Reorder dendrogram in R

Question

I want to plot a heatmap where the x-axis is clustered by "normal" and "KIRP" from left to right. Currently, my code clusters by dendrogram/similarity, and unfortunately I have one outlier "KIRP" on the left-most. I want to move this "KIRP" sample so that it appears after all the "normal" samples. Nevertheless, both groups "normal" and "KIRP" should still be clustered and arranged based on group similarity.

[![enter image description here][1]][1]

Code:

dge <- DGEList(counts=mat, group=group)
keep <- filterByExpr(dge)
v <- voom(mat, design, plot=TRUE)
vfit <- lmFit(v, design)
vfit <- contrasts.fit(vfit, contrasts=contrasts)
efit <- eBayes(vfit)
tfit <- treat(vfit, lfc=1)
dt <- decideTests(tfit)
de.common <- which(dt[,1]!=0)


kirp.vs.normal <- topTreat(tfit, coef=1, n=Inf)

topgenes <- rownames(kirp.vs.normal)[1:150]
i <- which(rownames(dge) %in% topgenes)
mycol <- colorpanel(1000,"#FFA500","white","#2E2787")
heatmap.2(lcpm[i,], scale="row",
          labRow=rownames(dge)[i], labCol=group, 
          col=mycol, trace="none", density.info="none", 
          margin=c(8,1), lhei=c(2,10), dendrogram="column", main="Differential Gene Expression in\nNormal vs KIRP Type II CIMP samples", cex.main=0.3, lmat=rbind(c(0,3,4), c(2,1,0)), lwid=c(0.5,10,3))

Data:

dput(dge[1:50,1:50])

new("DGEList", .Data = list(structure(c(2L, 47L, 5L, 185L, 124L, 
272L, 197L, 405L, 59L, 258L, 270L, 226L, 112L, 322L, 381L, 281L, 
145L, 53L, 325L, 107L, 103L, 375L, 70L, 298L, 131L, 79L, 297L, 
2L, 345L, 390L, 113L, 289L, 58L, 400L, 389L, 414L, 228L, 188L, 
392L, 222L, 86L, 355L, 20L, 49L, 211L, 311L, 96L, 304L, 378L, 
145L, 3L, 363L, 199L, 22L, 313L, 305L, 182L, 338L, 32L, 266L, 
314L, 35L, 384L, 361L, 37L, 241L, 4L, 340L, 356L, 26L, 100L, 
212L, 27L, 273L, 25L, 43L, 355L, 5L, 211L, 155L, 372L, 253L, 
180L, 380L, 105L, 13L, 242L, 221L, 401L, 215L, 197L, 233L, 345L, 
136L, 254L, 183L, 111L, 390L, 392L, 298L, 1L, 308L, 89L, 118L, 
306L, 219L, 50L, 100L, 352L, 286L, 229L, 340L, 135L, 194L, 130L, 
124L, 323L, 54L, 105L, 279L, 91L, 99L, 391L, 291L, 395L, 83L, 
353L, 1L, 322L, 185L, 196L, 263L, 33L, 274L, 362L, 265L, 234L, 
356L, 297L, 154L, 81L, 65L, 293L, 144L, 2L, 132L, 270L, 360L, 
371L, 5L, 2L, 95L, 1L, 93L, 248L, 317L, 269L, 373L, 71L, 192L, 
375L, 340L, 60L, 108L, 42L, 128L, 3L, 292L, 312L, 173L, 363L, 
178L, 17L, 387L, 143L, 329L, 385L, 2L, 252L, 118L, 413L, 16L, 
87L, 339L, 88L, 75L, 347L, 184L, 337L, 297L, 136L, 229L, 85L, 
358L, 8L, 283L, 162L, 316L, 45L, 7L, 1L, 319L, 2L, 117L, 137L, 
199L, 300L, 114L, 291L, 92L, 125L, 168L, 153L, 238L, 3L, 259L, 
192L, 360L, 125L, 230L, 80L, 262L, 34L, 266L, 220L, 237L, 272L, 
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288L, 145L, 372L, 192L, 307L, 203L, 2L, 277L, 280L, 233L, 368L, 
6L, 2L, 83L, 2L, 192L, 141L, 297L, 203L, 338L, 323L, 210L, 289L, 
275L, 91L, 263L, 3L, 4L, 2L, 28L, 259L, 264L, 317L, 198L, 361L, 
365L, 373L, 312L, 300L, 2L, 283L, 63L, 123L, 324L, 286L, 251L, 
253L, 104L, 284L, 143L, 371L, 237L, 325L, 314L, 16L, 208L, 1L, 
191L, 134L, 279L, 348L, 180L, 2L, 126L, 1L, 369L, 368L, 377L, 
305L, 314L, 38L, 24L, 407L, 223L, 320L, 66L, 3L, 136L, 2L, 240L, 
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357L, 211L, 2L, 287L, 273L, 275L, 339L, 2L, 2L, 302L, 203L, 210L, 
190L, 276L, 351L, 51L, 361L, 155L, 232L, 213L, 184L, 330L, 130L, 
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366L, 280L, 85L, 43L, 357L, 1L, 304L, 9L, 41L, 259L, 326L, 310L, 
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312L, 282L, 23L, 318L, 72L, 301L, 4L, 146L, 96L, 302L, 379L, 
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182L, 144L, 334L, 5L, 283L, 195L, 59L, 310L, 179L, 192L, 30L, 
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6L, 324L, 176L, 188L, 127L, 108L, 331L, 307L, 214L, 93L, 390L, 
2L, 300L, 80L, 349L, 344L, 109L, 398L, 339L, 173L, 244L, 323L, 
272L, 259L, 185L, 172L, 366L, 226L, 129L, 35L, 303L, 404L, 391L, 
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335L, 32L, 258L, 325L, 256L, 73L, 52L, 110L, 288L, 8L, 58L, 113L, 
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355L, 219L, 43L, 32L, 389L, 199L, 3L, 96L, 386L, 290L, 388L, 
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"ARFRP1", "ARHGAP29", "ARHGDIB", "ARL11", "ARL4D", "ARRDC3", 
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), class = "data.frame")))

