Editorial: Integrative Analysis of Single-cell And/or Bulk Multi-omics Sequencing Data

Overview

Journal Front Genet

Date 2023 Jan 23

PMID 36685891

Authors

Geng Chen

Rongshan Yu

Xingdong Chen

Affiliations

Soon will be listed here.

Citing Articles

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics.

Sanches P, de Melo N, M Porcari A, de Carvalho L Biology (Basel). 2024; 13(11).

PMID: 39596803 PMC: 11592251. DOI: 10.3390/biology13110848.

Applications of single-cell multi-omics in liver cancer.

Peeters F, Cappuyns S, Pique-Gili M, Phillips G, Verslype C, Lambrechts D JHEP Rep. 2024; 6(7):101094.

PMID: 39022385 PMC: 11252522. DOI: 10.1016/j.jhepr.2024.101094.

References

Hasin Y, Seldin M, Lusis A . Multi-omics approaches to disease. Genome Biol. 2017; 18(1):83. PMC: 5418815. DOI: 10.1186/s13059-017-1215-1. View

Decamps C, Prive F, Bacher R, Jost D, Waguet A, Houseman E . Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software. BMC Bioinformatics. 2020; 21(1):16. PMC: 6958785. DOI: 10.1186/s12859-019-3307-2. View

Jiang Y, Wang J, Sun M, Zuo D, Wang H, Shen J . Multi-omics analysis identifies osteosarcoma subtypes with distinct prognosis indicating stratified treatment. Nat Commun. 2022; 13(1):7207. PMC: 9684515. DOI: 10.1038/s41467-022-34689-5. View

Aibar S, Gonzalez-Blas C, Moerman T, Huynh-Thu V, Imrichova H, Hulselmans G . SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017; 14(11):1083-1086. PMC: 5937676. DOI: 10.1038/nmeth.4463. View

Mustachio L, Roszik J . Single-Cell Sequencing: Current Applications in Precision Onco-Genomics and Cancer Therapeutics. Cancers (Basel). 2022; 14(3). PMC: 8833749. DOI: 10.3390/cancers14030657. View

Stuart T, Satija R . Integrative single-cell analysis. Nat Rev Genet. 2019; 20(5):257-272. DOI: 10.1038/s41576-019-0093-7. View

Lei Y, Tang R, Xu J, Wang W, Zhang B, Liu J . Applications of single-cell sequencing in cancer research: progress and perspectives. J Hematol Oncol. 2021; 14(1):91. PMC: 8190846. DOI: 10.1186/s13045-021-01105-2. View

Li Y, Ma L, Wu D, Chen G . Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine. Brief Bioinform. 2021; 22(5). DOI: 10.1093/bib/bbab024. View

Zhang Y, Parmigiani G, Johnson W . : batch effect adjustment for RNA-seq count data. NAR Genom Bioinform. 2020; 2(3):lqaa078. PMC: 7518324. DOI: 10.1093/nargab/lqaa078. View

10.

Leek J, Johnson W, Parker H, Jaffe A, Storey J . The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012; 28(6):882-3. PMC: 3307112. DOI: 10.1093/bioinformatics/bts034. View

11.

Zaitsev K, Bambouskova M, Swain A, Artyomov M . Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures. Nat Commun. 2019; 10(1):2209. PMC: 6525259. DOI: 10.1038/s41467-019-09990-5. View

12.

Haghverdi L, Lun A, Morgan M, Marioni J . Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol. 2018; 36(5):421-427. PMC: 6152897. DOI: 10.1038/nbt.4091. View

13.

Wang X, Park J, Susztak K, Zhang N, Li M . Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat Commun. 2019; 10(1):380. PMC: 6342984. DOI: 10.1038/s41467-018-08023-x. View

14.

Leng D, Zheng L, Wen Y, Zhang Y, Wu L, Wang J . A benchmark study of deep learning-based multi-omics data fusion methods for cancer. Genome Biol. 2022; 23(1):171. PMC: 9361561. DOI: 10.1186/s13059-022-02739-2. View

15.

Weinstein J, Collisson E, Mills G, Mills Shaw K, Ozenberger B, Ellrott K . The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013; 45(10):1113-20. PMC: 3919969. DOI: 10.1038/ng.2764. View

16.

Wen L, Tang F . Recent advances in single-cell sequencing technologies. Precis Clin Med. 2022; 5(1):pbac002. PMC: 9267251. DOI: 10.1093/pcmedi/pbac002. View

17.

Lin Y, Li H, Xiao X, Zhang L, Wang K, Zhao J . DAISM-DNN: Highly accurate cell type proportion estimation with data augmentation and deep neural networks. Patterns (N Y). 2022; 3(3):100440. PMC: 9058910. DOI: 10.1016/j.patter.2022.100440. View