- Convenient Excel Add-in
- Can be applied to data from Oligo or cDNA arrays, SNP arrays,
protein arrays, etc.
- Correlates expression data to clinical parameters including
treatment, diagnosis categories, survival time,
paired (before and after), quantitative (eg. tumor volume) and
one-class. Both parameteric and non-parametric tests are offered.
- Correlates expression data with time, to study time trends.
The experimental units can fall into one or two classes, or be paired.
- Automatic imputation of missing data via nearest neighbor
algorithm (better, faster in SAM version 2.0)
- Adjustable threshold determines number of genes called significant
- Uses data permutations to provide estimate of False Discovery Rate for multiple testing
- New version 2.0 reports local false discovery rates and miss rates.
- Can deal with blocked designs, for example, when treatments are applied
within different batches of arrays
- Pattern discovery via eigengenes (principal components)
- Gene lists in Excel workbook form, easily exportable into TreeView, Cluster or other software
- Genes are web-linked to Stanford SOURCE database
- Developed at Stanford University Statistics and Biochemistry Labs.
Based on paper of Tusher, Tibshirani and Chu (2001)
"Significance analysis of microarrays applied to the ionizing radiation response". PNAS, Apr 24, 2001