bioRxivCancer BiologyPreprint
Fingerprints of cancer by persistent homology
Abstract
We have carried out a topological data analysis of gene expressions for different databases based on the Fermat distance between the z scores of different tissue samples. There is a critical value of the filtration parameter at which all clusters collapse in a single one. This critical value for healthy samples is gapless and smaller than that for cancerous ones. After collapse in a single cluster, topological holes persist for larger filtration parameter values in cancerous samples. Barcodes, persistence diagrams and Betti numbers as functions of the filtration parameter are different for different types of cancer and constitute fingerprints thereof.
- assay: bulk RNA-seq
- research focus: clustering
- research focus: differential expression
- approach: deep learning
- approach: statistical model
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