencing (scRNA-seq) and the availability of large amounts of bulk RNA-seq data in disease-relevant tissues, various deconvolution methods have been developed. However, the performance of existing methods heavily relies on the quality of information provided by external data sources, such as the selection of scRNA-seq data as a reference and prior biological information. Results We present the Integrated and Robust Deconvolution (InteRD) algorithm to infer cell-type proportions from target bulk RNA-seq data. Owing to the innovative use of penalized regression with a new evaluation criterion for deconvolution, InteRD has three primary advantages. First, it is able to effectively integrate deconvolution results from multiple scRNA-seq datasets. Second, InteRD calibrates estimates from reference-based deconvolution by taking into account extra biological information as priors. Third, the proposed algorithm is robust to inaccurate external information imposed in the deconvolution system. Extensive numerical evaluations and real data applications demonstrate that InteRD yields more accurate and robust cell-type proportion estimates that agree well with known biology. Availability and implementation The proposed InteRD framework is implemented in R and the package is available at https://cran.r-project.org/web/packages/InteRD/index.html. Supplementary information Supplementary Materials including pseudo algorithms, more simulation results, and extra discussion and information are available at Bioinformatics online.
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Ivich, Adriana, Natalie R. Davidson, Laurie Grieshober, et al. "Missing cell types in single-cell references impact deconvolution of bulk data but are detectable." Genome Biology 26, no. 1 (2025). https://doi.org/10.1186/s13059-025-03506-9.
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Abstract Background Advancements in RNA sequencing have expanded our ability to study gene expression profiles of biological samples in bulk tissue and single cells. Deconvolution of bulk data with single-cell references provides the ability to study relative cell-type proportions, but most methods assume a reference is present for every cell type in bulk data. This is not true in all circumstances—cell types can be missing in single-cell profiles for many reasons. In this study, we examine the impact of missing cell types on deconvolution methods. Results Using paired single-cell and single-nucleus data, we simulate realistic scenarios where cell types are missing since single-nucleus RNA sequencing is able to capture cell types that would otherwise be missing in a single-cell counterpart. Single-nucleus sequencing captures cell types absent in single-cell counterparts, allowing us to study their effects on deconvolution. We evaluate three different methods and find that performance is influenced by both the number and similarity of missing cell types. Additionally, missing cell-type profiles can be recovered from residuals using a simple non-negative matrix factorization strategy. We also analyzed real bulk data of cancerous and non-cancerous samples. We observe results consistent with simulation, namely that expression patterns from cell types likely to be missing appear present in residuals. Conclusions We expect our results to provide a starting point for those developing new deconvolution methods and help improve their to better account for the presence of missing cell types. Our results suggest that deconvolution methods should consider the possibility of missing cell types.
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Kim, Jeff J. H., and Yang Dai. "Cell‐Type Deconvolution Reveals Dynamic Changes in MASLD." Liver International Communications 6, no. 2 (2025). https://doi.org/10.1002/lci2.70012.
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ABSTRACTMetabolic‐associated steatotic liver disease (MASLD) is among the most prevalent liver disorders worldwide, with many patients progressing to metabolic‐associated steatohepatitis (MASH) characterised by fibrosis and inflammation. The current lack of effective treatments for MASH highlights the urgent need to deepen our understanding of its underlying mechanisms. Examining cellular dynamics—specifically, changes in cell type proportions across disease stages—offers a promising avenue for gaining such insights. However, previous deconvolution analyses have been limited to a few cell types, and a comprehensive analysis encompassing diverse cell populations and their unique subtypes has yet to be conducted. In this study, we employed MuSiC deconvolution to analyse two bulk RNA sequencing datasets spanning the MASLD spectrum across both fibrosis staging and Non‐Alcoholic Fatty Liver Disease Activity Score (NAS) staging. Our analysis reveals distinct proportional trends in 10 different cell types, including hepatocytes, cholangiocytes, two subpopulations of hepatic stellate cells, endothelial cells, and immune cells such as kupffer cells, TREM2+ macrophages, and plasma B cells. In addition to deconvolution analysis, we integrated cell type proportion data with transcriptomic profiles, significantly enhancing the performance of random forest models in classifying fibrosis stages compared to using transcriptomic data alone. The study's findings highlight critical cellular dynamic changes across MASLD progression, advancing our understanding of the disease mechanisms and potentially informing the development of more effective therapeutic strategies.