Answer 1

I used some simulated data to try this, since it seems that the question can be generalized to other datasets. I also ran into errors with your subset of data.

set.seed(123)
data <- as.matrix(data.frame(
    a1 = rnorm(100, 0, 1),
    a2 = rnorm(100, 0, 1),
    a3 = rnorm(100, 0, 1),
    b1 = rnorm(100, 0, 1.2),
    b2 = rnorm(100, 0, 1.2),
    b3 = rnorm(100, 0, 1.2)
))

Here we see that b1 would look nicer next to b3 and b2.

library(gplots)
ht <- heatmap.2(
    x = data,
    ColSideColors = c(rep("#b66363", 3), rep("#8fa8c0", 3)),
    col = colorpanel(1000,"#FFA500","white","#2E2787"),
    trace = "none",
    key = FALSE,
    dendrogram = "column",
    main = "Example Data",
    labRow = FALSE
)

Looking at the structure of the heatmap output, the dendrogram is stored as a "dendrogram" class object, which can be manipulated with with the reorder() generic.

The documentation doesn't reveal too much, but the second argument wts describes arbitrary weights to determine a reordered dendrogram. From what I can tell, large values generally get placed to the right. From trial-and-error, it appears that supplying weights in the original order of the columns worked out. This essentially flips branches without affecting the distance metrics.

colden <- ht$colDendrogram
colden_reordered <- reorder(colden, c(10, 1, 1, 100, 300, 200))
plot(colden, main = "original dendrogram")
plot(colden_reordered, main = "modified dendrogram")

When can then plot the heatmap with the new dendrogram using the Colv option.

ht2 <- heatmap.2(
    x = data,
    ColSideColors = c(rep("#b66363", 3), rep("#8fa8c0", 3)),
    col = colorpanel(1000,"#FFA500","white","#2E2787"),
    trace = "none",
    key = FALSE,
    dendrogram = "column",
    Colv = colden_reordered,
    main = "Reordered manually",
    labRow = FALSE
)

The dendsort package may be a better go-to tool for sorting dendrograms. In short, the dendsort() moves clusters with smaller average distances to the left. Using this alone seems to solve the issue. With larger heatmaps, the benefit may be apparent when looking for patterns in the data. Seems preferable to manual reordering, when possible. Below I've modified the option Colv to use this function.

library(dendsort)

ht3 <- heatmap.2(
    x = data,
    ColSideColors = c(rep("#b66363", 3), rep("#8fa8c0", 3)),
    col = colorpanel(1000,"#FFA500","white","#2E2787"),
    trace = "none",
    density.info = "none",
    key = FALSE,
    dendrogram = "column",
    Colv = dendsort(colden),
    main = "Reordered w/ dendsort()",
    labRow = FALSE
)

For more options for heatmaps concerning clustering and groups, I use the ComplexHeatmap package, which has wonderful documentation. They have many options for splitting columns; for instance, slicing up the columns into groups first, and then clustering within those slices. See this section 2.7: Chapter 2 A Single Heatmap | ComplexHeatmap Complete Reference.