{"id":"W2971748632","doi":"10.1101/753582","title":"A simple method to quantify protein abundances from one thousand 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":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; Zegar Family Foundation; National Institutes of Health; Fondation Leducq; York University; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; American Heart Association","keywords":"Proteomics; Workflow; Embryonic stem cell; Quantitative proteomics; Protocol (science); Abundance (ecology); Lysis; Simple (philosophy)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005399928,0.0006612937,0.000727154,0.0001302633,0.0001296141,0.0002461134,0.0008427328,0.0009045033,0.00005071737],"category_scores_gemma":[0.00008917709,0.0007240364,0.0002619749,0.0002200862,0.00007271976,0.000009110184,0.0005394148,0.0005677884,0.0001237477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007344591,"about_ca_system_score_gemma":0.0005116244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005738038,"about_ca_topic_score_gemma":0.00003269325,"domain_scores_codex":[0.9966075,0.0002488538,0.0005616519,0.001591925,0.0003574641,0.000632553],"domain_scores_gemma":[0.9973671,0.00003663836,0.0003110769,0.001636292,0.0003240134,0.0003248393],"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":[0.0001982143,0.0001935417,0.002134631,0.0002204118,0.0002401463,0.000008201532,0.00001080163,0.0003877972,0.9961413,0.00003860779,0.0004178248,0.000008501413],"study_design_scores_gemma":[0.0005445883,0.0001903095,0.005136576,0.0002420693,0.00009743994,4.271871e-9,0.000002888528,0.0002129643,0.9604591,0.000005191399,0.03220401,0.0009049262],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.905981,0.001245593,0.0893141,0.0001108965,0.0009405803,0.00137489,0.000899262,0.000118114,0.00001555978],"genre_scores_gemma":[0.9358716,0.0001369852,0.06211438,0.000457351,0.0009893423,0.0002043046,0.000005065021,0.0001868614,0.00003412031],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03568228,"threshold_uncertainty_score":0.9995211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01906210057560935,"score_gpt":0.2463456607772007,"score_spread":0.2272835602015914,"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."}}