Standardizing workflows in imaging transcriptomics with the abagen toolbox
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2021-11-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/72129 |
| _version_ | 1811180372482850816 |
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| author | Ross D Markello Aurina Arnatkeviciute Jean-Baptiste Poline Ben D Fulcher Alex Fornito Bratislav Misic |
| author_facet | Ross D Markello Aurina Arnatkeviciute Jean-Baptiste Poline Ben D Fulcher Alex Fornito Bratislav Misic |
| author_sort | Ross D Markello |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T09:02:12Z |
| format | Article |
| id | doaj.art-c416e6f15f4d4c81bdf3c99cab557229 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-11T09:02:12Z |
| publishDate | 2021-11-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-c416e6f15f4d4c81bdf3c99cab5572292022-12-22T04:32:45ZengeLife Sciences Publications LtdeLife2050-084X2021-11-011010.7554/eLife.72129Standardizing workflows in imaging transcriptomics with the abagen toolboxRoss D Markello0https://orcid.org/0000-0003-1057-1336Aurina Arnatkeviciute1Jean-Baptiste Poline2https://orcid.org/0000-0002-9794-749XBen D Fulcher3https://orcid.org/0000-0002-3003-4055Alex Fornito4Bratislav Misic5https://orcid.org/0000-0003-0307-2862McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, CanadaSchool of Psychological Sciences & Monash Biomedical Imaging, Monash University, Clayton, AustraliaMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, CanadaSchool of Physics, University of Sydney, Sydney, AustraliaSchool of Psychological Sciences & Monash Biomedical Imaging, Monash University, Clayton, AustraliaMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, CanadaGene 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.https://elifesciences.org/articles/72129transcriptomicsneuroimagingMRIprocessing variabilitysoftware |
| spellingShingle | Ross D Markello Aurina Arnatkeviciute Jean-Baptiste Poline Ben D Fulcher Alex Fornito Bratislav Misic Standardizing workflows in imaging transcriptomics with the abagen toolbox eLife transcriptomics neuroimaging MRI processing variability software |
| title | Standardizing workflows in imaging transcriptomics with the abagen toolbox |
| title_full | Standardizing workflows in imaging transcriptomics with the abagen toolbox |
| title_fullStr | Standardizing workflows in imaging transcriptomics with the abagen toolbox |
| title_full_unstemmed | Standardizing workflows in imaging transcriptomics with the abagen toolbox |
| title_short | Standardizing workflows in imaging transcriptomics with the abagen toolbox |
| title_sort | standardizing workflows in imaging transcriptomics with the abagen toolbox |
| topic | transcriptomics neuroimaging MRI processing variability software |
| url | https://elifesciences.org/articles/72129 |
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