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Sang-aram, Chananchida, Robin Browaeys, Ruth Seurinck, and Yvan Saeys. "Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics." eLife 12 (May 24, 2024). http://dx.doi.org/10.7554/elife.88431.3.
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Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).
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Chiu, Yen-Jung, Chung-En Ni, and Yen-Hua Huang. "HArmonized single-cell RNA-seq Cell type Assisted Deconvolution (HASCAD)." BMC Medical Genomics 16, S2 (2023). http://dx.doi.org/10.1186/s12920-023-01674-w.
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Abstract Background Cell composition deconvolution (CCD) is a type of bioinformatic task to estimate the cell fractions from bulk gene expression profiles, such as RNA-seq. Many CCD models were developed to perform linear regression analysis using reference gene expression signatures of distinct cell types. Reference gene expression signatures could be generated from cell-specific gene expression profiles, such as scRNA-seq. However, the batch effects and dropout events frequently observed across scRNA-seq datasets have limited the performances of CCD methods. Methods We developed a deep neural network (DNN) model, HASCAD, to predict the cell fractions of up to 15 immune cell types. HASCAD was trained using the bulk RNA-seq simulated from three scRNA-seq datasets that have been normalized by using a Harmony-Symphony based strategy. Mean square error and Pearson correlation coefficient were used to compare the performance of HASCAD with those of other widely used CCD methods. Two types of datasets, including a set of simulated bulk RNA-seq, and three human PBMC RNA-seq datasets, were arranged to conduct the benchmarks. Results HASCAD is useful for the investigation of the impacts of immune cell heterogeneity on the therapeutic effects of immune checkpoint inhibitors, since the target cell types include the ones known to play a role in anti-tumor immunity, such as three subtypes of CD8 T cells and three subtypes of CD4 T cells. We found that the removal of batch effects in the reference scRNA-seq datasets could benefit the task of CCD. Our benchmarks showed that HASCAD is more suitable for analyzing bulk RNA-seq data, compared with the two widely used CCD methods, CIBERSORTx and quanTIseq. We applied HASCAD to analyze the liver cancer samples of TCGA-LIHC, and found that there were significant associations of the predicted abundance of Treg and effector CD8 T cell with patients’ overall survival. Conclusion HASCAD could predict the cell composition of the PBMC bulk RNA-seq and classify the cell type from pure bulk RNA-seq. The model of HASCAD is available at https://github.com/holiday01/HASCAD.
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Ma, Ying, and Xiang Zhou. "Spatially informed cell-type deconvolution for spatial transcriptomics." Nature Biotechnology, May 2, 2022. http://dx.doi.org/10.1038/s41587-022-01273-7.
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Livne, Dani, Tom Snir, and Sol Efroni. "YADA - Reference Free Deconvolution of RNA Sequencing Data." Current Bioinformatics 19 (June 5, 2024). http://dx.doi.org/10.2174/0115748936304034240405034414.
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Introduction: We present YADA, a cellular content deconvolution algorithm for estimating cell type proportions in heterogeneous cell mixtures based on gene expression data. YADA utilizes curated gene signatures of cell type-specific marker genes, either obtained intrinsically from pure cell type expression matrices or provided by the user. Method: YADA implements an accessible and extensible deconvolution framework uniquely capable of handling marker genes alone as inputs. Adoption barriers are lowered significantly by relying solely on literature-supported cell type-specific signatures rather than full transcriptomic profiles from purified isolates. However, flexible inputs do not necessitate sacrificing rigor - predictions match metrics of current methodologies through an integrated optimization scheme balancing multiple inference algorithms. Efficiency optimizations via compiled runtimes enable rapid execution. Packaging as an importable Python toolkit promotes community enhancement while retaining codebase extensibility. Result: Validation studies demonstrate that YADA matches or exceeds the performance of current deconvolution methods on benchmark datasets. To demonstrate the utility and enable immediate usage, we provide an online Jupyter Notebook implementation coupled with tutorials. Conclusion: YADA provides an accurate, efficient, and extensible Python-based toolkit for cellular deconvolution analysis of heterogeneous gene expression data.
