Computational methods for the integrative analysis of single-cell data

Forcato, Mattia; Romano, Oriana; Bicciato, Silvio

doi:10.1093/bib/bbaa042

Cited by 53 publications

(44 citation statements)

References 76 publications

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“…The workflow of scRNA-seq consists of single-cell capture, mRNA reverse transcription, cDNA amplification, library preparation, high-throughput sequencing, and data analysis. The number of sequenced reads, which represents the gene expression level, has been counted as a digital gene expression matrix for bioinformatic analysis (8,9). In this review, we will outline the recent findings on tumor-infiltrating immune cells based on scRNA-seq in human breast cancers, and their connections with immunotherapy and potential clinical applications.…”

Section: Introductionmentioning

confidence: 99%

Single-Cell Profiling to Explore Immunological Heterogeneity of Tumor Microenvironment in Breast Cancer

et al. 2021

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Immune infiltrates in the tumor microenvironment (TME) of breast cancer (BRCA) have been shown to play a critical role in tumorigenesis, progression, invasion, and therapy resistance, and thereby will affect the clinical outcomes of BRCA patients. However, a wide range of intratumoral heterogeneity shaped by the tumor cells and immune cells in the surrounding microenvironment is a major obstacle in understanding and treating BRCA. Recent progress in single-cell technologies such as single-cell RNA sequencing (scRNA-seq), mass cytometry, and digital spatial profiling has enabled the detailed characterization of intratumoral immune cells and vastly improved our understanding of less-defined cell subsets in the tumor immune environment. By measuring transcriptomes or proteomics at the single-cell level, it provides an unprecedented view of the cellular architecture consist of phenotypical and functional diversities of tumor-infiltrating immune cells. In this review, we focus on landmark studies of single-cell profiling of immunological heterogeneity in the TME, and discuss its clinical applications, translational outlook, and limitations in breast cancer studies.

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Section: Introductionmentioning

confidence: 99%

Single-Cell Profiling to Explore Immunological Heterogeneity of Tumor Microenvironment in Breast Cancer

et al. 2021

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“…It provides a well-established workflow to characterize these sources of variation, especially when analyzing datasets with complex group structure. Also, MOFA+ has been extensively used and cited in more than 80 research studies and comparative reviews [ 160 , 161 , 162 , 163 ]. Furthermore, the MOFA+ stable Bioconductor installation is utilizing basilisk to automatically set up the necessary Python-R connection, which facilitates interoperability.…”

Section: Resultsmentioning

confidence: 99%

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

Vlachavas

Bohn

Ückert

et al. 2021

IJMS

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Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

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“…Robust computational and statistical models are needed to extract biological information from other irrelevant signals (e.g., technical noises, batch effect) and for integrating the multimodal data of different characteristics, dimensionalities, and coverages to model them in a single space. Methods addressing these challenges are rapidly emerging (reviewed in Forcato et al, 2020 ; Hao et al, 2020 ) but still in the early stages in terms of accommodating all different data types and features. Both technical improvements of assay sensitivity and the development of analytic methods are essential for successfully applying these single-cell genomics techniques to understanding enhancer biology.…”

Section: Discussion Concluding Remarks and Future Perspectivesmentioning

confidence: 99%

Heart Enhancers: Development and Disease Control at a Distance

2021

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Bound by lineage-determining transcription factors and signaling effectors, enhancers play essential roles in controlling spatiotemporal gene expression profiles during development, homeostasis and disease. Recent synergistic advances in functional genomic technologies, combined with the developmental biology toolbox, have resulted in unprecedented genome-wide annotation of heart enhancers and their target genes. Starting with early studies of vertebrate heart enhancers and ending with state-of-the-art genome-wide enhancer discovery and testing, we will review how studying heart enhancers in metazoan species has helped inform our understanding of cardiac development and disease.

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Computational methods for the integrative analysis of single-cell data

Cited by 53 publications

References 76 publications

Single-Cell Profiling to Explore Immunological Heterogeneity of Tumor Microenvironment in Breast Cancer

Single-Cell Profiling to Explore Immunological Heterogeneity of Tumor Microenvironment in Breast Cancer

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

Heart Enhancers: Development and Disease Control at a Distance

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