All major components of SWIFT (weighted iterative sampling, the incremental EM iterations, and efficient LDA-based merging) are designed to be efficiently scalable to big datasets, providing a significant improvement over the existing soft clustering methods 9– 14, 14.
Thus cluster properties are consistent between replicate samples, yet SWIFT sensitively detects differences between non-identical samples, e.g., from different normal subjects.
If validated in an independent sample, the SWIFT score may improve the precision of MICU patient transfer decisions.
As model-based clustering of complex flow cytometry populations can have multiple valid solutions, repeated clustering of the same sample in SWIFT does not result in identical cluster locations, and individual cells can be assigned to different clusters.
The computational complexity for SWIFT's weighted iterative sampling scales less than linearly in the number of data points.
SWIFT's weighted iterative sampling addresses these twin challenges by scaling the EM algorithm to large datasets, while allowing better detection of small subpopulations.
While Mexico struggles to confirm cases of swine flu and sends samples to the United States, Hong Kong is already performing swift genetic tests on patient samples and will have laboratories doing so at six local hospitals by Thursday.
Thus, even if SWIFT clustering of the consensus sample requires significant time, e.g. 2 h (see Supporting Information for sample run times), analysis of hundreds of samples can be completed rapidly.
AlgorithmWeighted iterative sampling based EM in SWIFT Upon completion of the weighted iterative sampling based EM procedure for GMM fitting, SWIFT performs a few (typically 10) EM iterations on the entire dataset to improve the fit taking the entire data into account.
Atorvastatin was obtained as gift sample from Ind-Swift Lab, Mohali.
