# Plot Pathway Mean and Confidence Intervals

### Description

Functions for plotting the mean and confidence intervals of a set of pathways.

### Usage

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
plotCIs(QSarray,
path.index=1:numPathways(QSarray),
sort.by=c("mean","p","none"),
lowerBound=0.025,
upperBound=1-lowerBound,
col=NULL,
use.p.colors=TRUE,
p.breaks=NULL,
p.adjust.method = "fdr",
addLegend=use.p.colors,
lowerColorBar="none",
lowerColorBar.cols=NULL,
addGrid=TRUE,
x.labels=NULL,
cex.xaxis=1,
shift=0.0,
add=FALSE,
ylim=NULL, xlim=NULL,
ylab=NULL, xlab=NULL,
main=NULL,
sub=NULL,
type="p",
...
)
``` |

### Arguments

`QSarray` |
QSarray object |

`path.index` |
vector describing which pathways to plot. Can either be numeric or a character vector containing the names of the pathways to plot. |

`sort.by` |
One of c("mean","p","none") indicating the order that the pathways should be plotted in. If "none", the pathways will not be reordered, and the order specified in path.index will be maintained |

`lowerBound, upperBound` |
numeric indicating the lower and upper bounds of the confidence intervals. Default is for a 95% confidence interval. |

`col` |
an optional vector indicating the color for the points. If |

`use.p.colors` |
logical indicating whether error bars should be colored based on the significance of the p-value. |

`p.breaks` |
a vector indicating where the breaks in the p-value color scheme should be. By default, breaks will be at 0.001, 0.005, 0.01, 0.05, & 0.1 |

`p.adjust.method` |
The method to use to adjust the p-values. Must be one of the methods in |

`addLegend` |
a logical specifying if a legend for the p-value color scheme be plotted |

`lowerColorBar` |
Options for plotting a color bar below each point. Automatically generated color bars have not yet been implemented, but custom bars can be created using the "lowerColorBar.cols" parameter. |

`lowerColorBar.cols` |
a vector of colors to plot as a bar below each point. |

`addGrid` |
Should guiding dashed lines be plotted? |

`x.labels` |
character vector of the same length as |

`cex.xaxis` |
set cex parameter seperately for x axis label |

`shift` |
a number between 0 and 1 decribing the amount to shift points with respects to the guiding lines and axis labels. Useful when |

`add` |
logical indicating whether a new plot should be created. If |

`xlim, ylim, xlab, ylab, main, sub, type,...` |
parameters to be passed on to |

### Details

This function uses the data produced by `aggregateGeneSet`

to plot the means and confidence intervals of the gene sets in `QSarray`

. By default, the gene sets will be ordered by decreasing mean, and the 95% confidence intervals of each point (as calculated by calcBayesCI) will be added. To specify a different order, `sort.by`

must be set to `"none"`

, and the order specified by `path.index`

will be used.

The points in the plot can be optionally color-coded by the significance of the (corrected) p-values. The p-values are adjusted using R's built in `p.adjust`

method, which uses the `p.adjust.method`

parameter to determine the algorithm being used. The colors of the points are based on the breaks specified in `p.breaks`

. By default, more significant p-values will be plotted in bright red/green. If `use.p.colors`

is specified and `addLegend=TRUE`

, a legend describing the p-values will be added to the top left corner of the plot. Alternatively, if you want to specify the colors of the points individually, you can provide a vector of colors to the `col`

parameter.

The `plotCIs`

function can also add a color bar along the bottom of the plot to provide additional information about the pathways. We are currently working on implementing various metrics which can be added automatically using the `lowerColorBar`

parameter, but in the mean time, the bar can be added manually by providing a vector of colors the same length as `path.index`

to the `lowerColorBar.cols`

parameter.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
##create example data
eset = matrix(rnorm(500*20),500,20, dimnames=list(1:500,1:20))
labels = c(rep("A",10),rep("B",10))
geneSets = list()
##create a number of gene sets with varying levels of differential expression.
for(i in 0:10){
genes = ((30*i)+1):(30*(i+1))
eset[genes,labels=="B"] = eset[genes,labels=="B"] + rnorm(1)
geneSets[[paste("Set",i)]] = genes
}
##calculate qusage results
results = qusage(eset,labels, "B-A", geneSets)
##Plot gene set CIs
plotCIs(results)
``` |