References

All citations in this documentation refer to published tools, datasets, and statistical methods used by the pipeline. Full citation details are in references.bib.

1.

Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

2.

Tran, N. M. et al. Single-cell profiles of retinal ganglion cells differing in resilience to injury reveal neuroprotective genes. Neuron 104, 1039–1055 (2019).

3.

Benhar, I., London, A., Ousman, S. & Schwartz, M. Temporal single-cell atlas of non-neuronal retinal cells reveals dynamic murine glial and immune cell crosstalk. Nature Immunology 24, 1559–1574 (2023).

4.

Keuthan, C. J. et al. Experimental glaucoma and optic nerve crush mouse models induce different gene expression programs in retina and optic nerve. International Journal of Molecular Sciences 24, 13423 (2023).

5.

Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nature Biotechnology 42, 293–304 (2024).

6.

Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nature Methods 15, 1053–1058 (2018).

7.

Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nature Biotechnology 38, 1408–1414 (2020).

8.

Weiler, P., Lange, M., Klein, M., Pe’er, D. & Theis, F. CellRank 2: Unified fate mapping in multiview single-cell data. Nature Methods 21, 1196–1205 (2024).

9.

Kaminow, B., Yunusov, D. & Dobin, A. STARsolo: Accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data. bioRxiv https://doi.org/10.1101/2021.05.05.442755 (2021) doi:10.1101/2021.05.05.442755.

10.

Anselin, L. Local indicators of spatial association — LISA. Geographical Analysis 27, 93–115 (1995).

11.

Rey, S. J. & Anselin, L. PySAL: A python library of spatial analytical methods. The Review of Regional Studies 37, 5–27 (2007).

12.

Wiggs, J. L. et al. Common variants at 9p21 and 8q22 are associated with increased susceptibility to optic nerve degeneration in glaucoma. PLoS Genetics 8, e1002654 (2012).

13.

Gharahkhani, P. et al. Genome-wide meta-analysis identifies 127 open-angle glaucoma loci with consistent effect across ancestries. Nature Communications 12, 1258 (2021).

14.

Harwerth, R. S., Carter-Dawson, L., Shen, F., Smith III, E. L. & Crawford, M. L. J. Ganglion cell losses underlying visual field defects from experimental glaucoma. Investigative Ophthalmology & Visual Science 40, 2242–2250 (1999).

15.

Büttner, M., Ostner, J., Müller, C. L., Theis, F. J. & Schubert, B. Efficient use of paired single cell multimodal omics with contrastive multiview learning. Nature Communications 12, 6796 (2021).

16.

Fang, Z., Liu, X. & Peltz, G. GSEApy: A comprehensive package for performing gene set enrichment analysis in python. Bioinformatics 39, (2023).