This article is a US government work and, as such, is in the public domain in the United States of America
SPICE: Exploration and analysis of post-cytometric complex multivariate datasets†
Version of Record online: 7 JAN 2011
Published 2011 Wiley-Liss, Inc.
Cytometry Part A
Volume 79A, Issue 2, pages 167–174, February 2011
How to Cite
Roederer, M., Nozzi, J. L. and Nason, M. C. (2011), SPICE: Exploration and analysis of post-cytometric complex multivariate datasets. Cytometry, 79A: 167–174. doi: 10.1002/cyto.a.21015
- Issue online: 24 JAN 2011
- Version of Record online: 7 JAN 2011
- Manuscript Accepted: 3 DEC 2010
- Manuscript Revised: 1 DEC 2010
- Manuscript Received: 17 SEP 2010
- data analysis;
Polychromatic flow cytometry results in complex, multivariate datasets. To date, tools for the aggregate analysis of these datasets across multiple specimens grouped by different categorical variables, such as demographic information, have not been optimized. Often, the exploration of such datasets is accomplished by visualization of patterns with pie charts or bar charts, without easy access to statistical comparisons of measurements that comprise multiple components. Here we report on algorithms and a graphical interface we developed for these purposes. In particular, we discuss thresholding necessary for accurate representation of data in pie charts, the implications for display and comparison of normalized versus unnormalized data, and the effects of averaging when samples with significant background noise are present. Finally, we define a statistic for the nonparametric comparison of complex distributions to test for difference between groups of samples based on multi-component measurements. While originally developed to support the analysis of T cell functional profiles, these techniques are amenable to a broad range of datatypes. Published 2011 Wiley-Liss, Inc.