Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data

Osorio, Daniel; Capasso, Anna; Eckhardt, S. Gail; Giri, Uma; Somma, Alexander; Pitts, Todd M.; Lieu, Christophe H.; Messersmith, Wells A.; Bagby, Stacey M.; Singh, Harinder; Das, Jishnu; Sahni, Nidhi; Yi, S. Stephen; Kuijjer, Marieke L.

doi:10.1101/2023.01.20.524974

Cited by 2 publications

(3 citation statements)

References 97 publications

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“…Of note, while we used PORCUPINE on networks modeled with PANDA and LIONESS, the tool is not limited to these specific methodologies, and could potentially also be used to analyze (bipartite) networks modeled with other single-sample approaches. For example, it can potentially be applied to gene regulatory networks from single cell RNA-seq data modeled with SCORPION ( 57 ). Of course, when applying PORCUPINE, one should consider cohort sample size as well as the use of an independent validation dataset, as we showed here by including an independent leiomyosarcoma dataset, which are both important to include to detect relevant and robust pathways.…”

Section: Resultsmentioning

confidence: 99%

Heterogeneity in the gene regulatory landscape of leiomyosarcoma

et al. 2023

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Characterizing inter-tumor heterogeneity is crucial for selecting suitable cancer therapy, as the presence of diverse molecular subgroups of patients can be associated with disease outcome or response to treatment. While cancer subtypes are often characterized by differences in gene expression, the mechanisms driving these differences are generally unknown. We set out to model the regulatory mechanisms driving sarcoma heterogeneity based on patient-specific, genome-wide gene regulatory networks. We developed a new computational framework, PORCUPINE, which combines knowledge on biological pathways with permutation-based network analysis to identify pathways that exhibit significant regulatory heterogeneity across a patient population. We applied PORCUPINE to patient-specific leiomyosarcoma networks modeled on data from The Cancer Genome Atlas and validated our results in an independent dataset from the German Cancer Research Center. PORCUPINE identified 37 heterogeneously regulated pathways, including pathways representing potential targets for treatment of subgroups of leiomyosarcoma patients, such as FGFR and CTLA4 inhibitory signaling. We validated the detected regulatory heterogeneity through analysis of networks and chromatin states in leiomyosarcoma cell lines. We showed that the heterogeneity identified with PORCUPINE is not associated with methylation profiles or clinical features, thereby suggesting an independent mechanism of patient heterogeneity driven by the complex landscape of gene regulatory interactions.

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Section: Resultsmentioning

confidence: 99%

Heterogeneity in the gene regulatory landscape of leiomyosarcoma

et al. 2023

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“…The first one is to construct metacells across all samples and conditions, allowing to mix samples and conditions within one metacell 85,87,89,98,[129][130][131][132] . The second one is to construct metacells per sample and/or per condition, obtaining sample-and/or conditionspecific metacells 51,52,78,86,94,123,133,134 . The choice of the method depends on the specific objectives of the downstream analysis and size of samples/conditions.…”

Section: Partitioning Data Into Metacellsmentioning

confidence: 99%

“…. The second one is to construct metacells per sample and/or per condition, obtaining sample-and/or conditionspecific metacells 51,52,78,86,94,123,133,134 . The choice of the method depends on the specific objectives of the downstream analysis and size of samples/conditions.…”

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confidence: 99%

Building and analyzing metacells in single-cell genomics data

Bilous,

Hérault,

Gabriel

et al. 2024

Preprint

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The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze such data is a constant challenge and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information. Here, we review recent studies that capitalize on the concept of metacells – and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to study single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on construction and analysis of metacells from single-cell RNA-seq data (https://github.com/GfellerLab/MetacellAnalysisTutorial) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods (https://github.com/GfellerLab/MetacellAnalysisToolkit).

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Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data

Cited by 2 publications

References 97 publications

Heterogeneity in the gene regulatory landscape of leiomyosarcoma

Heterogeneity in the gene regulatory landscape of leiomyosarcoma

Building and analyzing metacells in single-cell genomics data

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