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IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2025年第2期22卷 781-789页

作者： Liu, Yan Wei, Guo Wu, Jia-Shun Li, Chen Yan, He Shen, Long-Chen Gasser, Robin B. Song, Jiangning Chen, Dijun Yu, Dong-Jun Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Yangzhou Univ Dept Comp Sci Yangzhou 225100 Peoples R China Nanjing Univ Sch Life Sci Nanjing 210023 Peoples R China Monash Univ Biomed Discovery Inst Melbourne Vic 3800 Australia Monash Univ Dept Biochem & Mol Biol Melbourne Vic 3800 Australia Univ Melbourne Melbourne Vet Sch Dept Vet Biosci Parkville Vic 3010 Australia

single-cell transcriptomics is a powerful approach for characterizing gene transcription at cellular resolution. This approach requires efficient computational pipelines to undertake essential tasks, including clustering, dimensionality reduction, imputation, and denoising. Currently, most such pipelines undertake these computational tasks separately without considering the interdependence among these tasks. Here, we present an advanced pipeline, MUSIC-GCN, by employing a graph convolutional neural (GCN) network and autoencoder to perform multi-task single-cell RNA-sequencing (scRNA-seq) data analysis. The rationale is that multiple related tasks can be carried out simultaneously to enable enhanced learning and more effective representations through the 'sharing of knowledge' regarding individual tasks. Benchmarking experiments using various scRNA-seq datasets show that MUSIC-GCN can achieve a competitive performance on multi-tasks when benchmarked with state-of-the-art approaches.

关键词： Imputation Noise reduction data models Pipelines Multitasking Dimensionality reduction Decoding Bioinformatics Autoencoders Biological information theory single-cell transcriptomic data bioinformatics residual graph convolution network multi-task

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iEMNN: An Iterative Integration Method for single-cell transcriptomic data Based on Network Similarity Enhancement and Mutual Nearest Neighbors 20th

iEMNN: An Iterative Integration Method for Single-Cell Trans...

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20th International Conference on Intelligent Computing (ICIC)

作者： Lin, Xuesheng Jiang, Yusheng Guan, Jinting Xiamen Univ Dept Automat Xiamen 361102 Fujian Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

ISBN: (纸本)9789819756919;9789819756926

With the widespread application of single-cell RNA sequencing technology, integrating different batches of data has become a crucial step. Batch effects arise from non-biological variations such as different sequencing batches, sequencing protocols, sequencing depths, and so on. Batch effects introduce systematic biases and confound biological variations of interest, which have a detrimental impact on the validity of study findings. Eliminating batch effects can increase comparability and repeatability, prevent bias and confusion, and improve data quality and consistency. Currently, a great deal of batch effect removal techniques for single-cell transcriptome data has been developed based on finding mutual nearest neighbors (MNNs) across batches, the accuracy of which influences greatly the effect of data correction and the subsequent integrative analysis. To enhance the identification of MNNs, we propose an iterative integration method, called iEMNN, for single-cell transcriptome data by utilizing network similarity enhancement. iEMNN facilitates the detection of similar cells while separating distinct cells. iEMNN applies multiple iterations to improve the effectiveness of the integration process and can perform data correction in both high-dimensional and low-dimensional spaces. Through systematic experiments and comparisons with the existing methods, we demonstrate that iEMNN outperforms other approaches in improving batch correction removal and enhancing clustering performance across different scenarios. This study provides a new perspective and an effective solution for the field of single-cell transcriptomic data integration, with potential significance for a deeper understanding of cellular heterogeneity and dynamic changes.

关键词： single-cell transcriptomic data Mutual Nearest Neighbors Network Similarity Enhancement Batch Effect data Integration

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A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data

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BRIEFINGS IN BIOINFORMATICS 2022年第2期23卷 bbab568-bbab568页

作者： Zhao, Mengyuan He, Wenying Tang, Jijun Zou, Quan Guo, Fei Tianjin Univ Beijing Peoples R China Hubei Univ Technol Sch Artificial Intellingence Wuhan Peoples R China Univ South Carolina Columbia SC USA Univ Elect Sci & Technol China Chengdu Peoples R China Cent South Univ Changsha Peoples R China

Inferring gene regulatory networks (GRNs) based on gene expression profiles is able to provide an insight into a number of cellular phenotypes from the genomic level and reveal the essential laws underlying various life phenomena. Different from the bulk expression data, single-cell transcriptomic data embody cell-to-cell variance and diverse biological information, such as tissue characteristics, transformation of cell types, etc. Inferring GRNs based on such data offers unprecedented advantages for making a profound study of cell phenotypes, revealing gene functions and exploring potential interactions. However, the high sparsity, noise and dropout events of single-cell transcriptomic data pose new challenges for regulation identification. We develop a hybrid deep learning framework for GRN inference from single-cell transcriptomic data, DGRNS, which encodes the raw data and fuses recurrent neural network and convolutional neural network (CNN) to train a model capable of distinguishing related gene pairs from unrelated gene pairs. To overcome the limitations of such datasets, it applies sliding windows to extract valuable features while preserving the direction of regulation. DGRNS is constructed as a deep learning model containing gated recurrent unit network for exploring time-dependent information and CNN for learning spatially related information. Our comprehensive and detailed comparative analysis on the dataset of mouse hematopoietic stem cells illustrates that DGRNS outperforms state-of-the-art methods. The networks inferred by DGRNS are about 16% higher than the area under the receiver operating characteristic curve of other unsupervised methods and 10% higher than the area under the precision recall curve of other supervised methods. Experiments on human datasets show the strong robustness and excellent generalization of DGRNS. By comparing the predictions with standard network, we discover a series of novel interactions which are proved to be true in som

