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Per-channel basis normalization of flow cytometry data.

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 multi-center clinical trials. It is often hard to match biologically relevant cell populations across samples due to technical variation in sample acquisition and instrumentation differences. Thus normalization of data is a critical step prior to analysis, particularly in large-scale data sets from clinical trials, where group specific differences may be subtle and patient-to-patient variation common. 
This module implements a normalization method that remove technical between-sample variation by aligning prominent features (landmarks) in the raw data on a per-channel basis. For more details on the normalization method, see Hahne et al (2010).

Version 2 of the module uses netCDF to store FCS data, which makes it about 30% slower than version 1, but requires much less memory.

References
Hahne F, Khodobakhshi AH, Bashashati A, Wong CJ, Gascoyne RD, Weng AP, Seyfert-Margolis V, Bourcier K, Asare A, Lumley T, Gentleman R, Brinkman RR.  Per-channel basis normalization methods for flow cytometry data. Cytometry A. 2010;77:121-131.
Spidlen J, Moore W, Parks D, Goldberg M, Bray C, Bierre P, Gorombey P, Hyun B, Hubbard M, Lange S, Lefebvre R, Leif R, Novo D, Ostruszka L, Treister A, Wood J, Murphy RF, Roederer M, Sudar D, Zigon R, Brinkman RR.  Data file standard for ?ow cytometry, version FCS 3.1. Cytometry A.  2010;77:97-100.

Language:
R
Operating System:
any
Original Author:
Josef Spidlen & Alireza Khodabakhshi, BC Cancer Agency
Uploaded by:
jspidlen on February 05, 2015 15:36
Version Comment: