.. _method_overview: =========================================== Model Overview & Architecture =========================================== .. image:: _static/Supp_fig_2_1_v6_crop.png :align: center :width: 100% :alt: scDoRI Architecture and Training Overview **scDoRI** (single-cell Deep Multi-Omic Regulatory Inference) is a computational framework that jointly models paired single-cell RNA-seq and ATAC-seq profiles to infer **enhancer-mediated gene regulatory networks (eGRNs)**. Unlike existing pipelines that treat dimensionality reduction and regulatory inference as distinct modules, scDoRI unifies them in a single encoder--decoder architecture grounded in biological priors. At its core, the model learns **topics** -- regulatory modules that link co-accessible chromatin regions, their cis-mediated target genes, and upstream activator and repressor transcription factors (TFs). Each cell is represented as a **probabilistic mixture over topics**, allowing for a continuous and interpretable view of transcriptional regulation. Architectural Components ------------------------- scDoRI consists of two primary components: **Encoder** ^^^^^^^^^^ - Projects high-dimensional RNA and ATAC profiles into a shared latent topic space. - Comprised of parallel neural networks (one each for RNA and ATAC), with outputs concatenated and mapped into topic logits. - Final output is a topic mixture vector for each cell, constrained via a softmax activation (topics sum up to 1 per cell). **Decoder** ^^^^^^^^^^ The decoder reconstructs observed data modalities from the shared latent topic space, enforcing biologically constrained logic through four modules: **Module 1: ATAC Reconstruction** - Reconstructs peak accessibility using a topic--peak weight matrix. - Includes batch-specific offsets to account for experimental variability. - Applies L1 regularization on topic--peak weights to encourage sparsity for interpretability. **Module 2: RNA-from-ATAC Prediction** - Reconstructs gene expression based on predicted chromatin accessibility from Module 1. - Employs a learnable gene--peak linkage matrix, constrained by genomic proximity (e.g., peaks within 150kb of the gene). **Module 3: TF Expression Reconstruction** - Learns topic-to-TF expression mappings, allowing the latent space to capture transcription factor expression programs. **Module 4: Signed TF--Gene Network Inference** - Computes signed topic-specific TF--gene links by integrating: - Precomputed TF--peak motif scores (activators and repressors) - Topic-wise chromatin accessibility - Gene--peak associations - Topic-level TF expression - Refines scores using a learnable 3D TF--gene--topic matrix. - Produces a final **signed GRN**, used to reconstruct gene expression from TF expression per topic (Module 3). Training Phases --------------- **Phase 1: Topic Construction** ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - The encoder and Modules 1--3 are trained jointly using multimodal reconstruction losses. - Peak accessibility, gene expression, and TF expression are reconstructed from latent topics. - Objective functions include Poisson likelihood loss (ATAC) and Negative Binomial likelihood loss (RNA), with regularization to promote sparsity and interpretability. **Phase 2: GRN Refinement** ^^^^^^^^^^^^^^^^^^^^^^^^^^^ - Module 4 is introduced to learn topic-specific GRNs from chromatin context, peak - gene links and TF- peak links (from insilico-ChIP-seq, introduced in https://www.biorxiv.org/content/10.1101/2022.06.15.496239v1). - Adds trainable activator/repressor TF--gene link matrices per topic. - Predicts RNA from TF expression using inferred GRNs. - Earlier modules can optionally be frozen to preserve previously learned topic representations. Schematic Overview ------------------ The figure above provides a schematic overview of the scDoRI model. Modules are color-coded by training phase, and matrix roles are explicitly annotated. Phase 1 involves joint training of the encoder and Modules 1--3. Phase 2 fine-tunes Module 4 to enable topic-specific GRN inference. ---- :ref:`Back to Main `