Benchmarking of cell type deconvolution pipelines for transcriptomics data

Cobos, Francisco Avila; Alquicira-Hernández, José; Powell, Joseph E.; Mestdagh, Pieter; Preter, Katleen De

doi:10.1038/s41467-020-19015-1

Cited by 278 publications

(338 citation statements)

References 72 publications

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“…The stably expressed UEG clusters with higher expression levels showed lower single-cell sparsity and vice versa . This implies that these stably expressed UEG clusters might be a good model to study potential connections in gene expression between bulk and single-cell level, and that maybe useful for cell type deconvolution [46] and adjusting for potential dropouts bias [47]. Moreover, these gene clusters provide local context information for transcriptome profiles that can further improve the outlier analysis approaches [19, 30].…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Analysis of Ubiquitously Expressed Genes in Human, From a Data-Driven Perspective

Dai

et al. 2021

Preprint

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Comprehensive characterization of spatial and temporal gene expression patterns in humans is critical for uncovering regulatory codes of the human genome and understanding molecular mechanisms of human diseases. The ubiquitously expressed genes (UEGs) refer to those genes expressed across a majority, if not all, phenotypic and physiological conditions of an organism. It is known that many human genes are broadly expressed across tissues. However, most previous UEG studies have only focused on providing a list of UEGs without capturing their global expression patterns, thus limiting the potential use of UEG information. In this article, we propose a novel data-driven framework to leverage the extensive collection of ~40,000 human transcriptomes to derive a list of UEGs and their corresponding global expression patterns, which offers a valuable resource to further validate and characterize human UEGs. Our results suggest that about half (12,234; 49.01%) of the human genes are expressed in at least 80% of human transcriptomes and the median size of the human transcriptome is 16,342 (65.44%). This suggests that the average difference in gene content between human transcriptomes is only 16.43%. Through gene clustering, we identified a set of UEGs, named LoVarUEGs, that have stable expression across human transcriptomes and can be used as internal reference genes for expression measurement. To further demonstrate the usefulness of this resource, we evaluated the uniqueness of repression for 16 previously predicted disallowed genes in islets beta cells and found that seven of these genes showed relatively more varied expression patterns, suggesting that the repression of these genes may not be unique to islets beta cells. We have made our resource publicly available at https://github.com/macroant/HumanUEGs.

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Section: Discussionmentioning

confidence: 99%

Comprehensive Analysis of Ubiquitously Expressed Genes in Human, From a Data-Driven Perspective

Dai

et al. 2021

Preprint

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“…However, many GRN were inferred for whole organs or sorted cells based on marker expression and lack single cell resolution. To address this issue, several deconvoluting methods can be used to infer (sub-)cell types or clusters of cells with specific transcriptomic signatures from tissues or bulk cells that have been sequenced by RNA-seq ( Sun et al, 2019 ; Avila Cobos et al, 2020 ). scRNA-seq and deconvoluted RNA-seq data can then be systematically compared through the analysis of gene expression patterns, differentially expressed genes and reconstructed GRN using each dataset as input.…”

Section: Single-cell Omics Approachesmentioning

confidence: 99%

Unraveling Root Development Through Single-Cell Omics and Reconstruction of Gene Regulatory Networks

et al. 2021

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Over the last decades, research on postembryonic root development has been facilitated by “omics” technologies. Among these technologies, microarrays first, and RNA sequencing (RNA-seq) later, have provided transcriptional information on the underlying molecular processes establishing the basis of System Biology studies in roots. Cell fate specification and development have been widely studied in the primary root, which involved the identification of many cell type transcriptomes and the reconstruction of gene regulatory networks (GRN). The study of lateral root (LR) development has not been an exception. However, the molecular mechanisms regulating cell fate specification during LR formation remain largely unexplored. Recently, single-cell RNA-seq (scRNA-seq) studies have addressed the specification of tissues from stem cells in the primary root. scRNA-seq studies are anticipated to be a useful approach to decipher cell fate specification and patterning during LR formation. In this review, we address the different scRNA-seq strategies used both in plants and animals and how we could take advantage of scRNA-seq to unravel new regulatory mechanisms and reconstruct GRN. In addition, we discuss how to integrate scRNA-seq results with previous RNA-seq datasets and GRN. We also address relevant findings obtained through single-cell based studies and how LR developmental studies could be facilitated by scRNA-seq approaches and subsequent GRN inference. The use of single-cell approaches to investigate LR formation could help to decipher fundamental biological mechanisms such as cell memory, synchronization, polarization, or pluripotency.

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“…FARDEEP is an integrated R-package that infers cell type proportions from bulk tissue transcriptomic data sets. A recent independent comparison shows that FARDEEP is among the most robust computational tools currently available for cell type quantitation 17 . We also confirmed its rigor in providing precise immune cell profiling in a large clinical collection of cancer tissues (n = 520) 18 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes

et al. 2021

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Rationale: The endemic of peri-implantitis affects over 25% of dental implants. Current treatment depends on empirical patient and site-based stratifications and lacks a consistent risk grading system. Methods: We investigated a unique cohort of peri-implantitis patients undergoing regenerative therapy with comprehensive clinical, immune, and microbial profiling. We utilized a robust outlier-resistant machine learning algorithm for immune deconvolution. Results: Unsupervised clustering identified risk groups with distinct immune profiles, microbial colonization dynamics, and regenerative outcomes. Low-risk patients exhibited elevated M1/M2-like macrophage ratios and lower B-cell infiltration. The low-risk immune profile was characterized by enhanced complement signaling and higher levels of Th1 and Th17 cytokines. Fusobacterium nucleatum and Prevotella intermedia were significantly enriched in high-risk individuals. Although surgery reduced microbial burden at the peri-implant interface in all groups, only low-risk individuals exhibited suppression of keystone pathogen re-colonization. Conclusion: Peri-implant immune microenvironment shapes microbial composition and the course of regeneration. Immune signatures show untapped potential in improving the risk-grading for peri-implantitis.

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Benchmarking of cell type deconvolution pipelines for transcriptomics data

Cited by 278 publications

References 72 publications

Comprehensive Analysis of Ubiquitously Expressed Genes in Human, From a Data-Driven Perspective

Comprehensive Analysis of Ubiquitously Expressed Genes in Human, From a Data-Driven Perspective

Unraveling Root Development Through Single-Cell Omics and Reconstruction of Gene Regulatory Networks

Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes

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