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Jeong, Yunhee, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, et al. "Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes." Briefings in Bioinformatics, July 6, 2022. http://dx.doi.org/10.1093/bib/bbac248.
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Abstract DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair and single-molecule resolution. It has tremendous potential for cell-type heterogeneity analysis using intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, systematic evaluation has not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection, DXM, PRISM, csmFinder+coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman, as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation, thus each was individually assessed. With this elaborate evaluation, we aimed to establish which method achieves the highest performance in different scenarios of synthetic bulk samples. We found that cell-type deconvolution performance is influenced by different factors depending on the number of cell types within the mixture. Finally, we propose a best-practice deconvolution strategy for sequencing data and point out limitations that need to be handled. Array-based methods—both reference-based and reference-free—generally outperformed sequencing-based methods, despite the absence of read-level information. This implies that the current sequencing-based methods still struggle with correctly identifying cell-type-specific signals and eliminating confounding methylation patterns, which needs to be handled in future studies.
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Galvão, Isabella C., Ludmyla Kandratavicius, Lauana A. Messias, et al. "Identifying cellular markers of focal cortical dysplasia type II with cell-type deconvolution and single-cell signatures." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-40240-3.
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AbstractFocal cortical dysplasia (FCD) is a brain malformation that causes medically refractory epilepsy. FCD is classified into three categories based on structural and cellular abnormalities, with FCD type II being the most common and characterized by disrupted organization of the cortex and abnormal neuronal development. In this study, we employed cell-type deconvolution and single-cell signatures to analyze bulk RNA-seq from multiple transcriptomic studies, aiming to characterize the cellular composition of brain lesions in patients with FCD IIa and IIb subtypes. Our deconvolution analyses revealed specific cellular changes in FCD IIb, including neuronal loss and an increase in reactive astrocytes (astrogliosis) when compared to FCD IIa. Astrogliosis in FCD IIb was further supported by a gene signature analysis and histologically confirmed by glial fibrillary acidic protein (GFAP) immunostaining. Overall, our findings demonstrate that FCD II subtypes exhibit differential neuronal and glial compositions, with astrogliosis emerging as a hallmark of FCD IIb. These observations, validated in independent patient cohorts and confirmed using immunohistochemistry, offer novel insights into the involvement of glial cells in FCD type II pathophysiology and may contribute to the development of targeted therapies for this condition.
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Guo, Shuai, Xiaoqian Liu, Xuesen Cheng, et al. "A deconvolution framework that uses single-cell sequencing plus a small benchmark dataset for accurate analysis of cell type ratios in complex tissue samples." Genome Research, November 25, 2024, gr.278822.123. http://dx.doi.org/10.1101/gr.278822.123.
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Bulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we utilize an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using this well-matched, i.e., benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using two benchmark datasets of healthy retinas and ovarian cancer tissues suggest much-improved deconvolution accuracy. Leveraging tissue-specific benchmark datasets, we applied DeMixSC to a large cohort of 453 age-related macular degeneration patients and a cohort of 30 ovarian cancer patients with various responses to neoadjuvant chemotherapy. Only DeMixSC successfully unveiled biologically meaningful differences across patient groups, demonstrating its broad applicability in diverse real-world clinical scenarios. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched dataset to resolve this challenge. The developed DeMixSC framework is generally applicable for accurately deconvolving large cohorts of disease tissues, including cancers, when a well-matched benchmark dataset is available.
