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scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data

ISCIENCE 2020年第3期23卷 100882-100882页

作者： Shao, Xin Liao, Jie Lu, Xiaoyan Xue, Rui Ai, Ni Fan, Xiaohui Zhejiang Univ Coll Pharmaceut Sci Pharmaceut Informat Inst Hangzhou 310058 Peoples R China

Recent advancements in single-cell RNA sequencing (scRNA-seq) have facilitated the classification of thousands of cells through transcriptome profiling, wherein accurate cell type identification is critical for mechanistic studies. Inmost current analysis protocols, cell type-based cluster annotation is manually performed and heavily relies on prior knowledge, resulting in poor replicability of cell type annotation. This study aimed to introduce a single-cell Cluster-based Automatic Annotation Toolkit for Cellular Heterogeneity (scCATCH, https://***/ZJUFanLab/scCATCH). Using three benchmark datasets, the feasibility of evidence-based scoring and tissue-specific cellular annotation strategies were demonstrated by high concordance among cell types, and scCATCH outperformed Seurat, a popular method for marker genes identification, and cell-based annotation methods. Furthermore, scCATCH accurately annotated 67%-100% (average, 83%) clusters in six published scRNA-seq datasets originating from various tissues. The present results show that scCATCH accurately revealed cell identities with high reproducibility, thus potentially providing insights into mechanisms underlying disease pathogenesis and progression.

关键词： automation in bioinformatics bioinformatics Cell Biology Quantitative Genetics

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EcSeg: Semantic Segmentation of Metaphase Images Containing Extrachromosomal DNA

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ISCIENCE 2019年第November 22, 2019期21卷 428-+页

作者： Rajkumar, Utkrisht Turner, Kristen Luebeck, Jens Deshpande, Viraj Chandraker, Manmohan Mischel, Paul Bafna, Vineet Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA Univ Calif San Diego Ludwig Inst Canc Res San Diego La Jolla CA USA Univ Calif San Diego Dept Pathol La Jolla CA USA Univ Calif San Diego Moores Canc Ctr La Jolla CA 92093 USA Illumina Inc 5200 Illumina Way San Diego CA USA

Oncogene amplification is one of the most common drivers of genetic events in cancer, potently promoting tumor development, growth, and progression. The recent discovery that oncogene amplification commonly occurs on extrachromosomal DNA, driving intratumoral genetic heterogeneity and high copy number owing to its non-chromosomal mechanism of inheritance, raises important questions about how the subnuclear location of amplified oncogenes mediates tumor pathogenesis. Next-generation sequencing is powerful but does not provide spatial resolution for amplified oncogenes, and new approaches are needed for accurately quantifying oncogenes located on ecDNA. Here, we introduce ecSeg, an image analysis tool that integrates conventional microscopy with deep neural networks to accurately resolve ecDNA and oncogene amplification at the single cell level.

关键词： automation in bioinformatics bioinformatics Genomics

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eDetect: A Fast Error Detection and Correction Tool for Live Cell Imaging Data Analysis

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ISCIENCE 2019年第March 29, 2019期13卷 1-+页

作者： Han, Hongqing Wu, Guoyu Li, Yuchao Zi, Zhike Max Planck Inst Mol Genet Otto Warburg Lab D-14195 Berlin Germany

Live cell imaging has been widely used to generate data for quantitative understanding of cellular dynamics. Various applications have been developed to perform automated imaging data analysis, which often requires tedious manual correction. It remains a challenge to develop an efficient curation method that can analyze massive imaging datasets with high accuracy. Here, we present eDetect, a fast error detection and correction tool that provides a powerful and convenient solution for the curation of live cell imaging analysis results. In eDetect, we propose a gating strategy to distinguish correct and incorrect image analysis results by visualizing image features based on principal component analysis. We demonstrate that this approach can substantially accelerate the data correction process and improve the accuracy of imaging data analysis. eDetect is well documented and designed to be user friendly for non-expert users.

关键词： automation in bioinformatics bioinformatics Cell Biology

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Predicting Human Olfactory Perception from Activities of Odorant Receptors

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ISCIENCE 2020年第8期23卷 101361页

作者： Kowalewski, Joel Ray, Anandasankar Univ Calif Riverside Interdept Neurosci Program Riverside CA 92521 USA Univ Calif Riverside Dept Mol Cell & Syst Biol 3401 Watkins Dr Riverside CA 92521 USA

Odor perception in humans is initiated by activation of odorant receptors (ORs) in the nose. However, the ORs linked to specific olfactory percepts are unknown, unlike in vision or taste where receptors are linked to perception of different colors and tastes. The large family of ORs (similar to 400) and multiple receptors activated by an odorant present serious challenges. Here, we first use machine learning to screen similar to 0.5 million compounds for new ligands and identify enriched structural motifs for ligands of 34 human ORs. We next demonstrate that the activity of ORs successfully predicts many of the 146 different perceptual qualities of chem-icals. Although chemical features have been used to model odor percepts, we show that biologically relevant OR activity is often superior. Interestingly, each odor percept could be predicted with very few ORs, implying they contribute more to each olfactory percept. A similar model is observed in Drosophila where comprehensive OR-neuron data are available.

