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

Perform a bi-level meta-analysis in conjunction with any of the classical hypothesis testing methods, such as t-test, Wilcoxon test, etc.

1 2 | ```
bilevelAnalysisClassic(x, y = NULL, splitSize = 5, metaMethod = addCLT,
func = t.test, p.value = "p.value", ...)
``` |

`x` |
a list of numeric vectors |

`y` |
an optional list of numeric vectors |

`splitSize` |
the minimum number of size in each split sample. splitSize should be at least 3. By default, splitSize=5 |

`metaMethod` |
the method used to combine p-values. This should be one of addCLT (additive method [1]), fishersMethod (Fisher's method [5]), stoufferMethod (Stouffer's method [6]), max (maxP method [7]), or min (minP method [8]) |

`func` |
the name of the hypothesis test. By default func=t.test |

`p.value` |
the component that returns the p-value after performing the
test provided by the |

`...` |
additional parameters for |

This function performs a bi-level meta-analysis for the lists
of samples [1]. It performs intra-experiment analyses to compare the
vectors in x agains the corresponding vectors in y using the function
`intraAnalysisClassic`

in conjunction with the test provided
in *func*. For example, it compares the first vector in x with the
first vector in y, the second vector in x with the second vector in y, etc.
When y is null, then the comparisons are reduced to one-sample tests. After
these comparisons, we have a list of p-values, one for each comparision.
The function then combines these p-values to obtain a single p-value
using *metaMethod*.

the combined p-value

Tin Nguyen and Sorin Draghici

[1] T. Nguyen, R. Tagett, M. Donato, C. Mitrea, and S. Draghici. A novel bi-level meta-analysis approach – applied to biological pathway analysis. Bioinformatics, 32(3):409-416, 2016.

`intraAnalysisClassic`

, `intraAnalysisGene`

, `bilevelAnalysisGene`

1 2 3 4 5 6 7 8 9 | ```
set.seed(1)
l1 <- lapply(as.list(seq(3)),FUN=function (x) rnorm(n=10, mean=1))
l1
# one-sample t-test
lapply(l1, FUN=function(x) t.test(x, alternative="greater")$p.value)
# combining the p-values of one-sample t-tests:
addCLT(unlist(lapply(l1, FUN=function(x) t.test(x, alter="g")$p.value)))
#Bi-level meta-analysis
bilevelAnalysisClassic(x=l1, alternative="greater")
``` |

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