Multi-Objective Optimized Fuzzy Clustering for Detecting Cell Clusters from Single-Cell Expression Profiles.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31412637", "kind": "science-record", "revision": 8, "content_hash": "sha256:d65b3fbfa5425dff1c67781c0808d7cec69da4ee23abf596a281416e9b3c2f5f", "created_at": "2026-11-01T15:40:48Z", "updated_at": "2026-11-01T22:44:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Multi-Objective Optimized Fuzzy Clustering for Detecting Cell Clusters from Single-Cell Expression Profiles.", "doi": "10.3390/genes10080611", "pmid": "31412637", "authors": ["Saurav Mallik", "Zhongming Zhao"], "journal": "Genes", "year": 2019, "abstract": "Rapid advance in single-cell RNA sequencing (scRNA-seq) allows measurement of the expression of genes at single-cell resolution in complex disease or tissue. While many methods have been developed to detect cell clusters from the scRNA-seq data, this task currently remains a main challenge. We proposed a multi-objective optimization-based fuzzy clustering approach for detecting cell clusters from scRNA-seq data. First, we conducted initial filtering and SCnorm normalization. We considered various case studies by selecting different cluster numbers ( c l = 2 to a user-defined number), and applied fuzzy c-means clustering algorithm individually. From each case, we evaluated the scores of four cluster validity index measures, Partition Entropy ( P E ), Partition Coefficient ( P C ), Modified Partition Coefficient ( M P C ), and Fuzzy Silhouette Index ( F S I ). Next, we set the first measure as minimization objective (↓) and the remaining three as maximization objectives (↑), and then applied a multi-objective decision-making technique, TOPSIS, to identify the best optimal solution. The best optimal solution (case study) that had the highest TOPSIS score was selected as the final optimal clustering. Finally, we obtained differentially expressed genes (DEGs) using Limma through the comparison of expression of the samples between each resultant cluster and the remaining clusters. We applied our approach to a scRNA-seq dataset for the rare intestinal cell type in mice [GEO ID: GSE62270, 23,630 features (genes) and 288 cells]. The optimal cluster result (TOPSIS optimal score= 0.858) comprised two clusters, one with 115 cells and the other 91 cells. The evaluated scores of the four cluster validity indices, F S I , P E , P C , and M P C for the optimized fuzzy clustering were 0.482, 0.578, 0.607, and 0.215, respectively. The Limma analysis identified 1240 DEGs (cluster 1 vs. cluster 2). The top ten gene markers were Rps21, Slc5a1, Crip1, Rpl15, Rpl3, Rpl27a, Khk, Rps3a1, Aldob and Rps17. In this list, Khk (encoding ketohexokinase) is a novel marker for the rare intestinal cell type. In summary, this method is useful to detect cell clusters from scRNA-seq data.", "claims": [{"id": "claim-method", "statement": "In this step, we applied Limma statistical tool [40,54] that used Empirical Bayes test using the cluster information of the cells for each resultant cluster in compared to the rest of the clusters, and generated the p-value for each gene (feature). The p-values were then adjusted by the most stringent p-value correction strategy, Bonferroni method [55]. Those genes that contained adjusted p-value less than 0.05, were considered to be differentially expressed genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31412637", "locator": "sections[7] \"2.5. Identification of Differentially Expressed Genes through Statistical Test\" paragraph 2", "quote": "In this step, we applied Limma statistical tool [40,54] that used Empirical Bayes test using the cluster information of the cells for each resultant cluster in compared to the rest of the clusters, and generated the p-value for each gene (feature). The p-values were then adjusted by the most stringent p-value correction strategy, Bonferroni method [55]. Those genes that contained adjusted p-value less than 0.05, were considered to be differentially expressed genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Of note, our study was conducted on a publicly available dataset GEO ID: GSE62270 that was published by Grun et al. [23].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31412637", "locator": "sections[17] \"4. Discussion\" paragraph 3", "quote": "Of note, our study was conducted on a publicly available dataset GEO ID: GSE62270 that was published by Grun et al. [23]."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:31412637"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:31412637", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/genes10080611", "sha256": "sha256:b2d523d0cc892cd5e5ca90a41d875f38cbec810d4b4ef001e70ff6dcbb3a0e59", "bytes": 45155, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3390/genes10080611", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC6723724", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "In this step, we applied Limma statistical tool [40,54] that used Empirical Bayes test using the cluster information of the cells for each resultant cluster in compared to the rest of the clusters, and generated the p-value for each gene (feature). The p-values were then adjusted by the most stringent p-value correction strategy, Bonferroni method [55]. Those genes that contained adjusted p-value less than 0.05, were considered to be differentially expressed genes."}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["Of note, our study was conducted on a publicly available dataset GEO ID: GSE62270 that was published by Grun et al. [23]."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:31412637"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:48:44Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 3, "usage": {"prompt_tokens": 33465, "completion_tokens": 1377, "cached_tokens": 776}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:31412637", "type": "paper", "label": "pmid:31412637"}, {"id": "source:pmid:31412637", "type": "manuscript", "label": "PMID 31412637 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:31412637", "type": "paper", "label": "pmid:31412637"}, {"id": "source:pmid:31412637", "type": "manuscript", "label": "PMID 31412637 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-31412637", "type": "extraction", "used": ["source:pmid:31412637"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:31412637", "parent_hash": "sha256:2a675e8a540e1b7a9c30cf07800a2f6a5c6e4d30fdcde168e11d699639b8c6c9", "fork_of": null, "generation": 7}, "gaps": ["execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Rapid advance in single-cell RNA sequencing (scRNA-seq) allows measurement of the expres", "Rapid advance in single-cell RNA sequencing (scRNA-seq) allows measurement of the expression of gen", "n developed to detect cell clusters from the scRNA-seq data, this task currently remains a main cha", "ng approach for detecting cell clusters from scRNA-seq data. First, we conducted initial filtering"], "order_basis": "paper_text_order:109", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["Rapid advance in single-cell RNA sequencing (scRNA-seq) allows measurement of the expres"], "order_basis": "paper_text_order:109", "id": "assay:2"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.85, "evidence": ["eq dataset for the rare intestinal cell type in mice [GEO ID: GSE62270, 23,630 features (genes) a"], "order_basis": "paper_text_order:1543", "id": "assay:3"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["In this step, we applied Limma statistical tool [40,54] that used Empirical Bayes test using the cluster information of the cells for each resultant cluster in compared to the rest of the clusters, and generated the p-value for each gene (feature). The p-values were then adjusted by the most stringent p-value correction strategy, Bonferroni method [55]. Those genes that contained adjusted p-value less than 0.05, were considered to be differentially expressed genes."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}