samplesize {OCplus} | R Documentation

## FDR as a function of sample size

### Description

This function tabulates the false discovery rate (FDR) for selecting differentially expressed genes as a function of sample size and cutoff level. Additionally, the same information can be displayed through an attractive plot.

### Usage

    samplesize(n = seq(5, 50, by = 5), p0 = 0.99, sigma = 1, D, F0, F1,
               paired = FALSE, crit, crit.style = c("top percentage", "cutoff"),
    		   plot =TRUE, local.show=FALSE, nplot = 100, ylim = c(0, 1), main,
    		   legend.show = FALSE, grid.show = FALSE, ...)

### Arguments

`n` | sample size (as subjects per group)
`p0` | the proportion of non-differentially expressed genes
`sigma` | the standard deviation for the log expression values
`D` | assumed average log fold change (in units of `sigma`), by default 1; this is a shortcut for specifying a simple symmetrical alternative hypothesis through `F1`.
`F0` | the distribution of the log2 expression values under the null hypothesis; by default, this is normal with mean zero and standard deviation `sigma`, but mixtures of normals can be specified, see Details and Examples.
`F1` | the distribution of the log2 expression values under the alternative hypothesis; by default, this is an equal mixture of two normals with means `D` and -`D` and standard deviation `sigma`; mixture of normals are again possible, see Details and Examples.
`paired` | logical value indicating whether this is the independent sample case (default) or the paired sample case.
`crit` | a vector of cutoff values for selecting differentially expressed genes; the interpretation depends on `crit.style`.
`crit.style` | indicates how differentially expressed genes are selected: either by a fixed cutoff level for the absolute value of the t-statistic or as a fixed percentage of the absolute largest t-statistics.
`plot` | logical value indicating whether to do the plotting business
`local.show` | logical value indicating whether to show local or global false discovery rate (default: global).
`nplot` | number of points that are evaluated for the curves
`ylim` | the usual limits on the vertical axis
`main` | the main title of the plot
`legend.show` | logical value indicating whether to show a legend for the types of gene selection in the plot
`grid.show` | logical value indicating whether to draw grid lines showing the sample sizes `n` to be tabulated in the plot
`...` | the usual graphical parameters, passed to `plot`

### Details

This function plots the FDR as a function of the sample size when comparing the expression of multiple genes between two groups of subjects. This is based on a model assuming that a proportion `p0` of genes is not differentially expressed (regulated) between groups, and that 1-`p0` genes are. The logarithmized gene expression values of regulated and non regulated genes are assumed to be generated by mixtures of normal distributions; these mixtures can be specified through the parameters `F0`, `F1` or `D`, and `sigma`; please see `TOC` for details on the model and the specification of the mixtures. By default, the null distribution of the log expression values is a normal centered on zero, and the alternative an equal mixture of normals centered at `+D` and `-D`.

The list of nominally differentially expressed genes can be selected in two ways:

- all genes with absolute t-statistic larger than the specified critical cutoff values (`cutoff`),

- all genes that represent the specified critical top percentage of the absolutely largest t-statistics (`top percentage`).

  Multiple critical values correspond to multiple curves, each labeled by the critical value, but only one value can be specified for the proportion of non-regulated genes `p0` and the standard deviation `sigma`.

### Value

A matrix with rows corresponding to elements of `n` and columns corresponding to the specified critical values is returned. The matrix has the attribute `param` that contains the specified arguments, see Examples.

### Note

Both the curve labels and the legend may be squashed if the plotting device is too small. Increasing the size of the device and re-plotting should improve readability.

### Author(s)

Y. Pawitan and A. Ploner

### References

Pawitan Y, Michiels S, Koscielny S, Gusnanto A, Ploner A (2005) False Discovery Rate, Sensitivity and Sample Size for Microarray Studies. Bioinformatics, 21, 3017-3024.

Jung SH (2005) Sample size for FDR-control in microarray data analysis. Bioinformatics, 21, 3097-104.

### See Also

`FDR`, `TOC`, `EOC`

### Examples

    # Default assumes a proportion of 0.01 regulated genes equally split
    # between two-fold up- and down-regulated
    # We select the top 1, 2, 3 percent absolute largest t-statistics
    samplesize(crit=c(0.03,0.02, 0.01))

    # Same model, but using a hard cutoff for the t-statistics
    samplesize(crit=2:4, crit.style="cutoff")

    # Paired test of the same size has slightly better FDR (as expected)
    samplesize(paired=TRUE)

    # Compare the effect of p0 and effect size
    par(mfrow=c(2,2))
    samplesize(crit=c(0.03,0.02, 0.01), p0=0.95, D=1)
    samplesize(crit=c(0.03,0.02, 0.01), p0=0.99, D=1)
    samplesize(crit=c(0.03,0.02, 0.01), p0=0.95, D=2)
    samplesize(crit=c(0.03,0.02, 0.01), p0=0.99, D=2)

    # An asymmetric alternative distribution: 20 percent of the regulated genes
    # are expected to be (at least) four-fold up regulated
    # NB, no graphical output
    ret = samplesize(F1=list(D=c(-1,1,2), p=c(2,2,1)), p0=0.95, crit=0.05, plot=FALSE)
    ret
    # Look at the parameters
    attr(ret, "param")

    # A wide null distribution that allows to disregard genes with small effect
    # Here: |log2 fold change| < 0.25, i.e. fold change of less than 19 percent
    samplesize(F0=list(D=c(-0.25,0,0.25)), grid=TRUE)

    # This is close to Example 3 in Jung's paper (see References):
    # p0=0.99 and sensitivity=0.6, so we want a rejection rate of
    # around 0.006 from the top list.
    # Here we require around 40 arrays/group, compared to
    # around 37 in Jung's paper, most likely because we use
    # the t-distribution instead of normal. Jung's alternative
    # is only one-sided, so the exact correspondence is
    #
    samplesize(p0=0.99,crit.style="top", crit=0.006, F1=list(D=1, p=1), grid=TRUE)
    abline(h=0.01)

    #The result is very close to the symmetric alternatives:
    samplesize(p0=0.99,crit=0.006, D=1, grid=TRUE, ylim=c(0,0.9))

[Package OCplus version 1.61.0 Index]