scSpark: an AI-assisted cloud platform for traceable interpretation of single-cell transcriptomic results

Abstract

Single-cell RNA sequencing now routinely produces detailed maps of cell types and states, but interpreting a finished project remains harder than it should be. Once the analysis is done, the results are usually handed over as static reports, figure panels and supplementary tables. A biologist who later wants to revisit an annotation, recompute a cell-type proportion or check whether a pathway is specific to one group typically has to return to a bioinformatician rather than explore the data directly. We developed scSpark to close this gap. The platform takes the completed outputs of a single-cell project: cell annotations, embeddings, differential-expression tables, trajectories, cell-cell communication networks and enrichment results--and serves them through a web browser as an interactive workspace. Heavy computation stays upstream: scSpark indexes the precomputed objects under a single project structure and exposes them through six modules for cell annotation, differential analysis, trajectory exploration, cell-cell communication, functional interpretation and AI-assisted result interrogation. Every action in these modules, from a query to a label change, an export or an AI-generated summary, is linked to a specific project version, data object, parameter set and output file, so that any conclusion can be traced back to the evidence behind it. We illustrate the platform by reworking a published periodontitis dataset through this interface. scSpark does not replace upstream pipelines or expert judgement; it is a layer that makes their results easier to inspect, revise and reuse, and that turns a single-cell project from a one-off report into an interpretation others can follow and check. Significance StatementSingle-cell studies produce increasingly intricate maps of tissues, but those maps are hard to interrogate once they have been written up as static reports. scSpark tackles this post-analysis bottleneck by holding a projects annotations, marker evidence, differential results, pathways, communication networks, AI-generated summaries and publication-ready figures together in one workspace, where each item is linked to the data and settings that produced it. The platform is built to support expert decisions rather than to make them: its aim is to let researchers check, revise and reuse a result, and to see exactly how it was reached.