Standardizing workflows in imaging transcriptomics with the abagen toolbox

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.

Trips and neurotransmitters: Discovering principled patterns across 6850 hallucinogenic experiences

“…Publicly available human gene expression data from six whole postmortem brains of neurotypical donors [one female, aged 24 to 57 years (42.5 ± 13.38 years)] were obtained from the Allen Human Brain Atlas ( http://human.brain-map.org ). To strengthen reproducibility and comparability, we have used the abagen tool to map the receptor gene expression information to the 200 Schaefer-Yeo regions [ https://github.com/rmarkello/abagen ; ( 61 )]. Averages of invasive brain tissue probes were computed across all six donors for each of the 40 receptor systems of interest.…”

Whole-brain comparison of rodent and human brains using spatial transcriptomics

“…Human gene expression data was obtained from the Allen Human Brain Atlas (26) . The data were down-loaded from the Allen Institute’s API (http://api.brain-map.org) using the abagen package in Python (https://abagen.readthedocs.io/en/stable/) (51) . We used the microarray data from the brains of all six donors, each of which contains log2 expression values for 58692 gene probes across numerous tissue samples.…”

Integrating neuroimaging and gene expression data using the imaging transcriptomics toolbox

“…To reduce the potential for misassignment, sample-to-region matching was constrained by hemisphere and gross structural divisions (i.e., cortex, subcortex/brainstem, and cerebellum). All tissue samples not assigned to a brain region in the provided atlas were discarded ( Markello et al., 2021 ). Samples were then averaged across donors and normalized, resulting in a final single matrix with rows corresponding to brain regions and columns corresponding to the 15,633 genes.…”