Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference DOI Creative Commons

Xiaoru Dong,

Jack R. Leary,

Chuanhao Yang

и другие.

Briefings in Bioinformatics, Год журнала: 2024, Номер 25(3)

Опубликована: Март 27, 2024

Single-cell RNA sequencing (scRNA-seq) experiments have become instrumental in developmental and differentiation studies, enabling the profiling of cells at a single or multiple time-points to uncover subtle variations expression profiles reflecting underlying biological processes. Benchmarking studies compared many computational methods used reconstruct cellular dynamics; however, researchers still encounter challenges their analysis due uncertainty with respect selecting most appropriate parameters. Even among universal data processing steps by trajectory inference such as feature selection dimension reduction, methods' performances are highly dataset-specific. To address these challenges, we developed Escort, novel framework for evaluating dataset's suitability quantifying properties influenced decisions. Escort evaluates combined effects choices using trajectory-specific metrics. navigates single-cell through data-driven assessments, reducing much decision burden inherent analyses. is implemented an accessible R package R/Shiny application, providing necessary tools make informed decisions during new insights into dynamic processes resolution.

Язык: Английский

Best practices for single-cell analysis across modalities DOI Open Access
Lukas Heumos, Anna C. Schaar, Christopher Lance

и другие.

Nature Reviews Genetics, Год журнала: 2023, Номер 24(8), С. 550 - 572

Опубликована: Март 31, 2023

Язык: Английский

Процитировано

513
The technological landscape and applications of single-cell multi-omics DOI Open Access
Alev Baysoy, Zhiliang Bai, Rahul Satija

и другие.

Nature Reviews Molecular Cell Biology, Год журнала: 2023, Номер 24(10), С. 695 - 713

Опубликована: Июнь 6, 2023

Язык: Английский

Процитировано

446
Microfluidics-free single-cell genomics with templated emulsification DOI Creative Commons
Iain C. Clark, Kristina M. Fontanez,

Robert H. Meltzer

и другие.

Nature Biotechnology, Год журнала: 2023, Номер 41(11), С. 1557 - 1566

Опубликована: Март 6, 2023

Current single-cell RNA-sequencing approaches have limitations that stem from the microfluidic devices or fluid handling steps required for sample processing. We develop a method does not require specialized devices, expertise hardware. Our approach is based on particle-templated emulsification, which allows encapsulation and barcoding of cDNA in uniform droplet emulsions with only vortexer. Particle-templated instant partition sequencing (PIP-seq) accommodates wide range emulsification formats, including microwell plates large-volume conical tubes, enabling thousands samples millions cells to be processed minutes. demonstrate PIP-seq produces high-purity transcriptomes mouse-human mixing studies, compatible multiomics measurements can accurately characterize cell types human breast tissue compared commercial platform. Single-cell transcriptional profiling mixed phenotype acute leukemia using reveals emergence heterogeneity within chemotherapy-resistant subsets were hidden by standard immunophenotyping. simple, flexible scalable next-generation workflow extends new applications.

Язык: Английский

Процитировано

100
VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center in 2023 DOI Creative Commons
Jorge Álvarez-Jarreta, B Kirtley Amos,

Cristina Aurrecoechea

и другие.

Nucleic Acids Research, Год журнала: 2023, Номер 52(D1), С. D808 - D816

Опубликована: Ноя. 11, 2023

Abstract The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) is a Bioinformatics Center funded by the National Institutes of Health with additional funding from Wellcome Trust. VEuPathDB supports >600 organisms that comprise invertebrate vectors, eukaryotic pathogens (protists fungi) relevant free-living or non-pathogenic species hosts. Since 2004, has analyzed omics data public domain using contemporary bioinformatic workflows, including orthology predictions via OrthoMCL, integrated analysis results tools, visualizations, advanced search capabilities. unique mining platform coupled >3000 pre-analyzed sets facilitates exploration pertinent in support hypothesis driven research. Comparisons are easily made across sets, types organisms. A Galaxy workspace offers opportunity for private large-scale datasets porting to comparisons data. MapVEu tool provides spatially resolved such as vector surveillance insecticide resistance monitoring. To address growing body advances laboratory techniques, added several new types, searches features, improved environment, redesigned interface updated infrastructure accommodate these changes.

