Article
Multidisciplinary Sciences
Kai Battenberg, S. Thomas Kelly, Radu Abu Ras, Nicola A. Hetherington, Makoto Hayashi, Aki Minoda
Summary: Single-cell RNA-sequencing analysis has gained popularity, and UniverSC is a universal tool for processing single-cell RNA-seq data on any platform. It provides a command-line tool, docker image, and containerized graphical application for consistent and comprehensive integration, comparison, and evaluation of data from various platforms. Additionally, a cross-platform application with a graphical user interface is available to address the bottleneck of data processing for researchers without bioinformatics expertise.
NATURE COMMUNICATIONS
(2022)
Article
Biochemical Research Methods
Yuchen Yang, Gang Li, Yifang Xie, Li Wang, Taylor M. Lagler, Yingxi Yang, Jiandong Liu, Li Qian, Yun Li
Summary: Batch effect correction is crucial in integrative analysis of multiple single-cell RNA-sequencing data. The iterative supervised MNN (iSMNN) refinement approach presented in this study shows advantages in mixing cells of the same type across batches and facilitating the identification of differentially expressed genes. iSMNN proves to be a valuable method for integrating multiple scRNA-seq datasets for biological and medical studies at single-cell level.
BRIEFINGS IN BIOINFORMATICS
(2021)
Review
Biochemical Research Methods
Zoe A. Clarke, Tallulah S. Andrews, Jawairia Atif, Delaram Pouyabahar, Brendan T. Innes, Sonya A. MacParland, Gary D. Bader
Summary: This tutorial provides guidelines for interpreting single-cell transcriptomic maps to identify cell types, states and other biologically relevant patterns, with a recommended three-step workflow including automatic cell annotation, manual cell annotation, and verification. It also discusses frequently encountered challenges, strategies to address them, as well as guiding principles and specific recommendations for software tools and resources.
Article
Biotechnology & Applied Microbiology
Tracy M. Yamawaki, Daniel R. Lu, Daniel C. Ellwanger, Dev Bhatt, Paolo Manzanillo, Vanessa Arias, Hong Zhou, Oh Kyu Yoon, Oliver Homann, Songli Wang, Chi-Ming Li
Summary: Our systematic benchmarking of seven high-throughput single-cell RNA-seq methods with 21 libraries under identical conditions revealed that the 10x Genomics 5 'v1 and 3' v3 methods exhibit higher mRNA detection sensitivity and fewer dropout events. This facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures, providing useful metrics for selecting a high-throughput single-cell RNA-seq method for profiling complex immune-cell heterogeneity.
Article
Multidisciplinary Sciences
Richard C. Tyser, Elmir Mahammadov, Shota Nakanoh, Ludovic Vallier, Antonio Scialdone, Shankar Srinivas
Summary: The single-cell transcriptional profile of a human embryo between 16 and 19 days after fertilization shows similarities and differences in gastrulation compared to mouse and non-human primate models. This study provides new insights into human development and offers valuable information for directed differentiation of human cells in vitro.
Article
Multidisciplinary Sciences
Bang Tran, Duc Tran, Hung Nguyen, Seungil Ro, Tin Nguyen
Summary: Unsupervised clustering of scRNA-seq data is crucial for identifying cell types, but the challenges posed by large numbers of cells, high-dimensional data, and high dropout rates are significant. We introduce a new method called scCAN that accurately segregates different cell types in large and sparse scRNA-seq data, outperforming other state-of-the-art methods in terms of accuracy and scalability.
SCIENTIFIC REPORTS
(2022)
Article
Biochemistry & Molecular Biology
Xin Shao, Haihong Yang, Xiang Zhuang, Jie Liao, Penghui Yang, Junyun Cheng, Xiaoyan Lu, Huajun Chen, Xiaohui Fan
Summary: scDeepSort is a pre-trained tool for cell-type annotation in single-cell transcriptomics using deep learning and a weighted graph neural network. It demonstrates high performance and robustness across multiple datasets, achieving an accuracy of 83.79%.
