Multiscale methods for signal selection in single-cell data

arXiv2206.07760MaRDI QIDQ6402215

Lewis Marsh, Otto Sumray, Renee S. Hoekzema, Thomas M. Carroll, Xin Lu, Helen M. Byrne, Heather A. Harrington

Publication date: 15 June 2022

Abstract: Analysis of single-cell transcriptomics often relies on clustering cells and then performing differential gene expression (DGE) to identify genes that vary between these clusters. These discrete analyses successfully determine cell types and markers; however, continuous variation within and between cell types may not be detected. We propose three topologically motivated mathematical methods for unsupervised feature selection that consider discrete and continuous transcriptional patterns on an equal footing across multiple scales simultaneously. Eigenscores ($ext{eig}_i$) rank signals or genes based on their correspondence to low-frequency intrinsic patterning in the data using the spectral decomposition of the Laplacian graph. The multiscale Laplacian score (MLS) is an unsupervised method for locating relevant scales in data and selecting the genes that are coherently expressed at these respective scales. The persistent Rayleigh quotient (PRQ) takes data equipped with a filtration, allowing the separation of genes with different roles in a bifurcation process (e.g., pseudo-time). We demonstrate the utility of these techniques by applying them to published single-cell transcriptomics data sets. The methods validate previously identified genes and detect additional biologically meaningful genes with coherent expression patterns. By studying the interaction between gene signals and the geometry of the underlying space, the three methods give multidimensional rankings of the genes and visualisation of relationships between them.

Has companion code repository: https://github.com/osumray/multiscale-signal-selection-single-cell

This page was built for publication: Multiscale methods for signal selection in single-cell data

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6402215)