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Hu, Mengying, and Maria Chikina. "Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods." Genome Biology 25, no. 1 (2024). http://dx.doi.org/10.1186/s13059-024-03292-w.
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Abstract Background Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions. Results In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers. Conclusions Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516, enabling further developments in deconvolution methods.
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Yu, Shilin, Guanqun Meng, Wen Tang, et al. "cypress: an R/Bioconductor package for cell-type-specific differential expression analysis power assessment." Bioinformatics, August 17, 2024. http://dx.doi.org/10.1093/bioinformatics/btae511.
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Abstract Summary Recent methodology advances in computational signal deconvolution have enabled bulk transcriptome data analysis at a finer cell-type level. Through deconvolution, identifying cell-type-specific differentially expressed (csDE) genes is drawing increasing attention in clinical applications. However, researchers still face a number of difficulties in adopting csDE detection methods in practice, especially in their experimental design. Here we present cypress, the first experimental design and statistical power analysis tool in csDE identification. This tool can reliably model purified cell-type-specific (CTS) profiles, cell-type compositions, biological and technical variations, offering a high-fidelity simulator for bulk RNA-seq convolution and deconvolution. cypress conducts simulation and evaluates the impact of multiple influencing factors, by various biostatistical metrics, to help researchers optimize experimental design and conduct power analysis. Availability and Implementation cypress is an open-source R/Bioconductor package at https://bioconductor.org/packages/cypress/. Supplementary information Supplementary data are available at Bioinformatics online.
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Karkar, Slim, Ashwini Sharma, Carl Herrmann, Yuna Blum, and Magali Richard. "DECOMICS, a shiny application for unsupervised cell type deconvolution and biological interpretation of bulk omic data." Bioinformatics Advances, September 20, 2024. http://dx.doi.org/10.1093/bioadv/vbae136.
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Abstract Unsupervised deconvolution algorithms are often used to estimate cell composition from bulk tissue samples. However, applying cell type deconvolution and interpreting the results remains a challenge, even more without prior training in bioinformatics. We propose here a tool for estimating and identifying cell type composition from bulk transcriptomes or methylomes. DECOMICS is a shiny-web application dedicated to unsupervised deconvolution approaches of bulk omic data. It provides (i) a variety of existing algorithms to perform deconvolution on the gene expression or methylation-level matrix, (ii) an enrichment analysis module to aid biological interpretation of the deconvolved components, based on enrichment analysis, and (iii) some visualisation tools. Input data can be downloaded in csv format and pre-processed in the web application (normalisation, transformation and feature selection). The results of the deconvolution, enrichment and visualisation processes can be downloaded. DECOMICS is an R-shiny web application that can be launched (i) directly from a local R session using the R package available here https://gitlab.in2p3.fr/Magali.Richard/decomics (either by installing it locally, or via a virtual machine and a Docker image that we provide); or (ii) in the Biosphere—IFB Clouds Federation for Life Science, a multi-cloud environment scalable for high performance computing: Https://biosphere.france-bioinformatique.fr/catalogue/appliance/193/.
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Zhang, Wei, Xianglin Zhang, Qiao Liu, et al. "Deconer: An Evaluation Toolkit for Reference-based Deconvolution Methods Using Gene Expression Data." Genomics, Proteomics & Bioinformatics, February 18, 2025. https://doi.org/10.1093/gpbjnl/qzaf009.
