SAM: Significance Analysis of Microarrays

Parameter Information


SAM Models

Several experimental design models can be processed by SAM to provide results based on the selected design. Each design is represented as a tab in the SAM initialization dialog. Comprehensive coverage of the use of SAM and analysis of various experimental designs are covered in the TMeV manual and the reference list found in the 'references' selection of the 'help' menu of the main TMeV tool bar.

Two-class unpaired

This model provides selection buttons to indicate which experiments should be in group A, group B, or Neither group. Options exist (buttons below group selection controls) to save a grouping scheme, load a grouping scheme, or reset the grouping.

Two-class paired

This model provides selection buttons to indicate which experiments should be paired. Experiments which are paired are considered to be related observations, for instance, samples from a particular subject before and after drug treatment. Each experiment can be paired with only one other experiment. Once a pairing has been made the pair is put into the pairings list and selection of the two experiments is disabled. Pairings may be removed from the pairing list and reassigned. Options exist (buttons below group selection controls) to save a pairing scheme, load a pairing scheme, or reset the pairings.

Multi-class

This model requires one to initially specify an number of classes (>2). Once that is done selection buttons allow the assignment of experiments to the appropriate classes. Groupings can be saved, loaded, and reset using the buttons below the group selection controls.

Censored survival

The selection controls for this model allow the inclusion or exclusion of particular experiments using the check boxes to the left of the experiment names. For those experiments included a corresponding time and a state is selected (Censored, or Dead).

Number of Permutations

This integer number indicates the number of times each vector should be permuted and have a d-statistic computed.

Imputation Engine

SAM handles missing data by constructing or imputing missing values using one of two available methods.

K-nearest neighbor imputer

This option imputes a value based on the k nearest neighbors (using Euclidean distance) to the vector which is missing the value. The k nearest neighbors are selected such that each should have a value for the missing vector element.

Row average imputer

This option imputes by taking the mean of the other elements in the vector which is missing the value.

Hierarchical Clustering

This check box selects whether to perform hierarchical clustering on the elements in each cluster created.