A benchmark for RNA-seq deconvolution analysis under dynamic testing environments

Jin, Haijing; Liu, Zhandong

doi:10.1186/s13059-021-02290-6

Cited by 101 publications

(102 citation statements)

References 37 publications

(147 reference statements)

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“…We selected MuSiC 15 for cell type deconvolution based on recommendations from comprehensive benchmarking studies 17 , 18 . Accordingly, MuSiC does not require a priori defined gene lists as input and is one of the preferred methods for cell type deconvolution if suitable reference scRNA-Seq datasets are available.…”

Section: Resultsmentioning

confidence: 99%

Molecular characterization and cell type composition deconvolution of fibrosis in NAFLD

Pantano

Agyapong

Shen

et al. 2021

Sci Rep

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Non-alcoholic fatty liver disease (NAFLD) is the most common cause of liver disease worldwide. In adults with NAFLD, fibrosis can develop and progress to liver cirrhosis and liver failure. However, the underlying molecular mechanisms of fibrosis progression are not fully understood. Using total RNA-Seq, we investigated the molecular mechanisms of NAFLD and fibrosis. We sequenced liver tissue from 143 adults across the full spectrum of fibrosis stage including those with stage 4 fibrosis (cirrhosis). We identified gene expression clusters that strongly correlate with fibrosis stage including four genes that have been found consistently across previously published transcriptomic studies on NASH i.e. COL1A2, EFEMP2, FBLN5 and THBS2. Using cell type deconvolution, we estimated the loss of hepatocytes versus gain of hepatic stellate cells, macrophages and cholangiocytes with advancing fibrosis stage. Hepatocyte-specific functional analysis indicated increase of pro-apoptotic pathways and markers of bipotent hepatocyte/cholangiocyte precursors. Regression modelling was used to derive predictors of fibrosis stage. This study elucidated molecular and cell composition changes associated with increasing fibrosis stage in NAFLD and defined informative gene signatures for the disease.

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Section: Resultsmentioning

confidence: 99%

Molecular characterization and cell type composition deconvolution of fibrosis in NAFLD

Pantano

Agyapong

Shen

et al. 2021

Sci Rep

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“…In addition, low abundant cell types from the reference dataset might not provide reliable information to predict those cell types in the test dataset, especially if a cell type is missing from the reference, which might be solved with most recent methods such as MARS ( 106 ). These and several other challenges are being thoroughly investigated in recent cell deconvolution benchmarking papers ( 22 , 108 ). A similar line of development (e.g.…”

Section: Discussionmentioning

confidence: 99%

Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research

Mädler

Julien-Laferrière

Wyss

et al. 2021

NAR Genomics and Bioinformatics

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Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of disease biology. The promise it presents to also transform translational research requires highly standardized and robust software workflows. Here, we present the toolkit Besca, which streamlines scRNA-seq analyses and their use to deconvolute bulk RNA-seq data according to current best practices. Beyond a standard workflow covering quality control, filtering, and clustering, two complementary Besca modules, utilizing hierarchical cell signatures and supervised machine learning, automate cell annotation and provide harmonized nomenclatures. Subsequently, the gene expression profiles can be employed to estimate cell type proportions in bulk transcriptomics data. Using multiple, diverse scRNA-seq datasets, some stemming from highly heterogeneous tumor tissue, we show how Besca aids acceleration, interoperability, reusability and interpretability of scRNA-seq data analyses, meeting crucial demands in translational research and beyond.

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“…One alternative approach to scRNA-seq is to extrapolate cellular components of the sample from bulk RNA-seq using deconvolution methods. There are more than 50 deconvolution methods published to date, that can be broadly categorized as marker-based (uses marker gene list for deconvolution), reference-based (for the deconvolution process, it uses cell type specific gene expression profiles and list of differentially expressed genes across the cell types in the reference), and reference-free (uses reference profiles for cluster annotation after the deconvolution step) [ 184 – 187 ].…”

Section: Transcriptomicsmentioning

confidence: 99%

A guide for the diagnosis of rare and undiagnosed disease: beyond the exome

2022

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Rare diseases affect 30 million people in the USA and more than 300–400 million worldwide, often causing chronic illness, disability, and premature death. Traditional diagnostic techniques rely heavily on heuristic approaches, coupling clinical experience from prior rare disease presentations with the medical literature. A large number of rare disease patients remain undiagnosed for years and many even die without an accurate diagnosis. In recent years, gene panels, microarrays, and exome sequencing have helped to identify the molecular cause of such rare and undiagnosed diseases. These technologies have allowed diagnoses for a sizable proportion (25–35%) of undiagnosed patients, often with actionable findings. However, a large proportion of these patients remain undiagnosed. In this review, we focus on technologies that can be adopted if exome sequencing is unrevealing. We discuss the benefits of sequencing the whole genome and the additional benefit that may be offered by long-read technology, pan-genome reference, transcriptomics, metabolomics, proteomics, and methyl profiling. We highlight computational methods to help identify regionally distant patients with similar phenotypes or similar genetic mutations. Finally, we describe approaches to automate and accelerate genomic analysis. The strategies discussed here are intended to serve as a guide for clinicians and researchers in the next steps when encountering patients with non-diagnostic exomes.

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A benchmark for RNA-seq deconvolution analysis under dynamic testing environments

Cited by 101 publications

References 37 publications

Molecular characterization and cell type composition deconvolution of fibrosis in NAFLD

Molecular characterization and cell type composition deconvolution of fibrosis in NAFLD

Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research

A guide for the diagnosis of rare and undiagnosed disease: beyond the exome

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