Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining
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Abstract
Single-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Thus, it would be of great interest being able to disclose biological information belonging to cell subpopulations, which can be defined by clustering analysis of scRNAseq data. In this manuscript, we report a tool that we developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA). This tool allows uncovering hidden features associated with scRNAseq data. We implemented two new metrics, QCC (Quality Control of Cluster) and QCM (Quality Control of Model), which allow quantifying the ability of SCA to reconstruct valuable cell clusters and to evaluate the quality of the neural network achievements, respectively. Our data indicate that SCA encoded space, derived by different experimentally validated data (TF targets, miRNA targets, Kinase targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.
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Effective integration of healthcare data with molecular, genomic, disease-level, and drug-level knowledge can help generate more accurate and interpretable predictions about biological systems of health and disease.
“Effective integration of healthcare data with knowledge about molecular, genomic, disease-level, and drug-level data can help generate more accurate and interpretable predictions about the biological systems of health and disease [224].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [224]
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Graph representation learning in biomedicine and healthcare
Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294 · see citing works
“Unlike methods for standard representation learning, GNNs can explicitly model dependencies (such as physical interactions) between proteins as well as single-cell gene expression 224,225 .”
- Authors
- Alessandri, L., Cordero, F., Beccuti, M., Licheri, N., Arigoni, M., Olivero, M., Di Renzo, M. F., Sapino, A., & Calogero, R.
- Affiliations
- University of Turin (IT)
- Candiolo Cancer Institute (IT)
- Corresponding author
- Calogero, Raffaele
- ORCID
- Venue
- npj Systems Biology and Applications
- Published
- 2021-01-05
- Volume
- 7
- Issue
- 1
- Pages
- 1-1
- ISSN
- 2056-7189 (ISSN-L)
- PMC
- PMC7785742
- OpenAlex
- W3119638436
- Cited by
- 40
- References
- 51
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 5 · 2022: 15 · 2021: 12
- FWCI
- 2.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Systems Biology and Applications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- single-cell RNA sequencing, cell clustering, cell subpopulations, quality control metrics, miRNA target genes, kinase targets, cellular heterogeneity
- MeSH terms
- Exome Sequencing, Algorithms, Base Sequence, Humans, Software, Cluster Analysis, Neural Networks, Computer, Sequence Analysis, RNA, Systems Biology, Data Mining, Single-Cell Analysis
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