Paired single-cell multi-omics data integration with Mowgli DOI Creative Commons
Geert-Jan Huizing,

Ina Maria Deutschmann,

Gabriel Peyré

et al.

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Nov. 24, 2023

The profiling of multiple molecular layers from the same set cells has recently become possible. There is thus a growing need for multi-view learning methods able to jointly analyze these data. We here present Multi-Omics Wasserstein inteGrative anaLysIs (Mowgli), novel method integration paired multi-omics data with any type and number omics. Of note, Mowgli combines integrative Nonnegative Matrix Factorization Optimal Transport, enhancing at time clustering performance interpretability Factorization. apply single-cell profiled 10X Multiome, CITE-seq, TEA-seq. Our in-depth benchmark demonstrates that Mowgli's competitive state-of-the-art in cell superior once considering biological interpretability. implemented as Python package seamlessly integrated within scverse ecosystem it available http://github.com/cantinilab/mowgli .

Language: Английский

scGPT: toward building a foundation model for single-cell multi-omics using generative AI DOI
Haotian Cui, Xiaoming Wang, Hassaan Maan

et al.

Nature Methods, Journal Year: 2024, Volume and Issue: 21(8), P. 1470 - 1480

Published: Feb. 26, 2024

Language: Английский

Citations

295
MUON: multimodal omics analysis framework DOI Creative Commons
Danila Bredikhin, Ilia Kats, Oliver Stegle

et al.

Genome biology, Journal Year: 2022, Volume and Issue: 23(1)

Published: Feb. 1, 2022

Advances in multi-omics have led to an explosion of multimodal datasets address questions from basic biology translation. While these data provide novel opportunities for discovery, they also pose management and analysis challenges, thus motivating the development tailored computational solutions. Here, we present a standard framework multi-omics, MUON, designed organise, analyse, visualise, exchange data. MUON stores efficient yet flexible interoperable structure. enables versatile range analyses, preprocessing alignment.

Language: Английский

Citations

107
Understanding tumour endothelial cell heterogeneity and function from single-cell omics DOI
Qun Zeng, Mira Mousa,

Aisha Shigna Nadukkandy

et al.

Nature reviews. Cancer, Journal Year: 2023, Volume and Issue: 23(8), P. 544 - 564

Published: June 22, 2023

Language: Английский

Citations

98
A benchmark study of deep learning-based multi-omics data fusion methods for cancer DOI Creative Commons

Dongjin Leng,

Linyi Zheng,

Yuqi Wen

et al.

Genome biology, Journal Year: 2022, Volume and Issue: 23(1)

Published: Aug. 9, 2022

A fused method using a combination of multi-omics data enables comprehensive study complex biological processes and highlights the interrelationship relevant biomolecules their functions. Driven by high-throughput sequencing technologies, several promising deep learning methods have been proposed for fusing generated from large number samples.

Language: Английский

Citations

78
A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication DOI Creative Commons
Changde Cheng, Wenan Chen, Hongjian Jin

et al.

Cells, Journal Year: 2023, Volume and Issue: 12(15), P. 1970 - 1970

Published: July 30, 2023

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of heterogeneity, identification rare but significant cell types, and exploration cell-cell communications interactions. Its broad applications span both basic clinical research domains. In this comprehensive review, we survey current landscape scRNA-seq analysis methods tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, inference communication. We review challenges encountered in analysis, issues sparsity or low expression, reliability assumptions discuss potential impact suboptimal clustering differential expression tools downstream analyses, particularly identifying subpopulations. Finally, recent advancements future directions enhancing analysis. Specifically, highlight development novel annotating single-cell data, integrating interpreting multimodal datasets covering epigenomics, proteomics, inferring communication networks. By elucidating latest progress innovation, provide overview rapidly advancing field

Language: Английский

Citations

74
Advancing CAR T cell therapy through the use of multidimensional omics data DOI
Jingwen Yang, Yamei Chen, Ying Jing

et al.

Nature Reviews Clinical Oncology, Journal Year: 2023, Volume and Issue: 20(4), P. 211 - 228

Published: Jan. 31, 2023

Language: Английский

Citations

67
‘Multi-omics’ data integration: applications in probiotics studies DOI Creative Commons
Iliya Dauda Kwoji, Olayinka Ayobami Aiyegoro, Moses Okpeku

et al.

npj Science of Food, Journal Year: 2023, Volume and Issue: 7(1)

Published: June 5, 2023

Abstract The concept of probiotics is witnessing increasing attention due to its benefits in influencing the host microbiome and modulation immunity through strengthening gut barrier stimulation antibodies. These benefits, combined with need for improved nutraceuticals, have resulted extensive characterization leading an outburst data generated using several ‘omics’ technologies. recent development system biology approaches microbial science paving way integrating from different omics techniques understanding flow molecular information one level other clear on regulatory features phenotypes. limitations tendencies a ‘single omics’ application ignore influence processes justify ‘multi-omics’ selections action host. Different techniques, including genomics, transcriptomics, proteomics, metabolomics lipidomics, used studying their are discussed this review. Furthermore, rationale multi-omics integration platforms supporting analyses was also elucidated. This review showed that useful selecting functions microbiome. Hence, recommend approach holistically

Language: Английский

Citations

49
From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis DOI
Yirui Zhang, Kai Chang, Babatunde Ogunlade

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: 18(28), P. 18101 - 18117

Published: July 1, 2024

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, metabolome at single-cell level. We first review advances nanophotonics-including plasmonics, metamaterials, metasurfaces-enhance scattering for rapid, strong label-free spectroscopy. then discuss ML approaches precise spectral analysis, including neural networks, perturbation gradient algorithms, transfer learning. provide illustrative examples of phenotyping using nanophotonics ML, bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, immunotherapy efficacy toxicity predictions. Lastly, exciting prospects future spectroscopy, instrumentation, self-driving laboratories, data banks, uncovering biological insights.

Language: Английский

Citations

22
Pathological mechanisms of kidney disease in ageing DOI
Takeshi Yamamoto, Yoshitaka Isaka

Nature Reviews Nephrology, Journal Year: 2024, Volume and Issue: 20(9), P. 603 - 615

Published: July 18, 2024

Language: Английский

Citations

21
Single cell RNA‐sequencing: A powerful yet still challenging technology to study cellular heterogeneity DOI Creative Commons
May Sin Ke, Badran Elshenawy, Helen Sheldon

et al.

BioEssays, Journal Year: 2022, Volume and Issue: 44(11)

Published: Sept. 6, 2022

Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what observed at this macroscopic level can be the result of many interactions several different single cells. Thus, observable 'average' cannot outright used as representative 'average cell'. Rather, resulting emerging behaviour actions and Single-cell RNA sequencing (scRNA-Seq) enables comparison transcriptomes individual This provides high-resolution maps dynamic cellular programmes allowing us answer fundamental biological questions on their function evolution. It also allows address medical such role rare populations contributing disease progression therapeutic resistance. Furthermore, an understanding context-specific dependencies, namely a in specific context, which crucial understand some complex diseases, diabetes, cardiovascular cancer. Here, we provide overview scRNA-Seq, including comparative review technologies computational pipelines. We discuss current applications focus tumour heterogeneity clear example how scRNA-Seq new disease. Additionally, limitations highlight need powerful pipelines reproducible protocols for broader acceptance technique basic clinical research.

Language: Английский

Citations

51