IF 4.2 3区生物学

Methods Pub Date : 2025-01-27 DOI: 10.1016/j.ymeth.2025.01.008

Quang-Huy Ho, Thi-Nhu-Quynh Nguyen, Thi-Thao Tran, Van-Truong Pham

{"title":"LiteMamba-Bound: A lightweight Mamba-based model with boundary-aware and normalized active contour loss for skin lesion segmentation","authors":"Quang-Huy Ho, Thi-Nhu-Quynh Nguyen, Thi-Thao Tran, Van-Truong Pham","doi":"10.1016/j.ymeth.2025.01.008","DOIUrl":"10.1016/j.ymeth.2025.01.008","url":null,"abstract":"<div><div>In the field of medical science, skin segmentation has gained significant importance, particularly in dermatology and skin cancer research. This domain demands high precision in distinguishing critical regions (such as lesions or moles) from healthy skin in medical images. With growing technological advancements, deep learning models have emerged as indispensable tools in addressing these challenges. One of the state-of-the-art modules revealed in recent years, the 2D Selective Scan (SS2D), based on state-space models that have already seen great success in natural language processing, has been increasingly adopted and is gradually replacing Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Leveraging the strength of this module, this paper introduces LiteMamba-Bound, a lightweight model with approximately 957K parameters, designed for skin image segmentation tasks. Notably, the Channel Attention Dual Mamba (CAD-Mamba) block is proposed within both the encoder and decoder alongside the Mix Convolution with Simple Attention bottleneck block to emphasize key features. Additionally, we propose the Reverse Attention Boundary Module to highlight challenging boundary features. Also, the Normalized Active Contour loss function presented in this paper significantly improves the model's performance compared to other loss functions. To validate performance, we conducted tests on two skin image datasets, ISIC2018 and PH2, with results consistently showing superior performance compared to other models. Our code will be made publicly available at: <span><span>https://github.com/kwanghwi242/A-new-segmentation-model</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"235 ","pages":"Pages 10-25"},"PeriodicalIF":4.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MPEMDA: A multi-similarity integration approach with pre-completion and error correction for predicting microbe-drug associations

IF 4.2 3区生物学

Methods Pub Date : 2025-01-23 DOI: 10.1016/j.ymeth.2024.12.013

Yuxiang Li , Haochen Zhao , Jianxin Wang

{"title":"MPEMDA: A multi-similarity integration approach with pre-completion and error correction for predicting microbe-drug associations","authors":"Yuxiang Li , Haochen Zhao , Jianxin Wang","doi":"10.1016/j.ymeth.2024.12.013","DOIUrl":"10.1016/j.ymeth.2024.12.013","url":null,"abstract":"<div><div>Exploring the associations between microbes and drugs offers valuable insights into their underlying mechanisms. Traditional wet lab experiments, while reliable, are often time-consuming and labor-intensive, making computational approaches an attractive alternative. Existing similarity-based machine learning models for predicting microbe-drug associations typically rely on integrated similarities as input, neglecting the unique contributions of individual similarities, which can compromise predictive accuracy. To overcome these limitations, we develop MPEMDA, a novel method that pre-completes the microbe-drug association matrix using various similarity combinations and employs a label propagation algorithm with error correction to predict microbe-drug associations. Compared with existing methods, MPEMDA simultaneously utilizes the integrated and individual similarities obtained through the Similarity Network Fusion (SNF) method to pre-complete the known drug-microbe association matrix, followed by error correction to optimize the predictive scores generated by the label propagation algorithm. Experimental results on three benchmark datasets show that MPEMDA outperforms state-of-the-art methods in both the 5-fold cross-validation and <em>de novo</em> test. Additionally, case studies on drugs and microbes highlight the method's strong potential to identify novel microbe-drug associations. The MPEMDA code is available at <span><span>https://github.com/lyx8527/MPEMDA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"235 ","pages":"Pages 1-9"},"PeriodicalIF":4.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of unitary conductance of gap junction channels based on stationary fluctuation analysis

IF 4.2 3区生物学

Methods Pub Date : 2025-01-20 DOI: 10.1016/j.ymeth.2025.01.006

Orestas Makniusevicius , Lukas Gudaitis , Tadas Kraujalis , Lina Kraujaliene , Mindaugas Snipas

