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
Hai C. T. Nguyen, Bukyung Baik, Sora Yoon, Taesung Park, Dougu Nam
Summary: This study evaluates the integration of single-cell RNA sequencing data and suggests high-performance methods under different conditions. Batch effects, sequencing depth, and data sparsity significantly impact the analysis performance. The use of batch-corrected data rarely improves the analysis for sparse data, while batch covariate modeling improves the analysis for substantial batch effects. Several high-performance methods are proposed based on simulation and real data analyses.
NATURE COMMUNICATIONS
(2023)
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
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
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.
Article
Biochemistry & Molecular Biology
Mengwei Li, Xiaomeng Zhang, Kok Siong Ang, Jingjing Ling, Raman Sethi, Nicole Yee Shin Lee, Florent Ginhoux, Jinmiao Chen
Summary: DISCO is an integrated database of single-cell omics data, offering an integrated cell atlas and harmonized metadata that users can utilize for comprehensive single-cell data analysis and exploration.
NUCLEIC ACIDS RESEARCH
(2022)
Article
Multidisciplinary Sciences
Lieke Michielsen, Marcel J. T. Reinders, Ahmed Mahfouz
Summary: scHPL is a hierarchical progressive learning method that can learn cellular hierarchies from multiple datasets while preserving the original annotations.
NATURE COMMUNICATIONS
(2021)
Article
Biochemistry & Molecular Biology
Tian Tian, Cheng Zhong, Xiang Lin, Zhi Wei, Hakon Hakonarson
Summary: In this study, a model-based deep learning approach called scDHMap is proposed to visualize the complex hierarchical structures of single-cell RNA-seq data in low-dimensional hyperbolic space. The evaluations show that scDHMap outperforms existing dimensionality-reduction methods in various analytical tasks for scRNA-seq data. Additionally, scDHMap can be extended to visualize single-cell ATAC-seq data.
Article
Biochemical Research Methods
Dakota Y. Hawkins, Daniel T. Zuch, James Huth, Nahomie Rodriguez-Sastre, Kelley R. McCutcheon, Abigail Glick, Alexandra T. Lion, Christopher F. Thomas, Abigail E. Descoteaux, William Evan Johnson, Cynthia A. Bradham
Summary: In this study, a novel unsupervised algorithm called ICAT is proposed, which can accurately identify and resolve cell states in single-cell RNA sequencing experiments, especially in perturbation experiments, where cell states need to be matched and population substructure needs to be removed. Through validation using simulated and real datasets, it is shown that ICAT outperforms current integration workflows and is robust to various conditions. Empirical validation in a developmental model demonstrates that only ICAT can identify perturbation-unique cellular responses.
Article
Biochemical Research Methods
Qianqian Zhang, Xing Xu, Li Lin, Jian Yang, Xing Na, Xin Chen, Lingling Wu, Jia Song, Chaoyong Yang
Summary: Cilo-seq is a high-performance platform for single-cell RNA sequencing library construction. It utilizes digital microfluidics in a single device, enabling convenient single-cell isolation, efficient nucleic acid amplification, low-loss nucleic acid purification, and high-quality library preparation.
Article
Cell Biology
Yasuko Akiyama-Oda, Takanori Akaiwa, Hiroki Oda
Summary: Patterning along an axis of polarity is crucial in the development of multicellular animal embryos. This study used single-cell and single-nucleus RNA sequencing to analyze the early spider embryo and revealed the impact of embryo polarity on cell states and pattern formation processes. The results demonstrated the important role of Hedgehog signaling in embryo polarity and provided valuable data resources for further research.
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
(2022)
Article
Biotechnology & Applied Microbiology
Will Macnair, Mark Robinson
Summary: SampleQC is a quality control method for single-cell RNA sequencing (scRNA-seq) processing pipelines, which reduces biased exclusion of rare cell types and improves sensitivity. It robustly fits a Gaussian mixture model across multiple samples and demonstrates less susceptibility to exclusion of rarer cell types through simulations and a real dataset. SampleQC is a general method implemented in R and can be applied to other data types.
Article
Biochemical Research Methods
Jordan W. Squair, Michael A. Skinnider, Matthieu Gautier, Leonard J. Foster, Gregoire Courtine
Summary: This study introduces a method called Augur for prioritizing the cell types most responsive to an experimental perturbation in complex tissue. By analyzing single-cell RNA-seq data, a list of cell types ranked based on separability following perturbation can be obtained. The study also demonstrates the application of this method in various workflows, including experimental designs, prioritization, and single-cell transcriptome imaging data.
Article
Biochemical Research Methods
Alexander Gerniers, Orian Bricard, Pierre Dupont
Summary: This study presents a data mining method, MicroCellClust, to identify small subpopulations of cells with highly specific expression profiles. Through controlled experiments, it is shown to achieve a high F-1 score in identifying rare subpopulations of human T cells, specific CD4 T cells from breast cancer samples, and a subpopulation related to a specific stage in the cell cycle. Additionally, three rare subpopulations in mouse embryonic stem cells are successfully identified with MicroCellClust, demonstrating its effectiveness in identifying small subsets of cells with highly specific expression profiles.
Article
Biochemical Research Methods
Siqi Chen, Xuhua Yan, Ruiqing Zheng, Min Li
Summary: Single-cell RNA sequencing technology (scRNA-seq) has the drawback of large sparsity, which leads to dropout events and affects downstream analyses. To address this, we propose Bubble, which identifies and imputes dropout events using expression rate and coefficient of variation, and leverages bulk RNA-seq data as a constraint. Bubble improves recovery of missing values, correlations, and reduces false positive signals. It enhances differential expression analysis, clustering, visualization, and aids cellular trajectory inference. Moreover, Bubble provides fast and scalable imputation with minimal memory usage.
BRIEFINGS IN BIOINFORMATICS
(2023)