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R - heatmap and heatmap.2
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  Examples from the official documentation

stats::heatmap

Example 1 (Basic usage)

require(graphics); require(grDevices)
x  <- as.matrix(mtcars)
rc <- rainbow(nrow(x), start = 0, end = .3)
cc <- rainbow(ncol(x), start = 0, end = .3)
hv <- heatmap(x, col = cm.colors(256), scale = "column",
              RowSideColors = rc, ColSideColors = cc, margins = c(5,10),
              xlab = "specification variables", ylab =  "Car Models",
              main = "heatmap(<Mtcars data>, ..., scale = \"column\")")

heatmap: Example 1 (opens new window)

utils::str(hv) # the two re-ordering index vectors
# List of 4
#  $ rowInd: int [1:32] 31 17 16 15 5 25 29 24 7 6 ...
#  $ colInd: int [1:11] 2 9 8 11 6 5 10 7 1 4 ...
#  $ Rowv  : NULL
#  $ Colv  : NULL

Example 2 (no column dendrogram (nor reordering) at all)

heatmap(x, Colv = NA, col = cm.colors(256), scale = "column",
        RowSideColors = rc, margins = c(5,10),
        xlab = "specification variables", ylab =  "Car Models",
        main = "heatmap(<Mtcars data>, ..., scale = \"column\")")

heatmap: Example 2 (opens new window)

Example 3 ("no nothing")

heatmap(x, Rowv = NA, Colv = NA, scale = "column",
        main = "heatmap(*, NA, NA) ~= image(t(x))")

heatmap: Example 3 (opens new window)

Example 4 (with reorder())

round(Ca <- cor(attitude), 2)
#            rating complaints privileges learning raises critical advance
# rating       1.00       0.83       0.43     0.62   0.59     0.16    0.16
# complaints   0.83       1.00       0.56     0.60   0.67     0.19    0.22
# privileges   0.43       0.56       1.00     0.49   0.45     0.15    0.34
# learning     0.62       0.60       0.49     1.00   0.64     0.12    0.53
# raises       0.59       0.67       0.45     0.64   1.00     0.38    0.57
# critical     0.16       0.19       0.15     0.12   0.38     1.00    0.28
# advance      0.16       0.22       0.34     0.53   0.57     0.28    1.00
symnum(Ca) # simple graphic
#            rt cm p l rs cr a
# rating     1                
# complaints +  1             
# privileges .  .  1          
# learning   ,  .  . 1        
# raises     .  ,  . , 1      
# critical             .  1   
# advance          . . .     1
# attr(,"legend")
# [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1
heatmap(Ca,               symm = TRUE, margins = c(6,6))

heatmap: Example 4 (opens new window)

Example 5 (NO reorder())

heatmap(Ca, Rowv = FALSE, symm = TRUE, margins = c(6,6))

heatmap: Example 5 (opens new window)

Example 6 (slightly artificial with color bar, without ordering)

cc <- rainbow(nrow(Ca))
heatmap(Ca, Rowv = FALSE, symm = TRUE, RowSideColors = cc, ColSideColors = cc,
    margins = c(6,6))

heatmap: Example 6 (opens new window)

Example 7 (slightly artificial with color bar, with ordering)

heatmap(Ca,        symm = TRUE, RowSideColors = cc, ColSideColors = cc,
    margins = c(6,6))

heatmap: Example 7 (opens new window)

Example 8 (For variable clustering, rather use distance based on cor())

symnum( cU <- cor(USJudgeRatings) )
#      CO I DM DI CF DE PR F O W PH R
# CONT 1                             
# INTG    1                          
# DMNR    B 1                        
# DILG    + +  1                     
# CFMG    + +  B  1                  
# DECI    + +  B  B  1               
# PREP    + +  B  B  B  1            
# FAMI    + +  B  *  *  B  1         
# ORAL    * *  B  B  *  B  B 1       
# WRIT    * +  B  *  *  B  B B 1     
# PHYS    , ,  +  +  +  +  + + + 1   
# RTEN    * *  *  *  *  B  * B B *  1
# attr(,"legend")
# [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1

hU <- heatmap(cU, Rowv = FALSE, symm = TRUE, col = topo.colors(16),
             distfun = function(c) as.dist(1 - c), keep.dendro = TRUE)

heatmap: Example 8 (opens new window)

