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  1. Article ; Online: FRMC: a fast and robust method for the imputation of scRNA-seq data.

    Wu, Honglong / Wang, Xuebin / Chu, Mengtian / Xiang, Ruizhi / Zhou, Ke

    RNA biology

    2021  Volume 18, Issue sup1, Page(s) 172–181

    Abstract: The high-resolution feature of single-cell transcriptome sequencing technology allows researchers to observe cellular gene expression profiles at the single-cell level, offering numerous possibilities for subsequent biomedical investigation. However, the ...

    Abstract The high-resolution feature of single-cell transcriptome sequencing technology allows researchers to observe cellular gene expression profiles at the single-cell level, offering numerous possibilities for subsequent biomedical investigation. However, the unavoidable technical impact of high missing values in the gene-cell expression matrices generated by insufficient RNA input severely hampers the accuracy of downstream analysis. To address this problem, it is essential to develop a more rapid and stable imputation method with greater accuracy, which should not only be able to recover the missing data, but also effectively facilitate the following biological mechanism analysis. The existing imputation methods all have their drawbacks and limitations, some require pre-assumed data distribution, some cannot distinguish between technical and biological zeros, and some have poor computational performance. In this paper, we presented a novel imputation software FRMC for single-cell RNA-Seq data, which innovates a fast and accurate singular value thresholding approximation method. The experiments demonstrated that FRMC can not only precisely distinguish 'true zeros' from dropout events and correctly impute missing values attributed to technical noises, but also effectively enhance intracellular and intergenic connections and achieve accurate clustering of cells in biological applications. In summary, FRMC can be a powerful tool for analysing single-cell data because it ensures biological significance, accuracy, and rapidity simultaneously. FRMC is implemented in Python and is freely accessible to non-commercial users on GitHub: https://github.com/HUST-DataMan/FRMC.
    MeSH term(s) Gene Expression Profiling ; Humans ; RNA-Seq/methods ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods ; Software ; Whole Exome Sequencing/methods
    Language English
    Publishing date 2021-08-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2159587-2
    ISSN 1555-8584 ; 1555-8584
    ISSN (online) 1555-8584
    ISSN 1555-8584
    DOI 10.1080/15476286.2021.1960688
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: FRMC: a fast and robust method for the imputation of scRNA-seq data

    Wu, Honglong / Wang, Xuebin / Chu, Mengtian / Xiang, Ruizhi / Zhou, Ke

    RNA Biology. 2021 Oct. 15, v. 18, no. S1 p.172-181

    2021  

    Abstract: The high-resolution feature of single-cell transcriptome sequencing technology allows researchers to observe cellular gene expression profiles at the single-cell level, offering numerous possibilities for subsequent biomedical investigation. However, the ...

    Abstract The high-resolution feature of single-cell transcriptome sequencing technology allows researchers to observe cellular gene expression profiles at the single-cell level, offering numerous possibilities for subsequent biomedical investigation. However, the unavoidable technical impact of high missing values in the gene-cell expression matrices generated by insufficient RNA input severely hampers the accuracy of downstream analysis. To address this problem, it is essential to develop a more rapid and stable imputation method with greater accuracy, which should not only be able to recover the missing data, but also effectively facilitate the following biological mechanism analysis. The existing imputation methods all have their drawbacks and limitations, some require pre-assumed data distribution, some cannot distinguish between technical and biological zeros, and some have poor computational performance. In this paper, we presented a novel imputation software FRMC for single-cell RNA-Seq data, which innovates a fast and accurate singular value thresholding approximation method. The experiments demonstrated that FRMC can not only precisely distinguish ‘true zeros’ from dropout events and correctly impute missing values attributed to technical noises, but also effectively enhance intracellular and intergenic connections and achieve accurate clustering of cells in biological applications. In summary, FRMC can be a powerful tool for analysing single-cell data because it ensures biological significance, accuracy, and rapidity simultaneously. FRMC is implemented in Python and is freely accessible to non-commercial users on GitHub: https://github.com/HUST-DataMan/FRMC.
    Keywords RNA ; computer software ; gene expression ; sequence analysis ; transcriptome ; Imputation1 ; scRNA-seq2 ; dropout event3 ; low-rank matrix optimization4 ; singular value thresholding iteration5 ; sparsity6
    Language English
    Dates of publication 2021-1015
    Size p. 172-181.
    Publishing place Taylor & Francis
    Document type Article ; Online
    ZDB-ID 2159587-2
    ISSN 1555-8584
    ISSN 1555-8584
    DOI 10.1080/15476286.2021.1960688
    Database NAL-Catalogue (AGRICOLA)

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  3. Article: A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data.

    Xiang, Ruizhi / Wang, Wencan / Yang, Lei / Wang, Shiyuan / Xu, Chaohan / Chen, Xiaowen

    Frontiers in genetics

    2021  Volume 12, Page(s) 646936

    Abstract: Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology performed at the level of an individual cell, which can have a potential to understand cellular heterogeneity. However, scRNA-seq data are high-dimensional, noisy, and ... ...

