UDRN: Unified Dimensional Reduction Neural Network for feature selection and feature projection

Structure-preserving visualization for single-cell RNA-Seq profiles using deep manifold transformation with batch-correction

“…In this paper, we address the following application scenarios: Firstly, we develop a machine learning method with the ability to preserve geometric structure of the high dimensional scRNA-seq data in the dimensionality reduced space and visualization of scRNA-seq data that can be applied to both cell clustering and trajectory inference tasks. These two scenarios are closely related yet have different technical goals: (1) For cell clustering is to explore the relationship between different cell types at a given time [4][5][6][7][8][9][10][11][12][13][14][15][16][17] , which we call the static (at a time point) scenario. It is to learn a low-dimensional embedding in which cells belonging to the same type should be close to each other whereas those of different types be away from each other.…”

“…In recent years, deep neural networks (DNNs) 23 have been utilized as effective non-linear dimensionality reduction and visualization tools for processing large datasets, incorporating different factors, and improving the scalable ability of models. This field mainly involves two mainstream directions, including (1) Deep manifold learning methods, such as parametric UMAP 10 , Markov-Lipschitz deep learning (MLDL) 11 , deep manifold transformation (DMT) 12 , deep local-flatness manifold embedding (DLME) 24 , EVNet 13 , unified dimensional reduction neural-network (UDRN) 14 and IVIS 15 , and (2) Deep reconstruction learning methods, which covers various (variational) autoencoders 16,25,26 . Generally speaking, the latter seeks to reconstruct the input data distribution and often ignores the importance of intrinsic geometric structure in input data.…”