This function calculates the significance of a top-scoring pair. It can be run after tspcalc() to calculate how strong a TSP is.
1 |
dat |
Can take two values: (a) an m genes by n arrays matrix of expression data or (b) an eSet object |
grp |
Can take one of two values: (a) A group indicator incharacter or numeric form, (b) an integer indicating the column of pData(dat) to use as the group indicator |
B |
The number of permutations to perform in calculation of the p-value, default is 50. |
seed |
If this is a numeric argument, the seed will be set for reproducible p-values. |
tspsig() only works for two group classification. The computation time grows rapidly in the number of genes, so for large gene expression matrices one should be prepared to wait or do a pre-filtering step. A progress bar is shown which gives some indication of the time until the calculation is complete. The top scoring pairs methodology was originally described in Geman et al. (2004).
p |
A p-value for testing the null hypothesis that there is no TSP for the data set dat. |
nullscores |
The null TSP scores from the permutation test. |
Jeffrey T. Leek jtleek@jhu.edu
D. Geman, C. d'Avignon, D. Naiman and R. Winslow, "Classifying gene expression profiles from pairwise mRNA comparisons," Statist. Appl. in Genetics and Molecular Biology, 3, 2004.
tspplot
, ts.pair
, tspcalc
,predict.tsp
, summary.tsp
1 2 3 4 5 6 7 8 9 10 11 12 |
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.