&lt;p&gt;Our paper contributes to understanding differential expression gene (DEG) signatures, a crucial element in the study of gene expression patterns. One of the key findings of our research, which has not been previously published, is the assertion that the DEG signature space significantly manifests hyperbolic properties. This discovery has far-reaching implications for both the fields of bioinformatics and machine learning, such as drug-target interaction prediction and visualizations for drug discovery.&lt;/p&gt; 

Hyperbolic Manifold Learning on Differential Expression Signatures

ABSTRACT Dimensionality reduction and visualization play an important role in biological data analysis, such as data interpretation of single-cell RNA sequence (scRNA-seq). It is desired to have a visualization method that can not only be applicable to various application scenarios, including cell clustering and trajectory inference, but also satisfy a variety of technical requirements, especially the ability to preserve inherent structure of data and handle with batch effects. However, no existing methods can accommodate these requirements in a unified framework. In this paper, we propose a general visualization method, deep visualization (DV), that possesses the ability to preserve inherent structure of data and handle batch effects and is applicable to a variety of datasets from different application domains and dataset scales. The method embeds a given dataset into a 2- or 3-dimensional visualization space, with either a Euclidean or hyperbolic metric depending on a specified task type with type static (at a time point) or dynamic (at a sequence of time points) scRNA-seq data, respectively. Specifically, DV learns a structure graph to describe the relationships between data samples, transforms the data into visualization space while preserving the geometric structure of the data and correcting batch effects in an end-to-end manner. The experimental results on nine datasets in complex tissue from human patients or animal development demonstrate the competitiveness of DV in discovering complex cellular relations, uncovering temporal trajectories, and addressing complex batch factors. We also provide a preliminary attempt to pre-train a DV model for visualization of new incoming data. 

Structure-Preserving Visualization for Single-cell RNA-Seq Profiles Using Deep Manifold Transformation with Batch-Correction

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Structure-Preserving Visualization for Single-cell RNA-Seq Profiles Using Deep Manifold Transformation with Batch-Correction