    Abstract Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology performed at the level of an individual cell, which can have a potential to understand cellular heterogeneity. However, scRNA-seq data are high-dimensional, noisy, and sparse data. Dimension reduction is an important step in downstream analysis of scRNA-seq. Therefore, several dimension reduction methods have been developed. We developed a strategy to evaluate the stability, accuracy, and computing cost of 10 dimensionality reduction methods using 30 simulation datasets and five real datasets. Additionally, we investigated the sensitivity of all the methods to hyperparameter tuning and gave users appropriate suggestions. We found that t-distributed stochastic neighbor embedding (t-SNE) yielded the best overall performance with the highest accuracy and computing cost. Meanwhile, uniform manifold approximation and projection (UMAP) exhibited the highest stability, as well as moderate accuracy and the second highest computing cost. UMAP well preserves the original cohesion and separation of cell populations. In addition, it is worth noting that users need to set the hyperparameters according to the specific situation before using the dimensionality reduction methods based on non-linear model and neural network.
    Language English
    Publishing date 2021-03-23
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2021.646936
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: GPMeta: a GPU-accelerated method for ultrarapid pathogen identification from metagenomic sequences.

    Wang, Xuebin / Wang, Taifu / Xie, Zhihao / Zhang, Youjin / Xia, Shiqiang / Sun, Ruixue / He, Xinqiu / Xiang, Ruizhi / Zheng, Qiwen / Liu, Zhencheng / Wang, Jin'An / Wu, Honglong / Jin, Xiangqian / Chen, Weijun / Li, Dongfang / He, Zengquan

    Briefings in bioinformatics

    2023  Volume 24, Issue 2

    Abstract: Metagenomic sequencing (mNGS) is a powerful diagnostic tool to detect causative pathogens in clinical microbiological testing owing to its unbiasedness and substantially reduced costs. Rapid and accurate classification of metagenomic sequences is a ... ...

    Abstract Metagenomic sequencing (mNGS) is a powerful diagnostic tool to detect causative pathogens in clinical microbiological testing owing to its unbiasedness and substantially reduced costs. Rapid and accurate classification of metagenomic sequences is a critical procedure for pathogen identification in dry-lab step of mNGS test. However, clinical practices of the testing technology are hampered by the challenge of classifying sequences within a clinically relevant timeframe. Here, we present GPMeta, a novel GPU-accelerated approach to ultrarapid pathogen identification from complex mNGS data, allowing users to bypass this limitation. Using mock microbial community datasets and public real metagenomic sequencing datasets from clinical samples, we show that GPMeta has not only higher accuracy but also significantly higher speed than existing state-of-the-art tools such as Bowtie2, Bwa, Kraken2 and Centrifuge. Furthermore, GPMeta offers GPMetaC clustering algorithm, a statistical model for clustering and rescoring ambiguous alignments to improve the discrimination of highly homologous sequences from microbial genomes with average nucleotide identity >95%. GPMetaC exhibits higher precision and recall rate than others. GPMeta underlines its key role in the development of the mNGS test in infectious diseases that require rapid turnaround times. Further study will discern how to best and easily integrate GPMeta into routine clinical practices. GPMeta is freely accessible to non-commercial users at https://github.com/Bgi-LUSH/GPMeta.
    MeSH term(s) Metagenome ; Microbiota ; High-Throughput Nucleotide Sequencing/methods ; Metagenomics/methods ; Sensitivity and Specificity
    Language English
    Publishing date 2023-03-13
    Publishing country England
    Document type Journal Article
    ZDB-ID 2068142-2
    ISSN 1477-4054 ; 1467-5463
    ISSN (online) 1477-4054
    ISSN 1467-5463
    DOI 10.1093/bib/bbad092
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Ag nanoparticles decorated urchin-like cobalt carbonate hydroxide composites for highly efficient oxygen evolution reaction.

    Wang, Wei / Zhu, Sainan / Chen, Xingliang / Zhang, Xinyu / Tao, Yourong / Zhang, Yanxin / Xiang, Ruizhi / Wu, Xingcai

    Nanotechnology

    2020  Volume 31, Issue 47, Page(s) 475402

    Abstract: Herein, a novel composite of small amounts of Ag nanoparticles (NPs) decorated urchin-like cobalt carbonate hydroxide hydrate (CCHH) was developed for highly-efficient alkaline oxygen evolution reaction (OER). Not only can Ag colloids, as template agents, ...

    Abstract Herein, a novel composite of small amounts of Ag nanoparticles (NPs) decorated urchin-like cobalt carbonate hydroxide hydrate (CCHH) was developed for highly-efficient alkaline oxygen evolution reaction (OER). Not only can Ag colloids, as template agents, modify the morphologies of urchin-like CCHH microspheres to expose more active sites available, but also the supported Ag NPs formed by Ag colloids can transfer the electron to CCHH surfaces, accelerating the transformation of surface Co
    Language English
    Publishing date 2020-08-28
    Publishing country England
    Document type Journal Article
    ZDB-ID 1362365-5
    ISSN 1361-6528 ; 0957-4484
    ISSN (online) 1361-6528
    ISSN 0957-4484
    DOI 10.1088/1361-6528/abaf80
    Database MEDical Literature Analysis and Retrieval System OnLINE

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