Evidence map›Paper›PMID 38168368›Full record

ArticlebioRxiv : the preprint server for biology2023

Bayesian Hidden Mark Interaction Model for Detecting Spatially Variable Genes in Imaging-Based Spatially Resolved Transcriptomics Data.

Jie Yang, Xi Jiang, Kevin W Jin, Sunyoung Shin, Qiwei Li

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, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 2 countries.

Jie YangDepartment of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas, U.S.A.
Xi JiangDepartment of Statistics and Data Science, Southern Methodist University, Dallas, Texas, U.S.A.
Kevin W JinProgram in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, U.S.A.
Sunyoung ShinDepartment of Mathematics, Pohang University of Science and Technology, Pohang, South Korea.
Qiwei LiDepartment of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas, U.S.A.ORCID 0000-0002-1020-3050
The University of Texas at Dallas · USPohang University of Science and Technology · KRSouthern Methodist University · USYale University · US

Funding

Developing novel algorithms for spatial molecular profiling technologiesR01GM141519 · NIGMS · UT SOUTHWESTERN MEDICAL CENTER · PI XIAO, GUANGHUA · 2021 to 2024
$1.4M
NIGMS NIH HHS R01 GM141519
6 · The paper itself

Abstract

Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on

Indexed as

Bayesian mark interaction modeldouble Metropolis-Hastings algorithmenergy functionspatial transcriptomicsZero-inflated negative binomial mixture model

Identifiers

PMID38168368
PMCPMC10760150
OpenAlexW4389870465

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.