## The Correlation matrix with same reordering:
round(100 * cU[hU[[1]], hU[[2]]])
#      CONT INTG DMNR PHYS DILG CFMG DECI RTEN ORAL WRIT PREP FAMI
# CONT  100  -13  -15    5    1   14    9   -3   -1   -4    1   -3
# INTG  -13  100   96   74   87   81   80   94   91   91   88   87
# DMNR  -15   96  100   79   84   81   80   94   91   89   86   84
# PHYS    5   74   79  100   81   88   87   91   89   86   85   84
# DILG    1   87   84   81  100   96   96   93   95   96   98   96
# CFMG   14   81   81   88   96  100   98   93   95   94   96   94
# DECI    9   80   80   87   96   98  100   92   95   95   96   94
# RTEN   -3   94   94   91   93   93   92  100   98   97   95   94
# ORAL   -1   91   91   89   95   95   95   98  100   99   98   98
# WRIT   -4   91   89   86   96   94   95   97   99  100   99   99
# PREP    1   88   86   85   98   96   96   95   98   99  100   99
# FAMI   -3   87   84   84   96   94   94   94   98   99   99  100


## The column dendrogram:
utils::str(hU$Colv)
# --[dendrogram w/ 2 branches and 12 members at h = 1.15]
#   |--leaf "CONT" 
#   `--[dendrogram w/ 2 branches and 11 members at h = 0.258]
#      |--[dendrogram w/ 2 branches and 2 members at h = 0.0354]
#      |  |--leaf "INTG" 
#      |  `--leaf "DMNR" 
#      `--[dendrogram w/ 2 branches and 9 members at h = 0.187]
#         |--leaf "PHYS" 
#         `--[dendrogram w/ 2 branches and 8 members at h = 0.075]
#            |--[dendrogram w/ 2 branches and 3 members at h = 0.0438]
#            |  |--leaf "DILG" 
#            |  `--[dendrogram w/ 2 branches and 2 members at h = 0.0189]
#            |     |--leaf "CFMG" 
#            |     `--leaf "DECI" 
#            `--[dendrogram w/ 2 branches and 5 members at h = 0.0584]
#               |--leaf "RTEN" 
#               `--[dendrogram w/ 2 branches and 4 members at h = 0.0187]
#                  |--[dendrogram w/ 2 branches and 2 members at h = 0.00657]
#                  |  |--leaf "ORAL" 
#                  |  `--leaf "WRIT" 
#                  `--[dendrogram w/ 2 branches and 2 members at h = 0.0101]
#                     |--leaf "PREP" 
#                     `--leaf "FAMI"
  Tuning parameters in heatmap.2

Given:

x  <- as.matrix(mtcars)

One can use heatmap.2 - a more recent optimized version of heatmap, by loading the following library:

require(gplots)
heatmap.2(x)

enter image description here (opens new window)

To add a title, x- or y-label to your heatmap, you need to set the main, xlab and ylab:

heatmap.2(x, main = "My main title: Overview of car features", xlab="Car features", ylab = "Car brands")

If you wish to define your own color palette for your heatmap, you can set the col parameter by using the colorRampPalette function:

heatmap.2(x, trace="none", key=TRUE, Colv=FALSE,dendrogram = "row",col =  colorRampPalette(c("darkblue","white","darkred"))(100))

enter image description here (opens new window)

As you can notice, the labels on the y axis (the car names) don't fit in the figure. In order to fix this, the user can tune the margins parameter:

heatmap.2(x, trace="none", key=TRUE,col = colorRampPalette(c("darkblue","white","darkred"))(100), margins=c(5,8))

enter image description here (opens new window)

Further, we can change the dimensions of each section of our heatmap (the key histogram, the dendograms and the heatmap itself), by tuning lhei and lwid :

enter image description here (opens new window)

If we only want to show a row(or column) dendogram, we need to set Colv=FALSE (or Rowv=FALSE) and adjust the dendogram parameter:

heatmap.2(x, trace="none", key=TRUE, Colv=FALSE, dendrogram = "row", col = colorRampPalette(c("darkblue","white","darkred"))(100), margins=c(5,8), lwid = c(5,15), lhei = c(3,15))

enter image description here (opens new window)

For changing the font size of the legend title,labels and axis, the user needs to set cex.main, cex.lab, cex.axis in the par list:

par(cex.main=1, cex.lab=0.7, cex.axis=0.7)
heatmap.2(x, trace="none", key=TRUE, Colv=FALSE, dendrogram = "row", col = colorRampPalette(c("darkblue","white","darkred"))(100), margins=c(5,8), lwid = c(5,15), lhei = c(5,15))

enter image description here (opens new window)

 

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