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Article
Biochemical Research Methods
Malte D. Luecken et al.
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
Biochemical Research Methods
Mengqi Zhang et al.
Summary: Single-cell sequencing has revolutionized the identification of differentially expressed genes (DEGs) by providing high resolution for heterogeneous cell tissues. However, current analysis mainly focuses on comparing different cell types within the same individual. This study proposes a nonparametric method called barycenter single-cell differential expression (BSDE) to identify DEGs in case-control studies. The method overcomes the limitations of parametric approaches and accurately detects differential expressions. It is demonstrated through simulations and real data analysis that BSDE can effectively identify cell type-specific DEGs. The availability of the R package and datasets further facilitate its application in research.
Article
Biochemistry & Molecular Biology
David J. Ahern et al.
Summary: The study presents a comprehensive blood atlas for patients with varying severity of COVID-19, compared to influenza, sepsis patients, and healthy volunteers. The results identify immune signatures and correlates of host response, including cells, inflammatory mediators, immune repertoire, and metabolic and coagulation features. The study also reveals that persistent immune activation is a specific feature of COVID-19. Plasma proteomic analysis enables sub-phenotyping and prediction of severity and outcome. Integrative analyses show feature groupings linked with severity and specificity compared to influenza and sepsis.
Article
Biochemical Research Methods
Yue Cao et al.
Summary: This study presents a method called scFeatures that creates interpretable cellular and molecular representations of single-cell and spatial data at the sample level. Summarizing a broad collection of features at the sample level is important for understanding disease mechanisms in different experimental studies and accurately classifying disease status of individuals.
Correction
Biochemistry & Molecular Biology
Xianwen Ren et al.
Article
Multidisciplinary Sciences
Marco Seeland et al.
Summary: This study proposes a classification scheme that relies on fusing visual information captured from different perspectives, using convolutional neural networks to extract and encode visual features, and investigating three fusion strategies. The experiments show that integrating information fusion into the network has greater benefits compared to post-processing of classification scores, and already trained networks can be easily extended by the best fusion strategy to outperform other approaches.
Article
Multidisciplinary Sciences
Suoqin Jin et al.
Summary: By constructing an interaction database and developing the tool CellChat, the authors quantitatively inferred and analyzed cell-cell communication networks, revealing important signaling inputs and outputs between cells, and achieved classification of conserved and context-specific pathways.
NATURE COMMUNICATIONS
(2021)
Article
Biochemical Research Methods
Andrew L. Thurman et al.
Summary: The study shows that using a naive approach to differential expression analysis can inflate the false discovery rate when the gene expression distribution varies between subjects. Comparison of multiple differential expression testing methods on scRNA-seq datasets from human samples and animal models suggests that an approach based on pseudobulk counts has better FDR control.
Article
Biochemistry & Molecular Biology
Emily Stephenson et al.
Summary: Transcriptomic and proteomic profiling of blood samples from individuals with COVID-19 reveals immune cell and hematopoietic progenitor cell alterations that are differentially associated with disease severity.
Article
Multidisciplinary Sciences
Jun Zhao et al.
Summary: Comprehensive and accurate comparisons of transcriptomic distributions of cells from samples taken from two different biological states, such as healthy versus diseased individuals, are a challenging task in single-cell RNA sequencing analysis. Current methods for detecting differentially abundant subpopulations between samples rely on initial clustering of all cells, which may not capture important differences between the two states. DA-seq is a targeted approach that identifies differentially abundant subpopulations not limited to clusters, providing improved ability to detect differences between distinct phenotypes in various biological contexts.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Immunology
Aaron J. Wilk et al.
Summary: The study revealed immune system dysfunctions in COVID-19 patients at different disease severity stages, particularly in severe and fatal cases. It also found a lack of pro-inflammatory cytokine production in severe cases, potentially due to chromatin accessibility changes as a mechanism.
JOURNAL OF EXPERIMENTAL MEDICINE
(2021)
Review
Oncology
Guangshun Sun et al.
Summary: Cancer poses a significant threat to human health, necessitating new technologies for further understanding its mechanisms and developing improved detection and treatment strategies. Single-cell RNA sequencing has emerged as an effective method to dissect human tumor tissue, offering new perspectives for research and avenues for explaining cancer biology.
MOLECULAR THERAPY-ONCOLYTICS
(2021)
Review
Biotechnology & Applied Microbiology
Pengyi Yang et al.
Summary: Recent advances in single-cell biotechnologies have led to high-dimensional datasets with increased complexity, making feature selection an essential technique for single-cell data analysis. This review revisits and summarizes feature selection techniques, their applications, challenges, and future directions in the single-cell era. The scalability and general recommendations for each type of feature selection method are also considered for stimulating future research and applications in single-cell analysis.
Review
Biotechnology & Applied Microbiology
Luke Zappia et al.
Summary: In recent years, there has been a revolution in single-cell RNA-sequencing technologies, datasets, and analysis methods. The number of tools for analyzing scRNA-seq data has surpassed 1000, showcasing the evolution of the field towards integrating multiple samples and utilizing reference datasets. Open science practices are found to reward developers with increased recognition and help accelerate progress in the field.
Review
Genetics & Heredity
Erick Armingol et al.
Summary: Intercellular interactions and communication can be inferred from RNA sequencing data, such as ligand-receptor pairs, which has led to new insights and methodologies for studying cell-cell interactions.
NATURE REVIEWS GENETICS
(2021)
Article
Multidisciplinary Sciences
Terrence J. Sejnowski
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2020)
Article
Biochemistry & Molecular Biology
Jonas Schulte-Schrepping et al.
Article
Multidisciplinary Sciences
Elizabeth J. Williamson et al.
Article
Mathematical & Computational Biology
Valentine Svensson et al.
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
(2020)
Review
Computer Science, Artificial Intelligence
Yue Cao et al.
NATURE MACHINE INTELLIGENCE
(2020)
Article
Biochemical Research Methods
Sara Aibar et al.
Review
Oncology
Sibo Zhu et al.
Review
Multidisciplinary Sciences
Yann LeCun et al.
Article
Biochemistry & Molecular Biology
Matthew E. Ritchie et al.
NUCLEIC ACIDS RESEARCH
(2015)
Article
Biochemistry & Molecular Biology
Arthur Liberzon et al.
Article
Biotechnology & Applied Microbiology
Davide Risso et al.
NATURE BIOTECHNOLOGY
(2014)
Review
Biochemical Research Methods
Pengyi Yang et al.
CURRENT BIOINFORMATICS
(2010)
Article
Computer Science, Interdisciplinary Applications
Max Kuhn
JOURNAL OF STATISTICAL SOFTWARE
(2008)
