title: Per-channel basis normalization methods for flow cytometry data.
abstract: Between-sample variation in high-throughput flow cytometry data poses a significant challenge for analysis of large-scale data sets, such as those derived from multicenter clinical trials. It is often hard to match biologically relevant cell populations across samples because of technical variation in sample acquisition and instrumentation differences. Thus, normalization of data is a critical step before analysis, particularly in large-scale data sets from clinical trials, where group-specific differences may be subtle and patient-to-patient variation common. We have developed two normalization methods that remove technical between-sample variation by aligning prominent features (landmarks) in the raw data on a per-channel basis. These algorithms were tested on two independent flow cytometry data sets by comparing manually gated data, either individually for each sample or using static gating templates, before and after normalization. Our results show a marked improvement in the overlap between manual and static gating when the data are normalized, thereby facilitating the use of automated analyses on large flow cytometry data sets. Such automated analyses are essential for high-throughput flow cytometry.
authors.affiliation: Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
authors.forename: Florian
authors.initials: F
authors.lastname: Hahne
authors.forename: Alireza Hadj
authors.initials: AH
authors.lastname: Khodabakhshi
authors.forename: Ali
authors.initials: A
authors.lastname: Bashashati
authors.forename: Chao-Jen
authors.initials: CJ
authors.lastname: Wong
authors.forename: Randy D
authors.initials: RD
authors.lastname: Gascoyne
authors.forename: Andrew P
authors.initials: AP
authors.lastname: Weng
authors.forename: Vicky
authors.initials: V
authors.lastname: Seyfert-Margolis
authors.forename: Katarzyna
authors.initials: K
authors.lastname: Bourcier
authors.forename: Adam
authors.initials: A
authors.lastname: Asare
authors.forename: Thomas
authors.initials: T
authors.lastname: Lumley
authors.forename: Robert
authors.initials: R
authors.lastname: Gentleman
authors.forename: Ryan R
authors.initials: RR
authors.lastname: Brinkman
mesh_terms: D000465:Algorithms; D000906:Antibodies; D015703:Antigens, CD / Q000276:immunology; D001773:Blood Cells / Q000166:cytology / Q000378:metabolism; D002469:Cell Separation; D001330:Electronic Data Processing / Q000379:methods; D005434:Flow Cytometry / Q000379:methods* / Q000706:statistics & numerical data; D006684:HLA-DR Antigens / Q000276:immunology; D006801:Humans; D008198:Lymph Nodes / Q000166:cytology / Q000378:metabolism
chemical_list: D000906:Antibodies; D015703:Antigens, CD; D006684:HLA-DR Antigens