Consistent quantitative gene product expression: #1. Automated identification of regenerating bone marrow cell populations using support vector machines.

ABSTRACT: Identification and quantification of maturing hematopoietic cell populations in flow cytometry data sets is a complex and sometimes irreproducible step in data analysis. Supervised machine learning algorithms present promise to automatically classify cells into populations, reducing subjective bias in data analysis. We describe the use of support vector machines (SVMs), a supervised algorithm, to reproducibly identify two distinctly different populations of normal hematopoietic cells, mature lymphocytes and uncommitted progenitor cells, in the challenging setting of pediatric bone marrow specimens obtained 1 month after chemotherapy. Four-color flow cytometry data were collected on a FACS Calibur for 77 randomly selected postchemotherapy pediatric patients enrolled on the Children's Oncolo

SUBMITTER: Voigt AP 

PROVIDER: S-EPMC5132084 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature