Abstract
Single-cell sequencing reveals cellular heterogeneity but not cell localization. However, by combining single-cell transcriptomic data with a reference atlas of a small set of genes, it would be possible to predict the position of individual cells and reconstruct the spatial expression profile of thousands of genes reported in the single-cell study. To develop new algorithms for this purpose, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium organized a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC). In the spirit of this framework, we describe here the proposed procedures for adequate reference genes selection, and an iterative procedure to predict spatial expression profile of other genes.
Highlights
Multicellular organisms show throughout their development a crescent cellular heterogeneity, distributed and organized in different organs and tissues
Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge data Expression patterns used as a reference atlas correspond to 84 driver genes obtained from in situ hybridization experiments; the data correspond to The Berkeley Drosophila Transcription Network Project (BDTNP)[8]
The expression level of gen g at the bin position i is given by the weighted average of the normalized gene expression across N putative positions corresponding to that bin, being the weight proportional to the associated
Summary
Multicellular organisms show throughout their development a crescent cellular heterogeneity, distributed and organized in different organs and tissues. This spatial heterogeneity has been explored using different techniques, such as immunohistochemistry and single-molecule fluorescence in situ hybridization (FISH)[1] These approaches allow quantification of gene expression in many cells but, these techniques can currently only be assayed to a small number of genes. With the advent of emergent methods in genomics, it has become possible to assess the transcriptomic profile of complex tissues with unprecedented resolution, thereby allowing insights into complex processes such as: differentiation trajectories, cell fate decisions, and spatial relationships. In this sense, high-throughput single-cell RNA-seq (sc-RNA-seq) is becoming an established experimental technique[2]. More recently several computational techniques, coupled to in situ RNA patterns facilitate this reconstruction with better resolution[5,6,7]
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