Heatmap

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Clustering

Task View

https://cran.r-project.org/web/views/Cluster.html

k-means clustering

k-medoids/Partitioning Around Medoids (PAM)

Number of clusters: Intraclass Correlation/ Intra Cluster Correlation (ICC)

mbkmeans

Hierarchical clustering

For the kth cluster, define the Error Sum of Squares as [math]\displaystyle{ ESS_m = }[/math] sum of squared deviations (squared Euclidean distance) from the cluster centroid. [math]\displaystyle{ ESS_m = \sum_{l=1}^{n_m}\sum_{k=1}^p (x_{ml,k} - \bar{x}_{m,k})^2 }[/math] in which [math]\displaystyle{ \bar{x}_{m,k} = (1/n_m) \sum_{l=1}^{n_m} x_{ml,k} }[/math] the mean of the mth cluster for the k-th variable, [math]\displaystyle{ x_{ml,k} }[/math] being the score on the kth variable [math]\displaystyle{ (k=1,\dots,p) }[/math] for the l-th object [math]\displaystyle{ (l=1,\dots,n_m) }[/math] in the mth cluster [math]\displaystyle{ (m=1,\dots,g) }[/math].

If there are C clusters, define the Total Error Sum of Squares as Sum of Squares as [math]\displaystyle{ ESS = \sum_m ESS_m, m=1,\dots,C }[/math]

Consider the union of every possible pair of clusters.

Combine the 2 clusters whose combination combination results in the smallest increase in ESS.

Comments:

  1. The default linkage is "complete" in R.
  2. Ward's method tends to join clusters with a small number of observations, and it is strongly biased toward producing clusters with the same shape and with roughly the same number of observations.
  3. It is also very sensitive to outliers. See Milligan (1980).

Take pomeroy data (7129 x 90) for an example: 

library(gplots)

lr = read.table("C:/ArrayTools/Sample datasets/Pomeroy/Pomeroy -Project/NORMALIZEDLOGINTENSITY.txt")
lr = as.matrix(lr)
method = "average" # method <- "complete"; method <- "ward.D"; method <- "ward.D2"
hclust1 <- function(x) hclust(x, method= method)
heatmap.2(lr, col=bluered(75), hclustfun = hclust1, distfun = dist,
              density.info="density", scale = "none",               
              key=FALSE, symkey=FALSE, trace="none", 
              main = method)

It seems average method will create a waterfall like dendrogram. Ward method will produce a tight clusters. Complete linkage produces a more 中庸 result.

Hc ave.png Hc com.png Hc ward.png

Wards agglomeration/linkage method

Density based clustering

http://www.r-exercises.com/2017/06/10/density-based-clustering-exercises/

Optimal number of clusters

Silhouette score/width

kBET: k-nearest neighbour batch effect test

  • Buttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A. & Theis, F. J. A test metric for assessing single-cell RNA-seq batch correction. Nat. Methods 16, 43–49 (2019).
  • https://github.com/theislab/kBET
  • quantify mixability; how well cells of the same type from different batches were grouped together

Alignment score

  • Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
  • quantify mixability; how well cells of the same type from different batches were grouped together

Compare 2 clustering methods, ARI

Benchmark clustering algorithms

Using clusterlab to benchmark clustering algorithms

Louvain algorithm: graph-based method

Mahalanobis distance

Mahalanobis distance.

You probably don't understand heatmaps

Evaluate the effect of centering & scaling

Different distance measures

9 Distance Measures in Data Science

1-correlation distance

Effect of centering and scaling on clustering of genes and samples in terms of distance. 'Yes' means the distance was changed compared to the baseline where no centering or scaling was applied.

clustering genes clustering samples
centering on each genes No Yes*
scaling on each genes No Yes*
  • Note: Cor(X, Y) = Cor(X + constant scalar, Y). If the constant is not a scalar, the equation won't hold. Or think about plotting data in a 2 dimension space. If the X data has a constant shift for all observations/genes, then the linear correlation won't be changed.

