A novel method for integrating transcriptomics and neuroimaging
Bibliographic record
Abstract
Abstract Background Positron emission tomography (PET) imaging has been playing a fundamental role in diagnosis of Alzheimer’s disease (AD). Also, in the context of AD, blood‐based biomarkers that are capable of predicting PET brain imaging findings are of high interest. Both PET imaging and transcriptomics are rich sources of different biological information. Hence, an interesting strategy to find potential novel blood biomarkers is by integrating PET imaging data with blood transcriptomics. We aim to develop a method for combining blood transcriptomics profile and PET imaging data, which may highlight novel AD biomarkers. Here, we hypothesize that by integrating blood transcriptomics and PET imaging data we will be able to identify clinically relevant novel peripheral biomarkers. Method Imaging and transcriptomics data were acquired from Alzheimer’s Disease Neuroimaging Initiative (ADNI). Microarray gene expression profiling from blood samples of 69 cognitively unimpaired (CU) individuals and 158 mild cognitively impaired (MCI) were submitted to differential expression (DE) analysis using the limma R package. The [F18]FDG‐PET standardized uptake ratio (SUVr), using cerebellum as the reference region, were calculated. Genes obtained from DE analysis were selected to undergo integration with [F18]FDG‐PET images using voxel‐wise generalized linear regressions (GLR) (RMINC package). Results The DE analysis resulted in 1232 differentially expressed genes (DEGs) (p‐value < 0.05). The GLR computed the associations between gene expression and [F18]FDG for each voxel, resulting in t‐value maps. Afterwards, only gray matter voxels presenting absolute t‐values higher than 2.3 were retained. Then, we transformed t‐value maps into proportion maps. In brief, each volume of interest (VOI) shows the percentage of voxels statistically correlated with gene expression (see Figure 1). Finally, we ranked DEGs according to the amount of VOIs with a proportion higher than 30%. Conclusion With the use of the proposed method, we were able to integrate blood transcriptomics with PET neuroimaging. The implementation of this method shows great potential in the search for new blood biomarkers, which can accelerate early diagnosis and provides a template for future research in the field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".