Page 309 - Contributed Paper Session (CPS) - Volume 4

CPS2233 Sharon Lee
5. Dempster, A.P., Laird, N.M., Rubin, D.B. (1977). Maximum likelihood from
incomplete data via the EM algorithm. Journal of the Royal Statistical
Society B 39, 1–38. Hahne F, Khodabakhshi AH, Bashashati A, Wong CJ,
Gascoyne RD, Weng AP, Seyfert-Margolis V, Bourcier K, Asare A, et al.
(2010). Per-channel basis normalization methods for flow cytometry
data. Cytometry A 77:121–131.
6. Lee, S.X., McLachlan, G.J. (2013). On mixtures of skew-normal and skew
t-distributions. Advances in Data Analysis and Classification 7, 241-266
7. Pyne S, Hu X, Wang K, Rossin E, Lin TI, Maier LM, Baecher-Allan C,
McLachlan GJ, Tamayo P, Hafler DA, et al. (2009). Automated high-
dimensional flow cytometric data analysis. Proceedings of the National
Academy of Science 106:8519–8524.
8. Lo K, Hahne F, Brinkman RR, Gottardo R. (2009). flowClust: A
Bioconductor package for automated gating of flow cytometry data.
BMC Bioinformatics10:145.
9. Mosmann TR, Naim I, Rebhahn J, Datta S, Cavenaugh JS, Weaver JM, et
al. (2014). SWIFT— Scalable clustering for automated identification of
rare cell populations in large, highdimensional flow cytometry datasets,
Part 2: Biological evaluation. Cytometry Part A 85A:422– 433.
10. Weber, L.M., Robinson, M.D. (2016). Comparison of clustering methods
for high-dimensional singlecell flow and mass cytometry data.
Cytometry A 89A, 1084-1096

298 | I S I W S C 2 0 1 9