Description Usage Arguments Details Value Author(s) See Also Examples
Obtaining the optimal p-value cut-off for individual tests to achieve a given Type I error level of obtaining connected nodes in the graph
1 2 3 4 5 6 7 8 9 | choisir.seuil.equiv( n.genes, taille.groupes,
mu = 10, sigma = 0.5, Delta = 0.5,
alpha.cible = 0.05,
seuil.p = (10:40)/100,
B = 3000, conf.level = 0.95,
f.p = equiv.fpc,
en.log = TRUE,
n.coeurs = 1,
... )
|
n.genes |
Number of genes to be quantified simultaneously |
taille.groupes |
An integer vector containing the sample size
for each group. The number of groups is determined by the length of
this vector. Unused if |
mu |
A numeric vector giving the mean amount for each component in the first condition, in the log scale (μ\). If a single value is provided, it is used for each component. Otherwise, the length of the vector must be equal to the number of components. It can also be a two-lines matrix giving the mean amounts for each component (columns) in the first (firt row) and second (second row) condition. |
sigma |
A numeric vector giving the standard deviation for the amount of each component in both conditions, in the log scale (σ\). If a single value is provided, it is used for each component. Otherwise, the length of the vector must be equal to the number of components. It can also be a two-lines matrix giving the mean amounts for each component (columns) in the first (firt row) and second (second row) condition. |
Delta |
The limit for the equivalence region, Δ, in the log scale. |
alpha.cible |
The target type I error level of obtaining disjoint subnetworks under the null hypothesis that gene expressions are the same in all groups. Should be between 0 and 1. |
seuil.p |
A numeric vector of candidate cutoffs. Values outside the [0,1] interval are automatically removed. The default (from 0.05 to 0.30) is suited for a target type I error of 0.05 and less than 30 genes, roughly. |
B |
How many simulations to do. |
conf.level |
The confidence level of the interval given as a result (see Details). |
f.p |
The function to use for individual tests of each
ratio. See |
en.log |
If |
n.coeurs |
The number of CPU cores to use in computation, with parallelization using forks (does not work on Windows) with the help of the parallel package. |
... |
additional arguments, to be used by the analysis function f.p |
The choisir.seuil.equiv
function simulates B
datasets of n.genes
“quantities” measured several times,
under the null hypothesis that variations between samples of two
conditions are given by the difference between the two rows of the
μ matrix. If μ was given as a single row (or a single
value), the second row is defined as (μ, μ + Δ, μ +
2Δ…) – correspondong to the null hypothesis that all
components have a different change between the two conditions, and
that this change is equal to the equivalence region limit
(Δ). For each of these B
datasets,
creer.Mp
is called with the provided test function, then
converted to a graph using in turn all cut-offs given in
seuil.p
and the number of edges of the graph is
determined. Having at least one edge is a type I error, since under
the null hypothesis there is no couple of genes having the same
change.
For each cut-off in seuil.p
, the proportion of false-positive
is then determined, along with its confidence interval (using the
exact, binomial formula). The optimal cut-off to achieve the target
type I error is then found by linear interpolation.
Data are generated using a normal (Gaussian) distribution, independantly for each component and each condition.
choisir.seuil.equiv
returns a data.frame with four
columns, corresponding to the candidate cut-offs, the corresponding
estimated type-I error and its lower and upper confidence bounds, and
attributes giving the estimated optimal cut-off, its confidence
interval and details on simulation condition. This data.frame has the
additional class SARPcompo.H0
, allowing specific print
and plot
methods to be used.
Emmanuel Curis (emmanuel.curis@parisdescartes.fr)
See choisir.seuil
for the case of difference tests and
disjoing subgraphs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # What would be the optimal cut-off for 5 genes quantified in two
# groups of 5 replicates?
# Null hypothesis : mean = 0, sd = 1, Delta = 2
# For speed reason, only 50 simulations are done here,
# but obviously much more are needed to have a good estimate f the cut-off.
seuil <- choisir.seuil.equiv( 5, c( 5, 5 ),
mu = 1, sigma = 1, Delta = 1,
B = 50 )
seuil
# Get the cut-off and its confidence interval
attr( seuil, "seuil" )
# Plot the results
plot( seuil )
|
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