Cell type identification from single-cell transcriptomic data is a common goal of single-cell RNA sequencing (scRNAseq) data analysis. Neural networks have been employed to identify cell types from scRNAseq data with high performance. However, it requires a large mount of individual cells with accurate and unbiased annotated types to build the identification models. Unfortunately, labeling the scRNAseq data is cumbersome and time-consuming as it involves manual inspection of marker genes. To overcome this challenge, we propose a semi-supervised learning model to use unlabeled scRNAseq cells and limited amount of labeled scRNAseq cells to implement cell identification. Firstly, we transform the scRNAseq cells to "gene sentences", which is inspired by similarities between natural language system and gene system. Then genes in these sentences are represented as gene embeddings to reduce data sparsity. With these embeddings, we implement a semi-supervised learning model based on recurrent convolutional neural networks (RCNN), which includes a shared network, a supervised network and an unsupervised network. The proposed model is evaluated on macosko2015, a large scale single-cell transcriptomic dataset with ground truth of individual cell types. It is observed that the proposed model is able to achieve encouraging performance by learning on very limited amount of labeled scRNAseq cells together with a large number of unlabeled scRNAseq cells.
Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. RNA-seq based transcriptome sequencing has been extensively used for identification of lncRNAs. However, accurate identification of lncRNAs in RNA-seq datasets is crucial for exploring their characteristic functions in the genome as most coding potential computation (CPC) tools fail to accurately identify them in transcriptomic data. Well-known CPC tools such as CPC2, lncScore, CPAT are primarily designed for prediction of lncRNAs based on the GENCODE, NONCODE and CANTAT…
Masked Graph Autoencoders with Contrastive Augmentation for Spatially Resolved Transcriptomics Data
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；College of Nursing Health Sciences, Yunnan Open University（云南开放大学护理与健康科学学院）
The cell cycle is one of the most fundamental biological processes important for understanding normal physiology and various pathologies such as cancer. Single cell RNA sequencing technologies give an opportunity to analyse the cell cycle transcriptome dynamics in an unprecedented range of conditions (cell types and perturbations), with thousands of publicly available datasets. Here we review the main computational tasks in such analysis: 1) identification of cell cycle phases, 2) pseudotime inference, 3) identification and profiling of cell cycle-related genes, 4) removing cell cycle effect, …
In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control,…
Background: Identification of biological specimens is a major requirement for a range of applications. Reference-free methods analyse unprocessed sequencing data without relying on prior knowledge, but generally do not scale to arbitrarily large genomes and arbitrarily large phylogenetic distances. Results: We present Cnidaria, a practical tool for clustering genomic and transcriptomic data with no limitation on genome size or phylogenetic distances. We successfully simultaneously clustered 169 genomic and transcriptomic datasets from 4 kingdoms, achieving 100% identification accuracy at supra…
Spatial transcriptomics has the potential to transform our understanding of RNA expression in tissues. Classical array-based technologies produce multiple-cell-scale measurements requiring deconvolution to recover single cell information. However, rapid advances in subcellular measurement of RNA expression at whole-transcriptome depth necessitate a fundamentally different approach. To integrate single-cell RNA-seq data with nanoscale spatial transcriptomics, we present a topological method for automatic cell type identification (TopACT). Unlike popular decomposition approaches to multicellular…
Noncoding RNAs are integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional…
Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation
