TransFlow: Source-Initialized Flow Transport for Cross-Modality Translation in Single-Cell Multi-Omics

Single-cell multi-omics provides complementary molecular measurements, but many cells are profiled in only one modality, motivating cross-modality translation of unmeasured molecular states. Conditional generative approaches often use the observed source modality as context while initializing the target-generation trajectory from noise. For paired data, however, the source and target measurements describe the same cell, making the encoded source a natural candidate for the initial state. We propose TransFlow, a source-initialized flow framework that transports paired RNA and ATAC representations between modality-specific latent spaces. Block-wise variational autoencoders encode the two modalities, and a shared bidirectional interval-conditioned velocity field models transport in both directions. A controlled noise-initialized variant receives the same source information as conditioning but begins from Gaussian noise, separating trajectory initialization from source availability. Multiple-run evaluations on BMMC and PBMC10k show that TransFlow obtains the lowest distributional scores in the main comparison, while feature-level results vary across datasets and directions. A single-run MiniAtlas experiment shows a similar distributional trend, and TransFlow obtains the best reported results in the evaluated BMMC held-out-cell-type setting. These findings support source-initialized transport as a useful formulation for distribution-level alignment, while fine-grained feature recovery remains challenging.