Detecting Very Weak Signals: A Mixed Strategy to Deal with Biologically Relevant Information

In many biological investigations, the relevant information does not coincide with the most powerful signals (most elevated eigenvalues, dominant frequencies, most populated clusters...), but very often hides in minor features that are difficult to discriminate from random noise. Here we propose an...

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Published inAlgorithms Vol. 18; no. 9; p. 581
Main Authors Vici, Alessandro, Zeuner, Ann, Giuliani, Alessandro
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 13.09.2025
Subjects
Algorithms
Cluster analysis
Clustering
Data analysis
Datasets
Eigenvalues
Gene expression
Random noise
Online AccessGet full text
ISSN1999-4893
1999-4893
DOI10.3390/a18090581

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Summary:In many biological investigations, the relevant information does not coincide with the most powerful signals (most elevated eigenvalues, dominant frequencies, most populated clusters...), but very often hides in minor features that are difficult to discriminate from random noise. Here we propose an algorithm that, by the combined use of a non-linear cluster analysis procedure and a strategy to discriminate minor signal components from noise, allows singling out biologically relevant hidden information. We tested the algorithm on a sparse data set corresponding to single-cell RNA-Seq measures, being able to identify a very small population of cells in charge of the immune response toward cancer tissue.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a18090581