Description: We developed a lightweight machine learning tool for RNA profiling of acute lymphoblastic leukemia (ALL), however, it can be used for any problem where multiple classes need to be identified from multi-dimensional data. The methodology is described in Makinen V-P, Rehn J, Breen J, Yeung D, White DL (2022) Multi-cohort transcriptomic subtyping of B-cell acute lymphoblastic leukemia, medRxiv, <doi:10.1101/2022.02.17.22270919>. The classifier contains optimized mean profiles of the classes (centroids) as observed in the training data, and new samples are matched to these centroids using the shortest Euclidean distance. Centroids derived from a dataset of 1,598 ALL patients are included, but users can train the models with their own data as well. The output includes both numerical and visual presentations of the classification results. Samples with mixed features from multiple classes or atypical values are also identified.

Description: We developed a lightweight machine learning tool for RNA profiling of acute lymphoblastic leukemia (ALL), however, it can be used for any problem where multiple classes need to be identified from multi-dimensional data. The methodology is described in Makinen V-P, Rehn J, Breen J, Yeung D, White DL (2022) Multi-cohort transcriptomic subtyping of B-cell acute lymphoblastic leukemia, International Journal of Molecular Sciences 23:4574, <doi:10.3390/ijms23094574>. The classifier contains optimized mean profiles of the classes (centroids) as observed in the training data, and new samples are matched to these centroids using the shortest Euclidean distance. Centroids derived from a dataset of 1,598 ALL patients are included, but users can train the models with their own data as well. The output includes both numerical and visual presentations of the classification results. Samples with mixed features from multiple classes or atypical values are also identified.

Authors@R: person("Stéphane", "Laurent", email = "laurent_step@outlook.fr", role = c("aut", "cre"))
Description: Computation of the alpha hull of a set of points in the 3D
space, that is to say "something like the shape formed by these
points". The alpha hull depends on a positive parameter alpha. When
alpha goes to zero, the alpha hull degenerates to the set of points,
while it is the convex hull of the set of points when alpha goes to
infinity. Computations are performed by the 'CGAL' 'C++' library
LinkingTo: BH, Rcpp, RcppCGAL, RcppEigen