Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 5 March 2021 / Approved: 5 March 2021 / Online: 5 March 2021 (21:21:59 CET)

How to cite: Zhao, Y.; Zhang, S.; Cannistraci, C.V.; Han, J.J. De Novo Spatial Reconstruction of Single Cells by Developmental Coalescent Embedding of Transcriptomic Networks. Preprints 2021, 2021030196 (doi: 10.20944/preprints202103.0196.v1). Zhao, Y.; Zhang, S.; Cannistraci, C.V.; Han, J.J. De Novo Spatial Reconstruction of Single Cells by Developmental Coalescent Embedding of Transcriptomic Networks. Preprints 2021, 2021030196 (doi: 10.20944/preprints202103.0196.v1).

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 localization is hitherto landmark based, and de novo spatial reconstruction is a compelling computational open problem. Here we show that a new algorithm - named D-CE - for coalescent embedding of single cell transcriptomic networks can address this open problem. We rely merely on the spatial information encoded in the expression patterns of developmental signal transcription factor (DST) genes, and we find that D-CE of cell-cell association DST-transcriptomic networks reliably reconstructs the Geo-seq or single cell samples’ 3D spatial tissue distribution. Comparison to the novoSpaRC and CSOmap (recent and only available de novo 3D spatial reconstruction methods) on 16 datasets and 681 reconstructions, reveals a significantly distinctive superior performance of D-CE.

Supplementary and Associated Material

https://github.com/JackieHanLab/D-CE: github link of our executable program

Subject Areas

Single cell RNA-seq; spatial reconstruction; development; coalescent embedding

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