Description Usage Arguments Value Author(s) See Also Examples
Draws a heatmap using fluctile as the workhorse and offers the possibility to add rectangles which visualize the biclusters.
1 2 3 4 5 | heattile(x, biclust = NULL, Is = NULL, shape = "r", fluct = FALSE, gap.prop = 0,
border = c(0.05, 0.03, 0.03, 0.05), label = c(TRUE,FALSE) ,
lab.opt = list(abbrev = 24, lab.cex = 1, rot = 0), bg.col = "lightgrey", sym = FALSE,
breaks = 20+ 10*sym, clust.col = NULL, clust.palette = "rgb", hm.palette = "div",
clust.col.opt = list(), hm.col.opt = list(revert = TRUE))
|
x |
A two-was data matrix. |
biclust |
A biclustering object. The matrix is displayed in its original order. |
Is |
Instead of |
shape |
Shape of the tiles, see fluctile. |
fluct |
Plots polygons whose sizes are proportional to their corresponding values, see fluctile.
If |
gap.prop |
gaps between the tiles, see fluctile. |
border |
plot margins, see fluctile. |
label |
Whether or not to draw labels, see fluctile. |
lab.opt |
Label options, see fluctile. |
bg.col |
A background color, see fluctile. |
sym |
Whether or not the colors should be on a symmetric scale around zero. |
breaks |
The matrix entries are cut into intervals via fluctile. see fluctile. |
clust.col |
A color vector for the cluster rectangles. |
clust.palette |
If no colors are specified a palette is used to obtain them: Usually a quantitative palette is a reasonable choice, e.g.
|
hm.palette |
The color vector for the heatmap or a color palette.
Usually |
clust.col.opt |
Options for the cluster color palette. See |
hm.col.opt |
Options for the heatmap color palette. See |
TRUE
Alexander Pilhoefer
fluctile
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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | ## Not run:
ss <- sample(1:nrow(plants), 500)
M <- t(as.matrix(plants[ ss, -1]))
M <- optME(M)
heattile(M, hm.palette = "seq")
require(biclust)
GE <- t(na.omit(GeneEx[,3:52]))
# draw a sample of 1000 genes
ss <- sample(1:ncol(GE),1000)
EY <- GE[,ss]
SEY <- scale(EY)
# compute sensible initial row and column orders:
require(seriation)
s1 <- seriate(dist(SEY),method="GW")
s2 <- seriate(dist(t(SEY)),method="GW")
o1 <- get_order(s1,1)
o2 <- get_order(s2,1)
SEY <- SEY[o1,o2]
# A plaid model with row effects
b1 <- biclust(SEY,method=BCPlaid(),row.release=0.4,
col.release=0.4, fit.model = y ~ m + a )
# index sets from b1
Is2 <- getIs(b1,dim(SEY), nstart = 1)
# clusters in seriated matirx:
heattile(SEY,biclust=b1,clust.palette="hsv",hm.palette="div",
label = TRUE, border = c(0.1,0.01,0.03,0.03))
#clusters in optimized matrix
heattile(SEY,Is=Is2,clust.palette="hsv",hm.palette="div",
label = TRUE, border = c(0.1,0.01,0.03,0.03))
## End(Not run)
|
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