matest {maanova} | R Documentation

## Statistical test for Microarray experiment

### Description

This is the function to perform F or T test on one or multiple experimental factor(s). Permutation test will be carried upon request.

### Usage

    matest(data, model, term, Contrast, n.perm=1000, nnodes=1,
           critical=.9, test.type = c("ttest", "ftest"),
           shuffle.method=c("sample", "resid"),
           MME.method=c("REML","noest","ML"),
           test.method=c(1,0,1,1),
           pval.pool=TRUE, verbose=TRUE)

### Arguments

`data` | An object of class `madata`.
`model` | An object of class `mamodel`.
`term` | The term(s) to be tested. It can be multiple terms. Note that the tested term must be fixed. If the term to be tested is a random term, it will be converted to a fixed term than do test.
`Contrast` | The contrast matrix for the term. The number of columns equals to the number of levels in the term. The number of rows is the number of T-test you want to carry. Note that it must be a matrix. Use `matrix` command to make it. Note that the the hypothesis test can be formulated as H0: Lb=0 versus alternative. This contrast matrix is L. For testing a covariate, use a one by one contrast matrix of 1.
`n.perm` | An integer for number of permuatations.
`nnodes` | Number of nodes in the MPI cluster. If 1, the permutation test will be running on the local computer.
`critical` | percentile of F-distribution used to get a subset to calculate p-value. Default is 90th percentile of F-distribution, and permutation analysis is conducted based on genes whose test statistics are smaller than 90th percentile of the F-distribution.
`test.type` | Test type. It could be F-test or T-test. If the Contrast matrix is missing, this should be a "ftest" and the contrast matrix is generated automatically to cover the whole linear space except for testing covariates. If the Contrast matrix is given, this could be "ftest" or "ttest". The default is "ttest" (for backward compatability). For T-test, the code will do a series of T-test, where each T-test corresponds to a row in the contrast matrix.
`shuffle.method` | Data shuffling method. "sample" for sample shuffling and "resid" for residual shuffling. Read "Data Shuffling" section for detail.
`MME.method` | The method used to solve the Mixed Model Equations. See `fitmaanova` for detail. This parameter only applies for mixed effects model permutation test. Default method is "REML". The variance components for observed data will be used for permuted data. It will greatly increase the speed but you may lose power in statistical test in some cases.
`test.method` | An integer vector of four elements to indicate which F test to carry. Default is c(1,0,1,1), which means do F1, F3 and Fs test.
`pval.pool` | A logical value to indicate whether to use pooled permutation F values to calculate the P values.
`verbose` | A logical value to indicate whether to display some message for calculation progress.

### Details

If user provide a comparision matrix, this function will perform T-test on the given comparison(s). Otherwise, this function will perform F-test for the given term.

There are four types of tests available. All four tests are based on the gene-specific ANOVA model. F1 is the usual F statistic. F3 assumes common error variance across all genes. F2 is the hybrid of the F1 and F3 tests. Fs is based on the James-Stein shrinkage estimates of the error variance.

Permutation tests can run on MPI cluster. This feature is only available for Unix/Linux system. Several other R packages (such like SNOW, Rmpi, etc.) are needed for using cluster. You may need help from your system administrator to setup LAM-MPI correctly. For detailed information on LAM-MPI cluster setup and the cluster usage in R, read "MPI_README" distributed with the package.

### Value

An object of class `matest`, which is a list of the following components:

`model` | Input model object.
`term` | The input term(s) to be tested.
`dfde` | Denominator's degree of freedom for the test.
`dfnu` | Numerator's degree of freedom for the test. Note that this is always 1 for T-test.
`obsAnova` | An object of `maanova`, which is the ANOVA model fitting result on the original data.
`Contrast` | The contrast matrix used in the test.
`n.perm` | Number of permutations.
`shuffle` | Shuffle style
`pval.pool` | Use pooled P value or not.
`F1, F2, F3, Fs` | Objects of four different F tests results. All or any of them could be there according to the requested F test method. Each of them contains the following fields: Fobs F value for the observed data. Ptab Tabulated P values for the observed data. Pvalperm Nominal permutation P values for each gene. This field will be unavailable if user didnot do permutation test. Pvalmax FWER one-step adjusted P values from the permutation test. All the F values and P values are matrices. The number of rows in the matrices equals to the number of genes. For F-test, the number of columns will be one. For T-test, the number of columns equals to the number of tests carried.

### Data Shuffling

Data shuffling method is a crucial part in the permutation test. Currently there are two shuffling method available, residual shuffing and sample shuffing. Fixed-effects models permutation test can use either of the method. For mixed-effects models, residual shuffing will be incorrect so only sample shuffing is available.

Residual shuffing is to shuffle the null model residuals globally without replacement.

Sample shuffing is to shuffle the samples based on the nesting relationship among the experimental factors in the model. For sample shuffling, you need to make sure you have a good sample size. Otherwise the result may be biased.

### Author(s)

Hao Wu; Hyuna Yang, hyuna.yang@jax.org

### References

Cui, X. and Churchill,GA (2003), Statistical tests for differential expression in cDNA Microarray Expeirments, Genome Biology 4:210.

Cui, X., Hwang, J.T.G., Blades N., Qiu J. and Churchill GA (2003), Improved statistical tests for differential gene expression by shrinking variance components, to be submitted.

### See Also

`makeModel`, `fitmaanova`

### Examples

    # load in Paigen's data
    data(paigen)
    # make data object with rep 2
    paigen <- createData(paigen.raw, 2)
    # Note that the data is normalized so normalization is skipped

    ##################################
    # fixed model test
    ##################################
    # make an additive model
    model.add.fix <- makeModel(data=paigen, formula=~Dye+Array+Strain+Diet)
    # test strain effect
    ## Not run: test.Strain.fix <- matest(paigen, model.add.fix, term="Strain", n.perm=100)
    # volcano plot
    ## Not run: idx <- volcano(test.Strain.fix)

    # test pairwise comparisions for Strain, using a MPI cluster with 8 nodes
    ## Not run: C <- matrix(c(1,-1,0,1,0,-1, 0,1,-1), nrow=3, byrow=TRUE)
    ## Not run:
    ttest.strain.fix <- matest(paigen, model.add.fix,
             term="Strain", Contrast=C, n.perm=100, nnodes=8)
    ## End(Not run)
    ## Not run: volcano(ttest.strain.fix)

    # a user specified F-test on Strain
    # note that the F- and P-values generated in this test is exactly the
    # same as the above F-test. But the volcano plot looks a little
    # different because the X-axis values are different
    ## Not run:
    C <- matrix(c(1,-1/2,-1/2,1,0,-1), nrow=2, byrow=TRUE)
    test.Strain.fix <- matest(paigen, model.add.fix, term="Strain",
    Contrast=C, test.type="ftest", n.perm=100)
    ## End(Not run)

    ##################################
    # mixed model test
    ##################################
    # mixed model permutation test is very slow
    # I will skip the example for that
    # the syntax of the function will be the same
    # except the input model object is for mixed effects model

[Package maanova version 1.6.0 Index]