Evidence map›Paper›PMID 39574747›Full record

ArticlebioRxiv : the preprint server for biology2024

Addressing the mean-variance relationship in spatially resolved transcriptomics data with

Kinnary Shah, Boyi Guo, Stephanie C Hicks

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

3 authors.

Kinnary ShahDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.ORCID 0000-0001-7098-2116
Boyi GuoDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.ORCID 0000-0003-2950-2349
Stephanie C HicksDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.ORCID 0000-0002-7858-0231

Funding

Laminar dissection of cortical human brain gene expression in neuropsychiatric disordersR01MH126393 · NIMH · LIEBER INSTITUTE, INC. · PI MARTINOWICH, KERI · 2021 to 2025
$3.9M
Registration of spatial gene expression in key nodes of reward-related circuitry in the human brainR01DA053581 · NIDA · LIEBER INSTITUTE, INC. · PI MARTINOWICH, KERI · 2021 to 2025
$3.7M
Characterization of high-grade serous ovarian cancer subtypes via single-cell profilingR01CA237170 · NCI · UNIVERSITY OF PENNSYLVANIA · PI DOHERTY, JENNIFER A., GREENE, CASEY S · 2019 to 2024
$3.0M
NCI NIH HHS R01 CA237170NIDA NIH HHS R01 DA053581NIMH NIH HHS R01 MH126393
6 · The paper itself

Abstract

An important task in the analysis of spatially resolved transcriptomics data is to identify spatially variable genes (SVGs), or genes that vary in a 2D space. Current approaches rank SVGs based on either

Indexed as

empirical BayesGaussian process regressionmean-variance biasspatially variable genespatial transcriptomics

Identifiers

PMID39574747
PMCPMC11580860

What OpenQuestion holds

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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.