{"title":"Evaluation of unitary conductance of gap junction channels based on stationary fluctuation analysis","authors":"Orestas Makniusevicius , Lukas Gudaitis , Tadas Kraujalis , Lina Kraujaliene , Mindaugas Snipas","doi":"10.1016/j.ymeth.2025.01.006","DOIUrl":"10.1016/j.ymeth.2025.01.006","url":null,"abstract":"<div><div>Gap junction (GJ) channels, formed of connexin (Cx) protein, enable direct intercellular communication in most vertebrate tissues. One of the key biophysical characteristics of these channels is their unitary conductance, which can be affected by mutations in Cx genes and various biochemical factors, such as posttranslational modifications. Due to the unique intercellular configuration of GJ channels, recording single-channel currents is challenging, and precise data on unitary conductances of some Cx isoforms remain limited. In this study, we applied stationary noise analysis, a method successfully used for ion channels with very low unitary conductances, to GJ channels. We modified this technique to account for the residual conductance of GJ channels and present three strategies for estimating unitary conductance, including model-based evaluation of open-state probability and subtraction of residual conductance. To assess the validity, advantages, and limitations of these approaches, we performed mathematical analysis and simulation experiments. We also addressed practical issues such as the underestimation of sample variance in autocorrelated recordings and channel rundown, proposing solutions to these issues. Finally, we applied these strategies to electrophysiological data recorded from cells expressing Cx45. Our findings showed that noise-based estimates of Cx45 unitary conductance from macroscopic currents align well with those obtained from single-channel recordings.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"235 ","pages":"Pages 81-91"},"PeriodicalIF":4.2,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143021544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

In silico identification of Histone Deacetylase inhibitors using Streamlined Masked Transformer-based Pretrained features 使用基于预训练特征的流式掩码变换器对组蛋白去乙酰化酶抑制剂进行硅学识别。

IF 4.2 3区生物学

Methods Pub Date : 2024-11-23 DOI: 10.1016/j.ymeth.2024.11.009

Tuan Vinh , Thanh-Hoang Nguyen-Vo , Viet-Tuan Le , Xuan-Phuc Phan-Nguyen , Binh P. Nguyen

{"title":"In silico identification of Histone Deacetylase inhibitors using Streamlined Masked Transformer-based Pretrained features","authors":"Tuan Vinh , Thanh-Hoang Nguyen-Vo , Viet-Tuan Le , Xuan-Phuc Phan-Nguyen , Binh P. Nguyen","doi":"10.1016/j.ymeth.2024.11.009","DOIUrl":"10.1016/j.ymeth.2024.11.009","url":null,"abstract":"<div><div>Histone Deacetylases (HDACs) are enzymes that regulate gene expression by removing acetyl groups from histones. They are involved in various diseases, including neurodegenerative, cardiovascular, inflammatory, and metabolic disorders, as well as fibrosis in the liver, lungs, and kidneys. Successfully identifying potent HDAC inhibitors may offer a promising approach to treating these diseases. In addition to experimental techniques, researchers have introduced several <em>in silico</em> methods for identifying HDAC inhibitors. However, these existing computer-aided methods have shortcomings in their modeling stages, which limit their applications. In our study, we present a <u>S</u>treamlined <u>M</u>asked <u>T</u>ransformer-based Pretrained (SMTP) encoder, which can be used to generate features for downstream tasks. The training process of the SMTP encoder was directed by masked attention-based learning, enhancing the model's generalizability in encoding molecules. The SMTP features were used to develop 11 classification models identifying 11 HDAC isoforms. We trained SMTP, a lightweight encoder, with only 1.9 million molecules, a smaller number than other known molecular encoders, yet its discriminant ability remains competitive. The results revealed that machine learning models developed using the SMTP feature set outperformed those developed using other feature sets in 8 out of 11 classification tasks. Additionally, chemical diversity analysis confirmed the encoder's effectiveness in distinguishing between two classes of molecules.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 1-9"},"PeriodicalIF":4.2,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust feature learning using contractive autoencoders for multi-omics clustering in cancer subtyping 利用收缩自编码器进行稳健特征学习，用于癌症亚型中的多组学聚类。

