scoring {macat} | R Documentation |
This function computes for all genes in an expression matrix the (regularized) t-scores (statistics) with the given class labels and a number of permutations of these labels. Each gene is also assigned a p-value either empirically from the permutation scores or from a t-distribution.
scoring(data, labels, method = "SAM", pcompute = "tdist", nperms = 1000, memory.limit = TRUE, verbose = TRUE)
data |
Expression matrix with rows = genes and columns = samples |
labels |
Vector or factor of class labels; Scoring works only with two classes! |
method |
Either "SAM" to compute regularized t-scores, or "t.test" to compute Student's t-statistic |
pcompute |
Method to compute p-values for each genes, either "empirical" to do permutations and compute p-values from them, or "tdist" to compute p-values based on respective t-distribution |
nperms |
Number of permutations of the labels to be investigated, if argument 'pcompute="empirical"' |
memory.limit |
Logical, if you have a really good computer (>2GB RAM), setting this FALSE will increase speed of computations |
verbose |
Logical, if progress should be reported to STDOUT |
If 'pcompute="empirical"', the statistic is computed based on the given class labels, afterwards for 'nperms' permutations of the labels. The p-value for each gene is then the proportion of permutation statistics that are higher or equal than the statistic from the real labels. For each gene the 2.5%- and the 97.5%-quantile of the permutation statistics are also returned as lower and upper 'significance threshold'.
If 'pcompute="tdist", the statistic is computed only based on the given class labels, and the p-value is computed from the t-distribution with (Number of samples - 2) degrees of freedom.
A list, with four components:
observed |
(Regularized) t-scores for all genes based on the given labels |
pvalues |
P-values for all genes, either from permutations or t-distribution |
expected.lower |
2.5%-quantile of permutation test-statistics, supposed to be a lower 'significance border' for the gene; or NULL if p-values were computed from t-distribution |
expected.upper |
97.5%-quantile of permutation test-statistics, supposed to be an upper 'significance border' for the gene; or NULL if p-values were computed from t-distribution |
In package macat
, this function is only called internally
by the function evalScoring
MACAT development team
Regarding the regularized t-score please see the macat
vignette.
data(stjd) # compute gene-wise regularized t-statistics for # T- vs. B-lymphocyte ALL: isT <- as.numeric(stjd$labels=="T") TvsB <- scoring(stjd$expr,isT,method="SAM",pcompute="none") summary(TvsB$observed)