Euclidean distance

clustering genes clustering samples
centering on each genes Yes No1
scaling on each genes Yes Yes2

Note

  1. [math]\displaystyle{ \sum(X_i - Y_i)^2 = \sum(X_i-c_i - (Y_i-c_i))^2 }[/math]
  2. [math]\displaystyle{ \sum(X_i - Y_i)^2 \neq \sum(X_i/c_i - Y_i/c_i)^2 }[/math]

Parallel distance Matrix in R

Supporting R code

1. 1-Corr distance 

source("http://www.bioconductor.org/biocLite.R")
biocLite("limma"); biocLite("ALL")

library(limma); library(ALL)
data(ALL)

eset <- ALL[, ALL$mol.biol %in% c("BCR/ABL", "ALL1/AF4")]

f <- factor(as.character(eset$mol.biol))
design <- model.matrix(~f)
fit <- eBayes(lmFit(eset,design))
selected  <- p.adjust(fit$p.value[, 2]) < 0.05
esetSel <- eset [selected, ]   # 165 x 47
heatmap(exprs(esetSel))

esetSel2 <- esetSel[sample(1:nrow(esetSel), 20), sample(1:ncol(esetSel), 10)] # 20 x 10

dist.no <- 1-cor(t(as.matrix(esetSel2))) 
dist.mean <- 1-cor(t(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2))))) # gene variance has not changed!
dist.median <- 1-cor(t(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median))))

range(dist.no - dist.mean) # [1] -1.110223e-16  0.000000e+00
range(dist.no - dist.median) # [1] -1.110223e-16  0.000000e+00
range(dist.mean - dist.median) # [1] 0 0

So centering (no matter which measure: mean, median, ...) genes won't affect 1-corr distance of genes. 

dist.mean <- 1-cor(t(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2)), "/")))
dist.median <- 1-cor(t(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median), "/")))
range(dist.no - dist.mean) # [1] -8.881784e-16  6.661338e-16
range(dist.no - dist.median) # [1] -6.661338e-16  6.661338e-16
range(dist.mean - dist.median) # [1] -1.110223e-15  1.554312e-15

So scaling after centering (no matter what measures: mean, median,...) won't affect 1-corr distance of genes.

How about centering / scaling genes on array clustering? 

dist.no <- 1-cor(as.matrix(esetSel2))
dist.mean <- 1-cor(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2)))) # array variance has changed!
dist.median <- 1-cor(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median)))

range(dist.no - dist.mean) # [1] -1.547086  0.000000
range(dist.no - dist.median) # [1] -1.483427  0.000000
range(dist.mean - dist.median) # [1] -0.5283601  0.6164602

dist.mean <- 1-cor(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2)), "/"))
dist.median <- 1-cor(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median), "/"))
range(dist.no - dist.mean) # [1] -1.477407  0.000000
range(dist.no - dist.median) # [1] -1.349419  0.000000
range(dist.mean - dist.median) # [1] -0.5419534  0.6269875

2. Euclidean distance 

dist.no <- dist(as.matrix(esetSel2))
dist.mean <- dist(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2))))
dist.median <- dist(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median)))

range(dist.no - dist.mean) # [1] 7.198864e-05 2.193487e+01
range(dist.no - dist.median) # [1] -0.3715231 21.9320846
range(dist.mean - dist.median) # [1] -0.923717629 -0.000088385

Centering does affect the Euclidean distance. 

dist.mean <- dist(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2)), "/"))
dist.median <- dist(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median), "/"))

range(dist.no - dist.mean) # [1]  0.7005071 24.0698991
range(dist.no - dist.median) # [1]  0.636749 24.068920
range(dist.mean - dist.median) # [1] -0.22122869  0.02906131

And scaling affects Euclidean distance too.

How about centering / scaling genes on array clustering? 

dist.no <- dist(t(as.matrix(esetSel2)))
dist.mean <- dist(t(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2)))))
dist.median <- dist(t(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median))))

range(dist.no - dist.mean) # 0 0
range(dist.no - dist.median)  # 0 0
range(dist.mean - dist.median) # 0 0

dist.mean <- dist(t(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2)), "/")))
dist.median <- dist(t(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median), "/")))

range(dist.no - dist.mean) # [1] 1.698960 9.383789
range(dist.no - dist.median)  # [1] 1.683028 9.311603
range(dist.mean - dist.median) # [1] -0.09139173  0.02546394

Euclidean distance and Pearson correlation relationship

http://analytictech.com/mb876/handouts/distance_and_correlation.htm. In summary,

[math]\displaystyle{ r(X, Y) = 1 - \frac{d^2(X, Y)}{2n} }[/math]

where [math]\displaystyle{ r(X, Y) }[/math] is the Pearson correlation of variables X and Y and [math]\displaystyle{ d^2(X, Y) }[/math] is the squared Euclidean distance of X and Y.

Simple image plot using image() function

https://chitchatr.wordpress.com/2010/07/01/matrix-plots-in-r-a-neat-way-to-display-three-variables/

Simpleimage.png Simpleimage2.png 

### Create Matrix plot using colors to fill grid
# Create matrix.   Using random values for this example.
rand <- rnorm(286, 0.8, 0.3)
mat <- matrix(sort(rand), nr=26)
dim(mat)  # Check dimensions
 
# Create x and y labels
yLabels <- seq(1, 26, 1)
xLabels <- c("a", "b", "c", "d", "e", "f", "g", "h", "i",
"j", "k");
 
# Set min and max values of rand
 min <- min(rand, na.rm=T)
 max <- max(rand, na.rm=T)
 
# Red and green range from 0 to 1 while Blue ranges from 1 to 0
 ColorRamp <- rgb(seq(0.95,0.99,length=50),  # Red
                  seq(0.95,0.05,length=50),  # Green
                  seq(0.95,0.05,length=50))  # Blue
 ColorLevels <- seq(min, max, length=length(ColorRamp))
 
# Set layout.  We are going to include a colorbar next to plot.
layout(matrix(data=c(1,2), nrow=1, ncol=2), widths=c(4,1),
         heights=c(1,1))
#plotting margins.  These seem to work well for me.
par(mar = c(5,5,2.5,1), font = 2)
 
# Plot it up!
image(1:ncol(mat), 1:nrow(mat), t(mat),
     col=ColorRamp, xlab="Variable", ylab="Time",
     axes=FALSE, zlim=c(min,max),
     main= NA)
 
# Now annotate the plot
box()
axis(side = 1, at=seq(1,length(xLabels),1), labels=xLabels,
      cex.axis=1.0)
axis(side = 2, at=seq(1,length(yLabels),1), labels=yLabels, las= 1,
      cex.axis=1)
 
# Add colorbar to second plot region
par(mar = c(3,2.5,2.5,2))
 image(1, ColorLevels,
      matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1),
      col=ColorRamp,xlab="",ylab="",xaxt="n", las = 1)

If we define ColorRamp variable using the following way, we will get the 2nd plot. 

require(RColorBrewer) # get brewer.pal()
ColorRamp <- colorRampPalette( rev(brewer.pal(9, "RdBu")) )(25)

Note that

  • colorRampPalette() is an R's built-in function. It interpolate a set of given colors to create new color palettes. The return is a function that takes an integer argument (the required number of colors) and returns a character vector of colors (see rgb()) interpolating the given sequence (similar to heat.colors() or terrain.colors()).
# An example of showing 50 shades of grey in R
greys <- grep("^grey", colours(), value = TRUE)
length(greys)
# [1] 102
shadesOfGrey <- colorRampPalette(c("grey0", "grey100"))
shadesOfGrey(2)
# [1] "#000000" "#FFFFFF"
# And 50 shades of grey?
fiftyGreys <- shadesOfGrey(50)
mat <- matrix(rep(1:50, each = 50))
image(mat, axes = FALSE, col = fiftyGreys)
box()
  • (For dual channel data) brewer.pal(9, "RdBu") creates a diverging palette based on "RdBu" with 9 colors. See help(brewer.pal, package="RColorBrewer") for a list of palette name. The meaning of the palette name can be found on colorbrew2.org website. In genomics, we will add rev() such as rev(brewer.pal(9, "RdBu")).
  • (For single channel data) brewer.pal(9, "Blues") is good. See an example.

gplots package and heatmap.2()

The following example is extracted from DESeq2 package.

