This paper deals with the problem of maximum likelihood estimation for a mixture of skew Student-t-normal distributions, which is a novel model-based tool for clustering heterogeneous (multiple groups) data in the presence of skewed and heavy-tailed outcomes. We present two analytically simple EM-type algorithms for iteratively computing the maximum likelihood estimates. The observed information matrix is derived for obtaining the asymptotic standard errors of parameter estimates. A small simulation study is conducted to demonstrate the superiority of the skew Student-t-normal distribution compared to the skew t distribution. The proposed methodology is particularly useful for analyzing multimodal asymmetric data as produced by major biotechnological platforms like flow cytometry. We provide such an application with the help of an illustrative example.

Su.86. Automated High-dimensional Flow Cytometric Data Analysis

Maximum likelihood inference for mixtures of skew Student-t-normal distributions through practical EM-type algorithms

Flow cytometry has been widely used for the diagnosis of various hematopoietic diseases. Although there have been advances in the number of biomarkers that can be analyzed simultaneously and technologies that enable fast performance, the diagnostic data are still interpreted by a manual gating strategy. The process is labor-intensive, time-consuming, and subject to human error. We used 80 sets of flow cytometry data from 44 healthy donors, 21 patients with chronic lymphocytic leukemia (CLL), and 15 patients with follicular lymphoma (FL). Approximately 15% of data from each group were used to build the profiles. Our approach was able to successfully identify 36/37 healthy donor cases, 18/18 CLL cases, and 12/13 FL cases. This proof-of-concept study demonstrated that an automated diagnosis of CLL and FL can be obtained by examining the cell capture rates of a test case using the computational method based on the multi-profile detection algorithm. The testing phase of our system is efficient and can facilitate diagnosis of B-lymphocyte neoplasms.

/pdf/automatic-b-cell-lymphoma-detection-using-flow-cytometry-4kl63kl71v.pdf

Automatic B cell lymphoma detection using flow cytometry data

Flow cytometry has been widely used for the diagnosis of various hematopoietic diseases. Although there have been advances in the number of markers that can be analyzed simultaneously, the data is still interpreted by manual gating. This is labor-intensive, time-consuming, and subject to human error. We propose a computational model to detect B-lymphocyte neoplasms using flow cytometry data by building healthy and sick profiles. A cell capture rate was defined to measure the fitness of a test subject using a particular profile. By examining the cell capture rate of a test case with all profiles, the disease type can be determined.

/pdf/automatic-b-cell-lymphoma-detection-using-flow-cytometry-34ao16yyt2.pdf

Su.86. Automated High-dimensional Flow Cytometric Data Analysis

Citations

Maximum likelihood inference for mixtures of skew Student-t-normal distributions through practical EM-type algorithms

Automatic B cell lymphoma detection using flow cytometry data

Automatic B cell lymphoma detection using flow cytometry data

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