关键词： gene regulatory network single-cell transcriptomic data network inference methods deep learning

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Localization of aquaglyceroporins in human and murine white adipose tissue

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HISTOCHEMISTRY AND cell BIOLOGY 2022年第6期157卷 623-639页

作者： Iena, Francesco Maria Kalucka, Joanna Nielsen, Laerke Sondergaard, Esben Nielsen, Soren Lebeck, Janne Aarhus Univ Dept Biomed Wilhelm Meyers Alle 3 DK-8000 Aarhus C Denmark Aarhus Univ Hosp Steno Diabet Ctr Aarhus Aarhus Denmark Aarhus Univ Aarhus Univ Adv Studies AIAS Aarhus Denmark

The glycerol channel AQP7 facilitates glycerol efflux from adipose tissue (AT), and AQP7 deficiency has been suggested to promote obesity. However, the release of glycerol from AT is not fully blocked in AQP7-deficient mice, which suggests that either alternative glycerol channels are present in AT or significant simple diffusion of glycerol occurs. Previous investigations of the expression of other aquaglyceroporins (AQP3, AQP9, AQP10) than AQP7 in AT are contradictory. Therefore, we here aim at determining the cellular localization of AQP3 and AQP9 in addition to AQP7 in human and mouse AT using well-characterized antibodies for immunohistochemistry (IHC) and immunoblotting as well as available single-cell transcriptomic data from human and mouse AT. We confirm that AQP7 is expressed in endothelial cells and adipocytes in human AT and find ex vivo evidence for interaction between AQP7 and perilipin-1 in adipocytes. In addition, labeling for AQP7 in human AT also includes CD68-positive cells. No labeling for AQP3 or AQP9 was identified in endothelial cells or adipocytes in human or mouse AT using IHC. Instead, in human AT, AQP3 was predominantly found in erythrocytes, whereas AQP9 expression was observed in a small number of CD15-positive cells. The transcriptomic data revealed that AQP3 mRNA was found in a low number of cells in most of the identified cell clusters, whereas AQP9 mRNA was found in myeloid cell clusters as well as in clusters likely representing mesothelial progenitor cells. No AQP10 mRNA was identified in human AT. In conclusion, the presented results do not suggest a functional overlap between AQP3/AQP9/AQP10 and AQP7 in human or mouse white AT.

关键词： Adipose tissue Aquaglyceroporins Aquaporin 7 Immunohistochemistry single-cell transcriptomic data

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Multi-objective Optimization for Marker Panel Identification in single-cell data

Multi-objective Optimization for Marker Panel Identification...

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IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB)

作者： Tangherloni, Andrea Riva, Simone G. Myers, Brynelle Cazzaniga, Paolo Univ Bergamo Dept Human & Social Sci Bergamo Italy Weatherall Inst Mol Med Radcliffe Dept Med Oxford England Univ Oxford Wellcome Ctr Human Genet Oxford England Bicocca Bioinformat Biostat & Bioimaging Ctr B4 Milan Italy

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484626

The computational analyses of single-cell data, aimed at elucidating and characterizing the functional roles of known and putative novel cell types, are enabling a thorough understanding of the processes driving cell development and pathology progression. The isolation of specific cell types is a crucial step to perform detailed analyses but requires the identification of succinct marker panels, which include genes that refer to cell surface proteins and clusters of differentiation molecules. This still represents a challenging NP-hard computational problem, which can be tackled through global optimization techniques. In this work, we formulate the marker panel identification problem as a bi-objective optimization problem, where the first objective regards the capability of the marker panels to accurately discriminate different cell types, while the second objective is related to the number of genes to include in the panel. In particular, we compared the performance of two multi-objective optimization algorithms, as well as of Genetic Algorithms (GAs) when considering only the first objective, employing two different representations for the candidate solutions. Our results show that the multi-objective optimization algorithms are better than GAs, considering both the quality and the consistency of the obtained marker panels;moreover, the collected results point out that different representations of the candidate solutions have a relevant impact on the performance of the optimization algorithms.

