Article in Cell reports methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
7 authors.
Nghia MillardCenter for Data Sciences, Brigham and Women's Hospital, Boston, MA 02115, USA.
Ilya KorsunskyCenter for Data Sciences, Brigham and Women's Hospital, Boston, MA 02115, USA.
Kathryn WeinandCenter for Data Sciences, Brigham and Women's Hospital, Boston, MA 02115, USA.
Chamith Y FonsekaCenter for Data Sciences, Brigham and Women's Hospital, Boston, MA 02115, USA.
Aparna NathanCenter for Data Sciences, Brigham and Women's Hospital, Boston, MA 02115, USA.
Joyce B KangCenter for Data Sciences, Brigham and Women's Hospital, Boston, MA 02115, USA.
Soumya RaychaudhuriCenter for Data Sciences, Brigham and Women's Hospital, Boston, MA 02115, USA.
Funding
Medical Scientist Training ProgramT32GM007753 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI WALENSKY, LOREN DAVID · 1985 to 2021
$50.0M
Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
MECHANISMS OF ARTHRITIC &DERMATOLOGY DISORDERST32AR007530 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI Michael B. Brenner, Ellen M Gravallese · 1986 to 2026
$10.4M
PEARL: Pathway Exploration and Analysis in Renal LupusUH2AR067688 · NIAMS · FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH · PI DIAMOND, BETTY, WOFSY, DAVID · 2014 to 2020
$9.0M
Tissue Acquisition Research GroupUM2AR067678 · NIAMS · STANFORD UNIVERSITY · PI HOLERS, VERNON MICHAEL, UTZ, PAUL JOSEPH · 2014 to 2020
$8.2M
Multi-Ethnic Translational Research Optimization (METRO) Lupus ConsortiumUH2AR067689 · NIAMS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI BUYON, JILL P, PUTTERMAN, CHAIM · 2014 to 2020
$6.3M
Evolving Adaptive and Effector Mechanisms from Pre-RA Through Established DiseaseUH2AR067681 · NIAMS · UNIVERSITY OF COLORADO DENVER · PI HOLERS, VERNON MICHAEL · 2014 to 2020
$5.5M
Discovery and Functional Impact of Common and Rare Variants in RAR01AR063759 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI Soumya Raychaudhuri · 2013 to 2026
$5.4M
Molecular and Cellular Dissection of Early Rheumatoid ArthritisUH2AR067694 · NIAMS · FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH · PI BRENNER, MICHAEL B., GREGERSEN, PETER K. · 2014 to 2020
$5.4M
Powering whole genome sequence-based genetic discovery for common human diseases- Extended 2021-2022.U01HG009088 · NHGRI · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIN, XIHONG, NEALE, BENJAMIN MICHAEL · 2016 to 2021
$5.1M
RA-SLE Molecular Deconstruction Leadership CenterUH2AR067677 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI BRENNER, MICHAEL B., RAYCHAUDHURI, SOUMYA · 2014 to 2020
$4.3M
Predicting the impact of genetic variants, genes and pathways on human DiseaseU01HG012009 · NHGRI · BRIGHAM AND WOMEN'S HOSPITAL · PI ALKES L PRICE, Soumya Raychaudhuri · 2021 to 2026
To estimate a study design's power to detect differential abundance, we require a framework that simulates many multi-sample single-cell datasets. However, current simulation methods are challenging for large-scale power analyses because they are computationally resource intensive and do not support easy simulation of multi-sample datasets. Current methods also lack modeling of important inter-sample variation, such as the variation in the frequency of cell states between samples that is observed in single-cell data. Thus, we developed single-cell POwer Simulation Tool (scPOST) to address these limitations and help investigators quickly simulate multi-sample single-cell datasets. Users may explore a range of effect sizes and study design choices (such as increasing the number of samples or cells per sample) to determine their effect on power, and thus choose the optimal study design for their planned experiments.
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Maximizing statistical power to detect differentially abundant cell states with scPOST. · full record | OpenQuestion