Polar Gini Curve: A Technique to Discover Gene Expression Spatial Patterns from Single-Cell RNA-Seq Data

Abstract

In this work, we describe the development of Polar Gini Curve, a method for characterizing cluster markers by analyzing single-cell RNA sequencing (scRNA-seq) data. Polar Gini Curve combines the gene expression and the 2D coordinates (“spatial”) information to detect patterns of uniformity in any clustered cells from scRNA-seq data. We demonstrate that Polar Gini Curve can help users characterize the shape and density distribution of cells in a particular cluster, which can be generated during routine scRNA-seq data analysis. To quantify the extent to which a gene is uniformly distributed in a cell cluster space, we combine two polar Gini curves (PGCs)—one drawn upon the cell-points expressing the gene (the “foreground curve”) and the other drawn upon all cell-points in the cluster (the “background curve”). We show that genes with highly dissimilar foreground and background curves tend not to uniformly distributed in the cell cluster—thus having spatially divergent gene expression patterns within the cluster. Genes with similar foreground and background curves tend to uniformly distributed in the cell cluster—thus having uniform gene expression patterns within the cluster. Such quantitative attributes of PGCs can be applied to sensitively discover biomarkers across clusters from scRNA-seq data. We demonstrate the performance of the Polar Gini Curve framework in several simulation case studies. Using this framework to analyze a real-world neonatal mouse heart cell dataset, the detected biomarkers may characterize novel subtypes of cardiac muscle cells. The source code and data for Polar Gini Curve could be found at http://discovery.informatics.uab.edu/PGC/ or https://figshare.com/projects/Polar_Gini_Curve/76749.

Biological Network Mining

“…In the context of literature retrieval, WIPER uses the PubMed score as a measure of semantic validation [105]. The p-value evaluation in WIPER can be adapted to work with any ranking algorithm tools, such as the Polar Gini Curve [118].…”

Detecting Data Embedding Spatial Patterns and Identifying Biomarkers with BioRSP

“…We can identify and pinpoint marker genes with specific spatial expression patterns in certain subpopulations by analyzing these patterns considering the cell population shape and density distribution. Previous work, such as the Polar Gini Curve (PGC), has laid the groundwork for such analysis (Nguyen et al, 2021). PGCs are constructed by calculating the Gini coefficient between foreground and background cells at various angles, then rotating the data 360 degrees to derive a curve.…”

Advanced Single-Cell Omics Technologies and Informatics Tools for Genomics, Proteomics, and Bioinformatics Analysis

“…Li and Li proposed the scLink method, which used statistical network modeling to understand the co-expression relationships among genes and construct sparse gene co-expression networks from single-cell gene expression data [24] . Nguyen et al described the development of Polar Gini Curve, a method for characterizing cluster markers by analyzing scRNA-seq data, which can help users characterize the shape and density distribution of cells in a particular cluster [25] . Conchouso et al provided engineering details and organized protocols for integrating three droplet-based microfluidic technologies into the metagenomic pipeline to enable functional screening of bioproducts at high throughput [26] .…”