Guo, W.-F. | bioRxiv Science

bioRxiv2022

LANTSA: Landmark-based transferable subspace analysis for single-cell and spatial transcriptomics

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) technologies provide new insights to understand tissue organization and biological function. Accurately capturing the relationships of samples (e.g., sequenced cells, spatial locations) will result in reliable and consistent outcomes in downstream analyses. However, this undertaking remains a challenge for large-volume or cross-platform datasets due to transcriptional heterogeneity and high computational demands. Here, we introduce landmark-based transferable subspace analysis (LANTSA) to solve such challenges for scRNA-seq and ST datasets. Specifically, LANTSA constructs a representation graph of samples for clustering and visualization based on a novel subspace model, which can learn a more accurate representation and is theoretically proven to be linearly proportional to data size in terms of the time consumption. Furthermore, LANTSA uses a dimensionality reduction technique as an integrative method to extract the discriminants underlying the representation structure, which enables label transfer from one (learning) dataset (i.e., scRNA-seq profiles) to the other (prediction) datasets (e.g., scRNA-seq or ST profiles), thus solving the massive-volume or cross-platform problem. We demonstrated the superiority of LANTSA to identify accurate data structures via clustering evaluation on benchmark datasets of various scRNA-seq protocols, 10x Visium, and Slide-seq ST platforms. Moreover, we confirmed the integration capability of LANTSA to transfer cell annotation on large-scale and cross-platform scRNA-seq datasets. Finally, we validated the effectiveness of LANTSA for the identification of multiple mouse brain areas as well as the spatial mapping of cell types within cortical layers by integrating scRNA-seq and ST data.

bioinformatics↗

bioRxiv2018

A novel structure-based control method for analyzing nonlinear dynamics in biological networks

Exploring complex biological systems requires adequate knowledge of the systems underlying wiring diagram but not its specific functional forms. Thus, exploration actually requires the concepts and approaches delivered by structure-based network control, which investigates the controllability of complex networks through a minimum set of input nodes. Traditional structure-based control methods focus on the structure of complex systems with linear dynamics and may not match the meaning of control well in some biological systems. Here we took into consideration the nonlinear dynamics of some biological networks and formalized the nonlinear control problem of undirected dynamical networks (NCU). Then, we designed and implemented a novel and general graphic-theoretic algorithm (NCUA) from the perspective of the feedback vertex set to discover the possible minimum sets of the input nodes in controlling the network state. We applied our NCUA to both synthetic networks and real-world networks to investigate how the network parameters, such as the scaling exponent and the degree heterogeneity, affect the control characteristics of networks with nonlinear dynamics. The NCUA was applied to analyze the patient-specific molecular networks corresponding to patients across multiple datasets from The Cancer Genome Atlas (TCGA), which demonstrates the advantages of the nonlinear control method to characterize and quantify the patient-state change over the other state-of-the-art linear control methods. Thus, our model opens a new way to control the undesired transition of cancer states and provides a powerful tool for theoretical research on network control, especially in biological fields.\n\nAuthor summaryComplex biological systems usually have nonlinear dynamics, such as the biological gene (protein) interaction network and gene co-expression networks. However, most of the structure-based network control methods focus on the structure of complex systems with linear dynamics. Thus, the ultimate purpose to control biological networks is still too complicated to be directly solved by such network control methods. We currently lack a framework to control the biological networks with nonlinear and undirected dynamics theoretically and computationally. Here, we discuss the concept of the nonlinear control problem of undirected dynamical networks (NCU) and present the novel graphic-theoretic algorithm from the perspective of a feedback vertex set for identifying the possible sets with minimum input nodes in controlling the networks. The NCUA searches the minimum set of input nodes to drive the network from the undesired attractor to the desired attractor, which is different from conventional linear network control, such as that found in the Maximum Matching Sets (MMS) and Minimum Dominating Sets (MDS) algorithms. In this work, we evaluated the NCUA on multiple synthetic scale-free networks and real complex networks with nonlinear dynamics and found the novel control characteristics of the undirected scale-free networks. We used the NCUA to thoroughly investigate the sample-specific networks and their nonlinear controllability corresponding to cancer samples from TCGA which are enriched with known driver genes and known drug target as controls of pathologic phenotype transitions. We found that our NCUA control method has a better predicted performance for indicating and quantifying the patient biological system changes than that of the state-of-the-art linear control methods. Our approach provides a powerful tool for theoretical research on network control, especially in a range of biological fields.

systems biology↗