Heatmap deseq2.png 

## ----loadDESeq2, echo=FALSE----------------------------------------------
# in order to print version number below
library("DESeq2")

## ----loadExonsByGene, echo=FALSE-----------------------------------------
library("parathyroidSE")
library("GenomicFeatures")
data(exonsByGene)

## ----locateFiles, echo=FALSE---------------------------------------------
bamDir <- system.file("extdata",package="parathyroidSE",mustWork=TRUE)
fls <- list.files(bamDir, pattern="bam$",full=TRUE)


## ----bamfilepaired-------------------------------------------------------
library( "Rsamtools" )
bamLst <- BamFileList( fls, yieldSize=100000 )


## ----sumOver-------------------------------------------------------------
library( "GenomicAlignments" )
se <- summarizeOverlaps( exonsByGene, bamLst,
                         mode="Union",
                         singleEnd=FALSE,
                         ignore.strand=TRUE,
                         fragments=TRUE )

## ----libraries-----------------------------------------------------------
library( "DESeq2" )
library( "parathyroidSE" )

## ----loadEcs-------------------------------------------------------------
data( "parathyroidGenesSE" )
se <- parathyroidGenesSE
colnames(se) <- se$run

## ----fromSE--------------------------------------------------------------
ddsFull <- DESeqDataSet( se, design = ~ patient + treatment )

## ----collapse------------------------------------------------------------
ddsCollapsed <- collapseReplicates( ddsFull,
                                    groupby = ddsFull$sample, 
                                    run = ddsFull$run )

## ----subsetCols----------------------------------------------------------
dds <- ddsCollapsed[ , ddsCollapsed$time == "48h" ]

## ----subsetRows, echo=FALSE----------------------------------------------
idx <- which(rowSums(counts(dds)) > 0)[1:4000]
dds <- dds[idx,]

## ----runDESeq, cache=TRUE------------------------------------------------
dds <- DESeq(dds)

rld <- rlog( dds)

library( "genefilter" )
topVarGenes <- head( order( rowVars( assay(rld) ), decreasing=TRUE ), 35 )

## ----beginner_geneHeatmap, fig.width=9, fig.height=9---------------------
library(RColorBrewer)
library(gplots)
heatmap.2( assay(rld)[ topVarGenes, ], scale="row", 
           trace="none", dendrogram="column", 
           col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255))

heatmap.2() vs heatmap()

It looks the main difference is heatmap.2() can produce color key on the top-left corner. See Heatmap in R: Static and Interactive Visualization.

Self-defined distance/linkage method

https://stackoverflow.com/questions/6806762/setting-distance-matrix-and-clustering-methods-in-heatmap-2 

heatmap.2(..., hclustfun = function(x) hclust(x,method = 'ward.D2'), ...)

Missing data

Generate sequential colors & grDevices::colorRampPalette()

?colorRampPalette. See an example Building heatmap with R. 

require(RColorBrewer)  # brewer.pal
ColorRamp <- colorRampPalette( brewer.pal(9, "Blues") )(25)
# The above will generate 25 colors by interpolating the colors 
#   defined by brewer.pal(9, "Blues")
heatmap.2(..., col = ColorRamp)

Hmapseqcolor.png

Change breaks in scale

https://stackoverflow.com/questions/17820143/how-to-change-heatmap-2-color-range-in-r

Con: it'll be difficult to interpret the heatmap

Font size, rotation

See the help page 

cexCol=.8   # reduce the label size from 1 to .8
offsetCol=0 # reduce the offset space from .5 to 0
adjRow, adjCol # similar to offSetCol ??
            # 2-element vector giving the (left-right, top-bottom) justification of row/column labels
adjCol=c(1,0)  # align to top; only meaningful if we rotate the labels
adjCol=c(0,1)  # align to bottom; some long text may go inside the figure 
adjCol=c(1,1)  # how to explain it?
srtCol=45   # Rotate 45 degrees

keysize=2    # increase the keysize from the default 1.5
key = TRUE   # default 
key.xlab=NA  # default is NULL
key.title=NA

Color labels and side bars

https://stackoverflow.com/questions/13206335/color-labels-text-in-r-heatmap. See the options in an example in ?heatmap.2.

