Evidence map›Paper›PMID 36993340›Full record

ArticlebioRxiv : the preprint server for biology2023

Gene panel selection for targeted spatial transcriptomics.

Yida Zhang, Viktor Petukhov, Evan Biederstedt, Richard Que, Kun Zhang, Peter V Kharchenko

Abstract 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.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Yida ZhangDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Viktor PetukhovDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Evan BiederstedtDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Richard QueDepartment of Bioengineering, University of California San Diego, La Jolla, CA, USA.
Kun ZhangDepartment of Bioengineering, University of California San Diego, La Jolla, CA, USA.
Peter V KharchenkoDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

Funding

Organ Specific ProjectU54HL145608 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SOLDATOV, RUSLAN · 2018 to 2021
$5.6M
NHLBI NIH HHS U54 HL145608
6 · The paper itself

Abstract

Targeted spatial transcriptomics hold particular promise in analysis of complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is that they rely on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method to perform gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.

Indexed as

cell type hierarchygene panel selectionplatform effectsingle cell RNA sequencingtargeted spatial transcriptomics

Identifiers

PMID36993340
PMCPMC10054990

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.