Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.
VenueRepository KITopen (Karlsruhe Institute of Technology) · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsnot available
FundersInstitute of GeneticsNational Human Genome Research InstituteCancer Research UK Cambridge Institute, University of CambridgeHelmholtz Zentrum MünchenBC Cancer AgencyNational Health and Medical Research CouncilEngineering and Physical Sciences Research CouncilMedical Research CouncilCanadian Cancer Society Research InstituteNational Institutes of HealthOncode InstituteBC Cancer FoundationWageningen University and ResearchCancer Research UKLorentz CenterTerry Fox Research InstituteI.M. Sechenov First Moscow State Medical UniversityBroad InstituteUniversität Duisburg-EssenAlan Turing InstituteSwiss Institute of BioinformaticsUniversität ZürichKlaus Tschira StiftungCycle for SurvivalRadboud Universitair Medisch CentrumUniversitair Medisch Centrum GroningenLeids Universitair Medisch CentrumCanadian Institutes of Health ResearchDeutsche ForschungsgemeinschaftNederlandse Organisatie voor Wetenschappelijk OnderzoekEidgenössische Technische Hochschule ZürichBundesministerium für Bildung und ForschungUniversität des SaarlandesInstitute for Research in BiomedicineTechnische Universiteit DelftUniversiteit UtrechtRijksuniversiteit GroningenDeutsche KrebshilfeSystemsX.chUniversiteit LeidenSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungMemorial Sloan-Kettering Cancer CenterWellcome TrustRadboud UniversiteitBarcelona Institute of Science and TechnologyChan Zuckerberg InitiativeEuropean Molecular Biology LaboratoryDeutsches KrebsforschungszentrumUniversiteit van AmsterdamGeorgia State UniversitySilicon Valley Community FoundationJohns Hopkins UniversityPrinceton UniversityUniversity of EdinburghNational Science FoundationMassachusetts General HospitalImperial College London
KeywordsCompendiumField (mathematics)Big dataOpen scienceGrand ChallengesBoomSearch engine indexing
Abstract
fetched live from OpenAlexThe recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands—or even millions—of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.859
Codex and Gemma teacher scores by category
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
