FACT: Gated Fusion-Augmented Causal Mask Transformer for Pseudotime Analysis

Accurately inferring latent orderings from unordered, high-dimensional tabular data is a core challenge in Big Data analytics, spanning domains from biology to finance, healthcare, and recommendation. We present FACT (Fusion-Augmented Causal Mask Transformer), a scalable framework that integrates domain-specific embeddings, gating-based fusion, and causality-inspired attention to infer interpretable trajectories under weak supervision. FACT combines pretrained language model embeddings with raw features, balances them via adaptive gating, and extends causal masking to permutation-invariant Set Transformers to model directional dependencies. To address supervision sparsity, we introduce a hybrid Sinkhorn-margin loss, aligning global orderings while enforcing local consistency. Experiments on biological and non-biological tabular datasets demonstrate that FACT improves ordering accuracy, interpretability, and computational efficiency compared to state-of-the-art baselines. While individual modules are based on established ideas, their integration delivers a practical, generalizable solution for ordering tasks across diverse Big Data applications.