Recovering Gene Interactions from Single-Cell Data Using Data Diffusion

Dijk, David van; Sharma, Roshan; Nainys, Juozas; Yim, Kristina; Kathail, Pooja; Carr, A. J. H.; Burdziak, Cassandra; Moon, Kevin R.; Chaffer, Christine L.; Pattabiraman, Diwakar R.; Bierie, Brian; Mažutis, Linas; Wolf, Guy; Krishnaswamy, Smita; Pe’er, Dana

doi:10.1016/j.cell.2018.05.061

Cited by 1,369 publications

(1,053 citation statements)

References 49 publications

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“…Inferring dropout events, replacing these zeros with appropriate expression values, and reducing the noise in the dataset have been the target of several recent tools (MAGIC: van Dijk et al , ; DCA: Eraslan et al , ; scVI: Lopez et al , ; SAVER: Huang et al , ; scImpute: Li & Li, ). Performing expression recovery has been shown to improve the estimation of gene–gene correlations (van Dijk et al , ; Eraslan et al , ). Furthermore, this step can be integrated with normalization, batch correction and other downstream analysis as implemented in the scVI tool (Lopez et al , ).…”

Section: Introductionmentioning

confidence: 99%

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Luecken

Theis

2019

Molecular Systems Biology

1,597

1,386

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Single‐cell RNA ‐seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single‐cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up‐to‐date workflow to analyse one's data. Here, we detail the steps of a typical single‐cell RNA ‐seq analysis, including pre‐processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell‐ and gene‐level downstream analysis. We formulate current best‐practice recommendations for these steps based on independent comparison studies. We have integrated these best‐practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial . This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

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Section: Introductionmentioning

confidence: 99%

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Luecken

Theis

2019

Molecular Systems Biology

1,597

1,386

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“…Typically, this can be achieved by comparing against a known process progression (27,44) or features derived from the original data such as clusters (46). Instead, what we proposed here is to employ the distance correlation, a statistical measure that directly assesses the independence between the ordering vector and the input data without the requirement of background knowledge or performing any other data analysis (e.g.…”

Section: Quality Of the Inferred Trajectories And Association With Thmentioning

confidence: 99%

Learning Pathway Dynamics from Single‐Cell Proteomic Data: A Comparative Study

2020

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Single‐cell platforms provide statistically large samples of snapshot observations capable of resolving intrercellular heterogeneity. Currently, there is a growing literature on algorithms that exploit this attribute in order to infer the trajectory of biological mechanisms, such as cell proliferation and differentiation. Despite the efforts, the trajectory inference methodology has not yet been used for addressing the challenging problem of learning the dynamics of protein signaling systems. In this work, we assess this prospect by testing the performance of this class of algorithms on four proteomic temporal datasets. To evaluate the learning quality, we design new general‐purpose evaluation metrics that are able to quantify performance on (i) the biological meaning of the output, (ii) the consistency of the inferred trajectory, (iii) the algorithm robustness, (iv) the correlation of the learning output with the initial dataset, and (v) the roughness of the cell parameter levels though the inferred trajectory. We show that experimental time alone is insufficient to provide knowledge about the order of proteins during signal transduction. Accordingly, we show that the inferred trajectories provide richer information about the underlying dynamics. We learn that established methods tested on high‐dimensional data with small sample size, slow dynamics, and complex structures (e.g. bifurcations) cannot always work in the signaling setting. Among the methods we evaluate, Scorpius and a newly introduced approach that combines Diffusion Maps and Principal Curves were found to perform adequately in recovering the progression of signal transduction although their performance on some metrics varies from one dataset to another. The novel metrics we devise highlight that it is difficult to conclude, which one method is universally applicable for the task. Arguably, there are still many challenges and open problems to resolve. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

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“…Cells were visualized using PHATE (Moon et al, 2019) with default parameters and gene expression was denoised using MAGIC (van Dijk et al, 2018) with default parameters. GSEA analyses were carried out comparing gene lists from scRNA-seq generated as described in the figure legend and applied to gene ontology sets (C5 biological processes and cellular components) in MSigDB (http://software.broadinstitute.org/gsea/) with default settings.…”

Section: Single Cell Rna-sequencing Of Pancreatic Isletsmentioning

confidence: 99%

Endocrine-exocrine signaling drives obesity-associated pancreatic ductal adenocarcinoma

Chung¹,

Singh

Lawres

et al. 2019

Preprint

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SUMMARYObesity is a major modifiable risk factor for pancreatic ductal adenocarcinoma (PDAC), yet how and when obesity contributes to PDAC progression is not well understood. Leveraging an autochthonous mouse model, we demonstrate a causal and reversible role for obesity in early PDAC progression, showing that obesity markedly enhances tumorigenesis, while genetic or dietary induction of weight loss intercepts cancer development. Bulk and single cell molecular analyses of human and murine samples define microenvironmental consequences of obesity that promote tumor development rather than new driver gene mutations. We observe increased inflammation and fibrosis and also provide evidence for significant pancreatic islet cell adaptation in obesity-associated tumors. Specifically, we identify aberrant islet beta cell expression of the peptide hormone cholecystokinin (CCK) in tumors as an adaptive response to obesity. Furthermore, beta cell CCK expression promotes oncogenic Kras-driven pancreatic ductal tumorigenesis. Our studies argue that PDAC progression is driven by local obesity-associated changes in the tumor microenvironment – rather than systemic effects – and implicate endocrine-exocrine signaling beyond insulin in PDAC development. Furthermore, our demonstration that these obesity-associated adaptations are reversible supports the use of anti-obesity strategies to intercept PDAC early during progression.

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Recovering Gene Interactions from Single-Cell Data Using Data Diffusion

Cited by 1,369 publications

References 49 publications

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Learning Pathway Dynamics from Single‐Cell Proteomic Data: A Comparative Study

Endocrine-exocrine signaling drives obesity-associated pancreatic ductal adenocarcinoma

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