{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T17:38:51Z","timestamp":1793727531394,"version":"4.6.1"},"reference-count":44,"publisher":"SAGE Publications","issue":"11-12","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"US National Institute of Health","award":["R01-LM012980"],"award-info":[{"award-number":["R01-LM012980"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Biology"],"published-print":{"date-parts":[[2026,12,1]]},"abstract":"<jats:p>\n                    Novel technologies for the acquisition of protein expression data at the single cell level are emerging rapidly. Although there exists a substantial body of computational algorithms and tools for the analysis of single cell gene expression (scRNAseq) data, tools for even basic tasks such as clustering or cell type identification for single cell proteomic (scProteomics) data are relatively scarce. Adoption of algorithms that have been developed for scRNAseq into scProteomics is challenged by the larger number of drop-outs, missing data, and noise in single cell proteomic data. Graph contrastive learning (GCL) on cell-to-cell similarity graphs derived from single cell protein expression profiles show promise in cell type identification. However, missing edges and noise in the cell-to-cell similarity graph requires careful design of convolution matrices to overcome the imperfections in these graphs. Here, we introduce\n                    <jats:sc>sc<\/jats:sc>\n                    P\n                    <jats:sc>rofiterole<\/jats:sc>\n                    (Single Cell Proteomics Clustering via Spectral Filters), a computational framework to facilitate effective use of spectral graph filters in GCL-based clustering of single cell proteomic data. Since clustering assumes a homophilic network topology, we consider three types of homophilic filters: (i) random walks, (ii) heat kernels (HK), (iii) beta kernels (BK). Direct implementation of these filters is computationally prohibitive, thus, the filters are either truncated or approximated in practice. To overcome this limitation,\n                    <jats:sc>sc<\/jats:sc>\n                    P\n                    <jats:sc>rofiterole<\/jats:sc>\n                    uses Arnoldi orthonormalization to implement polynomial interpolations of any given spectral graph filter. Our results on comprehensive single cell proteomic data show that (i) GCL with learnable polynomial coefficients that are carefully initialized improves the effectiveness and robustness of cell type identification, (ii) HK and BK improve clustering performance over adjacency matrices or random walks, and (iii) polynomial interpolation of spectral filters outperforms approximation or truncation. The source code for\n                    <jats:sc>sc<\/jats:sc>\n                    P\n                    <jats:sc>rofiterole<\/jats:sc>\n                    and\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/journals.sagepub.com\/doi\/suppl\/10.1177\/15578666261491608\">Supplementary Data<\/jats:ext-link>\n                    are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/mustafaCoskunAgu\/scProfiterole\">https:\/\/github.com\/mustafaCoskunAgu\/scProfiterole<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1177\/15578666261491608","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T13:30:41Z","timestamp":1791552641000},"page":"1035-1070","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>sc<\/scp>\n                    P\n                    <scp>rofiterole<\/scp>\n                    : Clustering of Single-Cell Proteomic Data Using Graph Contrastive Learning via Spectral Filters"],"prefix":"10.1177","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4805-1416","authenticated-orcid":false,"given":"Mustafa","family":"Co\u015fkun","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence and Data Engineering, Ankara University, Ankara, T\u00fcrkiye."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Filipa Blasco","family":"Lopes","sequence":"additional","affiliation":[{"name":"Department of Nutrition, Case Western Reserve University, Cleveland, Ohio, USA."},{"name":"Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, Ohio, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P\u0131nar","family":"Kubilay Tolunay","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Ankara University School of Medicine, Ankara, T\u00fcrkiye."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark R.","family":"Chance","sequence":"additional","affiliation":[{"name":"Department of Nutrition, Case Western Reserve University, Cleveland, Ohio, USA."},{"name":"Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, Ohio, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehmet","family":"Koyut\u00fcrk","sequence":"additional","affiliation":[{"name":"Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, Ohio, USA."},{"name":"Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, Ohio, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mcpro.2023.100518"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-020-0258-4"},{"key":"e_1_3_2_4_1","unstructured":"Chien E Peng J Li P et al. 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