Some theoretical and practical considerations for multivariate statistical cell classification useful in autologous stem cell transplantation and tumor cell purging.

Abstract

As flow cytometric data becomes more complex, it becomes increasingly difficult to classify cells using conventional flow cytometry data techniques based on visual classification of the data by user-drawn regions. This paper shows some simple applications of multivariate statistical classification to classify flow cytometric data. Discriminant Function Analysis (DFA) and Logistic Regression (LR) analysis techniques were evaluated with respect to their potential utility in the problem of detecting human breast cancer cells within normal bone marrow cells. Data sets having defined properties were employed to evaluate the potential utility of these statistical classification techniques whose performance was measured by ROC analysis. Two extreme but reasonable situations are presented: (1) data where the separation of cells was obvious by visual inspection and (2) data where major overlaps in the values of the individual FCM parameters made intuitive classification improbable. Both DFA and LR analysis were able to classify the cells of each type with acceptable accuracy and yield. The excellent empirical performance of both DFA and LR techniques, suggests that they offer promising approaches for classifying multiparameter FCM data using objective rules that may represent an improvement over commonly employed ad hoc approaches.