Overlapping Stochastic Community Finding

McDaid, Aaron; Hurley, Neil; Murphy, Brendan

doi:10.1109/asonam.2014.6921554

Cited by 12 publications

(12 citation statements)

References 9 publications

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“…We project cells based on their computed latent space representation into two dimensions with PHATE (Moon et al, 2019), which gives a more interpretable visualization than applying PHATE to the original data by learning a lower-dimensional representation of the data based on both transcriptomic similarity, and similarity across ground truth conditions. We can also directly apply k -means clustering to H (0) , obtaining more informative clusters as evidenced by higher normalized mutual information (NMI) scores (McDaid et al, 2011), a common metric for benchmarking clustering algorithms that measures how concordant the clusters were with the original ground truth sample assignments. W (1) is an h × m learned weight matrix, where m is the number of genes.…”

Section: Methodsmentioning

confidence: 99%

“…Cellograph outperforms existing approaches in quantifying the effects of perturbations and offers a novel GNN framework to cluster and visualize single-cell data. In the following sections, we discuss the workflow of Cellograph, demonstrate its performance on three published scRNA-seq datasets, and benchmark it against previously published methods using cross-categorical entropy and normalized mutual information (McDaid et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Cellograph: A Semi-supervised Approach to Analyzing Multi-condition Single-cell RNA Sequencing Data Using Graph Neural Networks

Shahir

Stanley

Purvis

2023

Preprint

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With the growing number of single-cell datasets collected under more complex experimental conditions, there is an opportunity to leverage single-cell variability to reveal deeper insights into how cells respond to perturbations. Many existing approaches rely on discretizing the data into clusters for differential gene expression (DGE), effectively ironing out any information unveiled by the single-cell variability across cell-types. In addition, DGE often assumes a statistical distribution that, if erroneous, can lead to false positive differentially expressed genes. Here, we present Cellograph: a semi-supervised framework that uses graph neural networks to quantify the effects of perturbations at single-cell granularity. Cellograph not only measures how prototypical cells are of each condition but also learns a latent space that is amenable to interpretable data visualization and clustering. The learned gene weight matrix from training reveals pertinent genes driving the differences between conditions. We demonstrate the utility of our approach on publicly-available datasets including cancer drug therapy, stem cell reprogramming, and organoid differentiation. Cellograph outperforms existing methods for quantifying the effects of experimental perturbations and offers a novel framework to analyze single-cell data using deep learning.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cellograph: A Semi-supervised Approach to Analyzing Multi-condition Single-cell RNA Sequencing Data Using Graph Neural Networks

Shahir

Stanley

Purvis

2023

Preprint

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“…To evaluate the quality of the tested algorithms, we employ an implementation of the Normalized Mutual Information measure for sets of overlapping clusters (ONMI) [ 20 ]. We used it to measure the difference between the covering produced by the examined algorithm and the known labels.…”

Section: Methodsmentioning

confidence: 99%

Overlapping community detection in networks based on link partitioning and partitioning around medoids

Ponomarenko¹,

Pitsoulis

Shamshetdinov³

2021

PLoS ONE

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In this paper, we present a new method for detecting overlapping communities in networks with a predefined number of clusters called LPAM (Link Partitioning Around Medoids). The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph employing link partitioning and partitioning around medoids which are done through the use of a distance function defined on the set of nodes. We consider both the commute distance and amplified commute distance as distance functions. The performance of the LPAM method is evaluated with computational experiments on real life instances, as well as synthetic network benchmarks. For small and medium-size networks, the exact solution was found, while for large networks we found solutions with a heuristic version of the LPAM method.

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“…6a) and numbers of SEACells (Supplementary Fig. 6b), for both RNA and ATAC modalities, based on normalized mutual information (NMI) score 18 . Another key performance metric is the ability to capture rare cell states.…”

Section: Seacells Metacells Represent Accurate and Robust Cell Statesmentioning

confidence: 99%

“…5a). Stem cells exhibit extensive priming of lineage gene regulatory elements, whereby enhancers are accessible for lineage-specific expression 18,33,34 . We used SEACells to better elucidate how the permissive epigenomic landscape of hematopoietic stem cells (HSCs) dynamically reconfigures to a sharply restricted landscape in differentiated cells.…”

Section: Seacells Reveals Accessibility Dynamics In Differentiationmentioning

confidence: 99%

SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data

et al. 2023

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Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here we present single-cell aggregation of cell states (SEACells), an algorithm for identifying metacells that overcome the sparsity of single-cell data while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying comprehensive, compact and well-separated metacells in both RNA and assay for transposase-accessible chromatin (ATAC) modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene–peak associations, compute ATAC gene scores and infer the activities of critical regulators during differentiation. Metacell-level analysis scales to large datasets and is particularly well suited for patient cohorts, where per-patient aggregation provides more robust units for data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a Coronavirus Disease 2019 (COVID-19) patient cohort.

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Overlapping Stochastic Community Finding

Cited by 12 publications

References 9 publications

Cellograph: A Semi-supervised Approach to Analyzing Multi-condition Single-cell RNA Sequencing Data Using Graph Neural Networks

Cellograph: A Semi-supervised Approach to Analyzing Multi-condition Single-cell RNA Sequencing Data Using Graph Neural Networks

Overlapping community detection in networks based on link partitioning and partitioning around medoids

SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data

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