{"references": ["Qiu, C., Cao, J., Martin, B.K., Li, T., Welsh, I.C., Srivatsan, S., Huang, X., Calderon, D., Noble, W.S., Disteche, C.M. and Murray, S.A., 2022. Systematic reconstruction of cellular trajectories across mouse embryogenesis. Nature genetics, 54(3), pp.328-341.", "Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. and Regev, A., 2015. Spatial reconstruction of single-cell gene expression data. Nature biotechnology, 33(5), pp.495-502.", "Wolf, F.A., Angerer, P. and Theis, F.J., 2018. SCANPY: large-scale single-cell gene expression data analysis. Genome biology, 19(1), pp.1-5.", "Briggs, J.A., Weinreb, C., Wagner, D.E., Megason, S., Peshkin, L., Kirschner, M.W. and Klein, A.M., 2018. The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science, 360(6392), p.eaar5780."]}

Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qiu et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package. If you use this data, please cite Briggs et al. 2018 and Qiu et al. 2022.