node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking.

Computational methods in biology can infer large molecular interaction networks from multiple data sources and at different resolutions, creating unprecedented opportunities to explore the mechanisms driving complex biological phenomena. Networks can be built to represent distinct conditions and compared to uncover graph-level differences-such as when comparing patterns of gene-gene interactions that change between biological states. Given the importance of the graph comparison problem, there is a clear and growing need for robust and scalable methods that can identify meaningful differences. We introduce node2vec2rank (n2v2r), a method for graph differential analysis that ranks nodes according to the disparities of their representations in joint latent embedding spaces. Improving upon previous bag-of-features approaches, we take advantage of recent advances in machine learning and statistics to compare graphs in higher-order structures and in a data-driven manner. Formulated as a multi-layer spectral embedding algorithm, n2v2r is computationally efficient, incorporates stability as a key feature, and can provably identify the correct ranking of differences between graphs in an overall procedure that adheres to veridical data science principles. By better adapting to the data, node2vec2rank clearly outperformed the commonly used node degree in finding complex differences in simulated data. In the real-world applications of breast cancer subtype characterization, analysis of cell cycle in single-cell data, and searching for sex differences in lung adenocarcinoma, node2vec2rank found meaningful biological differences enabling the hypothesis generation for therapeutic candidates. Software and analysis pipelines implementing n2v2r and used for the analyses presented here are publicly available.

Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data.

Effective dimension reduction is essential for single cell RNA-seq (scRNAseq) analysis. Principal component analysis (PCA) is widely used, but requires continuous, normally-distributed data; therefore, it is often coupled with log-transformation in scRNAseq applications, which can distort the data and obscure meaningful variation. We describe correspondence analysis (CA), a count-based alternative to PCA. CA is based on decomposition of a chi-squared residual matrix, avoiding distortive log-transformation. To address overdispersion and high sparsity in scRNAseq data, we propose five adaptations of CA, which are fast, scalable, and outperform standard CA and glmPCA, to compute cell embeddings with more performant or comparable clustering accuracy in 8 out of 9 datasets. In particular, we find that CA with Freeman-Tukey residuals performs especially well across diverse datasets. Other advantages of the CA framework include visualization of associations between genes and cell populations in a "CA biplot," and extension to multi-table analysis; we introduce corralm for integrative multi-table dimension reduction of scRNAseq data. We implement CA for scRNAseq data in corral, an R/Bioconductor package which interfaces directly with single cell classes in Bioconductor. Switching from PCA to CA is achieved through a simple pipeline substitution and improves dimension reduction of scRNAseq datasets.

Impact of Data Preprocessing on Integrative Matrix Factorization of Single Cell Data.

Integrative, single-cell analyses may provide unprecedented insights into cellular and spatial diversity of the tumor microenvironment. The sparsity, noise, and high dimensionality of these data present unique challenges. Whilst approaches for integrating single-cell data are emerging and are far from being standardized, most data integration, cell clustering, cell trajectory, and analysis pipelines employ a dimension reduction step, frequently principal component analysis (PCA), a matrix factorization method that is relatively fast, and can easily scale to large datasets when used with sparse-matrix representations. In this review, we provide a guide to PCA and related methods. We describe the relationship between PCA and singular value decomposition, the difference between PCA of a correlation and covariance matrix, the impact of scaling, log-transforming, and standardization, and how to recognize a horseshoe or arch effect in a PCA. We describe canonical correlation analysis (CCA), a popular matrix factorization approach for the integration of single-cell data from different platforms or studies. We discuss alternatives to CCA and why additional preprocessing or weighting datasets within the joint decomposition should be considered.

Additive manufacturing of hydrogel-based materials for next-generation implantable medical devices.

Implantable microdevices often have static components rather than moving parts, and exhibit limited biocompatibility. This paper demonstrates a fast manufacturing method which can produce features in biocompatible materials down to tens of microns in scale, with intricate and composite patterns in each layer. By exploiting unique mechanical properties of hydrogels, we developed a "locking mechanism" for precise actuation and movement of freely moving parts, which can provide functions such as valves, manifolds, rotors, pumps, and delivery of payloads. Hydrogel components could be tuned within a wide range of mechanical and diffusive properties, and can be controlled after implantation without a sustained power supply. In a mouse model of osteosarcoma, triggering of release of doxorubicin from the device over ten days showed high treatment efficacy and low toxicity, at one-tenth of a standard systemic chemotherapy dose. Overall, this platform, called "iMEMS", enables development of biocompatible implantable microdevices with a wide range of intricate moving components that can be wirelessly controlled on demand, in a manner that solves issues of device powering and biocompatibility.

Breast cancer cells alter the dynamics of stromal fibronectin-collagen interactions.

Breast cancer cells recruit surrounding stromal cells, such as cancer-associated fibroblasts (CAFs), to remodel their extracellular matrix (ECM) and promote invasive tumor growth. Two major ECM components, fibronectin (Fn) and collagen I (Col I), are known to interact with each other to regulate cellular behavior. In this study, we seek to understand how Fn and Col I interplay and promote a dysregulated signaling pathway to facilitate tumor progression. Specifically, we investigated the evolution of tumor-conditioned stromal ECM composition, structure, and relaxation. Furthermore, we assessed how evolving Fn-Col I interactions gradually affected pro-angiogenic signaling. Our data first indicate that CAFs initially assembled a strained, viscous, and unfolded Fn matrix. This early altered Fn matrix was later remodeled into a thick Col I-rich matrix that was characteristic of a dense tumor mass. Next, our results suggest that this ECM remodeling was primarily mediated by matrix metalloproteinases (MMPs). This MMP activity caused profound structural and mechanical changes in the developing ECM, which then modified vascular endothelial growth factor (VEGF) secretion by CAFs and matrix sequestration. Collectively, these findings enhance our understanding of the mechanisms by which Fn and Col I synergistically interplay in promoting a sustained altered signaling cascade to remodel the breast tumor stroma for invasive breast tumor growth.