Polaratio: A magnitude-contingent monotonic correlation metric and its improvements to scRNA-seq clustering

Motivation: Single-cell RNA sequencing (scRNA-seq) technologies and analysis tools have allowed researchers to achieve remarkably detailed understandings of the roles and relationships between cells and genes. However, conventional distance metrics, such as Euclidean, Pearson, and Spearman distances, fail to simultaneously take into account the high dimensionality, monotonicity, and magnitude of gene expression data. To address several shortcomings in these commonly used metrics, we present a magnitude-contingent monotonic correlation metric called Polaratio which is designed to enhance the quality of scRNA-seq data analysis. Results: We integrate three state-of-the-art interpretable clustering algorithms – Single-Cell Consensus Clustering (SC3), Hierarchical Clustering (HC), and K-Medoids (KM) – through a consensus cell clustering procedure, which we evaluate on various biological datasets to benchmark Polaratio against several well-known metrics. Our results demonstrate Polaratio’s ability to improve the accuracy of cell clustering on 5 out of 7 publicly available datasets. Availability: https://github.com/dubai03nsr/Polaratio Contact: pcicalese{at}uh.edu

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Graph Clustering Biase et al Polaratio Consensus Clustering Adjusted Rand Index 1.000 # 1
Graph Clustering Bozec et al Polaratio Consensus Clustering Adjusted Rand Index 0.571 # 1
Graph Clustering Deng et al Polaratio Consensus Clustering Adjusted Rand Index 0.459 # 1
Graph Clustering Goolam et al Polaratio Consensus Clustering Adjusted Rand Index 0.914 # 1
Graph Clustering Pollen et al Polaratio Consensus Clustering Adjusted Rand Index 0.953 # 1
Graph Clustering Treutlein et al Polaratio Consensus Clustering Adjusted Rand Index 0.812 # 1
Graph Clustering Yan et al Polaratio Consensus Clustering Adjusted Rand Index 0.811 # 1

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