Single-cell RNA sequencing enables cellular-level gene expression analysis, but high sparsity and noise pose significant challenges for accurate cell-type identification. Here, we propose sxFusion, a structure-aware clustering method that integrates low-rank representation with orthogonal non-negative matrix factorization. sxFusion first integrates graph-based features using a graph convolutional network and nonlinear embeddings from a multilayer perceptron via dimension-wise gating to enhance feature discriminability and mitigating over-smoothing; it then employs a structure-guided low-rank self-expression model co-optimized with orthogonal non-negative matrix factorization, aligning learned representation directly with clustering assignments, and outputs clustering results straightforward. Benchmarked against eleven state-of-the-art methods on eleven single-cell datasets, sxFusion consistently achieved higher accuracy. Ablation and visualization analyses confirmed the advantages of the fusion and co-optimization modules. These results demonstrate that sxFusion effectively identifies biologically meaningful cell populations, providing robust and interpretable clusters.

Breast cancer subtyping presents a significant clinical and scientific challenge. The prevalent expression-based Prediction Analysis of the Microarray of 50 genes (PAM50) system and its Immunohistochemistry (IHC) surrogate tests showed substantial inconsistencies and did not apply to the rapidly progressing circulating tumor DNA screenings. We developed Unified Genetic and Epigenetic Subtyping (UGES), a new intrinsic subtype classifier, by integrating large-scale DNA-level omics data with a hierarchy learning algorithm. Our benchmarks showed that both multi-step hierarchical learning and using all DNA-level alteration data are crucial, improving the overall AUC score by over 8.3% compared to the one-step multi-classification method. Based on these insights, we developed UGES, a three-step classifier based on 50831 DNA features of 2065 samples, including mutations, copy number aberrations, and methylations. UGES achieved an overall AUC score of 0.963 and greatly improved the clinical stratification of real-world patients, as each subtype strata's survival difference became statistically more significant, P = 9.7e-55 (UGES) vs. 2.2e-47 (PAM50). Finally, UGES identified 52 subtype-specific DNA biomarkers that can be targeted in early screening technology to expand the time window for precision care. The UGES code is freely available at https://github.com/labxscut/UGES.

MOTIVATION: Single-cell multi-omics data provide a comprehensive molecular view of cells. However, single-cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging. RESULTS: In this study, we present a novel algorithm, termed UnionCom, for the unsupervised topological alignment of single-cell multi-omics integration. UnionCom does not require any correspondence information, either among cells or among features. It first embeds the intrinsic low-dimensional structure of each single-cell dataset into a distance matrix of cells within the same dataset and then aligns the cells across single-cell multi-omics datasets by matching the distance matrices via a matrix optimization method. Finally, it projects the distinct unmatched features across single-cell datasets into a common embedding space for feature comparability of the aligned cells. To match the complex non-linear geometrical distorted low-dimensional structures across datasets, UnionCom proposes and adjusts a global scaling parameter on distance matrices for aligning similar topological structures. It does not require one-to-one correspondence among cells across datasets, and it can accommodate samples with dataset-specific cell types. UnionCom outperforms state-of-the-art methods on both simulated and real single-cell multi-omics datasets. UnionCom is robust to parameter choices, as well as subsampling of features. AVAILABILITY AND IMPLEMENTATION: UnionCom software is available at https://github.com/caokai1073/UnionCom. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The advance in single-cell profiling technologies and the development in computational algorithms provide the opportunity to reconstruct pseudo temporal trajectory with branch point of cellular development. On the other hand, theories such as dynamical network biomarkers (DNB) theory have been recently proposed to characterize the pre-transition state in biological systems. Few studies have validated whether the branch point identified in pseudo time is the critical point in dynamical system. In this study, the dynamical behavior of the branch point on the pseudo trajectory has been investigated. We study the pseudo temporal trajectories reconstructed by Wishbone and diffusion pseudotime analysis (DPT) algorithms, as well as the simulated trajectory. DNB theory is applied to justify the bifurcating event on the pseudo trajectories. Our results demonstrate that the branch point recovered by Wishbone and DPT algorithms is confirmed as a transition state in cell differentiation process by DNB theory. Furthermore, we show that an appropriate DNB group will amplify the comprehensive index of critical event as defined in DNB theory. Our study provides biological insights on pseudo trajectory with branch point in a dynamical view and also indicates that DNB theory may serve as a benchmark to check the validity of branch point.

MOTIVATION: Cell fate determination is a continuous process in which one cell type diversifies to other cell types following a hierarchical path. Advancements in single-cell technologies provide the opportunity to reveal the continuum of cell progression which forms a structured continuous tree (SCTree). Computational algorithms, which are usually based on a priori assumptions on the hidden structures, have previously been proposed as a means of recovering pseudo trajectory along cell differentiation process. However, there still lack of statistical framework on the assessments of intrinsic structure embedded in high-dimensional gene expression profile. Inherit noise and cell-to-cell variation underlie the single-cell data, however, pose grand challenges to testing even basic structures, such as linear versus bifurcation. RESULTS: In this study, we propose an adaptive statistical framework, termed SCTree, to test the intrinsic structure of a high-dimensional single-cell dataset. SCTree test is conducted based on the tools derived from metric geometry and random matrix theory. In brief, by extending the Gromov-Farris transform and utilizing semicircular law, we formulate the continuous tree structure testing problem into a signal matrix detection problem. We show that the SCTree test is most powerful when the signal-to-noise ratio exceeds a moderate value. We also demonstrate that SCTree is able to robustly detect linear, single and multiple branching events with simulated datasets and real scRNA-seq datasets. Overall, the SCTree test provides a unified statistical assessment of the significance of the hidden structure of single-cell data. AVAILABILITY AND IMPLEMENTATION: SCTree software is available at https://github.com/XQBai/SCTree-test. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Visualizing and reconstructing cell developmental trajectories intrinsically embedded in high-dimensional expression profiles of single-cell RNA sequencing (scRNA-seq) snapshot data are computationally intriguing, but challenging. RESULTS: We propose DensityPath, an algorithm allowing (i) visualization of the intrinsic structure of scRNA-seq data on an embedded 2-d space and (ii) reconstruction of an optimal cell state-transition path on the density landscape. DensityPath powerfully handles high dimensionality and heterogeneity of scRNA-seq data by (i) revealing the intrinsic structures of data, while adopting a non-linear dimension reduction algorithm, termed elastic embedding, which can preserve both local and global structures of the data; and (ii) extracting the topological features of high-density, level-set clusters from a single-cell multimodal density landscape of transcriptional heterogeneity, as the representative cell states. DensityPath reconstructs the optimal cell state-transition path by finding the geodesic minimum spanning tree of representative cell states on the density landscape, establishing a least action path with the minimum-transition-energy of cell fate decisions. We demonstrate that DensityPath can ably reconstruct complex trajectories of cell development, e.g. those with multiple bifurcating and trifurcating branches, while maintaining computational efficiency. Moreover, DensityPath has high accuracy for pseudotime calculation and branch assignment on real scRNA-seq, as well as simulated datasets. DensityPath is robust to parameter choices, as well as permutations of data. AVAILABILITY AND IMPLEMENTATION: DensityPath software is available at https://github.com/ucasdp/DensityPath. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.