NUCLEIC ACIDS RESEARCH
(2021)
Article
Biochemical Research Methods
Xinyi Xu, Xiangjie Li
Summary: Dimension reduction is crucial in single-cell RNA sequencing. This study proposes a novel method called SPDR, which can simultaneously identify cell types, preserve data structure, and handle batch effects. Comprehensive evaluations demonstrate that SPDR outperforms other existing methods in removing batch effects, preserving biological variation, facilitating visualization, and improving clustering accuracy.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Elvis Han Cui, Dongyuan Song, Weng Kee Wong, Jingyi Jessica Li
Summary: This article proposes a single-cell generalized trend model (scGTM) for capturing gene expression trends, helping to interpret biological processes along cell pseudotime. The model has excellent interpretability and flexibility, making it useful for analyzing gene expression data.
Article
Biochemical Research Methods
Malte D. Luecken, M. Buettner, K. Chaichoompu, A. Danese, M. Interlandi, M. F. Mueller, D. C. Strobl, L. Zappia, M. Dugas, M. Colome-Tatche, Fabian J. Theis
Summary: This study benchmarked 68 method and preprocessing combinations on 85 batches of gene expression data, highlighting the importance of highly variable gene selection in improving method performance. When dealing with complex integration tasks, scANVI, Scanorama, scVI, and scGen consistently performed well, while the performance of single-cell ATAC-sequencing integration was strongly influenced by the choice of feature space.
Article
Genetics & Heredity
Bin Zou, Tongda Zhang, Ruilong Zhou, Xiaosen Jiang, Huanming Yang, Xin Jin, Yong Bai
Summary: DeepMNN is a novel deep learning-based method for batch effect correction in scRNA-seq data, which has shown better or comparable performance compared to other state-of-the-art batch correction methods in various scenarios.
FRONTIERS IN GENETICS
(2021)
Article
Multidisciplinary Sciences
Kiya W. Govek, Patrick Nicodemus, Yuxuan Lin, Jake Crawford, Artur B. Saturnino, Hannah Cui, Kristi Zoga, Michael P. Hart, Pablo G. Camara
Summary: The authors present a computational approach for cell morphometry and multi-modal analysis, which is based on concepts from metric geometry. They demonstrate the utility of this approach in integrating cell morphology data into single-cell omics analyses. The approach involves building cell morphology latent spaces using metric geometry, which facilitate the integration of single-cell morphological data and inference of relations with other data.
NATURE COMMUNICATIONS
(2023)
Article
Genetics & Heredity
Ruizhi Xiang, Wencan Wang, Lei Yang, Shiyuan Wang, Chaohan Xu, Xiaowen Chen
Summary: The study compared the performance of different dimensionality reduction methods in scRNA-seq data analysis. t-SNE showed the best accuracy and computing cost, while UMAP demonstrated high stability and preserved the cohesion and separation of cell populations.
FRONTIERS IN GENETICS
(2021)
Article
Biochemical Research Methods
Stephanie C. Hicks, Ruoxi Liu, Yuwei Ni, Elizabeth Purdom, Davide Risso
Summary: Single-cell RNA-Sequencing (scRNA-seq) is a widely used technology for measuring gene expression at the single-cell level, with analyses often detecting distinct cell subpopulations through clustering algorithms. The development of the mbkmeans package offers a solution for handling large datasets without requiring full data loading into memory. This package provides efficient computation and performance comparisons with other clustering methods.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Biochemistry & Molecular Biology
Jacob C. Kimmel, David R. Kelley
Summary: scNym is a semi-supervised adversarial neural network that can transfer cell identity annotations between different experiments by learning rich representations of cell identities from both labeled and unlabeled datasets. It shows superior performance in transferring annotations across experiments and can synthesize information from multiple datasets to improve accuracy. Additionally, scNym models are well calibrated, interpretable, and can be enhanced with saliency methods.