deds.stat.linkC {DEDS} | R Documentation

## Differentail Expression via Distance Summary of Multiple Statistics

### Description

`deds.stat.linkC` integrates different statistics of differential expression (DE) to rank and select a set of DE genes.

### Usage

    deds.stat.linkC(X, L, B = 1000, tests = c("t", "fc", "sam"), tail =
    c("abs", "lower", "higher"), extras = NULL, distance = c("weuclid",
    "euclid"), adj = c("fdr", "adjp"), nsig = nrow(X), quick = TRUE)

### Arguments

`X` | A matrix, with m rows corresponding to variables (hypotheses) and n columns corresponding to observations. In the case of gene expression data, rows correspond to genes and columns to mRNA samples. The data can be read using `read.table`.
`L` | A vector of integers corresponding to observation (column) class labels. For k classes, the labels must be integers between 0 and k-1.
`B` | The number of permutations. For a complete enumeration, `B` should be 0 (zero) or any number not less than the total number of permutations.

`tests`

A character vector specifying the statistics to be used to test the null hypothesis of no association between the variables and the class labels, `test` could be any of the following:

`"t"`: | one or two sample t-statistics;
`"f"`: | F-statistics;
`"fc"`: | fold changes among classes;
`"sam"`: | SAM-statistics;
`"modt"`: | moderated t-statistics;
`"modt"`: | moderated F-statistics;
`"B"`: | B-statistics.

`tail`

A character string specifying the type of rejection region.
If `side="abs"`, two-tailed tests, the null hypothesis is rejected for large absolute values of the test statistic.
If `side="higher"`, one-tailed tests, the null hypothesis is rejected for large values of the test statistic.
If `side="lower"`, one-tailed tests, the null hypothesis is rejected for small values of the test statistic.

`extras` | Extra parameter needed for the test specified; see `deds.genExtra`.
`distance` | A character string specifying the type of distance measure used for the calculation of the distance to the extreme point (E). If `distance="weuclid"`, weighted euclidean distance, the weight for statistic t is 1/MAD(t); If `distance="euclid"`, euclidean distance.
`adj` | A character string specifying the type of multiple testing adjustment. If `adj="fdr"`, False Discovery Rate is controled and q values are returned. If `adj="adjp"`, ajusted p values that controls family wise type I error rate are returned.
`nsig` | If `adj = "fdr"`, `nsig` specifies the number of top differentially expressed genes whose q values will be calculated; we recommend setting `nsig < m`, as the computation of q values will be extensive. q values for the rest of genes will be approximated to 1. If `adj = "adjp"`, the calculation of the adjusted p values will be for the whole dataset.
`quick` | A logical variable specifying if a quick but memory requiring procedure will be selected. If `quick=TRUE`, permutation will be carried out once and stored in memory; If `quick=FALSE` a fixed seeded sampling procedure will be employed, which requires more computation time as the permutation will be carried out twice, but will not use extra memory for storage.

### Details

`deds.stat.linkC` summarizes multiple statistical measures for the evidence of DE. The DEDS methodology treats each gene as a point corresponding to a gene's vector of DE measures. An "extreme origin" is defined as the maxima of all statistics and the distance from all points to the extreme is computed and ranking of a gene for DE is determined by the closeness of the gene to the extreme. To determine a cutoff for declaration of DE, null referent distributions are generated by permuting the data matrix.

Statistical measures currently in the DEDS package include t statistics (`tests="t"`), fold changes (`tests="fc"`), F statistics (`tests="f"`), SAM (`tests="sam"`), moderated t (`tests="modt"`), moderated F statistics (`tests="modf"`), and B statistics (`tests="B"`). The function `deds.stat.linkC` interfaces to C functions for the tests and the computation of DEDS. For more flexibility, the user can also use `deds.stat` which has the same functionality as `deds.stat.linkC` but is written completely in R (therefore slower) and the user can supply their own function for a statistic not covered in the DEDS package.

DEDS can also summarize p values from different statistical models, see `deds.pval`.

### Value

An object of class `DEDS`. See `DEDS-class`.

### Author(s)

Yuanyuan Xiao, yxiao@itsa.ucsf.edu,
Jean Yee Hwa Yang, jean@biostat.ucsf.edu.

### References

Yang, Y.H., Xiao, Y. and Segal M.R.: Selecting differentially expressed genes from microarray experiment by sets of statistics. Bioinformatics 2005 21:1084-1093.

### See Also

`deds.pval`, `deds.stat`.

### Examples

    X <- matrix(rnorm(1000,0,0.5), nc=10)
    L <- rep(0:1,c(5,5))

    # genes 1-10 are differentially expressed
    X[1:10,6:10]<-X[1:10,6:10]+1
    # DEDS summarizing t, fc and sam
    d <- deds.stat.linkC(X, L, B=200)

[Package DEDS version 1.6.0 Index]