The Experts below are selected from a list of 15543 Experts worldwide ranked by ideXlab platform
Zuoheng Wang - One of the best experts on this subject based on the ideXlab platform.
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g2s3 a gene graph based Imputation Method for single cell rna sequencing data
PLOS Computational Biology, 2021Co-Authors: Yunqing Liu, Qile Dai, Xiting Yan, Zuoheng WangAbstract:Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new Method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing Imputation Methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for Imputation in large-scale single-cell transcriptomic datasets.
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g2s3 a gene graph based Imputation Method for single cell rna sequencing data
bioRxiv, 2020Co-Authors: Qile Dai, Yunqing Liu, Xiting Yan, Zuoheng WangAbstract:Single-cell RNA sequencing provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses. We propose a novel Method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and other existing Methods to seven single-cell datasets to compare their performance. Our results demonstrated that G2S3 is superior in recovering true expression levels, identifying cell subtypes, improving differential expression analyses, and recovering gene regulatory relationships, especially for mildly expressed genes.
Zhandong Liu - One of the best experts on this subject based on the ideXlab platform.
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prime a probabilistic Imputation Method to reduce dropout effects in single cell rna sequencing
Bioinformatics, 2020Co-Authors: Hyundoo Jeong, Zhandong LiuAbstract:Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel Imputation Method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this Method, called PRIME (PRobabilistic Imputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed Method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.
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prime a probabilistic Imputation Method to reduce dropout effects in single cell rna sequencing
bioRxiv, 2020Co-Authors: Hyundoo Jeong, Zhandong LiuAbstract:Abstract Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data therefore need to be carefully processed before in-depth analysis. Here we describe a novel Imputation Method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local community of cells of the same type. We comprehensively evaluated this Method, called PRIME (PRobabilistic Imputation to reduce dropout effects in Expression profiles of single cell sequencing), on six datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise.
Yunqing Liu - One of the best experts on this subject based on the ideXlab platform.
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g2s3 a gene graph based Imputation Method for single cell rna sequencing data
PLOS Computational Biology, 2021Co-Authors: Yunqing Liu, Qile Dai, Xiting Yan, Zuoheng WangAbstract:Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new Method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing Imputation Methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for Imputation in large-scale single-cell transcriptomic datasets.
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g2s3 a gene graph based Imputation Method for single cell rna sequencing data
bioRxiv, 2020Co-Authors: Qile Dai, Yunqing Liu, Xiting Yan, Zuoheng WangAbstract:Single-cell RNA sequencing provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses. We propose a novel Method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and other existing Methods to seven single-cell datasets to compare their performance. Our results demonstrated that G2S3 is superior in recovering true expression levels, identifying cell subtypes, improving differential expression analyses, and recovering gene regulatory relationships, especially for mildly expressed genes.
Qile Dai - One of the best experts on this subject based on the ideXlab platform.
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g2s3 a gene graph based Imputation Method for single cell rna sequencing data
PLOS Computational Biology, 2021Co-Authors: Yunqing Liu, Qile Dai, Xiting Yan, Zuoheng WangAbstract:Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new Method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing Imputation Methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for Imputation in large-scale single-cell transcriptomic datasets.
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g2s3 a gene graph based Imputation Method for single cell rna sequencing data
bioRxiv, 2020Co-Authors: Qile Dai, Yunqing Liu, Xiting Yan, Zuoheng WangAbstract:Single-cell RNA sequencing provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses. We propose a novel Method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and other existing Methods to seven single-cell datasets to compare their performance. Our results demonstrated that G2S3 is superior in recovering true expression levels, identifying cell subtypes, improving differential expression analyses, and recovering gene regulatory relationships, especially for mildly expressed genes.
Xiting Yan - One of the best experts on this subject based on the ideXlab platform.
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g2s3 a gene graph based Imputation Method for single cell rna sequencing data
PLOS Computational Biology, 2021Co-Authors: Yunqing Liu, Qile Dai, Xiting Yan, Zuoheng WangAbstract:Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new Method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing Imputation Methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for Imputation in large-scale single-cell transcriptomic datasets.
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g2s3 a gene graph based Imputation Method for single cell rna sequencing data
bioRxiv, 2020Co-Authors: Qile Dai, Yunqing Liu, Xiting Yan, Zuoheng WangAbstract:Single-cell RNA sequencing provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses. We propose a novel Method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and other existing Methods to seven single-cell datasets to compare their performance. Our results demonstrated that G2S3 is superior in recovering true expression levels, identifying cell subtypes, improving differential expression analyses, and recovering gene regulatory relationships, especially for mildly expressed genes.