BioMedical Machine Learning Laboratory, School of Biomedical Engineering, UNSW Sydney（新南威尔士大学生物医学工程学院生物医学机器学习实验室）；School of Biotechnology and Biomolecular Sciences, UNSW Sydney（新南威尔士大学生物技术与分子科学学院）；School of Computing, Macquarie University（麦考瑞大学计算机学院）；Strands（23Strands）；Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institute of Advanced Technology（深圳先进技术研究院高性能数据挖掘重点实验室）；School of Biomedical Engineering, UNSW Sydney（新南威尔士大学生物医学院）；Center of Excellence in Precision Medicine and Digital Health, Faculty of Dentistry, Chulalongkorn University（朱拉隆功大学牙科学院精准医疗与数字健康卓越中心）；UNSW AI Institute, University of New South Wales（新南威尔士大学人工智能研究所）；Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院上海免疫学研究所）
UnPaSt: unsupervised patient stratification by biclustering of omics data
University of Hamburg（汉堡大学）；Technical University of Munich（慕尼黑工业大学）；Netherlands Cancer Institute（荷兰癌症研究所）；ACMetric；University of Campinas（坎皮纳斯大学）；University of British Columbia（不列颠哥伦比亚大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大大学埃尔兰根-纽伦堡）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Altius Institute for Biomedical Sciences（Altius生物医学科学研究所）；University of Liège（列日大学）；Aarhus University（奥胡斯大学）；Aarhus University Hospital（奥胡斯大学医院）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Simon Fraser University（西蒙弗雷泽大学）；Vancouver Prostate Centre（温哥华前列腺中心）；Leibniz Institute of Virology（莱布尼茨病毒学研究所）；University of Southern Denmark（南丹麦大学）
A Deep Learning Pipeline for Epilepsy Genomic Analysis Using GPT-2 XL and NVIDIA H100
Connetquot Central School District of Long Island, NY 11716 USA（纽约长岛康奈克特中央学区）；Florida Atlantic University（佛罗里达大学）；Kansas State University（堪萨斯州立大学）；Brookhaven National Laboratory（布鲁赫斯国家实验室）
There is increased interest in the identification and analysis of gene fusions and chimeric RNA transcripts. While most recent efforts focused on the analysis of genomic and transcriptomic data, identi-fication of novel peptides corresponding to such events in mass spectrometry-based proteomic datasets would provide complemen-tary, protein-level evidence. The process of identifying fusion pro-teins from mass spectrometry data is inherently difficult because such events are rare. It is also complicated due to large amount of spectra collected and the explosion in the number of candidate peptide…
Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer
Washington University in St. Louis（圣路易斯华盛顿大学）；The Hong Kong Polytechnic University（香港理工大学）；Institute for Informatics (I2)（信息学研究所（I2））；Peking University（北京大学）；Washington University School of Medicine（华盛顿大学医学院）
A molecular and cellular understanding of how SARS-CoV-2 variably infects and causes severe COVID-19 remains a bottleneck in developing interventions to end the pandemic. We sought to use deep learning to study the biology of SARS-CoV-2 infection and COVID-19 severity by identifying transcriptomic patterns and cell types associated with SARS-CoV-2 infection and COVID-19 severity. To do this, we developed a new approach to generating self-supervised edge features. We propose a model that builds on Graph Attention Networks (GAT), creates edge features using self-supervised learning, and ingests …
Gene expression represents a fundamental interface between genes and environment in the development and ongoing plasticity of the human organism. Individual differences in gene expression are likely to underpin much of human diversity, including psychiatric illness. Gene expression shows a distinct regulatory pattern in different tissues. Therefore, brain tissue analysis provides insights into brain disorder mechanisms. Furthermore, mechanistic understanding of gene regulatory pattern can be provided through studying the underlying relationships as a complex network. Identification of brain sp…
Institut Charles Delaunay, Universite de Technologie de Troyes（特鲁瓦理工大学夏尔·德洛纳研究所）；Biomedicine Research Institute of Buenos Aires - Max Planck Partner Institute（布宜诺斯艾利斯生物医学研究所-马克斯·普朗克伙伴研究所）；Universidad Tecnologica Nacional Buenos Aires（布宜诺斯艾利斯国立理工大学）
The transcriptomics of cancer tumors are characterized with tens of thousands of gene expression features. Patient prognosis or tumor stage can be assessed by machine learning techniques like supervised classification tasks given a gene expression profile. Feature selection is a useful approach to select the key genes which helps to classify tumors. In this work we propose a feature selection method based on Multiple Kernel Learning that results in a reduced subset of genes and a custom kernel that improves the classification performance when used in support vector classification. During the f…
