{"id":"W2980515354","doi":"10.1101/797597","title":"An engineered CRISPR/Cas9 mouse line for simultaneous readout of lineage histories and gene expression profiles in single cells","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Natural Sciences and Engineering Research Council of Canada; Leukemia and Lymphoma Society","keywords":"Biology; CRISPR; Stem cell; Computational biology; Context (archaeology); Transcriptome; Function (biology); Lineage (genetic); Gene; Genome editing; Single-cell analysis; Genetics; Cell; Gene expression","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":[0.0005482485,0.000737279,0.0004911632,0.00145944,0.0005191074,0.000801086,0.0008214344,0.0008954923,0.003875485],"category_scores_gemma":[0.0002329947,0.0005675199,0.0004370364,0.0004526734,0.0007218737,0.0003920546,0.0007154885,0.001462966,0.002300732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005744983,"about_ca_system_score_gemma":0.0006084067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001136271,"about_ca_topic_score_gemma":0.002531244,"domain_scores_codex":[0.9994352,0.00004113335,0.00006174028,0.000195731,0.0001967769,0.00006935479],"domain_scores_gemma":[0.9995384,0.0000924494,0.000134723,0.0001054225,0.00004335781,0.00008555115],"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":[0.00004453311,0.00001872438,0.0001238886,0.00001863926,0.000004171326,0.0000596273,0.00002096156,0.00009664926,0.9970414,0.00072927,0.000179607,0.001662442],"study_design_scores_gemma":[0.00001238442,0.00003212007,0.0007120226,0.000007252781,0.000007260726,0.0002179683,0.00001088493,0.001132911,0.9894549,0.0001518075,0.008248586,0.0000118695],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5552263,0.0009377515,0.4043628,0.0008591286,0.0003559273,0.0005717892,0.01336396,0.00969258,0.01462967],"genre_scores_gemma":[0.6626326,0.001047301,0.263117,0.0003489949,0.00006133616,0.0009594755,0.01129414,0.003114548,0.05742462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003875485,"threshold_uncertainty_score":0.01296484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01271049021169588,"score_gpt":0.216022905898356,"score_spread":0.2033124156866602,"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."}}