  • colRow, colCol
  • RowSideColors, ColSideColors
## Color the labels to match RowSideColors and ColSideColors
hv <- heatmap.2(x, col=cm.colors(255), scale="column",
          RowSideColors=rc, ColSideColors=cc, margin=c(5, 10),
        xlab="specification variables", ylab= "Car Models",
        main="heatmap(<Mtcars data>, ..., scale=\"column\")",
          tracecol="green", density="density", colRow=rc, colCol=cc,
          srtCol=45, adjCol=c(0.5,1))

Moving colorkey

https://stackoverflow.com/questions/15351575/moving-color-key-in-r-heatmap-2-function-of-gplots-package

Dendrogram width and height

# Default
lhei <- c(1.5, 4)
lwid <- c(1.5, 4)

Note these are relative. Recall heatmap.2() makes a 2x2 grid: color key, dendrograms (left & top) and the heatmap (right & bottom).

Modify the margins for column/row names

# Default
margins <- c(5, 5) # (column, row)

Note par(mar) does not work.

White strips (artifacts)

On my Linux (Dell Precision T3500, NVIDIA GF108GL, Quadro 600, 1920x1080), the heatmap shows several white strips when I set a resolution 800x800 (see the plot of 10000 genes shown below). Note if I set a higher resolution 1920x1920, the problem is gone but the color key map will be quite large and the text font will be small.

On MacBook Pro (integrated Intel Iris Pro, 2880x1800), there is no artifact even with 800x800 resolution.

Heatmap ani.gif

How about saving the plots using

  • a different format (eg tiff) or even the lossless compression option - not help
  • Cairo package - works. Note that the default background is transparent.

RowSideColors and ColSideColors options

A short tutorial for decent heat maps in R

Annotation

legend(). 

legend("topright",      
    legend = unique(dat$GO),
    col = unique(as.numeric(dat$GO)), 
    lty= 1,             
    lwd = 5,           
    cex=.7)

Another example from video which makes use of an archived package heatmap.plus. 

legend(0.8,1,
       legend=paste(treatment_times,"weeks"),
       fill=treatment_color_options,
       cex=0.5)
legend(0.8,0.9,
       legend=c("Control","Treatment"),
       fill=c('#808080','#FFC0CB'),
       cex=0.5)

heatmap.plus()

How to Make an R Heatmap with Annotations and Legend. ColSideColors can be a matrix (n x 2). So it is possible to draw two side colors on the heatmap. Unfortunately the package was removed from CRAN in 2021-04. The package was used by TCGAbiolinks but now this package uses ComplexHeatmap instead. 

devtools::install_version("heatmap.plus", "1.3")

Output from heatmap.2 examples

ggplot2 package

ggplot2::geom_tile()

# Suppose dat=[x, y1, y2, y3] is a wide matrix
# and we want to make a long matrix like dat=[x, y, val]
library(tidyr)
dat <- dat %>% pivot_longer(!x, names_to = 'y', values_to='val')
ggplot(dat, aes(x, y)) +
  geom_tile(aes(fill = val), colour = "white") +
  scale_fill_gradient2(low = "blue", mid = "white", high = "red") +
  labs(y="Cell Line", fill= "Log GI50")
# white is the border color
# grey = NA by default
# labs(fill) is to change the title
# labs(y) is to change the y-axis label

NMF package

aheatmap() function.

ComplexHeatmap

Pros

Correlation matrix

The following code will guarantee the heatmap has a diagonal in the direction of top-left and bottom-right. The row dendrogram will be flipped automatically. There is no need to use cluster_rows = rev(mydend). 

mcor <- cor(t(lograt))
colnames(mcor) <- rownames(mcor) <- 1:ncol(mcor)
mydend <- as.dendrogram(hclust(as.dist(1-mcor)))
Heatmap( mcor, cluster_columns = mydend, cluster_rows = mydend,
     row_dend_reorder = FALSE, column_dend_reorder = FALSE,
     row_names_gp = gpar(fontsize = 6),
     column_names_gp = gpar(fontsize = 6),
     column_title = "", name = "value")

OncoPrint

InteractiveComplexHeatmap

InteractiveComplexHeatmap

tidyHeatmap

tidyHeatmap. This is a tidy implementation for heatmap. At the moment it is based on the (great) package 'ComplexHeatmap'.

Note: that ComplexHeatmap is on Bioconductor but tidyHeatmap is on CRAN.

