Leukemia {FCPS}

Leukemia distance matrix and classificiation used in [Thrun, 2018]

Description

Data is anonymized. Original dataset was published in [Haferlach et al., 2010]. Original dataset had around 12.000 dimensions. Detailed description of preprocessed dataset and its clustering challenge is provided in [Thrun/Ultsch, 2020].

Usage

data("Leukemia")

Details

554x554 distance matrix. Cls defines the following clusters:

1= APL Outlier

2=APL

3=Healthy

4=AML

5=CLL

6=CLL Outlier

References

[Thrun, 2018] Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, doctoral dissertation 2017, Springer, Heidelberg, ISBN: 978-3-658-20539-3, doi:10.1007/978-3-658-20540-9, 2018.

[Haferlach et al., 2010] Haferlach, T., Kohlmann, A., Wieczorek, L., Basso, G., Te Kronnie, G., Bene, M.-C., . . . Mills, K. I.: Clinical utility of microarray-based gene expression profiling in the diagnosis and subclassification of leukemia: report from the International Microarray Innovations in Leukemia Study Group, Journal of Clinical Oncology, Vol. 28(15), pp. 2529-2537. 2010.

[Thrun/Ultsch, 2020] Thrun, M. C., & Ultsch, A.: Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems, Data in Brief, Vol. 30(C), pp. 105501, doi:10.1016/j.dib.2020.105501, 2020.

Examples

data(Leukemia)
str(Leukemia)
Cls=Leukemia$Cls
Distance=Leukemia$DistanceMatrix
isSymmetric(Distance)