Evidence map›Paper›PMID 36993544›Full record

ArticlebioRxiv : the preprint server for biology2023

Dimension-agnostic and granularity-based spatially variable gene identification.

Juexin Wang, Jinpu Li, Skyler T Kramer, Li Su, Yuzhou Chang, Chunhui Xu, Qin Ma, Dong Xu

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Juexin WangDepartment of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA.
Jinpu LiMU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA.
Skyler T KramerMU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA.
Li SuMU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA.
Yuzhou ChangDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
Chunhui XuMU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA.
Qin MaDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
Dong XuDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.
University of Missouri · USThe Ohio State University Comprehensive Cancer Center – Arthur G. James Cancer Hospital and Richard J. Solove Research Institute · USIndiana University – Purdue University Indianapolis · US

Funding

Multi-view self-supervised deep learning for biological sequences and beyondR35GM126985 · NIGMS · UNIVERSITY OF SOUTH FLORIDA · PI DONG XU · 2018 to 2026
$3.8M
Construction of cell specific gene co-regulations signatures based on single cell transcriptomics analysisR01GM131399 · NIGMS · SOUTH DAKOTA STATE UNIVERSITY · PI MA, QIN · 2018 to 2021
$1.3M
NIGMS NIH HHS R01 GM131399NIGMS NIH HHS R35 GM126985
6 · The paper itself

Abstract

Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a spatial granularity-guided and non-parametric model to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This new method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.

Indexed as

granularitynon-parametric statistical modelSpatially variable genesthree-dimensional spatial transcriptomics

Identifiers

PMID36993544
PMCPMC10055351
OpenAlexW4360797543

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-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.