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Abstract In recent years, computational methods for quantifying cell type proportions from transcription data have gained significant attention, particularly those reference-based methods which have demonstrated high accuracy. However, there is currently a lack of comprehensive evaluation and guidance for available reference-based deconvolution methods in cell proportion deconvolution analysis. In this study, we introduce Deconvolution Evaluator (Deconer), a comprehensive toolkit for the evaluation of reference-based deconvolution methods. Deconer provides various simulated and real gene expression datasets, including both bulk and single-cell sequencing data, and offers multiple visualization interfaces. By utilizing Deconer, we conducted systematic comparisons of 16 reference-based deconvolution methods from different perspectives, including method robustness, accuracy in deconvolving rare components, signature gene selection, and building external reference. We also performed an in-depth analysis of the application scenarios and challenges in cell proportion deconvolution methods. Finally, we provided constructive suggestions for users in selecting and developing cell proportion deconvolution algorithms. This work presents novel insights to researchers, assisting them in choosing appropriate toolkits, applying solutions in clinical contexts, and advancing the development of deconvolution tools tailored to gene expression data. The tutorials, manual, source code, and demo data of Deconer are publicly available at https://honchkrow.github.io/Deconer/.
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Maié, Tiago, Marco Schmidt, Myriam Erz, Wolfgang Wagner, and Ivan G. Costa. "CimpleG: finding simple CpG methylation signatures." Genome Biology 24, no. 1 (2023). http://dx.doi.org/10.1186/s13059-023-03000-0.
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AbstractDNA methylation signatures are usually based on multivariate approaches that require hundreds of sites for predictions. Here, we propose a computational framework named CimpleG for the detection of small CpG methylation signatures used for cell-type classification and deconvolution. We show that CimpleG is both time efficient and performs as well as top performing methods for cell-type classification of blood cells and other somatic cells, while basing its prediction on a single DNA methylation site per cell type. Altogether, CimpleG provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution.
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Maden, Sean K., Louise A. Huuki-Myers, Sang Ho Kwon, Leonardo Collado-Torres, Kristen R. Maynard, and Stephanie C. Hicks. "lute: estimating the cell composition of heterogeneous tissue with varying cell sizes using gene expression." BMC Genomics 26, no. 1 (2025). https://doi.org/10.1186/s12864-025-11508-x.
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Abstract Background Relative cell type fraction estimates in bulk RNA-sequencing data are important to control for cell composition differences across heterogenous tissue samples. While there exist algorithms to estimate the cell type proportions in tissues, a major challenge is the algorithms can show reduced performance if using tissues that have varying cell sizes, such as in brain tissue. In this way, without adjusting for differences in cell sizes, computational algorithms estimate the relative fraction of RNA attributable to each cell type, rather than the relative fraction of cell types, leading to potentially biased estimates in cellular composition. Furthermore, these tools were built on different frameworks with non-uniform input data formats while addressing different types of systematic errors or unwanted bias. Results We present lute, a software tool to accurately deconvolute cell types with varying sizes. Our package lute wraps existing deconvolution algorithms in a flexible and extensible framework to enable easy benchmarking and comparison of existing deconvolution algorithms. Using simulated and real datasets, we demonstrate how lute adjusts for differences in cell sizes to improve the accuracy of cell composition. Conclusions Our software (https://bioconductor.org/packages/lute) can be used to enhance and improve existing deconvolution algorithms and can be used broadly for any type of tissue containing cell types with varying cell sizes.
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Lu, Yingying, Qin M. Chen, and Lingling An. "SPADE: spatial deconvolution for domain specific cell-type estimation." Communications Biology 7, no. 1 (2024). http://dx.doi.org/10.1038/s42003-024-06172-y.
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AbstractUnderstanding gene expression in different cell types within their spatial context is a key goal in genomics research. SPADE (SPAtial DEconvolution), our proposed method, addresses this by integrating spatial patterns into the analysis of cell type composition. This approach uses a combination of single-cell RNA sequencing, spatial transcriptomics, and histological data to accurately estimate the proportions of cell types in various locations. Our analyses of synthetic data have demonstrated SPADE’s capability to discern cell type-specific spatial patterns effectively. When applied to real-life datasets, SPADE provides insights into cellular dynamics and the composition of tumor tissues. This enhances our comprehension of complex biological systems and aids in exploring cellular diversity. SPADE represents a significant advancement in deciphering spatial gene expression patterns, offering a powerful tool for the detailed investigation of cell types in spatial transcriptomics.