Description Usage Arguments Details Value Author(s) See Also Examples

This function displays an expression data matrix as a heatmap with a column dendrogram. A given clustering will be shown in color. Additionally, a number of binary and interval scaled covariates can be added to characterize these clusters.

This function is just about to be deprecated. Please use functions `annHeatmap`

or `annHeatmap2`

for new projects.

1 2 3 4 5 |

`x` |
the numerical data matrix to be displayed. |

`addvar` |
data frame with (mostly binary) covariates. |

`covariate` |
integer indicating the one column in |

`picket.control` |
list of option for drawing the covariates, passed to |

`h` |
height at which to cut the dendrogram, as in |

`clus` |
an explicit vector of cluster memberships for the columns of |

`cluscol` |
a vector of colors used to indicate clusters. |

`cluslabel` |
labels to designate cluster names. |

`Rowv` |
either a dendrogram or a vector of reordering indexes for the rows. |

`Colv` |
either a dendrogram or a vector of reordering indexes for the columns. |

`reorder` |
logical vector of length two, indicating whether the rows and columns (in this order) should be reordered using |

`distfun` |
function to compute the distances between rows and columns. Defaults to |

`hclustfun` |
function used to cluster rows and columns. Defaults to |

`scale` |
indicates whether values should be scaled by either by row, column, or not at all. Defaults to |

`na.rm` |
logical indicating whther to remove NAs. |

`do.dendro` |
logical indicating whether to draw the column dendrogram. |

`col` |
the color scheme for |

`trim` |
Percentage of values to be trimmed. This helps to keep an informative color scale, see Details. |

`equalize` |
logical indicating whther to use the ranks of the data for setting the color scheme; alternative to |

`...` |
extra arguments to |

This is a heavily modified version of `heatmap_2`

, which is a heavily modfied version of an old version of `heatmap`

in package `stats`

, so some of the arguments are described in more detail there. The main distinguishing feature of this routine is the possibility to color a cluster solution, and to add a covariate display.

Covariates are assumed to be binary, coded as 0 and 1 (or `FALSE`

and `TRUE`

respectively). One of the covariates can be interval scaled, the column index of this variable is supplied via argument `covariate`

. The details of the added display are handled by the function `picketplot`

.

Setting `trim`

to a number between 0 and 1 uses equidistant classes between the (`trim`

)- and (1-`trim`

)-quantile, and lumps the values below and above this range into separate open-ended classes. If the data comes from a heavy-tailed distribution, this can save the display from putting too many values into to few classes. Alternatively, you can set `equal=TRUE`

, which uses an equidistant color scheme for the ranks of the values.

A list with components

`rowInd` |
indices of the rows of the display in terms of the rows of |

`colInd` |
ditto for the columns of the display. |

`clus` |
the cluster indices of the columns of the display. |

Original by Andy Liaw, with revisions by Robert Gentleman and Martin Maechler.

Alexander Ploner for the modifications documented here.

`heatmap_2`

, `heatmap`

, `oldPicketplot`

, `oldCutplot.dendrogram`

,
`RGBColVec`

, `annHeatmap`

, `annHeatmap2`

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 | ```
# create data
mm = matrix(rnorm(1000, m=1), 100,10)
mm = cbind(mm, matrix(rnorm(2000), 100, 20))
mm = cbind(mm, matrix(rnorm(1500, m=-1), 100, 15))
mm2 = matrix(rnorm(450), 30, 15)
mm2 = cbind(mm2, matrix(rnorm(900,m=1.5), 30,30))
mm=rbind(mm, mm2)
colnames(mm) = paste("Sample", 1:45)
rownames(mm) = paste("Gene", 1:130)
addvar = data.frame(Var1=rep(c(0,1,0),c(10,20,15)),
Var2=rep(c(1,0,0),c(10,20,15)),
Var3=rep(c(1,0), c(15,30)),
Var4=rep(seq(0,1,length=4), c(10,5,15,15))+rnorm(45, sd=0.5))
addvar[3,3] = addvar[17,2] = addvar[34,1] =NA
colnames(addvar) = c("Variable X","Variable Y", "ZZ","Interval")
# the lame default, without clustering
# Labels do not look too hot that way
heatmap_plus(mm)
# without labels, but with cluster
dimnames(mm)=NULL
heatmap_plus(mm, h=40)
# add some covariates, with nice names
heatmap_plus(mm, addvar=addvar, cov=4)
# covariates and clustering
heatmap_plus(mm, addvar=addvar, cov=4, h=20, col=RGBColVec(64), equal=TRUE)
# Clustering without the dendrogram
cc = cutree(hclust(dist(t(mm))), k=5)
heatmap_plus(mm, addvar=addvar, cov=4, clus=cc, do.dendro=FALSE)
``` |

Heatplus documentation built on Nov. 1, 2018, 3:45 a.m.

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