Evidence map›Paper›PMID 38978101›Full record

ArticleGenome biology2024

Detection of allele-specific expression in spatial transcriptomics with spASE.

Luli S Zou, Dylan M Cable, Irving A Barrera-Lopez, Tongtong Zhao, Evan Murray, Martin J Aryee, Fei Chen, Rafael A Irizarry

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Spatial architecture of development and disease.Nature reviews. Genetics · 2026
    Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Luli S Zou *Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Dylan M Cable *Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
Irving A Barrera-LopezBroad Institute of Harvard and MIT, Cambridge, MA, 02142, USA.
Tongtong ZhaoBroad Institute of Harvard and MIT, Cambridge, MA, 02142, USA.
Evan MurrayBroad Institute of Harvard and MIT, Cambridge, MA, 02142, USA.
Martin J AryeeDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Fei Chen *Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA.
Rafael A Irizarry *Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. rafa@ds.dfci.harvard.edu.ORCID 0000-0002-3944-4309

Funding

COVID19 Slide-seqR01HG010647 · NHGRI · BROAD INSTITUTE, INC. · PI Fei Chen, Evan Z Macosko · 2019 to 2026
$6.0M
Data Analysis Tools for Emerging High-Throughput TechnologiesR35GM131802 · NIGMS · DANA-FARBER CANCER INST · PI Rafael Angel Irizarry · 2019 to 2026
$4.1M
Overcoming bias and unwanted variability in next generation sequencingR01HG005220 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI IRIZARRY, RAFAEL ANGEL · 2010 to 2018
$3.6M
Characterizing glioma heterogeneity with novel multiplexed nanoscale imaging technologiesDP5OD024583 · OD · BROAD INSTITUTE, INC. · PI CHEN, FEI · 2017 to 2021
$2.2M
IMAT-ITCR Collaboration: An integrated experimental and computational platform for analyzing the spatial organization of tumor clonesR33CA246455 · NCI · BROAD INSTITUTE, INC. · PI ASHENBERG, ORR, CHEN, FEI · 2020 to 2022
$1.4M
NCI NIH HHS R33 CA246455NHGRI NIH HHS R01 HG005220NHGRI NIH HHS R01 HG010647NIGMS NIH HHS R35 GM131802NIH HHS DP5 OD024583NIH HHS R01HG005220NIH HHS R01HG010647NIH HHS R33CA246455NIH HHS R35GM131802NIH Office of the Director DP5OD024583
6 · The paper itself

Abstract

Spatial transcriptomics technologies permit the study of the spatial distribution of RNA at near-single-cell resolution genome-wide. However, the feasibility of studying spatial allele-specific expression (ASE) from these data remains uncharacterized. Here, we introduce spASE, a computational framework for detecting and estimating spatial ASE. To tackle the challenges presented by cell type mixtures and a low signal to noise ratio, we implement a hierarchical model involving additive mixtures of spatial smoothing splines. We apply our method to allele-resolved Visium and Slide-seq from the mouse cerebellum and hippocampus and report new insight into the landscape of spatial and cell type-specific ASE therein.

Indexed as

AllelesCerebellumTranscriptomeAnimalsGene Expression ProfilingHippocampusMiceSingle-Cell AnalysisAllele-specific expressionSpatial transcriptomics

Identifiers

PMID38978101
PMCPMC11229351

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Registered trials

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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.