SPICE: exploration and analysis of post-cytometric complex multivariate datasets
| Autor: | Joshua L. Nozzi, Martha Nason, Mario Roederer |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Multivariate statistics
Histology Multivariate analysis Bar chart Computer science Article Pathology and Forensic Medicine law.invention law Computer Graphics Humans Categorical variable Principal Component Analysis business.industry Pie chart Nonparametric statistics Computational Biology Pattern recognition Cell Biology Flow Cytometry Visualization Data Interpretation Statistical Principal component analysis Multivariate Analysis Artificial intelligence business Algorithms Software |
| Zdroj: | Cytometry. Part A : the journal of the International Society for Analytical Cytology. 79(2) |
| ISSN: | 1552-4930 |
| Popis: | 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 in order 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text