Description Usage Arguments Details Value Note Author(s) References See Also Examples

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.

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`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.

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```
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, basename, cbind, colMeans, colSums, colnames,
dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
intersect, is.unsorted, lapply, lengths, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: annotate
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: IRanges
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following object is masked from 'package:base':
expand.grid
Loading required package: XML
Loading MicroArray Chromosome Analysis Tool...
Loading required packages...
You need package 'stjudem' if you want to see the demo and examples.
Type 'loaddatapkg("stjudem")' for automatic install!
Type 'demo(macatdemo)' for a quick tour...
Compute observed test statistics...
Compute quantiles of empirical distributions...Done.
Min. 1st Qu. Median Mean 3rd Qu. Max.
-5.837462 -0.382940 0.021554 0.005255 0.450870 5.425687
```

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