IF 4.2 3区生物学

Methods Pub Date : 2024-11-20 DOI: 10.1016/j.ymeth.2024.11.013

Mengke Guo, Xiucai Ye, Dong Huang, Tetsuya Sakurai

{"title":"Robust feature learning using contractive autoencoders for multi-omics clustering in cancer subtyping","authors":"Mengke Guo, Xiucai Ye, Dong Huang, Tetsuya Sakurai","doi":"10.1016/j.ymeth.2024.11.013","DOIUrl":"10.1016/j.ymeth.2024.11.013","url":null,"abstract":"<div><div>Cancer can manifest in virtually any tissue or organ, necessitating precise subtyping of cancer patients to enhance diagnosis, treatment, and prognosis. With the accumulation of vast amounts of omics data, numerous studies have focused on integrating multi-omics data for cancer subtyping using clustering techniques. However, due to the heterogeneity of different omics data, extracting important features to effectively integrate these data for accurate clustering analysis remains a significant challenge. This study proposes a new multi-omics clustering framework for cancer subtyping, which utilizes contractive autoencoder to extract robust features. By encouraging the learned representation to be less sensitive to small changes, the contractive autoencoder learns robust feature representations from different omics. To incorporate survival information into the clustering analysis, Cox proportional hazards regression is used to further select the key features significantly associated with survival for integration. Finally, we utilize K-means clustering on the integrated feature to obtain the clustering result. The proposed framework is evaluated on ten different cancer datasets across four levels of omics data and compared to other existing methods. The experimental results indicate that the proposed framework effectively integrates the four omics datasets and outperforms other methods, achieving higher C-index scores and showing more significant differences between survival curves. Additionally, differential gene analysis and pathway enrichment analysis are performed to further demonstrate the effectiveness of the proposed method framework.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"233 ","pages":"Pages 52-60"},"PeriodicalIF":4.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Retinal Imaging: Evaluation of ultrasmall TiO2 nanoparticle- fluorescein conjugates for improved Fundus Fluorescein Angiography 优化视网膜成像：评估超小 TiO2 纳米粒子-荧光素共轭物对改进眼底荧光素血管造影的作用。

IF 4.2 3区生物学

Methods Pub Date : 2024-11-19 DOI: 10.1016/j.ymeth.2024.11.012

Marina França Dias , Rodrigo Ken Kawassaki , Lutiana Amaral de Melo , Koiti Araki , Robson Raphael Guimarães , Sílvia Ligorio Fialho

{"title":"Optimizing Retinal Imaging: Evaluation of ultrasmall TiO2 nanoparticle- fluorescein conjugates for improved Fundus Fluorescein Angiography","authors":"Marina França Dias , Rodrigo Ken Kawassaki , Lutiana Amaral de Melo , Koiti Araki , Robson Raphael Guimarães , Sílvia Ligorio Fialho","doi":"10.1016/j.ymeth.2024.11.012","DOIUrl":"10.1016/j.ymeth.2024.11.012","url":null,"abstract":"<div><div>Fundus Fluorescein Angiography (FFA) has been extensively used for the identification, management, and diagnosis of various retinal and choroidal diseases, such as age-related macular degeneration, diabetic retinopathy, retinopathy of prematurity, among others. This exam enables clinicians to evaluate retinal morphology and the pathophysiology of retinal vasculature. However, adverse events, including from mild to severe reactions to sodium fluorescein, have been reported. Titanium dioxide nanoparticles (NPTiO<sub>2</sub>) have shown significant potential in numerous biological applications. Coating or conjugating these nanoparticles with small molecules can enhance their stability, photochemical properties, and biocompatibility, as well as increase the hydrophilicity of the nanoparticles, making them more suitable for biomedical applications. This work demonstrates the potential use of ultrasmall titanium dioxide nanoparticles conjugated with sodium fluorescein to improve the quality of angiography exams. The strategy of conjugating fluorescein with NPTiO<sub>2</sub> successfully enhanced the fluorescence photostability of the contrast agent and increased its retention time in the retina. Preliminary <em>in vivo</em> and <em>in vitro</em> safety tests suggest that these nanoparticles are safe for the intended application demonstrating low tendency to hemolysis, and no significant changes in the retina thickness or in the electroretinography a-wave and b-wave amplitudes. Overall, the conjugation of fluorescein to NPTiO<sub>2</sub> has produced a nanomaterial with favorable properties for use as an innovative contrast agent in FFA examinations. By providing a clear description of our methodology of analysis, we also aim to offer better perspectives and reproducible conditions for future research.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"233 ","pages":"Pages 30-41"},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142680419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SITP: A single cell bioinformatics analysis flow captures proteasome markers in the development of breast cancer SITP：单细胞生物信息学分析流捕捉乳腺癌发展过程中的蛋白酶体标记。

