Standardizing workflows in imaging transcriptomics with the abagen toolbox

  1. Ross D Markello  Is a corresponding author
  2. Aurina Arnatkevičiūtė
  3. Jean-Baptiste Poline
  4. Ben D Fulcher
  5. Alex Fornito
  6. Bratislav Misic  Is a corresponding author
  1. McGill University, Canada
  2. Monash University, Australia
  3. University of Sydney, Australia

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 p ≥ 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.

Data availability

All datasets used in this study are publicly available. Detailed information about the datasets and how to access them are described in the manuscript.

The following previously published data sets were used

Article and author information

Author details

  1. Ross D Markello

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    For correspondence
    ross.markello@mail.mcgill.ca
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1057-1336
  2. Aurina Arnatkevičiūtė

    Monash University, Clayton, Australia
    Competing interests
    No competing interests declared.
  3. Jean-Baptiste Poline

    Montreal Neurological Institute & Hospital,, McGill University, Montreal, QC, Canada, Canada
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9794-749X
  4. Ben D Fulcher

    School of Physics, University of Sydney, Sydney, Australia
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3003-4055
  5. Alex Fornito

    Monash University, Clayton, Australia
    Competing interests
    Alex Fornito, Reviewing editor, eLife.
  6. Bratislav Misic

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    For correspondence
    bratislav.misic@mcgill.ca
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0307-2862

Funding

Natural Sciences and Engineering Research Council of Canada (017-04265)

  • Bratislav Misic

National Health and Medical Research Council (3274306)

  • Alex Fornito

National Institutes of Health (NIH-NIBIB P41 EB019936)

  • Jean-Baptiste Poline

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Saad Jbabdi, University of Oxford, United Kingdom

Publication history

  1. Preprint posted: July 9, 2021 (view preprint)
  2. Received: July 12, 2021
  3. Accepted: November 15, 2021
  4. Accepted Manuscript published: November 16, 2021 (version 1)
  5. Version of Record published: December 9, 2021 (version 2)

Copyright

© 2021, Markello et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,111
    Page views
  • 365
    Downloads
  • 31
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Ross D Markello
  2. Aurina Arnatkevičiūtė
  3. Jean-Baptiste Poline
  4. Ben D Fulcher
  5. Alex Fornito
  6. Bratislav Misic
(2021)
Standardizing workflows in imaging transcriptomics with the abagen toolbox
eLife 10:e72129.
https://doi.org/10.7554/eLife.72129

Further reading

    1. Neuroscience
    Jiawen Huang, Isabel Velarde ... Christopher Baldassano
    Research Article

    When forming a memory of an experience that is unfolding over time, we can use our schematic knowledge about the world (constructed based on many prior episodes) to predict what will transpire. We developed a novel paradigm to study how the development of a complex schema influences predictive processes during perception and impacts sequential memory. Participants learned to play a novel board game ('4-in-a-row') across six training sessions, and repeatedly performed a memory test in which they watched and recalled sequences of moves from the game. We found that participants gradually became better at remembering sequences from the game as their schema developed, driven by improved accuracy for schema-consistent moves. Eye tracking revealed that increased predictive eye movements during encoding, which were most prevalent in expert players, were associated with better memory. Our results identify prediction as a mechanism by which schematic knowledge can improve episodic memory.

    1. Neuroscience
    Tarek Amer, Lila Davachi
    Review Article

    Pattern separation, or the process by which highly similar stimuli or experiences in memory are represented by non-overlapping neural ensembles, has typically been ascribed to processes supported by the hippocampus. Converging evidence from a wide range of studies, however, suggests that pattern separation is a multistage process supported by a network of brain regions. Based on this evidence, considered together with related findings from the interference resolution literature, we propose the ‘cortico-hippocampal pattern separation’ (CHiPS) framework, which asserts that brain regions involved in cognitive control play a significant role in pattern separation. Particularly, these regions may contribute to pattern separation by (1) resolving interference in sensory regions that project to the hippocampus, thus regulating its cortical input, or (2) directly modulating hippocampal processes in accordance with task demands. Considering recent interest in how hippocampal operations are modulated by goal states likely represented and regulated by extra-hippocampal regions, we argue that pattern separation is similarly supported by neocortical–hippocampal interactions.