# Various plotting routines in the EBarrays package

### Description

Various plotting routines, used for diagnostic purposes

### Usage

1 2 3 4 5 6 7 8 9 10 11 12 | ```
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 |

`f` |
passed on to |

`fit, x` |
object of class “ebarraysEMfit”, typically produced by a
call to |

`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 |

`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 |

### 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`

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