Spatial Reconstruction of Oligo and Single Cells by De Novo Coalescent Embedding of Transcriptomic Networks

Abstract

Single cell RNA-seq (scRNA-seq) profiles conceal temporal and spatial tissue developmental information. De novo reconstruction of single cell temporal trajectory has been fairly addressed, but reverse engineering single cell 3D spatial tissue organization is hitherto landmark based, and de novo spatial reconstruction is a compelling computational open problem. Here it is shown that a proposed algorithm for de novo coalescent embedding (D-CE) of oligo/single cell transcriptomic networks can help to address this problem. Relying on the spatial information encoded in the expression patterns of genes, it is found that D-CE of cell-cell association transcriptomic networks, by preserving mesoscale network organization, captures spatial domains, identifies spatially expressed genes, reconstructs cell samples' 3D spatial distribution, and uncovers spatial domains and markers necessary for understanding the design principles on spatial organization and pattern formation. Comparison to the novoSpaRC and CSOmap (the only available de novo 3D spatial reconstruction methods) on 14 datasets and 497 reconstructions, reveals a significantly superior performance of D-CE.

Geometric Separability of Mesoscale Patterns in Embedding Representation and Visualization of Multidimensional Data and Complex Networks

“…Finally, a projection separability index (PSI) is defined by applying any bi-class separability measure (such as the area under the curve of precision-recall, AUPR 16 , or any other measure for evaluation of unbalanced data classification) directly on the projection line to measure the extent to which the two groups are linearly separable. For instance, PSI was adopted with merit to evaluate the geometric linear separability of spatially organized groups of single cells embedded in a 2D and 3D space by analyzing their transcriptome 17 .…”