Project name: Mus musculus

Description: We here systematically studied the interaction network of small intestine crypt cells.We first created a reference of cell types based on single-cell mRNA sequencing, which we used to create virtual doublets and triplets. These virtual combinations were then used to train a machine learning algorithm (random forests) in order to infer the cell types present in the actual picked doublets or triplets. We then counted the number of interactions in between defferent cell types, and compared it to label permution simulation to identify interactions that happens more or less than expected by chance.Overall design: After dissociation of the small intestine crypts, we sorted single-cells for mRNA sequencing. In addition, after mild dissociation of the small intestine crypts, we picked small interacting structures (cell doublets, triplets), and sequenced their mRNAs.

Data type: Transcriptome or Gene expression

Sample scope: Multiisolate

Relevance: ModelOrganism

Last updated: 2017-09-05