Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data

Abstract

Remarkable advances in single cell genomics have presented unique challenges and opportunities for interrogating a wealth of biomedical inquiries. High dimensional genomic data are inherently complex because of intertwined relationships among the genes. Existing methods, including emerging deep learning-based approaches, do not consider the underlying biological characteristics during data processing, which greatly compromises the performance of data analysis and hinders the maximal utilization of state-of-the-art genomic techniques. In this work, we develop an entropy-based cartography strategy to contrive the high dimensional gene expression data into a configured image format, referred to as genomap, with explicit integration of the genomic interactions. This unique cartography casts the gene-gene interactions into the spatial configuration of genomaps and enables us to extract the deep genomic interaction features and discover underlying discriminative patterns of the data. We show that, for a wide variety of applications (cell clustering and recognition, gene signature extraction, single cell data integration, cellular trajectory analysis, dimensionality reduction, and visualization), the proposed approach drastically improves the accuracies of data analyses as compared to the state-of-the-art techniques.

Leveraging cell-cell similarity for high-performance spatial and temporal cellular mappings from gene expression data

“…Most of the traditional and state-of-the-art techniques 7 , 8 , 50 , 51 , 52 , 53 , 54 , 55 , 56 perform the analysis of developmental and differentiation trajectories by first finding the data clusters. In these analyses, the idea is to first find the clusters, and then a minimum spanning tree is constructed on the clusters to determine the number and rough shape of lineages.…”

Revealing hidden patterns in deep neural network feature space continuum via manifold learning

“…8). It is noteworthy that like many other data analysis techniques such as PCA, FEM, GSE 50 , CCSF 39 , t-SNE, and UMAP, interactive relationships of the components inside an HD feature point are not considered explicitly during the MDA embedding process 51 . For some special applications such as the assessment of image similarity in a low dimensional embedding, DNN-based manifold learning techniques like Deep Manifold Embedding Method (DMEM) 52 and Deep Local-flatness Manifold Embedding (DLME) 53 could be used.…”

STFT, LASSO and EHO based Feature Extraction with Integrated Machine Learning and Metaheuristic Classification Techniques for Colon Cancer detection from Microarray Gene Expressions

“…In Liu et al [23], low accuracy is obtained to more number of redundant features. Islam et al [24] and Xiao et al [25] reported high accuracies due to the applied deep learning methods. Abdulla et al [30] has proposed a cost sensitive feature selection method with accuracy in the range of 95%.…”