Язык: Английский

Процитировано

91
CZ CELL×GENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data DOI Creative Commons

Shibla Abdulla,

Brian D. Aevermann, Pedro Assis

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Ноя. 2, 2023

Abstract Hundreds of millions single cells have been analyzed to date using high throughput transcriptomic methods, thanks technological advances driving the increasingly rapid generation single-cell data. This provides an exciting opportunity for unlocking new insights into health and disease, made possible by meta-analysis that span diverse datasets building on recent in large language models other machine learning approaches. Despite promise these emerging analytical tools analyzing amounts data, a major challenge remains sheer number inconsistent format, data accessibility. Many are available via unique portals platforms often lack interoperability. Here, we present CZ CellxGene Discover ( cellxgene.cziscience.com ), platform curated interoperable resource, free-to-use online portal, hosts growing corpus community contributed spans more than 50 million cells. Curated, standardized, associated with consistent cell-level metadata, this collection is largest its kind. A suite features enables accessibility reusability both computational visual interfaces allow researchers rapidly explore individual perform cross-corpus analysis. functionality enabling meta-analyses tens across studies tissues providing global views human at resolution

Язык: Английский

Процитировано

67
Single-cell profiling to explore pancreatic cancer heterogeneity, plasticity and response to therapy DOI
Stefanie Bärthel, Chiara Falcomatà, Roland Rad

и другие.

Nature Cancer, Год журнала: 2023, Номер 4(4), С. 454 - 467

Опубликована: Март 23, 2023

Язык: Английский

Процитировано

49
AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships DOI Creative Commons
You Wu, Lei Xie

Computational and Structural Biotechnology Journal, Год журнала: 2025, Номер 27, С. 265 - 277

Опубликована: Янв. 1, 2025

Despite the wealth of single-cell multi-omics data, it remains challenging to predict consequences novel genetic and chemical perturbations in human body. It requires knowledge molecular interactions at all biological levels, encompassing disease models humans. Current machine learning methods primarily establish statistical correlations between genotypes phenotypes but struggle identify physiologically significant causal factors, limiting their predictive power. Key challenges modeling include scarcity labeled generalization across different domains, disentangling causation from correlation. In light recent advances data integration, we propose a new artificial intelligence (AI)-powered biology-inspired multi-scale framework tackle these issues. This will integrate organism hierarchies, species genotype-environment-phenotype relationships under various conditions. AI inspired by biology may targets, biomarkers, pharmaceutical agents, personalized medicines for presently unmet medical needs.

Язык: Английский

Процитировано

4
scPriorGraph: constructing biosemantic cell–cell graphs with prior gene set selection for cell type identification from scRNA-seq data DOI Creative Commons
Xiyue Cao, Yu‐An Huang, Zhu‐Hong You

и другие.

Genome biology, Год журнала: 2024, Номер 25(1)

Опубликована: Авг. 5, 2024

Cell type identification is an indispensable analytical step in single-cell data analyses. To address the high noise stemming from gene expression data, existing computational methods often overlook biologically meaningful relationships between genes, opting to reduce all genes a unified space. We assume that such can aid characterizing cell features and improving recognition accuracy. this end, we introduce scPriorGraph, dual-channel graph neural network integrates multi-level biosemantics. Experimental results demonstrate scPriorGraph effectively aggregates feature values of similar cells using high-quality graphs, achieving state-of-the-art performance identification.

Язык: Английский

Процитировано

9
Recent advances in isolation and physiological characterization of planktonic anaerobic ammonia-oxidizing bacteria DOI
Yu Zhang, Zhihui Dong, Jing Lü

и другие.

Bioresource Technology, Год журнала: 2024, Номер unknown, С. 131919 - 131919

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

8
Single-cell and spatial transcriptomics uncover the role of B chromosomes in driving plant invasiveness DOI Creative Commons
Cui Wang, James Ord, Mengxiao Yan

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Abstract Invasive plants can profoundly disrupt native biodiversity, yet the genetic mechanisms underpinning their success remain poorly understood. To date, genomic studies have been conducted on only a limited number of invasive species, and no single-cell level applied. This research investigates drivers behind behavior common reed ( Phragmites australis ), hardy grass species that became in North America following its introduction from Europe. By integrating whole-genome sequencing with spatial transcriptomics, we developed comprehensive atlas reed’s shoot system. UMAP analysis identified 19 distinct cell clusters within Gene Ontology (GO) enrichment enabled annotation key types, including mesophyll, epidermal, bundle sheath, xylem cells, as well apical lateral bud meristems, auxillary meristems. RNA velocity highlighted multipotent nature mesophyll chlorenchyma Cluster 3 progenitor cells capable differentiating into various tissues 1 progressing towards aerenchyma formation. Comparative between European American populations revealed significant differences transcriptional activity gene expression, particularly associated meristem. exhibited higher prevalence B chromosomes, three genes IMPA-3, SSC3, DDE family endonuclease consistently upregulated across nearly all clusters, notably near meristematic regions. The fast mutation IMPA-3 which functions major receptor Resistance (R) may strengthened adaptability population America. These findings provide critical insights cellular development diversity underlying invasiveness reed, offering valuable information to guide ecological management strategies.

Язык: Английский

Процитировано

1