Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19

Cheng, Jinyu; Zhang, Ji; Wu, Zhongdao; Sun, Xiaoqiang

doi:10.1093/bib/bbaa327

Cited by 86 publications

(75 citation statements)

References 41 publications

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“…A number of computational tools and resources have emerged that can be further classified as those that predict CCC interactions alone [4][5][6][7][8][9][10][11][12][13] , and those that additionally estimate intracellular pathway activities related to CCC [14][15][16][17][18] . Here, we focus on the former (Table 1).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Dimitrov

Türei

Boys

et al. 2021

Preprint

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The growing availability of single-cell data has sparked an increased interest in the inference of cell-cell communication from this data. Many tools have been developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we created a framework, available at https://github.com/saezlab/ligrec_decoupler, to facilitate a comparative assessment of methods for inferring cell-cell communication from single cell transcriptomics data and then compared 15 resources and 6 methods. We found few unique interactions and a varying degree of overlap among the resources, and observed uneven coverage in terms of pathways and biological categories. We analysed a colorectal cancer single cell RNA-Seq dataset using all possible combinations of methods and resources. We found major differences among the highest ranked intercellular interactions inferred by each method even when using the same resources. The varying predictions lead to fundamentally different biological interpretations, highlighting the need to benchmark resources and methods.

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Section: Introductionmentioning

confidence: 99%

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Dimitrov

Türei

Boys

et al. 2021

Preprint

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“…Our proposed stage-weighted Gaussian kernel allows construction of diffusion-like random walks to quantify the temporal progression distance (TPD) between two patients (Eq (8)). The diffusion map, as a manifoldbased nonlinear dimension reduction method, has been recently applied to scRNA-seq data analysis [26,[57][58][59]. One major difficulty in applying diffusion maps for inferring pseudo trajectories lies in identifying the rooting point when using scRNA-seq data itself, and it often needs additional biological knowledge.…”

Section: Discussionmentioning

confidence: 99%

Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples

2021

Self Cite

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Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine.

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“…Zhang et al developed a novel scRNA-seq data-based approach to reconstruct a multilayer signaling network that contains pathways from intercellular ligand–receptor interactions, intracellular transcriptional factors, and their target genes ( Zhang et al, 2020 ). Meanwhile, the single-cell RNA-sequencing data based on multilayer network method (scMLnet) ( Cheng et al, 2021 ) also help to resolve tumor–microenvironment interactions and dissect the microenvironment-mediated intercellular and intracellular signaling pathways of tumor cells, which might help to investigate the influence of microenvironment on the tumor growth, drug resistance, and patient prognosis.…”

Section: Discussionmentioning

confidence: 99%

Establishment of Prognosis Model in Acute Myeloid Leukemia Based on Hypoxia Microenvironment, and Exploration of Hypoxia-Related Mechanisms

Zhong

et al. 2021

Front. Genet.

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Acute myeloid leukemia (AML) is a highly heterogeneous hematologic neoplasm with poor survival outcomes. However, the routine clinical features are not sufficient to accurately predict the prognosis of AML. The expression of hypoxia-related genes was associated with survival outcomes of a variety of hematologic and lymphoid neoplasms. We established an 18-gene signature-based hypoxia-related prognosis model (HPM) and a complex model that consisted of the HPM and clinical risk factors using machine learning methods. Both two models were able to effectively predict the survival of AML patients, which might contribute to improving risk classification. Differentially expressed genes analysis, Gene Ontology (GO) categories, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to reveal the underlying functions and pathways implicated in AML development. To explore hypoxia-related changes in the bone marrow immune microenvironment, we used CIBERSORT to calculate and compare the proportion of 22 immune cells between the two groups with high and low hypoxia-risk scores. Enrichment analysis and immune cell composition analysis indicated that the biological processes and molecular functions of drug metabolism, angiogenesis, and immune cell infiltration of bone marrow play a role in the occurrence and development of AML, which might help us to evaluate several hypoxia-related metabolic and immune targets for AML therapy.

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Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19

Cited by 86 publications

References 41 publications

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples

Establishment of Prognosis Model in Acute Myeloid Leukemia Based on Hypoxia Microenvironment, and Exploration of Hypoxia-Related Mechanisms

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