| Abstract |
As systems biology develops, various types of high-throughput -omics data become rapidly
available. An increasing challenge is to analyze such massive data, interpret the results and
validate the findings. Data analysis for most of the omics-techniques is in a fledgling immature
stage. Alone the dimensionality of the data tables calls for new ways to reveal structure in the
data, without cognitive overflow and excessive false discovery rate. Multi-block methods have
been developed and adapted in order to find common variation patterns in data and depict these
findings on graphical displays while providing tools to enhance the interpretation of the
outcomes. In particular, multi-block methods based on latent variables are powerful tools to study
block and global variation patterns, e.g. by inspecting block and global score plots. These
methods can be used to achieve a graphical overview over sample and variable variation patterns
in an efficient way. However, a visual detection of patterns may be subjective and, therefore,
there is a need for validation tools. In this paper tools for validation of visually identified patterns
in multi-block results are presented. Cross-validated estimates of Root Mean Square Error
(RMSE) for block results are introduced for estimating the number of relevant PCs of the
Consensus Principal Component Analysis (CPCA) models. Furthermore, important variables are
identified by approximate t-test based on Procrustes-corrected jackknifing. For the assessment of
the stability of score patterns, block stability plots are introduced. Outliers can be revealed
graphically on block and global level by stability plots. |
| Reference |
Hassani, S., Martens, H., Qannari, M., Hanafi, M., Borge, G.I., Kohler, A. 2010. Analysis of –omics data: Graphical interpretation- and validation tools in multi–block methods. Chemometrics and Intelligent Laboratory Systems, Vol 104, Issue 1, pp 140-153. |
Sahar Hassani
Harald Martens
Grethe Iren Borge
Achim Kohler
