ebplots {EBarrays} | R Documentation

## Various plotting routines in the EBarrays package

### Description

Various plotting routines, used for diagnostic purposes

### Usage

    checkCCV(data, useRank = FALSE, f = 1/2)
    checkModel(data, fit, model = c("gamma", "lognormal", "lnnmv"),
               number = 9, nb = 10, cluster = 1, groupid = NULL)
    checkVarsQQ(data, groupid, ...)
    checkVarsMar(data, groupid, xlab, ylab, ...)
    plotMarginal(fit, data, kernel = "rect", n = 100,
                 bw = "nrd0", adjust = 1, xlab, ylab,...)
    plotCluster(fit, data, cond = NULL, ncolors = 123, sep=TRUE,
                transform=NULL)

    ## S3 method for class 'ebarraysEMfit'
    plot(x, data, plottype="cluster", ...)

### Arguments

`data` | data, as a “matrix” or “ExpressionSet”
`useRank` | logical. If `TRUE`, ranks of means and c.v.-s are used in the scatterplot
`f` | passed on to `lowess`
`fit, x` | object of class “ebarraysEMfit”, typically produced by a call to `emfit`
`model` | which theoretical model use for Q-Q plot. Partial string matching is allowed
`number` | number of bins for checking model assumption.
`nb` | number of data rows included in each bin for checking model assumption
`cluster` | check model assumption for data in that cluster
`groupid` | an integer vector indicating which group each sample belongs to. groupid for samples not included in the analysis should be 0.
`kernel, n, bw, adjust` | passed on to `density`
`cond` | a vector specifying the condition for each replicate
`ncolors` | different number of colors in the plot
`xlab, ylab` | labels for x-axis and y-axis
`sep` | whether or not to draw horizontal lines between clusters
`transform` | a function to transform the original data in plotting
`plottype` | a character string specifying the type of the plot. Available options are "cluster" and "marginal". The default plottype "cluster" employs function 'plotCluster' whereas the "marginal" plottype uses function 'plotMarginal'.
`...` | extra arguments are passed to the `qqmath`, `histogram` and `xyplot` call used to produce the final result

### Details

`checkCCV` checks the constant coefficient of variation assumption made in the GG and LNN models. `checkModel` generates QQ plots for subsets of (log) intensities in a small window. They are used to check the Log-Normal assumption on observation component of the LNN and LNNMV models and the Gamma assumption on observation component of the GG model. `checkVarsQQ` generates QQ plot for gene specific sample variances. It is used to check the assumption of a scaled inverse chi-square prior on gene specific variances, made in the LNNMV model. `checkVarsMar` is another diagnostic tool to check this assumption. The density histogram of gene specific sample variances and the density of the scaled inverse chi-square distribution with parameters estimated from data will be plotted. `checkMarginal` generates predictive marginal distribution from fitted model and compares with estimated marginal (kernel) density of data. Available for the GG and LNN models only. `plotCluster` generate heatmap for gene expression data with clusters

### Value

`checkModel`, `checkVarsQQ` and `checkVarsMar` return an object of class “trellis”, using function in the Lattice package. Note that in certain situations, these may need to be explicitly ‘print’-ed to have any effect.

### Author(s)

Ming Yuan, Ping Wang, Deepayan Sarkar, Michael Newton, and Christina Kendziorski

### References

Newton, M.A., Kendziorski, C.M., Richmond, C.S., Blattner, F.R. (2001). On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data. Journal of Computational Biology 8:37-52.

Kendziorski, C.M., Newton, M.A., Lan, H., Gould, M.N. (2003). On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Statistics in Medicine 22:3899-3914.

Newton, M.A. and Kendziorski, C.M. Parametric Empirical Bayes Methods for Microarrays in The analysis of gene expression data: methods and software. Eds. G. Parmigiani, E.S. Garrett, R. Irizarry and S.L. Zeger, New York: Springer Verlag, 2003.

Newton, M.A., Noueiry, A., Sarkar, D., and Ahlquist, P. (2004). Detecting differential gene expression with a semiparametric hierarchical mixture model. Biostatistics 5: 155-176.

Yuan, M. and Kendziorski, C. (2006). A unified approach for simultaneous gene clustering and differential expression identification. Biometrics 62(4): 1089-1098.

### See Also

`emfit`, `lowess`

[Package EBarrays version 2.56.0 Index]