G2S3: A gene graph-based imputation method for single-cell RNA sequencing data

Wu, Weimiao; Liu, Yunqing; Dai, Qile; Yan, Xiting; Wang, Zuoheng

doi:10.1371/journal.pcbi.1009029

Cited by 10 publications

(3 citation statements)

References 51 publications

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“…Multi-omic bioinformatic tools have emerged as effective methods for identifying biomarkers ( Lin et al, 2017 ; Badhwar et al, 2020 ). Transcriptome-wide analyses offer a global overview of the cell state and how it changes after disease or treatment ( Paananen and Fortino, 2020 ), while single-cell RNA sequencing (scRNA-seq) enables the analysis of transcriptomes at the single-cell level, circumventing intercellular randomness ( Hu et al, 2020 ; Lam et al, 2020 ; Wu et al, 2021 ; Chen et al, 2022a , b ; Luo et al, 2022 ). Single-cell sequencing can be used to understand the development of neurodegenerative diseases at the single-cell level ( Wang et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Cromolyn prevents cerebral vasospasm and dementia by targeting WDR43

Wang

Kong

Lin

2023

Front. Aging Neurosci.

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BackgroundCerebral vasospasm (CV) can cause inflammation and damage to neuronal cells in the elderly, leading to dementia.PurposeThis study aimed to investigate the genetic mechanisms underlying dementia caused by CV in the elderly, identify preventive and therapeutic drugs, and evaluate their efficacy in treating neurodegenerative diseases.MethodsGenes associated with subarachnoid hemorrhage and CV were acquired and screened for differentially expressed miRNAs (DEmiRNAs) associated with aneurysm rupture. A regulatory network of DEmiRNAs and mRNAs was constructed, and virtual screening was performed to evaluate possible binding patterns between Food and Drug Administration (FDA)-approved drugs and core proteins. Molecular dynamics simulations were performed on the optimal docked complexes. Optimally docked drugs were evaluated for efficacy in the treatment of neurodegenerative diseases through cellular experiments.ResultsThe study found upregulated genes (including WDR43 and THBS1) and one downregulated gene associated with aneurysm rupture. Differences in the expression of these genes indicate greater disease risk. DEmiRNAs associated with ruptured aortic aneurysm were identified, of which two could bind to THBS1 and WDR43. Cromolyn and lanoxin formed the best docking complexes with WDR43 and THBS1, respectively. Cellular experiments showed that cromolyn improved BV2 cell viability and enhanced Aβ42 uptake, suggesting its potential as a therapeutic agent for inflammation-related disorders.ConclusionThe findings suggest that WDR43 and THBS1 are potential targets for preventing and treating CV-induced dementia in the elderly. Cromolyn may have therapeutic value in the treatment of Alzheimer’s disease and dementia.

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Section: Introductionmentioning

confidence: 99%

Cromolyn prevents cerebral vasospasm and dementia by targeting WDR43

Wang

Kong

Lin

2023

Front. Aging Neurosci.

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“…The experiments are conducted on two real datasets, and the results are compared with other eight state-of-the-art methods, including DrImpute [14], MAGIC [9], CNMF [12], ALRA [15], scRMD [16], G2S3 [17], VIPER [18], SAVER [11]. For the real datasets, we used tSNE + K-means to cluster and calculated the evaluation index to evaluate the performance of each method [19].…”

Section: Introductionmentioning

confidence: 99%

Data Smoothing Filling Method based on ScRNA-Seq Data Zero-Value Identification

Jiang,

Zhu

2023

Artificial Intelligence and Applications

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Single-cell RNA sequencing (scRNA-seq) determines RNA expression at single-cell resolution. It provides a powerful tool for studying immunity, regulation, and other life activities of cells. However, due to the limitations of the sequencing technique, the scRNA-seq data are represented with sparsity, which contains missing gene values, i.e., zero values, called dropout. Therefore, it is necessary to impute missing values before analyzing scRNA-seq data. However, existing imputation computation methods often only focus on the identification of technical zeros or imputing all zeros based on cell similarity. This study proposes a new method (SFAG) to reconstruct the gene expression relationship matrix by using graph regularization technology to preserve the high-dimensional manifold information of the data, and to mine the relationship between genes and cells in the data, and then uses a method of averaging the clustering results to fill in the identified technical zeros. Experimental results show that SFAG can help improve downstream analysis and reconstruct cell trajectory.

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“…Methods like Single-cell Analysis Via Expression Recovery (SAVER) [ 9 ], or Sparse Gene Graph of Smooth Signals (G2S3) [ 10 ], employ a different strategy that can overcome some of these limitations. Instead of using the whole transcriptome of a cell to predict the expression level of a given gene, these methods learn gene-gene relationships from the dataset and use only the specific subset of genes that are expected to be predictive for the particular gene at hand.…”

Section: Introductionmentioning

confidence: 99%

Regulatory network-based imputation of dropouts in single-cell RNA sequencing data

Leote

Beyer

2022

PLoS Comput Biol

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Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Further, it is unknown if all genes equally benefit from imputation or which imputation method works best for a given gene. Here, we show that a transcriptional regulatory network learned from external, independent gene expression data improves dropout imputation. Using a variety of human scRNA-seq datasets we demonstrate that our network-based approach outperforms published state-of-the-art methods. The network-based approach performs particularly well for lowly expressed genes, including cell-type-specific transcriptional regulators. Further, the cell-to-cell variation of 11.3% to 48.8% of the genes could not be adequately imputed by any of the methods that we tested. In those cases gene expression levels were best predicted by the mean expression across all cells, i.e. assuming no measurable expression variation between cells. These findings suggest that different imputation methods are optimal for different genes. We thus implemented an R-package called ADImpute (available via Bioconductor https://bioconductor.org/packages/release/bioc/html/ADImpute.html) that automatically determines the best imputation method for each gene in a dataset. Our work represents a paradigm shift by demonstrating that there is no single best imputation method. Instead, we propose that imputation should maximally exploit external information and be adapted to gene-specific features, such as expression level and expression variation across cells.

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G2S3: A gene graph-based imputation method for single-cell RNA sequencing data

Cited by 10 publications

References 51 publications

Cromolyn prevents cerebral vasospasm and dementia by targeting WDR43

Cromolyn prevents cerebral vasospasm and dementia by targeting WDR43

Data Smoothing Filling Method based on ScRNA-Seq Data Zero-Value Identification

Regulatory network-based imputation of dropouts in single-cell RNA sequencing data

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