Multiscale methods for signal selection in single-cell data. (arXiv:2206.07760v1 [q-bio.QM])

Web: http://arxiv.org/abs/2206.07760

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 ($\mathrm{eig}_i$) rank signals or genes based on
their correspondence …

arxiv bio data signal