References
Here, you will find a list of relevant references. For a complete list, you can read our paper.
Our Paper
If you decide to cytomulate it in your package, citing our paper is highly appreciated!
@article{Yang2023,
title = {Cytomulate: accurate and efficient simulation of CyTOF data},
volume = {24},
ISSN = {1474-760X},
url = {http://dx.doi.org/10.1186/s13059-023-03099-1},
DOI = {10.1186/s13059-023-03099-1},
number = {1},
journal = {Genome Biology},
publisher = {Springer Science and Business Media LLC},
author = {Yang, Yuqiu and Wang, Kaiwen and Lu, Zeyu and Wang, Tao and Wang, Xinlei},
year = {2023},
month = nov
}
Selected References
Lou X, Zhang G, Herrera I, Kinach R, Ornatsky O, Baranov V, et al. Polymer-based elemental tags for sensitive bioassays. Angew Chem Int Ed. 2007;46(32):6111-4.
Spitzer MH, Nolan GP. Mass cytometry: single cells, many features. Cell. 2016 May 5;165(4):780-91.
Bandura DR, Baranov VI, Ornatsky OI, Antonov A, Kinach R, Lou X, et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal Chem. 2009 Aug 15;81(16):6813-22.
Chester C, Maecker HT. Algorithmic Tools for Mining High-Dimensional Cytometry Data. J Immunol. 2015 Aug 1;195(3):773-9.
Finck R, Simonds EF, Jager A, Krishnaswamy S, Sachs K, Fantl W, et al. Normalization of mass cytometry data with bead standards. Cytometry A. 2013 May;83(5):483-94.
Trussart M, Teh CE, Tan T, Leong L, Gray DH, Speed TP. Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets. eLife. 2020 Sep 7;9.
Van Gassen S, Gaudilliere B, Angst MS, Saeys Y, Aghaeepour N. Cytonorm: A normalization algorithm for cytometry data. Cytometry A. 2020 Mar;97(3):268-78.
Shaham U, Stanton KP, Zhao J, Li H, Raddassi K, Montgomery R, et al. Removal of batch effects using distribution-matching residual networks. Bioinformatics. 2017 Aug 15;33(16):2539-46.
Upadhyay U, Jain A. Removal of Batch Effects using Generative Adversarial Networks. arXiv. 2019;
Van Gassen S, Callebaut B, Van Helden MJ, Lambrecht BN, Demeester P, Dhaene T, et al. FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A. 2015 Jul;87(7):636-45.
Abe K, Minoura K, Maeda Y, Nishikawa H, Shimamura T. Model-based clustering for flow and mass cytometry data with clinical information. BMC Bioinformatics. 2020 Sep 17;21(Suppl 13):393.
Ji D, Nalisnick E, Qian Y, Scheuermann RH, Smyth P. Bayesian trees for automated cytometry data analysis. BioRxiv. 2018 Sep 19;
Dai Y, Xu A, Li J, Wu L, Yu S, Chen J, et al. CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data. BMC Bioinformatics. 2021 Mar 22;22(1):138.
Wang, K., Yang, Y., Wu, F., Song, B., Wang, X., & Wang, T. (2023). Comparative analysis of dimension reduction methods for cytometry by time-of-flight data. Nature Communications, 14(1), 1836.
Laurens van der M, Geoffrey H. Visualizing Data using t-SNE. J Mach Learn Res. 2008;
McInnes L, Healy J, Melville J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv. 2018;
Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Springer; 2008.
Amodio M, Srinivasan K, van Dijk D, Mohsen H, Yim K, Muhle R, et al. Exploring Single-Cell Data with Multitasking Deep Neural Networks. BioRxiv. 2017 Dec 19;
Ding J, Condon A, Shah SP. Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nat Commun. 2018 May 21;9(1):2002.
Hahne F, LeMeur N, Brinkman RR, Ellis B, Haaland P, Sarkar D, et al. flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics. 2009 Apr 9;10:106.
Hao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S, Butler A, et al. Integrated analysis of multimodal single-cell data. Cell. 2021 Jun 24;184(13):3573-3587.e29.
Zappia L, Phipson B, Oshlack A. Splatter: simulation of single-cell RNA sequencing data. Genome Biol. 2017 Sep 12;18(1):174.
Data scientist’s primer to analysis of mass cytometry data [Internet]. [cited 2022 May 26]. Available from: https://biosurf.org/cytof_data_scientist.html
Lukas M. CS. HDCytoData. Bioconductor. 2018;
Leipold MD, Obermoser G, Fenwick C, Kleinstuber K, Rashidi N, McNevin JP, et al. Comparison of CyTOF assays across sites: Results of a six-center pilot study. J Immunol Methods. 2018 Feb;453:37-43.
Street K, Risso D, Fletcher RB, Das D, Ngai J, Yosef N, et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics. 2018 Jun 19;19(1):477.
Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014 Apr;32(4):381-6.
Saelens W, Cannoodt R, Todorov H, Saeys Y. A comparison of single-cell trajectory inference methods. Nat Biotechnol. 2019 May;37(5):547-54.
Bishop CM. Pattern Recognition and Machine Learning (Information Science and Statistics). Softcover reprint of the original 1st ed. 2006. Springer; 2016.
Liu X, Song W, Wong BY, Zhang T, Yu S, Lin GN, et al. A comparison framework and guideline of clustering methods for mass cytometry data. Genome Biol. 2019 Dec 23;20(1):297.
Weber LM, Robinson MD. Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Cytometry A. 2016 Dec 19;89(12):1084-96.
Lee H-C, Kosoy R, Becker CE, Dudley JT, Kidd BA. Automated cell type discovery and classification through knowledge transfer. Bioinformatics. 2017 Jun 1;33(11):1689-95.
Perez-Cruz F. Kullback-Leibler divergence estimation of continuous distributions. 2008 IEEE International Symposium on Information Theory. IEEE; 2008. p. 1666-70.
Dasgupta S. Learning Polytrees. arXiv. 2013;
Cormen TH, Leiserson CE, Rivest RL, Stein C. Introduction to Algorithms, 3rd Edition (The MIT Press). 3rd ed. Cambridge, Mass: The MIT Press; 2009.
Clauset A, Newman MEJ, Moore C. Finding community structure in very large networks. Phys Rev E. 2004 Dec 6;70(6).
Hagberg AA, Schult DA, Swart PJ. Exploring Network Structure, Dynamics, and Function using NetworkX. In: Varoquaux G, Vaught T, Millman J, editors. Proceedings of the 7th Python in Science conference. SciPy conference proceedings; 2008. p. 11-5.
Trefethen LN, David Bau III. Numerical Linear Algebra. 1st ed. Philadelphia: SIAM: Society for Industrial and Applied Mathematics; 1997.