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 in | Algorithms Vol. 18; no. 9; p. 581 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
13.09.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1999-4893 1999-4893 |
| DOI | 10.3390/a18090581 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Giuliani, Alessandro Zeuner, Ann Vici, Alessandro |
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| Copyright | 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| References_xml | – volume: 19 start-page: 267 year: 2021 ident: ref_15 article-title: Evaluation of cell type annotation R packages on single-cell RNA-seq data publication-title: Genom. Proteom. Bioinform. doi: 10.1016/j.gpb.2020.07.004 – ident: ref_14 doi: 10.3389/fgene.2022.828479 – ident: ref_7 doi: 10.1186/1471-2105-7-194 – ident: ref_10 doi: 10.20944/preprints202502.0391.v1 – volume: 42 start-page: 197 year: 2023 ident: ref_19 article-title: Dormancy, stemness, and therapy resistance: Interconnected players in cancer evolution publication-title: Cancer Metastasis Rev. – ident: ref_3 doi: 10.1101/066498 – volume: 23 start-page: 643 year: 2022 ident: ref_16 article-title: The ImmGen consortium OpenSource T cell project publication-title: Nat. Immunol. doi: 10.1038/s41590-022-01197-z – volume: 7 start-page: 54 year: 2024 ident: ref_1 article-title: On the (Apparently) Paradoxical Role of Noise in the Recognition of Signal Character of Minor Principal Components publication-title: Stats doi: 10.3390/stats7010004 – ident: ref_13 doi: 10.3389/fbinf.2025.1519468 – volume: 2 start-page: 762 year: 2012 ident: ref_5 article-title: Essential dynamics: Foundation and applications publication-title: WIREs Comput. Mol. Sci. doi: 10.1002/wcms.1099 – volume: 11 start-page: 733 year: 2010 ident: ref_4 article-title: Tackling the widespread and critical impact of batch effects in high-throughput data publication-title: Nat. Rev. Genet. doi: 10.1038/nrg2825 – ident: ref_8 doi: 10.1145/1015330.1015408 – volume: 24 start-page: 339 year: 1960 ident: ref_2 article-title: Size and shape variation in the painted turtle. A principal component analysis publication-title: Growth – ident: ref_6 doi: 10.3390/diagnostics11061071 – volume: 9 start-page: 25070 year: 2024 ident: ref_11 article-title: Addressing limitations of the K-means clustering algorithm: Outliers, non-spherical data, and optimal cluster selection publication-title: MATH doi: 10.3934/math.20241222 – volume: 16 start-page: 1378 year: 2025 ident: ref_12 article-title: Brd7 loss reawakens dormant metastasis initiating cells in lung by forging an immunosuppressive niche publication-title: Nat. Commun. doi: 10.1038/s41467-025-56347-2 – volume: 25 start-page: 25 year: 2000 ident: ref_18 article-title: Gene Ontology: Tool for the unification of biology publication-title: Nat. Genet. doi: 10.1038/75556 – volume: 37 start-page: 38 year: 2019 ident: ref_17 article-title: Dimensionality reduction for visualizing single-cell data using UMAP publication-title: Nat. Biotechnol. doi: 10.1038/nbt.4314 – volume: 507 start-page: 114 year: 2001 ident: ref_9 article-title: The main biological determinants of tumor line taxonomy elucidated by a principal component analysis of microarray data publication-title: FEBS Lett. doi: 10.1016/S0014-5793(01)02973-8 |
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| Title | Detecting Very Weak Signals: A Mixed Strategy to Deal with Biologically Relevant Information |
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