Deep inside every tissue, some of the most consequential cells are also the hardest to find. They exist at vanishingly low abundance, appear only transiently within narrow spatial niches, or emerge briefly in response to stress and injury, yet they can steer lineage commitment, drive tissue regeneration, fuel disease progression, enable immune evasion, underpin therapy tolerance, and trigger relapse. Single-cell RNA sequencing has, in principle, given biologists the resolution needed to capture this heterogeneity, but capturing a rare cell and reliably identifying it are two very different problems. A new framework described in the journal Advanced Biotechnology by a team led by Lingling Zheng and Xiao Feng at Sun Yat-sen University now tackles that second problem head-on, and the way it does so is forcing computational biologists to rethink a step most pipelines take for granted: how cells are connected to one another in the first place.
The tool, called scFormer, is a heterogeneous graph transformer framework designed specifically for the identification of rare cell states. To appreciate why it matters, it helps to understand where conventional approaches break down. Most existing methods for analyzing single-cell or spatial transcriptomic data begin by constructing a neighborhood graph based on intercellular similarity, then propagate information among similar cells to smooth out technical noise and learn robust representations. That strategy works well for abundant populations, which are surrounded by legions of near-identical neighbors. But rare cells often lack similar neighbors altogether. When information is propagated across such a graph, their distinctive expression signatures are prone to over-smoothing, and their representations are gradually absorbed into the dominant populations around them. The rare signal simply dissolves. In multi-sample analyses the problem compounds, because batch correction procedures, which are designed to align datasets collected in different experiments, can further weaken the already fragile signals of low-abundance cell states.
ScFormer’s central contribution lies in redefining how graph connectivity is established. Instead of building a global cell neighborhood first and then hunting for rare populations within it, the method prioritizes gene specificity. In the primary analysis, the model selects twenty highly specific candidate genes for each cell based on Z-scores, and then constructs bidirectional connections between cells and genes accordingly. This is a subtle but powerful shift. Even when a rare cell has no similar cellular neighbors, it can still establish indirect associations with other cells through shared gene nodes. Its specific expression pattern is therefore preserved at the graph construction stage rather than being eroded before analysis even begins. In effect, the genome itself becomes the bridge that keeps unusual cells tethered to the rest of the data.
On top of this specificity-driven graph, scFormer layers a heterogeneous graph transformer that employs attention mechanisms designed for distinct node types and relation types, learning low-dimensional representations of both cells and genes. This architecture builds on a growing family of graph-based approaches for single-cell biology, including heterogeneous graph transformers used for biological network inference, heterophily-aware graph neural networks for cell type annotation and novel cell detection, and single-cell graph transformers for rare population inference in multi-omics settings. ScFormer integrates representation learning, clustering, and optional batch correction into a single unified optimization framework. The clustering component applies discriminative constraints derived from pseudo-labels generated by an initial clustering pass, while simultaneously incorporating objectives for expression reconstruction and intra-cluster compactness, so that the learned representation is faithful to the underlying expression data and pulls cells of the same state tightly together.
For multi-batch datasets, the model can further incorporate losses for global distribution alignment and local structure preservation. This dual objective is designed to mitigate batch effects, the systematic technical differences that arise when cells are processed in separate experiments, while deliberately seeking to retain rare biological signals rather than averaging them away. The distinction is crucial. Traditional integration methods measure success by how well cells from different batches intermix, but aggressive alignment can erase exactly the low-abundance states that biologists most want to find. By optimizing for both alignment and the preservation of local structure, scFormer attempts to walk the narrow line between removing technical artifacts and destroying biological reality.
The benchmarking results are striking. Across 125 Splatter-simulated datasets generated with an extreme 99:1 imbalance ratio, a regime in which the target population constitutes just one percent of all cells, scFormer achieved a mean F1 score of 0.988 with an F1 variance of only 0.008, indicating both high accuracy and remarkable stability. On 18 real datasets, where reference rare populations were defined as cell types with an abundance of no more than five percent according to original study annotations, scFormer achieved the highest mean F1 score among the compared methods at 0.595, reaching 0.955 on the mouse retina dataset. These comparisons included dedicated rare-cell detection tools such as scCAD, a cluster decomposition-based anomaly detection method, putting scFormer’s performance in direct context with the state of the art.
The multi-batch tests may be the most consequential for everyday practice. On a peripheral blood mononuclear cell dataset comprising 30,669 cells across four batches, scFormer achieved an integrated least-squares… more precisely, an iLISI score of 2.695, exceeding the 2.191 achieved by Harmony, one of the most widely adopted batch integration tools in the field. At the same time, the framework delivered an F1 score of 0.675 for rare-cell identification, with precision of 0.977 and recall of 0.516. That precision figure deserves emphasis: when scFormer flags a cell as rare, it is almost always right, a property that matters enormously for experimentalists who must decide which computationally identified populations to spend limited resources validating at the bench. Collectively, these results demonstrate that scFormer delivers stable performance in rare-cell identification across complex, imbalanced datasets and multi-batch data, a combination that has historically forced researchers to choose between integration quality and rare-state sensitivity.
