scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

Li, Jingyi Jessica; Li, Yanzeng

doi:10.1101/2020.09.19.304956

Cited by 4 publications

(6 citation statements)

References 102 publications

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“…Our work sheds light on the increasing prevalent data from scRNA-sequencing in studying perturbation response (Azizi et al 2018, Li and Li 2020). Genome-scale high-dimensional measurements on single cells have been readily generated recently, which can be applied at relatively low cost to thousands and even tens of thousands of cells.…”

Section: Discussionmentioning

confidence: 90%

“…Studies in human disease and plant and animal stress response frequently use genome-wide gene expression data to study how co-expression changes and networks are rewired during environmental perturbation (Fukushima 2013, Southworth et al 2009, de la Fuente 2010, Amar et al 2013, Choi et al 2005, Kostka and Spang 2004, Deng et al 2015, Yan et al 2019, Cho et al 2009, Fukushima et al 2012, de la Fuente 2010, Li and Li 2020). Ultimately, the aim of such studies is to identify the cellular basis of environmental responses as a means to identify abnormal regulation in disease state and to improve medical interventions (Southworth et al 2009, de la Fuente 2010, Amar et al 2013, Kostka and Spang 2004), design breeding strategies (Fukushima et al 2012), or to parameterize models of molecular evolution (Wray et al 2003).…”

Section: Discussionmentioning

confidence: 99%

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Revisiting regulatory decoherence and phenotypic integration: accounting for temporal bias in co-expression analyses

Cai

2021

Preprint

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Transcriptional Regulatory Networks (TRNs) orchestrate the timing, magnitude, and rate of organismal response to many environmental perturbations. Regulatory interactions in TRNs are context-dependent, affording the opportunity to detect responsive links upon environmental perturbation. To measure the transition between regulatory status directly, one powerful approach is chromatin immuno‐precipitation binding, ChIP-Seq. However, genome-wide ChIP-Seq is costly and currently inaccessible for many transcription factors (TFs) across multiple conditions in most organisms. Seeking to exploit the abundance of RNASequencing data now available, many past studies have relied upon population-level statistics from cross-sectional studies, generating pairwise gene co-expression, to capture transient regulatory activity. Here, we employ a simple stochastic model of gene regulation and demonstrate that population correlations from cross-sectional studies may fail to capture transient regulatory activities. To characterize network rewiring in response to environmental perturbations, we use the dynamic correlation between time-series gene expression profiles in Oryza sativa in a network prior. Overall, stronger regulatory interactions are observed following environmental perturbation, which we term regulatory coherence. Previously known regulators with changing regulatory activities are reliably identified with our method. Exploiting dynamic correlations has the potential to prioritize stress-responsive regulators, affording greater detection power as compared to traditional differential expression approaches.

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Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Revisiting regulatory decoherence and phenotypic integration: accounting for temporal bias in co-expression analyses

Cai

2021

Preprint

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“…By looking at the ligand and receptor pairs expressed in cell types, we can identify interacting cell types utilizing autocrine, paracrine, and endocrine signaling. For example, CellPhoneDB and iTALK are two standard tools to calculate cell-cell interaction scores [ 128 , 129 ]; SCENIC uses transcription factor information and single cell transcriptome data to identify regulons at a cell-type-specific level [ 130 ]; scLink infers gene co-expression networks from a sparse gene expression matrix [ 131 ]; CytoTalk aims to construct both within cell-type and between cell-type signaling networks [ 132 ]. Benchmarking and applying these methods would bring mechanistic research of psychiatric disorders to a finer granularity from the brain region level to the cell subtype level.…”

Section: Discussionmentioning

confidence: 99%

Unveiling the Pathogenesis of Psychiatric Disorders Using Network Models

Zuo

Don

Zhu

et al. 2021

Genes

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Psychiatric disorders are complex brain disorders with a high degree of genetic heterogeneity, affecting millions of people worldwide. Despite advances in psychiatric genetics, the underlying pathogenic mechanisms of psychiatric disorders are still largely elusive, which impedes the development of novel rational therapies. There has been accumulating evidence suggesting that the genetics of complex disorders can be viewed through an omnigenic lens, which involves contextualizing genes in highly interconnected networks. Thus, applying network-based multi-omics integration methods could cast new light on the pathophysiology of psychiatric disorders. In this review, we first provide an overview of the recent advances in psychiatric genetics and highlight gaps in translating molecular associations into mechanistic insights. We then present an overview of network methodologies and review previous applications of network methods in the study of psychiatric disorders. Lastly, we describe the potential of such methodologies within a multi-tissue, multi-omics approach, and summarize the future directions in adopting diverse network approaches.

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“…were aligned to hg38 using Kallisto-BUS (26), followed by analysis using Scanpy (27). Linear correlation and co-expression networks were defined using pyScenic (28) and scLINK (29) and statistical significance of co-expression was confirmed using hypergeometric test.…”

Section: Aldefluor Assaymentioning

confidence: 99%

lncRNA BORG:TRIM28 Complexes Drive Metastatic Progression by Inducing α6 Integrin/CD49f Expression in Breast Cancer Stem Cells

Parker

Gooding

Valadkhan

et al. 2021

Molecular Cancer Research

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Triple-negative breast cancer (TNBC) is the most lethal subtype of breast cancer, with its aggressive phenotype being attributed to chemotherapy resistance, metastatic dissemination, and rapid disease recurrence. Breast cancer stem cells (BCSCs) are significant contributors to tumor initiation, as well as to the acquisition of aggressive tumorigenic phenotypes, namely due to their ability to self-replicate and to produce heterogeneous differentiated tumor cells. To elucidate the underlying mechanisms that drive BCSC tumorigenicity in TNBC, we identified the long noncoding RNA BMP/OP-Responsive Gene (BORG) as an enhancer of BCSC phenotypes. Indeed, we found BORG expression to (i) correlate with stem cell markers Nanog, Aldh1a3, and Itga6 ( 6 integrin/CD49f); (ii) enhance stem cell phenotypes in murine and human TNBC cells, and (iii) promote TNBC tumor initiation in mice. Mechanistically, BORG promoted BCSC phenotypes through its ability to interact physically with the E3 SUMO ligase TRIM28. Moreover, TRIM28 binding was observed in the promoter region of Itga6, whose genetic inactivation prevented BORG:TRIM28 complexes from (i) inducing BCSC self-renewal and expansion in vitro, and (ii) eliciting BCSC metastatic outgrowth in the lungs of mice. Collectively, these findings implicate BORG:TRIM28 complexes as novel drivers of BCSC phenotypes in developing and progressing TNBCs.Implications: This work establishes the lncRNA BORG as a driver of BCSC phenotypes and the aggressive behaviors of TNBCs, events critically dependent upon the formation of BORG:TRIM28 complexes and expression of 6 integrin.

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scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

Cited by 4 publications

References 102 publications

Revisiting regulatory decoherence and phenotypic integration: accounting for temporal bias in co-expression analyses

Revisiting regulatory decoherence and phenotypic integration: accounting for temporal bias in co-expression analyses

Unveiling the Pathogenesis of Psychiatric Disorders Using Network Models

lncRNA BORG:TRIM28 Complexes Drive Metastatic Progression by Inducing α6 Integrin/CD49f Expression in Breast Cancer Stem Cells

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