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Unsupervised gene selection for predicting cell spatial positions in the Drosophila embryo

Analyzing single cell RNA-seq data is important for deciphering the spatial relationships, expression patterns, and developmental processes of cells. Combining in situ hybridization-based gene expression atlas images, some works have successfully recovered spatial locations of cells in zebrafish and Drosophila embryos. In this article, we describe a highly ranked method in the DREAM Single Cell Transcriptomics Challenge for predicting cell positions in the Drosophila embryo. The method performs unsupervised feature extraction to select a small number of driver genes and then uses them to predict gene expression and spatial position of each individual cell. First, hierarchical clustering is used to select a subset of driver genes. Second, the similarity matrix of single cells in the bins of the reference atlas is computed. Based on the similarity matrix, the spatial positions of cells are then determined by hierarchical clustering. This method is evaluated on the cell positions and gene expressions in the DREAM Single Cell Transcriptomics Challenge. The comparison with the “silver standard” suggests that our method is effective in reconstructing the cell spatial positions and gene expression patterns in tissues.