{"id":"W1563425389","doi":"10.1016/j.stem.2015.04.004","title":"Combined Single-Cell Functional and Gene Expression Analysis Resolves Heterogeneity within Stem Cell Populations","year":2015,"lang":"en","type":"article","venue":"Cell stem cell","topic":"Hematopoietic Stem Cell Transplantation","field":"Medicine","cited_by":478,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Cambridge Institute for Medical Research, University of Cambridge; Medical Research Council; Directorate for Biological Sciences; Biotechnology and Biological Sciences Research Council; Hutchison Whampoa Limited; Leukaemia and Lymphoma Research; Canadian Institutes of Health Research; National Institute for Health and Care Research; Cancer Research UK; NIHR Cambridge Biomedical Research Centre; Wellcome Trust","keywords":"Biology; Stem cell; Cell sorting; Gene expression; Haematopoiesis; Single-cell analysis; Cell biology; Computational biology; Cell; Gene; Population; Hematopoietic stem cell; Gene expression profiling; Gene regulatory network; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000644895,0.0002984031,0.0006948864,0.001048164,0.0002918602,0.0008599103,0.0003587117,0.0005347339,0.0005693962],"category_scores_gemma":[0.0005352701,0.0001865184,0.0003474163,0.0007348582,0.0003891306,0.0004881721,0.000399148,0.0006781782,0.00026074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000292658,"about_ca_system_score_gemma":0.000266529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004024946,"about_ca_topic_score_gemma":0.001035773,"domain_scores_codex":[0.9996439,0.00003317701,0.00002574731,0.0001367255,0.0001155065,0.00004500542],"domain_scores_gemma":[0.9995866,0.0001516028,0.00007184476,0.00007458344,0.00007204559,0.00004326503],"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":[0.00003488966,0.00001850547,0.002764426,0.00003841659,0.00001829312,0.00001703788,0.00002320325,0.000562054,0.9926305,0.0001512462,0.00003981227,0.003701584],"study_design_scores_gemma":[0.000008945103,0.000130323,0.07136003,0.000008344387,0.00008996885,0.0001932505,0.00009731896,0.02526338,0.8994039,0.00146653,0.001951038,0.00002707328],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9347228,0.0006410769,0.06019168,0.0001354351,0.00001800058,0.00005223968,0.0028083,0.0003688217,0.001061568],"genre_scores_gemma":[0.9535161,0.0004422847,0.04193494,0.0001693772,0.0000227349,0.0001222467,0.003068561,0.0001031746,0.0006205786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001048164,"threshold_uncertainty_score":0.003410578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07425966831984375,"score_gpt":0.2619755341771907,"score_spread":0.1877158658573469,"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."}}