cell.plot: Cell Plot
In dieterich-lab/CellPlot: Segmented Bar Chart Plot Methods

Description Usage Arguments Author(s) Examples

Plots a horizontal barchart of strictly positive values in x. For each entry, a vector of additional values needs to be provided in a list. The additional values are plotted as cells subdividing the length of the corresponding bar. A typical usage scenario is plotting the enrichment of Gene Ontology terms, with individual cells reflecting the differential regulation of the constituent genes.

cell.plot(x, cells, x.col = NULL, cell.col = c("deepskyblue2", "white",
  "coral"), inf.shading = 30/cell.lwd, space = 0.1, x.mar = c(0.2, 0),
  y.mar = c(0.1, 0), x.bound = NULL, lab.cex = 1, xdes.cex = 1,
  xlab.cex = 1, xlab.ticks = 5, sym = FALSE, cell.lwd = 2,
  cell.outer = 2, cell.sort = T, cell.limit = 30, cell.bounds = NULL,
  elem.bounds = NULL, xlab = "GO Term Enrichment", key = T,
  key.lab = "Differential Expression", key.n = 11, spacers = NULL,
  bar.scale = 1, gridlines = T, ...)

`x`	Strictly positive numeric vector.
`cells`	List of vectors. Must be the same length as x.
`x.col`	Vector of color names. Must be the same length as x. Defaults to black.
`cell.col`	Character vector of length 3, specifying colors for the low end, zero, and high end of cell values. Defaults to c("blue","white","red").
`inf.shading`	Numeric. Density of shading lines for infinite values. Defaults to 30/cell.lwd.
`space`	Scaling factor for spacing between bars.
`x.mar`	Numeric vector defining the inside margin to the left and right of the main plot. Defaults to c(0.2,0.1).
`y.mar`	Numeric vector defining the inside margin to the top and bottom of the main plot. Defaults to c(0.08,0).
`x.bound`	Numeric, must be positive. Specifies upper bound of x-axis scale. If NULL (the default), this value is chosen automatically.
`lab.cex`	Scaling factor for label text size.
`xdes.cex`	Scaling factor for key legend text size.
`xlab.cex`	Scaling factor for x-label text size.
`xlab.ticks`	Number of ticks for the x-axis..
`sym`	Logical, if `TRUE` visualize cell values on a symmetrical scale.
`cell.lwd`	Size of the border of individual cells. Defaults to 1.
`cell.outer`	Size of outer border around bars. Defaults to 2.
`cell.sort`	Should cell values be sorted? Defaults to TRUE.
`cell.limit`	Number of cells above which separators between cells are omitted. Defaults to 50.
`cell.bounds`	Numeric vector of length 2, specifying lower and upper bound for cell value visualization. If set to NULL (the default), these values are chosen automatically.
`elem.bounds`	Vector of length 2 specifying a filter on minimum and maximum number of elements in a category. Defaults to NULL, in which case no filter is applied.
`xlab`	Label for x-axis.
`key`	Logical value. Show the key/legend for the cell colors.
`key.lab`	Label for color key.
`key.n`	Number of legend boxes for the color key. Minimum is 3 (5 when infinite values are present).
`spacers`	Numeric vector. Inserts empty space after the specified positions to visually group bars.
`bar.scale`	Numeric. Set bar height to a fixed value multiplied by this parameter. If the plotting area is too small clipping may occur.
`gridlines`	Logical value, if to show vertical grid lines.
`...`	Arguments passed through to the title() function. E.g. main = "Plot title".

Robert Sehlke [aut]
Sven E. Templer [ctb]

## Not run: 

### Random data example

# Generate random positive vector and name it
x = sort( runif(16, min = 1, max = 3), decreasing = T )
names(x) = paste("GO Term",1:16)

# Label colors
xcolor = c(rep("darkslategrey",4), rep("chartreuse4",4), rep("coral4",8))

# Generate list with random vectors, one for each entry in x
cells = list()
xc = round( runif(16, min=21, max=100) )
for (i in 1:length(xc)) { cells = c(cells, list(runif(xc[i],-5,5))) }
cells[[9]][1:2] = Inf
cells[[9]][3] = -Inf

# Plot with spacers
cell.plot( x, cells,
           xcolor, spacers = c(4,8), xlab.ticks = 5, cell.limit = 80,
           main="Cell Plot Demo", xlab="log(enrichment)" )
  
### golubstat data example

data(golubstat)
x <- subset(golubstat, p<=.05 & significant>4 & !duplicated(genes))
x <- head(x, 10)
cell.plot( x = setNames(x$loge, x$term), cells = x$deg.log2fc, 
           main = "Golub et al. (1999) - Gene Ontology Enrichment" )

## End(Not run)