Evidence map›Paper›PMID 38045476›Full record

ArticleArXiv2023

A SELECTIVE REVIEW OF RECENT DEVELOPMENTS IN SPATIALLY VARIABLE GENE DETECTION FOR SPATIAL TRANSCRIPTOMICS.

Sikta Das Adhikari, Jiaxin Yang, Jianrong Wang, Yuehua Cui

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 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.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Sikta Das AdhikariDepartment of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA.
Jiaxin YangDepartment of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.
Jianrong WangDepartment of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.
Yuehua CuiDepartment of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA.

Funding

Statistical modeling of long-range chromatin interactions on gene regulation and underlying molecularR01GM131398 · NIGMS · MICHIGAN STATE UNIVERSITY · PI WANG, JIANRONG · 2018 to 2021
$1.3M
NIGMS NIH HHS R01 GM131398
6 · The paper itself

Abstract

With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs. Over the past few years, a plethora of new methods have been proposed for the detection of SVGs, accompanied by numerous innovative concepts and discussions. This article provides a selective review of methods and their practical implementations, offering valuable insights into the current literature in this field.

Indexed as

Single cell RNA sequencingSpatially resolved transcriptomicsSpatially variable genesSpatial transcriptomics

Identifiers

PMID38045476
PMCPMC10690303

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.