Citations

If you use Finding eML/iML package in your research, please cite the following:

Finding eML

Foltz JA, Tran J, Wong P, et al. Cytokines drive the formation of memory-like NK cell subsets via epigenetic rewiring and transcriptional regulation. Science Immunology. 2024;9(96):eadk4893. https://doi.org/10.1126/sciimmunol.adk4893

TotalVI

Gayoso A, Steier Z, Lopez R, et al. Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nat Methods. 2021;18(3):272-282. https://doi.org/10.1038/s41592-020-01050-x

scVI (scvi-tools)

Gayoso A, Lopez R, Xing G, et al. A Python library for probabilistic analysis of single-cell omics data. Nat Biotechnol. 2022;40(2):163-166. https://doi.org/10.1038/s41587-021-01206-w

SCANPY

Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018;19(1):15. https://doi.org/10.1186/s13059-017-1382-0

Imbalanced-learn

Lemaitre G, Nogueira F, Aridas CK. Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. arXiv. 2016. https://doi.org/10.48550/ARXIV.1609.06570