Evidence map›Paper›PMID 34783653›Full record

ArticleeLife2021

Standardizing workflows in imaging transcriptomics with the abagen toolbox.

Ross D Markello, Aurina Arnatkeviciute, Jean-Baptiste Poline, Ben D Fulcher, Alex Fornito, Bratislav Misic

Abstract read
In one paragraph

Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 326 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
326citing papers in PubMed, 5 pooled it
–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

326 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Trial
  7. Trial
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

266 more citing papers are in PubMed but not listed here.

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

6 authors.

Ross D MarkelloMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.ORCID 0000-0003-1057-1336
Aurina ArnatkeviciuteSchool of Psychological Sciences & Monash Biomedical Imaging, Monash University, Clayton, Australia.
Jean-Baptiste PolineMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.ORCID 0000-0002-9794-749X
Ben D FulcherSchool of Physics, University of Sydney, Sydney, Australia.ORCID 0000-0002-3003-4055
Alex FornitoSchool of Psychological Sciences & Monash Biomedical Imaging, Monash University, Clayton, Australia.
Bratislav MisicMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.ORCID 0000-0003-0307-2862

Funding

Resource DiscoveryP41EB019936 · NIBIB · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI David Nelson Kennedy · 2016 to 2026
$15.2M
Large-scale meta-analysis of functional MRI dataR01MH096906 · NIMH · UNIVERSITY OF TEXAS AT AUSTIN · PI Alejandro De La Vega · 2012 to 2026
$7.9M
Exposing the Deep Content of the Publication: Knowledge Extraction for Neuroimaging in Child PsychiatryR01MH083320 · NIMH · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI FRAZIER, JEAN A, KENNEDY, DAVID NELSON · 2009 to 2020
$6.3M
The Neuroimaging Data Model: FAIR descriptors of Brain Initiative Imaging ExperimentsRF1MH120021 · NIMH · UNIVERSITY OF CALIFORNIA-IRVINE · PI KEATOR, DAVID BRYANT · 2019 to 2019
$944k
NIBIB NIH HHS P41 EB019936NIMH NIH HHS R01 MH083320NIMH NIH HHS R01 MH096906NIMH NIH HHS RF1 MH120021
6 · The paper itself

Abstract

Gene expression fundamentally shapes the structural and functional architecture of the human brain. Open-access transcriptomic datasets like the Allen Human Brain Atlas provide an unprecedented ability to examine these mechanisms in vivo; however, a lack of standardization across research groups has given rise to myriad processing pipelines for using these data. Here, we develop the abagen toolbox, an open-access software package for working with transcriptomic data, and use it to examine how methodological variability influences the outcomes of research using the Allen Human Brain Atlas. Applying three prototypical analyses to the outputs of 750,000 unique processing pipelines, we find that choice of pipeline has a large impact on research findings, with parameters commonly varied in the literature influencing correlations between derived gene expression and other imaging phenotypes by as much as ρ ≥ 1.0. Our results further reveal an ordering of parameter importance, with processing steps that influence gene normalization yielding the greatest impact on downstream statistical inferences and conclusions. The presented work and the development of the abagen toolbox lay the foundation for more standardized and systematic research in imaging transcriptomics, and will help to advance future understanding of the influence of gene expression in the human brain.

Indexed as

SoftwareBrainGene Expression ProfilingHumansReference StandardsTranscriptomeWorkflowhumanMRIneuroimagingneuroscienceprocessing variabilitysoftwaretranscriptomics

Identifiers

PMID34783653
PMCPMC8660024

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.