关键词： Sensory Neuroscience bioinformatics automation in bioinformatics Computational bioinformatics

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Generic medical concept embedding and time decay for diverse patient outcome prediction tasks

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ISCIENCE 2022年第9期25卷 104880页

作者： Li, Yupeng Dong, Wei Ru, Boshu Black, Adam Zhang, Xinyuan Guan, Yuanfang Merck & Co Inc Rahway NJ 07065 USA Arbor Algorithms Inc Ann Arbor MI 48104 USA Odysseus Data Serv Cambridge MA 02142 USA Univ Michigan Dept Computat Med & Bioinformat Ann Arbor MI 48109 USA RVAC Med Inc Waltham MA 02451 USA

Many fields, including Natural Language Processing (NLP), have recently witnessed the benefit of pre-training with large generic datasets to improve the accuracy of prediltion tasks. However, there exist key differences between the Ion-gitudinal healthcare data (e.g., claims) and NLP tasks, which make the direct app!:cation of NLP pre-training methods to healthcare data inappropriate. In this article, we developed a pre-training scheme for longitudinal healthcare data that leverages the pairing of medical history and a future event. We then conducted systematic evaluations of various methods on ten patient-level prediction tasks encompassing adverse events, misdiagnosis, disease risks, and readmission. In addition to substantially reducing model size, our results show that a universal medical concept embedding pretrained with generic big data as well as carefully designed time decay modeling improves the accuracy of different downstream prediction tasks.

关键词： Health sciences bioinformatics Medical informatics automation in bioinformatics

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Deep learning predicts chromosomal instability from histopathology images

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ISCIENCE 2021年第5期24卷 102394页

作者： Xu, Zhuoran Verma, Akanksha Naveed, Uska Bakhoum, Samuel F. Khosravi, Pegah Elemento, Olivier Weill Cornell Med Caryl & Israel Englander Inst Precis Med New York NY 10065 USA Weill Cornell Med Pathol & Lab Med New York NY 10065 USA Mem Sloan Kettering Canc Ctr Human Oncol & Pathogenesis Program New York NY 10021 USA Mem Sloan Kettering Canc Ctr Dept Radiat Oncol New York NY 10021 USA Mem Sloan Kettering Canc Ctr Dept Epidemiol & Biostat Computat Oncol New York NY 10021 USA

Chromosomal instability (CIN)is a hallmark of human cancer yet not readily testable for patients with cancer in routine clinical setting. In this study, we sought to explore whether CIN status can be predicted using ubiquitously available hematoxylin and eosin histology through a deep learning-based model. When applied to a cohort of 1,010 patients with breast cancer (Training set: n = 858, Test set: n = 152) from The Cancer Genome Atlas where 485 patients have high CIN status, our model accurately classified CIN status, achieving an area under the curve of 0.822 with 81.2% sensitivity and 68.7% specificity in the test set. Patch-level predictions of CIN status suggested intra-tumor heterogeneity within slides. Moreover, presence of patches with high predicted CIN score within an entire slide was more predictive of clinical outcome than the average CIN score of the slide, thus underscoring the clinical importance of intra-tumor heterogeneity.

关键词： Cell Biology automation in bioinformatics Neural Networks Cancer Systems Biology

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Generative pretraining from large-scale transcriptomes for single-cell deciphering

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ISCIENCE 2023年第5期26卷 106536页

作者： Shen, Hongru Liu, Jilei Hu, Jiani Shen, Xilin Zhang, Chao Wu, Dan Feng, Mengyao Yang, Meng Li, Yang Yang, Yichen Wang, Wei Zhang, Qiang Yang, Jilong Chen, Kexin Li, Xiangchun Tianjin Med Univ Tianjin Med Univ Canc Inst & Hosp Tianjin Canc Inst Tianjins Clin Res Ctr CancNatl Clin Res Ctr Canc Tianjin Peoples R China Tianjin Med Univ Tianjin Med Univ Canc Inst & Hosp Dept Bone & Soft Tissue Tumor Tianjins Clin Res Ctr CancNatl Clin Res Ctr Canc Tianjin Peoples R China Tianjin Med Univ Tianjin Med Univ Canc Inst & Hosp Dept Epidemiol & Biostat Natl Clin Res Ctr CancKey Lab Mol Canc Epidemiol Tianjin Peoples R China Tianjin Med Univ Tianjin Med Univ Canc Inst & Hosp Tianjins Clin Res Ctr Canc Dept Maxillofacial & Otorhinolaryngol OncolNatl C Tianjin Peoples R China

Exponential accumulation of single-cell transcriptomes poses great challenge for efficient assimilation. Here, we present an approach entitled generative pretrain-ing from transcriptomes (tGPT) for learning feature representation of transcrip-tomes. tGPT is conceptually simple in that it autoregressive models the ranking of a gene in the context of its preceding neighbors. We developed tGPT with 22.3 million single-cell transcriptomes and used four single-cell datasets to eval-utate its performance on single-cell analysis tasks. In addition, we examine its ap-plications on bulk tissues. The single-cell clusters and cell lineage trajectories derived from tGPT are highly aligned with known cell labels and states. The feature patterns of tumor bulk tissues learned by tGPT are associated with a wide range of genomic alteration events, prognosis, and treatment outcome of immunotherapy. tGPT represents a new analytical paradigm for integrating and deciphering massive amounts of transcriptome data and it will facilitate the inter-pretation and clinical translation of single-cell transcriptomes.

