Sma.pdf
Spectral Map Analysis
Overview
The Spectral Map Analysis command runs Spectral Map Analysis on the dataset. This is a unique function, because it is similar to PCA in that it provides data structure, but at the same time it shows the variables that are responsible for the structure, allowing the user to see which variables are responsible for the differences. This method allows for determining clusters of samples and genes, correlating samples with gene expression profiles.
For more information on Spectral Map Analysis, see Wouters, L., Goehlmann, H., Bijnens, L., Kass, S.U., Molenberghs, G., Lewi, P.J. (2003). Graphical exploration of gene expression data: a comparative study of three multivariate methods. Biometrics 59, 1131-1140). http://www.vetstat.ugent.be/workshop/Nairobi2004/Bijnens/Bijnens2004.pdf
To run this module, click MicroArray | Pattern | Spectral Map Analysis.
Input Data Requirements
This function works on -Omic data types.
General Options
Input/Output
- Project & Data: The window includes a dropdown box to select the Project and Data object to be filtered.
- Variables: Selections can be made on which variables should be included in the filtering (options include All variables, Selected variables, Visible variables, and Customized variables (select any pre-generated Lists)).
- Observations: Selections can be made on which observations should be included in the filtering (options include All observations, Selected observations, Visible observations, and Customized observations (select any pre-generated Lists).
- Output name: The user can choose to name the output data object.
Options
- Observation group: Define the factor from the design table to group observations. The observations in different groups will have different colors in the final output.
- Variable group: Define the factor from the annotation table to group variables. The variables in different groups will have different colors in the final output.
Output Results
Spectral map analysis (SMA) is a visualization tool to check the quality of a microarray experiment. A Scatter plot will automatically be generated. The plot shows both Variables (green symbols) and Observations (blue symbols), and is fully interactive in a 2-dimensional plot. The two dimensions (x-axis and y-axis) visualize the two largest variability in the dataset.
The size of the green dot represents the expression intensity of a given gene or probeset across samples; the size of blue dot represents the expression across all genes for a given sample. If the microarray experiment is good, the replicate samples of a given treatment or cell sources should be close to each other. If two samples are far apart, there are more differences between them, and the variability can be explained by the x-axis and the y-axis, along with the probesets (genes) on their directions. It allows user to see which variables are responsible for the differences.
OmicScript
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