By default, .scale = "row". See ?heatmap.

add_tile() to add a column or row (depending on the data) annotation.

cluster_rows=FALSE if we don't want to cluster rows. 

BiocManager::install('tidyHeatmap')
library(tidyHeatmap)

library(tidyr)
mtcars_tidy <- 
  mtcars |> 
  as_tibble(rownames="Car name") |>   
  # Scale
  mutate_at(vars(-`Car name`, -hp, -vs), scale) |>  
  # tidyfy
  pivot_longer(cols = -c(`Car name`, hp, vs), 
               names_to = "Property", 
               values_to = "Value")

# create another variable which will be added next to 'hp'
mtcars_tidy <- mtcars_tidy%>% 
  mutate(type = `Car name`)
mtcars_tidy$type <- substr(mtcars_tidy$type, 1, 1) 
mtcars_tidy

# NA case. Consider the cell on the top-right corner
mtcars_tidy %>% filter(`Car name` == 'Volvo 142E' & Property == 'am')
mtcars_tidy <- mtcars_tidy %>%                               # Replacing values
               mutate(Value = replace(Value, 
                               `Car name` == 'Volvo 142E' & Property == 'am', 
                               NA))
mtcars_tidy %>% filter(`Car name` == 'Volvo 142E' & Property == 'am')
# Re-draw data with missing value
mtcars_tidy |> 
heatmap(`Car name`, Property, Value, 
          palette_value = circlize::colorRamp2(
            seq(-2, 2, length.out = 11), 
            rev(RColorBrewer::brewer.pal(11, "RdBu")))) |>
  add_tile(hp) |>
  add_tile(type)


# two tiles on rows
mtcars_heatmap <- 
  mtcars_tidy |> 
  heatmap(`Car name`, Property, Value, 
          palette_value = circlize::colorRamp2(
              seq(-2, 2, length.out = 11), 
              rev(RColorBrewer::brewer.pal(11, "RdBu")))) |>
  add_tile(hp) |>
  add_tile(type)
mtcars_heatmap
# Other useful parameters
# heatmap(, cluster_rows = FALSE)
# heatmap(, .scale = F)

# Note add_tile(var) can decide whether the 'var' should go to 
# columns or rows - interesting! 
# one tile goes to columns and one tile goes to rows.
tidyHeatmap::pasilla |>
    # group_by(location, type) |>
    heatmap(
      .column = sample,
      .row = symbol,
      .value = `count normalised adjusted`
    ) |>
    add_tile(condition) |>
    add_tile(activation)

Correlation heatmap

Customizable correlation heatmaps in R using purrr and ggplot2

corrplot

This package is used for visualization of correlation matrix. See its vignette and Visualize correlation matrix using correlogram.

ggcorrplot

https://cran.r-project.org/web/packages/ggcorrplot/index.html

ztable

https://cran.r-project.org/web/packages/ztable/index.html

SubtypeDrug: Prioritization of Candidate Cancer Subtype Specific Drugs

https://cran.r-project.org/web/packages/SubtypeDrug/index.html

pheatmap

What is special?

  1. the color scheme is grey-blue to orange-red. pheatmap() default options, colorRampPalette in R
  2. able to include class labels in samples and/or genes
  3. the color key is thin and placed on the RHS (prettier for publications though it could miss information)
  4. borders for each cell are included (not necessary)

fheatmap

(archived)

Dot heatmap

Office/Excel

Apply a Color Scale Based on Values

Heatmap in the terminal

Crayonmaps

rasterImage

Interactive heatmaps

d3heatmap

This package has been removed from CRAN and archived on 2020-05-24.

A package generates interactive heatmaps using d3.js and htmlwidgets.

The package let you

  • Highlight rows/columns by clicking axis labels
  • Click and drag over colormap to zoom in (click on colormap to zoom out)
  • Optional clustering and dendrograms, courtesy of base::heatmap

The following screenshots shows 3 features.

  • Shows the row/column/value under the mouse cursor
  • Zoom in a region (click on the zoom-in image will bring back the original heatmap)
  • Highlight a row or a column (click the label of another row will highlight another row. Click the same label again will bring back the original image)

D3heatmap mouseover.png D3heatmap zoomin.png D3heatmap highlight.png

heatmaply

This package extends the plotly engine to heatmaps, allowing you to inspect certain values of the data matrix by hovering the mouse over a cell. You can also zoom into a region of the heatmap by drawing a rectangle over an area of your choice.