IF 4.2 3区生物学

Methods Pub Date : 2024-11-15 DOI: 10.1016/j.ymeth.2024.11.011

Xue-Jie Zhou , Xiao-Feng Liu , Xin Wang , Xu-Chen Cao

{"title":"SITP: A single cell bioinformatics analysis flow captures proteasome markers in the development of breast cancer","authors":"Xue-Jie Zhou , Xiao-Feng Liu , Xin Wang , Xu-Chen Cao","doi":"10.1016/j.ymeth.2024.11.011","DOIUrl":"10.1016/j.ymeth.2024.11.011","url":null,"abstract":"<div><div>Single cell sequencing and related databases have been widely used in the exploration of cancer occurrence and development, but there is still no in-depth explanation of specific and complicated cellular protein modification processes. Ubiquitin-Proteasome System (UPS), as a specific and precise protein modification and degradation process, plays an important role in the biological functions of cancer cell proliferation and apoptosis. Proteasomes, vital multi-catalytic proteinases in eukaryotic cells, play a crucial role in protein degradation and contribute to tumor regulation. The 26S proteasome, part of the ubiquitin–proteasome system. In this study, we have enrolled a common SITP process including analysis of single cell sequencing to elucidate a flow that can capture typical proteasome markers in the oncogenesis and progression of breast cancer. PSMD11, a key component of the 26S proteasome regulatory particle, has been identified as a critical survival factor in cancer cells. Results suggest that PSMD11’s rapid degradation is linked to acute apoptosis in cancer cells, making it a potential target for cancer treatment. Our study explored the potential mechanisms of PSMD11 in breast cancer development. The findings revealed the feasibility of disclosing ubiquitinating biomarkers from public database, as well as presented new evidence supporting PSMD11 as a potential therapeutic biomarker for breast cancer.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"233 ","pages":"Pages 1-10"},"PeriodicalIF":4.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ab-Amy 2.0: Predicting light chain amyloidogenic risk of therapeutic antibodies based on antibody language model Ab-amy 2.0：基于抗体语言模型预测治疗性抗体的轻链淀粉样蛋白致病风险。

IF 4.2 3区生物学

Methods Pub Date : 2024-11-15 DOI: 10.1016/j.ymeth.2024.11.005

Yuwei Zhou , Wenwen Liu , Chunmei Luo , Ziru Huang , Gunarathne Samarappuli Mudiyanselage Savini , Lening Zhao , Rong Wang , Jian Huang

{"title":"Ab-Amy 2.0: Predicting light chain amyloidogenic risk of therapeutic antibodies based on antibody language model","authors":"Yuwei Zhou , Wenwen Liu , Chunmei Luo , Ziru Huang , Gunarathne Samarappuli Mudiyanselage Savini , Lening Zhao , Rong Wang , Jian Huang","doi":"10.1016/j.ymeth.2024.11.005","DOIUrl":"10.1016/j.ymeth.2024.11.005","url":null,"abstract":"<div><div>Therapeutic antibodies have emerged as a promising treatment option for a wide range of diseases. However, the light chain of antibodies can potentially induce amyloidosis, a condition characterized by protein misfolding and aggregation, posing a significant safety concern. Therefore, it is crucial to assess the amyloidogenic risk of therapeutic antibodies during the early stages of drug development. In this study, we introduce AB-Amy 2.0, a new computational model with enhanced performance for assessing the light chain amyloidogenic risk of therapeutic antibodies. By employing pretrained protein language models (PLMs) embeddings, AB-Amy 2.0 achieves higher accuracy in amyloidogenic risk prediction compared with traditional features offering a crucial tool for early-stage identification of antibodies with low aggregation propensity. The AB-Amy 2.0 was trained on antiBERTy embeddings and utilizes the SVM algorithm, resulting in superior performance metrics. On an independent test dataset, the model achieved high sensitivity, specificity, ACC, MCC and AUC of 93.47%, 89.23%, 91.92%, 0.8261 and 0.9739, respectively. These results highlight the effectiveness and robustness of AB-Amy 2.0 in predicting light chain amyloidogenic risk accurately. To facilitate user-friendly access, we have developed an online web server (<span><span><u>http://i.uestc.edu.cn/AB-Amy2</u></span><svg><path></path></svg></span>) and a command line tool (<span><span><u>https://github.com/zzyywww/ABAmy2</u></span><svg><path></path></svg></span>). These resources enable the broader application of this advanced model and promise to enhance the development of safer therapeutic antibodies.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"233 ","pages":"Pages 11-18"},"PeriodicalIF":4.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data preprocessing methods for selective sweep detection using convolutional neural networks 使用卷积神经网络进行选择性扫频检测的数据预处理方法。

