Assessing the Added Value of scATAC-seq Data in Cell-Specific Gene Regulatory Network Inference for Alzheimer's Disease
Y. Lin (TU Delft - Electrical Engineering, Mathematics and Computer Science)
M.J.T. Reinders – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
I.B. Pronk – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
T. Verlaan – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
S.E. Verwer – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disorder and the most common form of dementia, accounting for an estimated 60-70% of all cases worldwide. Understanding how gene regulation changes in specific brain cell types during disease progression is essential to uncovering the molecular mechanisms of AD. Recent research has introduced new methods to extend Gene Regulatory Network (GRN) inference with ATAC-seq in addition to RNA-seq, including ScReNI.
This paper investigates whether integrating chromatin accessibility into GRN inference can help explain gene regulatory changes in specific brain cell types during AD progression by evaluating whether ScReNI's ATAC-derived terms improve biological support beyond RNA-only inference. Using a Python reimplementation, we decompose the published ScReNI formula and assess formula variants on the mouse retina development benchmark with ChIP-Atlas precision and network-clustering ARI. We then apply the same component analysis to SEA-AD MTG data and evaluate whether literature reported AD-related microglia genes survive feature selection.
The results suggest that removing the regulator-locus term and using TF-specific target-peak attribution improves performance of the ScReNI weight formula on the mouse retina development dataset. However, before ScReNI can support strong AD-specific regulatory claims in SEA-AD, feature selection must retain disease-relevant regulators and validation must be performed using human brain cell-type-specific benchmarks.