关键词： Candidate Solution Representation Multiobjective Optimization single-cell transcriptomic data Computational Biology Bioinformatics

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A self-attention-driven deep learning framework for inference of transcriptional gene regulatory networks

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BRIEFINGS IN BIOINFORMATICS 2024年第1期26卷 bbae639页

作者： Liu, Yong Zhong, Le Yan, Bin Chen, Zhuobin Yu, Yanjia Yu, Dan Qin, Jing Wang, Junwen Guangxi Minzu Univ Coll Elect Informat 188 East Univ Rd Nanning 530006 Guangxi Peoples R China Univ Hong Kong Fac Dent Div Appl Oral Sci & Community Dent Care 34 Hosp Rd Hong Kong Peoples R China Shenzhen Campus Sun Yat Sen Univ Sch Pharmaceut Sci Shenzhen 66 Gongchang Rd Shenzhen 518107 Guangdong Peoples R China Mayo Clin Ctr Individualized Med Dept Quantitat Hlth Sci 13400 Shea Blvd Scottsdale AZ 85259 USA Mayo Clin Comprehens Canc Ctr 13400 Shea Blvd Scottsdale AZ 85259 USA

The interactions between transcription factors (TFs) and the target genes could provide a basis for constructing gene regulatory networks (GRNs) for mechanistic understanding of various biological complex processes. From gene expression data, particularly single-cell transcriptomic data containing rich cell-to-cell variations, it is highly desirable to infer TF-gene interactions (TGIs) using deep learning technologies. Numerous models or software including deep learning-based algorithms have been designed to identify transcriptional regulatory relationships between TFs and the downstream genes. However, these methods do not significantly improve predictions of TGIs due to some limitations regarding constructing underlying interactive structures linking regulatory components. In this study, we introduce a deep learning framework, DeepTGI, that encodes gene expression profiles from single-cell and/or bulk transcriptomic data and predicts TGIs with high accuracy. Our approach could fuse the features extracted from Auto-encoder with self-attention mechanism and other networks and could transform multihead attention modules to define representative features. By comparing it with other models or methods, DeepTGI exhibits its superiority to identify more potential TGIs and to reconstruct the GRNs and, therefore, could provide broader perspectives for discovery of more biological meaningful TGIs and for understanding transcriptional gene regulatory mechanisms.

关键词： self-attention mechanism deep learning transcription factor-gene interaction gene regulatory networks single-cell transcriptomic data

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The Impact of Representation on the Optimization of Marker Panels for single-cell RNA data

The Impact of Representation on the Optimization of Marker P...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Tangherloni, Andrea Riva, Simone G. Spolaor, Simone Besozzi, Daniela Nobile, Marco S. Cazzaniga, Paolo Univ Bergamo Dept Human & Social Sci Bergamo Italy Univ Cambridge Dept Haematol Wellcome Trust MRC Cambridge Stem Cell InstWellcome Trust Sanger In Wellcome Trust Genome Campus Cambridge England Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy Bicocca Bioinformat Biostat & Bioimaging Ctr B4 Milan Italy Eindhoven Univ Technol Dept Ind Engn & Innovat Sci Eindhoven Netherlands

ISBN: (纸本)9781728183923

The increasing number of single-cell transcriptomic and single-cell RNA sequencing studies are allowing for a deeper understanding of the molecular processes underlying the normal development of an organism as well as the onset of pathologies. These studies continuously refine the functional roles of known cell populations, and provide their characterization as soon as putatively novel cell populations are detected. In order to isolate the cell populations for further tailored analysis, succinct marker panels-composed of a few cell surface proteins and clusters of differentiation molecules-must be identified. The identification of these marker panels is a challenging computational problem due to its intrinsic combinatorial nature, which makes it an NP-hard problem. Genetic Algorithms (GAs) have been successfully used in Bioinformatics and other biomedical applications to tackle combinatorial problems. We present here a GA-based approach to solve the problem of the identification of succinct marker panels. Since the performance of a GA is strictly related to the representation of the candidate solutions, we propose and compare three alternative representations, able to implicitly introduce different constraints on the search space. For each representation, we perform a fine-tuning of the parameter settings to calibrate the GA, and we show that different representations yield different performance, where the most relaxed representations-in which the GA can also evolve the number of genes in the panel-turn out to be the more effective, especially in the case of 0-knowledge problems. Our results also show that the marker panels identified by GAs can outperform manually curated solutions.

关键词： Candidate Solution Representation Genetic Algorithms single-cell transcriptomic data Computational Biology Bioinformatics

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Q&A: Array tomography

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BMC BIOLOGY 2018年第1期16卷 98-98页

作者： Smith, Stephen J. Allen Inst Brain Sci Seattle WA 98109 USA

Array tomography encompasses light and electron microscopy modalities that offer unparalleled opportunities to explore three-dimensional cellular architectures in extremely fine structural and molecular detail. Fluorescence array tomography achieves much higher resolution and molecular multiplexing than most other fluorescence microscopy methods, while electron array tomography can capture three-dimensional ultrastructure much more easily and rapidly than traditional serial-section electron microscopy methods. A correlative fluorescence/electron microscopy mode of array tomography furthermore offers a unique capacity to merge the molecular discrimination strengths of multichannel fluorescence microscopy with the ultrastructural imaging strengths of electron microscopy. This essay samples the first decade of array tomography, highlighting applications in neuroscience.

关键词： Array Tomography (AT) Molecular Multiplex Whole-mount Fluorescence Multiplex Sequencing single-cell transcriptomic data

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