IF 4.2 3区生物学

Methods Pub Date : 2024-11-15 DOI: 10.1016/j.ymeth.2024.11.003

Hanqing Zhao, Nikolaos Alachiotis

{"title":"Data preprocessing methods for selective sweep detection using convolutional neural networks","authors":"Hanqing Zhao, Nikolaos Alachiotis","doi":"10.1016/j.ymeth.2024.11.003","DOIUrl":"10.1016/j.ymeth.2024.11.003","url":null,"abstract":"<div><div>The identification of positive selection has been framed as a classification task, with Convolutional Neural Networks (CNNs) already outperforming summary statistics and likelihood-based approaches in accuracy. Despite the prevalence of CNN-based methods that manipulate the pixels of images representing raw genomic data as a preprocessing step to improve classification accuracy, the efficacy of these pixel-rearrangement techniques remains inadequately examined, particularly in the presence of confounding factors like population bottlenecks, migration and recombination hotspots. We introduce a set of pixel rearrangement algorithms aimed at enhancing CNN classification accuracy in detecting selective sweeps. These algorithms are employed to assess the performance of four CNN models for selective sweep detection. Our findings illustrate that the judicious application of rearrangement algorithms notably enhances the overall classification accuracy of a CNN across various datasets simulating confounding factors. We observed that sorting the columns of the genomic matrices has higher on CNN performance than rearranging the sequences. To some extent, these rearrangement algorithms are more robust to misspecified demographic models compared with the utilization of the default preprocessing algorithm as suggested by the respective authors of each CNN architecture. We provide the data rearrangement algorithms as a distinct package available for download at: <span><span>https://github.com/Zhaohq96/Genetic-data-rearrangement</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"233 ","pages":"Pages 19-29"},"PeriodicalIF":4.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SpaInGNN: Enhanced clustering and integration of spatial transcriptomics based on refined graph neural networks SpaInGNN：基于精炼图神经网络的空间转录组学增强聚类和整合。

IF 4.2 3区生物学

Methods Pub Date : 2024-11-13 DOI: 10.1016/j.ymeth.2024.11.006

Fangqin Zhang, Zhan Shen, Siyi Huang, Yuan Zhu, Ming Yi

{"title":"SpaInGNN: Enhanced clustering and integration of spatial transcriptomics based on refined graph neural networks","authors":"Fangqin Zhang, Zhan Shen, Siyi Huang, Yuan Zhu, Ming Yi","doi":"10.1016/j.ymeth.2024.11.006","DOIUrl":"10.1016/j.ymeth.2024.11.006","url":null,"abstract":"<div><div>Recent developments in spatial transcriptomics (ST) technology have markedly enhanced the proposed capacity to comprehensively characterize gene expression patterns within tissue microenvironments while crucially preserving spatial context. However, the identification of spatial domains at the single-cell level remains a significant challenge in elucidating biological processes. To address this, SpaInGNN was developed, a sophisticated graph neural network (GNN) framework that accurately delineates spatial domains by integrating spatial location data, histological information, and gene expression profiles into low-dimensional latent embeddings. Additionally, to fully leverage spatial coordinate data, spatial integration using graph neural network (SpaInGNN) refines the graph constructed for spatial locations by incorporating both tissue image distance and Euclidean distance, following a pre-clustering of gene expression profiles. This refined graph is then embedded using a self-supervised GNN, which minimizes self-reconfiguration loss. By applying SpaInGNN to refined graphs across multiple consecutive tissue slices, this study mitigates the impact of batch effects in data analysis. The proposed method demonstrates substantial improvements in the accuracy of spatial domain recognition, providing a more faithful representation of the tissue organization in both mouse olfactory bulb and human lateral prefrontal cortex samples.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"233 ","pages":"Pages 42-51"},"PeriodicalIF":4.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142611449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Methods最新文献