pairs-methods {DEDS} R Documentation

## Pairs Plot for DEDS Objects

### Description

The function `pairs-DEDS` produces pairs plots of statistics or p values for `DEDS-class` objects.

### Usage

```## S3 method for class 'DEDS':
pairs(x, subset=c(1:nrow(x\$stats)), labels =
colnames(x\$stats[,-1]), logit = FALSE,
diagonal = c("qqnorm", "boxplot", "density", "histogram", "none"),
lower = c("cor", "none"), groups.by.deds = TRUE, thresh = 0.05, reg.line
= NULL, smooth = FALSE, line.by.group = FALSE, diag.by.group = TRUE, lower.by.group =
FALSE, col = palette(), pch = 1:n.groups, lwd = 1, legend.plot =
length(levels(groups)) > 1, ...)
```

### Arguments

`x` An object of `DEDS`.
`subset` A numeric vector indicating the subset of points to be plotted.
`labels` A character vector specifying the names of the variables.
`logit` A logical variable, if `TRUE` the variables are logged, useful when plotting p values.
`diagonal` A character string specifying the type of plot to be applied in the diagonal panels.
 `diagonal="qqnorm"`: `qqnorm` on the diagonal `diagonal="boxplot"`: `boxplot` on the diagonal `diagonal="density"`: `density` on the diagonal `diagonal="histogram"`: `hist` on the diagonal `diagonal="none"`: no special plot will be applied on the diagonal
`lower` A character string specifying the function to be applied in the lower panels.
 `lower="cor"`: absolute correlation will be put on the lower panel `none="cor"`; no special function will be applied
`groups.by.deds` A logcial variable, if `TRUE`, points will be separated into groups according to their magnitude of q- or p-values by DEDS.
`thresh` A numeric variable, if `thresh`<1, it specifies the threshold of significance in differential expression (DE) for q- or p-values of the DEDS object; default is set at 0.05. If `thresh`>1, it specifies the number of top DE genes to be highlighted.
`reg.line` A function name specifying the type of regressio line to be plotted in the scatter plots. If `reg.line=lm`, linear regression line will be plotted; If `reg.line=NULL`, no regression line will be plotted in the scatter plot.
`smooth` A logical variable specifying if smooth regression lines will be plotted in the scatter plots. If `smooth=TRUE`, a `lowess` line will be applied.
`line.by.group` A logical variable specifying if the regression lines should be applied within groups.
`diag.by.group` A logical variable specifying if the plot in the diagonal panels whould be applied groupwise.
`lower.by.group` A logical variable, if `lower.by.group=TRUE` and `lower="cor"`, correlation coefficients will be calculated and printed separated according to groups in the lower panels.
`col` A specification for the colors to be used for plotting different groups, see `par`.
`pch` A specification for the type of points to be used for plotting different groups, see `par`.
`lwd` A specification for the width of lines to be used if lines are plotted; see `par`.
`legend.plot` A logical variable specifying if the legend will be plotted.
`...` Extra parameters for plotting.

### Details

The function `pairs.DEDS` implements a S3 method of `pairs` for `DEDS`. The `DEDS` class is a simple list-based class to store DEDS results and it is usually created by functions `deds.pval`, `deds.stat`, `deds.stat.linkC`. The list contains a "stat" component, which stores statistics or p values from various statistical tests. The function `pairs.DEDS` extracts the "stat" component and produces a matrix of scatterplot.

`pairs.DEDS` as a default highlights points (corresponding to genes) with adjusted p- or q-values less than a user defined threshold. The user can select among a series of options a plot for the diagonal panel; as a default, it produces a `qqnorm` for each column in the "stat" matrix. Both the diagonal and lower panels can be stratified by specifying the `diag.by.group` or `lower.by.group` arguments.

### Author(s)

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

`deds.stat`, `deds.pval`, `deds.stat.linkC`, `hist.DEDS`, `qqnorm.DEDS`

### 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