Installing this package requires to compile some dependent package.

The return object is heatmaply is 'plotly' and 'htmlwidget'. It does not return the ordering of rows/columns. It can not control whether to do clustering (d3heatmap package is better at this).

canvasXpress

InteractiveComplexHeatmap

InteractiveComplexHeatmap, article

Calendar heatmap

A heatmap as calendar

Dendrogram

Beautiful dendrogram visualizations in R

Flip dendrogram

https://www.biostars.org/p/279775/

Color labels

library(RColorBrewer)
# matrix contains genomics-style data where columns are samples 
#   (if otherwise remove the transposition below)
# labels is a factor variable going along the columns of matrix
plotHclustColors <- function(matrix,labels,...) {
  colnames(matrix) <- labels
  d <- dist(t(matrix))
  hc <- hclust(d, method = "ward.D2")
  labelColors <- brewer.pal(nlevels(labels),"Set1")
  colLab <- function(n) {
      if (is.leaf(n)) {
      a <- attributes(n)
      labCol <- labelColors[which(levels(labels) == a$label)]
      attr(n, "nodePar") <- c(a$nodePar, lab.col=labCol)
      }
      n
  }
  clusDendro <- dendrapply(as.dendrogram(hc), colLab)
  # I change cex because there are lots of samples
  op <- par(mar=c(5,3,1,.5)+.1, cex=.3)
  plot(clusDendro,...)
  par(op)
}

plotHclustColors(genedata, pheno)

Dendrogram with covariates

https://web.stanford.edu/~hastie/TALKS/barossa.pdf#page=41

dendextend* package

  • dendextend::plot(, horiz=TRUE) allows to rotate a dendrogram with tips on RHS.
  • plot_horiz.dendrogram() allows to rotate a dendrogram with tips on LHS.
  • The package has a function tanglegram() to compare two trees of hierarchical clusterings. See this post and its vignette.
  • Add colored bars

Simplified from dendextend's vignette or Label and color leaf dendrogram. 

library(dendextend)

set.seed(1234)
iris <- datasets::iris[sample(150, 30), ] # subset for better view
iris2 <- iris[, -5] # data
species_labels <- iris[, 5] # group for coloring
 
hc_iris <- hclust(dist(iris2), method = "complete")
iris_species <- levels(species_labels)

dend <- as.dendrogram(hc_iris)
colorCodes <- c("red", "green", "blue")   
labels_colors(dend) <- colorCodes[as.numeric(species_labels)][order.dendrogram(dend)]

labels(dend) <- paste0(as.character(species_labels)[order.dendrogram(dend)],
                           "(", labels(dend), ")")
# We hang the dendrogram a bit:
dend <- hang.dendrogram(dend, hang_height=0.1)
dend <- set(dend, "labels_cex", 1.0)

png("~/Downloads/iris_dextend.png", width = 1200, height = 600)
par(mfrow=c(1,2), mar = c(3,3,1,7))
plot(dend, main = "", horiz =  TRUE)
legend("topleft", legend = iris_species, fill = colorCodes)

par(mar=c(3,1,1,5)) 
plot(as.dendrogram(hc_iris),horiz=TRUE)
dev.off()

Iris dextend.png

Colors

A quick introduction to using color in density plots

http://sharpsightlabs.com/blog/quick-intro-color-density-plot/

display.brewer.pal() and brewer.pal() functions

While brewer.pal() will return colors (in hex) for a certain palette, display.brewer.pal() can display the colors on a graphical device. 

library(RColorBrewer)
display.brewer.pal(11, "BrBG") # Alternative colors used in correlation matrix

display.brewer.pal(9, "Set1") # Up to 9 classes are allowed

Missing data

In the dynamic heatmap tool of BRB-ArrayTools, the missing data is represented by the gray color.

Papers

Double dipping

Healthcare Access and Quality Index - Lancet

http://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(17)30818-8.pdf

Other survey

Heatmap in R: Static and Interactive Visualization

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