{"id":"W4412181795","doi":"10.1038/s41467-025-61118-0","title":"Quantification of transcript isoforms at the single-cell level using SCALPEL","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Surgical Specialties (Canada)","funders":"European Regional Development Fund; Agència de Gestió d'Ajuts Universitaris i de Recerca; Generalitat de Catalunya; European Commission; National Natural Science Foundation of China; Centres de Recerca de Catalunya; Agencia Estatal de Investigación; Ministerio de Ciencia, Innovación y Universidades","keywords":"Gene isoform; Single-cell analysis; Computational biology; Biology; Cell; Cell biology; Computer science; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001928628,0.0001163132,0.0001177019,0.00005198314,0.0003375441,0.00001913177,0.0008791052,0.000302295,0.00000685931],"category_scores_gemma":[0.00006227582,0.00009102088,0.000125675,0.0002399644,0.0002625076,0.000005124561,0.0001543995,0.000305207,0.000002244247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003407713,"about_ca_system_score_gemma":0.00008495736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004713707,"about_ca_topic_score_gemma":0.0006513012,"domain_scores_codex":[0.9992287,0.00008988775,0.0002692828,0.0001821762,0.00009523534,0.0001347287],"domain_scores_gemma":[0.9980916,0.00005507482,0.0001020489,0.001548077,0.0001786272,0.00002461549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003573806,0.0002147559,0.00197569,0.00002035379,0.00003225919,3.006534e-8,0.00006668251,0.00007112106,0.9929407,0.001328633,0.001726416,0.001587673],"study_design_scores_gemma":[0.0004450673,0.00004599169,0.003888356,0.00003296669,0.00007792762,0.000002551232,0.0001203578,0.0008930051,0.8820705,0.0001665213,0.1121136,0.0001430922],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9271538,0.02808697,0.02874622,0.003898915,0.0005898553,0.0005659424,0.0002405818,0.00003009849,0.01068768],"genre_scores_gemma":[0.9940625,0.0004978526,0.003334377,0.000459986,0.00002441515,0.00001132439,0.0003179777,0.00001396771,0.00127757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1108701,"threshold_uncertainty_score":0.3711724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05735735089794705,"score_gpt":0.3016922350769087,"score_spread":0.2443348841789616,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}