Beyond the quantitative benchmarks, the authors examined whether the method could recover biologically known populations and plausible candidate states in real data, and the results read like a tour of recent single-cell biology. In a mouse airway epithelium dataset, scFormer detected Foxi1-positive pulmonary ionocytes and goblet cells, and identified two candidate states associated with basal cells and club cells respectively, both exhibiting proliferative features. Notably, these populations remained partially detectable even after the authors removed a set of cell-cycle-related genes, suggesting the identifications were not merely artifacts of proliferation signatures. In a mouse intestinal crypt regeneration dataset, the model identified Clu- and Anxa1-expressing revival stem cells, the damage-responsive stem cell population discovered in prior work on the regenerating intestine, as well as low-abundance immune subpopulations with B-cell and plasmacytoid dendritic cell characteristics that were detected almost exclusively in unirradiated samples. These subpopulations exhibited compositional differences associated with tissue injury status, hinting at how rare immune states shift as tissue damage unfolds.
Perhaps the most visually compelling demonstration came from spatial transcriptomics. In a representative section of the sci-Space mouse embryo dataset, an embryo-scale spatial transcriptomic resource, scFormer mapped a biliary-epithelial-like state at 0.92 percent abundance and a pancreatic-marker-enriched state at 0.52 percent abundance back to their original spatial coordinates. The two states exhibited spatial self-enrichment of 35.74-fold and 89.52-fold respectively relative to a permuted background, indicating pronounced spatial aggregation within the section. In other words, these vanishingly rare states were not scattered randomly across the embryo but concentrated in coherent anatomical locations, exactly the pattern one would expect of biologically meaningful, spatially organized cell states. The finding showcases the promise of applying specificity-driven graph learning to spatial data, where the combination of low abundance and spatial context carries rich developmental information.
The significance of scFormer extends beyond accurate identification of rare cell states. By allowing highly specific genes to participate in defining connectivity, the method reduces dependence on high-abundance cell neighborhoods and offers a promising route to rare-state discovery in multimodal atlases, perturbation screens, and larger-scale tissue datasets. As single-cell and spatial atlases continue to grow in size and complexity, ensuring that low-abundance yet biologically meaningful states remain detectable will become increasingly important, both for basic developmental biology and for translational questions ranging from tumor heterogeneity to regenerative medicine. ScFormer represents a viable starting point in this regard. Its subsequent validation across multimodal data, perturbation screens, and clinical samples is expected to further test and extend the value of the specificity-driven approach, and if those validations hold, the humble step of deciding which nodes in a graph should be connected may prove to be where rare biology was hiding all along.
The benchmarking design itself reflects the difficulty of this problem. Simulated data, generated with tools such as Splatter, allow ground truth to be known exactly, which is why the 99:1 imbalance experiments could yield precise F1 measurements; real datasets, by contrast, rely on annotations from prior studies as reference labels, meaning that apparent false positives may sometimes represent genuinely novel states rather than errors. This helps explain why the gap between simulated and real-data performance, from 0.988 to a mean of 0.595, is not necessarily a weakness of the method alone but also a reflection of imperfect reference annotations. The high precision paired with lower recall on the multi-batch PBMC data suggests the framework behaves conservatively, favoring confident calls over exhaustive recovery.
The choice of Z-scores for selecting candidate genes is also notable. A Z-score measures how strongly a gene’s expression in a given cell deviates from its typical behavior, making it a natural way to identify genes that distinguish an individual cell from the population at large. This per-cell, rather than per-cluster, selection is what enables the method to preserve signals from states too small to form their own cluster during initial analysis, breaking the common dependency between clustering quality and downstream rare-cell detection that constrains many existing pipelines.
For experimentalists, the practical implication is that tools of this kind could serve as a screening layer, nominating small, spatially coherent populations for targeted follow-up by markers such as Foxi1 or Clu before committing resources to sequencing or functional assays.
Subject of Research: A specificity-driven cell-gene graph transformer framework for identifying rare cell states in single-cell and spatial transcriptomic data
Article Title: Specificity-driven graph learning for the identification of rare cell states
Article References: Hou, L., & Hu, Z. (2026). Specificity-driven graph learning for the identification of rare cell states. Advanced Biotechnology, 4(3), Article 35. https://doi.org/10.1007/s44307-026-00132-9
Image Credits: AI Generated
DOI: 10.1007/s44307-026-00132-9
Keywords: scFormer, rare cell states, single-cell RNA sequencing, spatial transcriptomics, heterogeneous graph transformer, graph learning, batch correction, clustering, mouse embryo atlas, revival stem cells, pulmonary ionocytes, computational biology
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Drew Townsend. (September 3, 2026). Graph Transformer Puts Rare Cell States on the Map in Single-Cell Data. Scienmag. https://scienmag.com/graph-transformer-puts-rare-cell-states-on-the-map-in-single-cell-data/
Drew Townsend. “Graph Transformer Puts Rare Cell States on the Map in Single-Cell Data.” Scienmag, 3 September 2026, https://scienmag.com/graph-transformer-puts-rare-cell-states-on-the-map-in-single-cell-data/. Accessed 3 September 2026.
Drew Townsend. “Graph Transformer Puts Rare Cell States on the Map in Single-Cell Data.” Scienmag. September 3, 2026. https://scienmag.com/graph-transformer-puts-rare-cell-states-on-the-map-in-single-cell-data/
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Tags: advanced biotechnology in single-cell databatch correctioncell connectivity in single-cell dataclusteringcomputational biologycomputational biology for rare cell detectiondisease progression and immune evasiongraph learninggraph-based machine learning in biologyheterogeneous graph transformerlineage commitment and tissue regenerationmouse embryo atlaspulmonary ionocytesrare cell state identificationrare cell statesrevival stem cellsscFormerscFormer frameworkSingle-Cell RNA Sequencingsingle-cell RNA sequencing analysisSpatial transcriptomicsspatial transcriptomics analysistissue heterogeneity and cell subpopulations
