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Single cell RNA sequencing (scRNA-seq) data analysis is important for building a global transcription landscape of all cell types in tissues, tracing cell lineages, and reconstructing cell spatial organizations. In this article, we propose an unsupervised learning method to predict spatial positions and gene expression of individual cells in Drosophila embryos using a small number of driver genes. Specifically, we develop a two-stage clustering approach, and compute a probability matrix of the spatial positions of single cells. This method is applied to dataset in the DREAM Single Cell Transcriptomics Challenge. The comparison with the “gold standard” suggests that our method is effective in reconstructing the cell positions and gene expression patterns in spatial tissues.

An unsupervised learning method for reconstructing cell spatial organization with application to the DREAM Single Cell Transcriptomics Challenge