Evidence map›Paper›PMID 37398321›Full record

ArticlebioRxiv : the preprint server for biology2024

Visualizing scRNA-Seq Data at Population Scale with GloScope.

Hao Wang, William Torous, Boying Gong, Elizabeth Purdom

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Hao WangDivision of Biostatistics, University of California, Berkeley, CA, USA.ORCID 0000-0002-0749-474X
William TorousDepartment of Statistics, University of California, Berkeley, CA, USA.
Boying GongDivision of Biostatistics, University of California, Berkeley, CA, USA.ORCID 0000-0001-7326-4603
Elizabeth PurdomDepartment of Statistics, University of California, Berkeley, CA, USA.ORCID 0000-0001-9455-7990

Funding

Generation of novel cell type specific mouse genetic toolsU19MH114830 · NIMH · ALLEN INSTITUTE · PI ZENG, HONGKUI · 2017 to 2021
$64.7M
Supplement for Programs of Gene Expression in Olfactory NeurogenesisR01DC007235 · NIDCD · UNIVERSITY OF CALIFORNIA BERKELEY · PI DUDOIT, SANDRINE · 2005 to 2022
$6.6M
A statistical framework for disease classification with scRNA-Seq dataR01GM144493 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI PURDOM, ELIZABETH · 2022 to 2025
$1.2M
NIDCD NIH HHS R01 DC007235NIGMS NIH HHS R01 GM144493
6 · The paper itself

Abstract

Increasingly, scRNA-Seq studies explore cell populations across different samples and the effect of sample heterogeneity on organism's phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call a GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples and demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.

Indexed as

batch effect detection and visualizationdensity estimationscRNA-SeqSingle cell sequencing data

Identifiers

PMID37398321
PMCPMC10312527

What OpenQuestion holds

Textmetadata
LicenceCC BY-ND
Read underepoch 390

Registered trials

None linked

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.