关键词： automation in bioinformatics Data processing in systems biology Transcriptomics

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ADEPT: Autoencoder with differentially expressed genes and imputation for robust spatial transcriptomics clustering

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ISCIENCE 2023年第6期26卷 106792页

作者： Hu, Yunfei Zhao, Yuying Schunk, Curtis T. Ma, Yingxiang Derr, Tyler Zhou, Xin Maizie Vanderbilt Univ Dept Comp Sci Nashville TN 37235 USA Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA Vanderbilt Univ Data Sci Inst Nashville TN 37235 USA

Advancements in spatial transcriptomics (ST) have enabled an in-depth understanding of complex tissues by quantifying gene expression at spatially localized spots. Several notable clustering methods have been introduced to utilize both spatial and transcriptional information in the analysis of ST datasets. However, data quality across different ST sequencing techniques and types of datasets influence the performance of different methods and benchmarks. To harness spatial context and transcriptional profile in ST data, we developed a graph-based, multi-stage framework for robust clustering, called ADEPT. To control and stabilize data quality, ADEPT relies on a graph autoencoder backbone and performs an iterative clustering on imputed, differentially expressed genes-based matrices to minimize the variance of clustering results. ADEPT outperformed other popular methods on ST data generated by different platforms across analyses such as spatial domain identification, visualization, spatial trajectory inference, and data denoising.

关键词： automation in bioinformatics Data processing in systems biology Transcriptomics

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New open-source software for subcellular segmentation and analysis of spatiotemporal fluorescence signals using deep learning

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ISCIENCE 2022年第5期25卷 104277页

作者： Kamran, Sharif Amit Hossain, Khondker Fariha Moghnieh, Hussein Riar, Sarah Bartlett, Allison Tavakkoli, Alireza Sanders, Kenton M. Baker, Salah A. Univ Nevada Reno Sch Med Dept Physiol & Cell Biol Anderson Med Bldg MS352 Reno NV 89557 USA Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0E9 Canada

Cellular imaging instrumentation advancements as well as readily available optogenetic and fluorescence sensors have yielded a profound need for fast, accurate, and standardized analysis. Deep-learning architectures have revolutionized the field of biomedical image analysis and have achieved state-of-the-art accuracy. Despite these advancements, deep learning architectures for the segmentation of subcellular fluorescence signals is lacking. Cellular dynamic fluorescence signals can be plotted and visualized using spatiotemporal maps (STMaps), and currently their segmentation and quantification are hindered by slow workflow speed and lack of accuracy, especially for large datasets. In this study, we provide a software tool that utilizes a deep-learningmethodology to fundamentally overcome signal segmentation challenges. The software framework demonstrates highly optimized and accurate calcium signal segmentation and provides a fast analysis pipeline that can accommodate different patterns of signals across multiple cell types. The software allows seamless data accessibility, quantification, and graphical visualization and enables large dataset analysis throughput.

关键词： Cell biology bioinformatics automation in bioinformatics Artificial intelligence

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Dimensionality reduction by UMAP for visualizing and aiding in classification of imaging flow cytometry data

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ISCIENCE 2022年第10期25卷 105142页

作者： Stolarek, Ireneusz Samelak-Czajka, Anna Figlerowicz, Marek Jackowiak, Paulina Polish Acad Sci Inst Bioorgan Chem Noskowskiego 12-14 PL-61704 Poznan Poland

Recent advances in imaging flow cytometry (IFC) have revolutionized high-throughput multiparameter analyses at single-cell resolution. Although enabling the discovery of population heterogeneities and the detection of rare events, IFC generates hyperdimensional datasets that demand innovative analytical approaches. Current methods work in a supervised manner, utilize only limited information content, or require large annotated reference datasets. Dimensionality reduction algorithms, including uniform manifold approximation and projection (UMAP), have been successfully applied to analyze the large number of parameters generated in various high-throughput techniques. Here, we apply a workflow incorporating UMAP to analyze different IFC datasets. We demonstrate that it out-competes other popular dimensionality reduction methods in speed and accuracy. Moreover, it enables fast visualization, clustering, and tagging of unannotated objects in large-scale experiments. We anticipate that our workflow will be a robust method to address complex IFC datasets, either alone or as an upstream addition to the deep learning approaches.

关键词： automation in bioinformatics bioinformatics Cell biology

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