Creating color palettes in R

In the R post, we will present how to create your own color palettes and how to work with other palettes such as RColorBrewer, wesanderson and hex codes from for exciting color palettes.

There are several color palettes available in R such as rainbow(), heat.colors(), terrain.colors(), and topo.colors(). We can visualize these  as 3D pie charts using the plotrix R package.

# Let's create a pie chart with n=7 colors using each palette
sliceValues <- rep(10, 7) # each slice value=10 for proportionate slices
pie3D(sliceValues,explode=0, theta=1.2, col=rainbow(n=7), main="rainbow()")

# Let's create a figure with all 4 base color palettes
par(mfrow=c(1, 4))
pie3D(sliceValues,explode=0, theta=1.2, col=rainbow(n=7), main="rainbow()")
pie3D(sliceValues,explode=0, theta=1.2, col=heat.colors(n=7), main="heat.colors()")
pie3D(sliceValues,explode=0, theta=1.2, col=terrain.colors(n=7), main="terrain.colors()")
pie3D(sliceValues,explode=0, theta=1.2, col=topo.colors(n=7), main="topo.colors()")

Screen Shot 2016-07-10 at 9.01.30 AM

Other popular color palettes include the RColorBrewer package that has a variety of sequential, divergent and qualitative palettes and the wesanderson package that has color palettes derived from his films.


# To see all palettes available in this package
par(mfrow=c(1, 1))

# To create pie charts from a sequential, divergent and qualitative RColorBrewer palette
par(mfrow=c(1, 4))
pie3D(sliceValues,explode=0, theta=1.2, col=brewer.pal(7, "RdPu"), main="Sequential RdPu")
pie3D(sliceValues,explode=0, theta=1.2, col=brewer.pal(7, "RdGy"), main="Divergent RdGy")
pie3D(sliceValues,explode=0, theta=1.2, col=brewer.pal(7, "Set1"), main="Qualitative Set1")

# And add pie chart with a wes_anderson palette
# we will only use 5 slices in the example since the Darjeeling palette only has 5 colors
pie3D(sliceValues[1:5],explode=0, theta=1.2, col=wes_palette("Darjeeling2"), main="Darjeeling2")

Screen Shot 2016-07-10 at 9.01.45 AMYou can also create your own color palettes in R with your colors of choice with the colors() function or creating a vector with the color names. A great review and cheat sheet for R colors can be found at

# To get an idea of the colors available
length(colors()) # 657

# To see all 657 colors as a color chart you can source the R script to generate a pdf version in your working directory

Screen Shot 2016-07-09 at 5.18.32 PM

# We can create choose a palette based on the R chart as follow:
mycols <- colors()[c(8, 5, 30, 53, 118, 72)] #
# or you could enter the color names directly
# mycols <- c("aquamarine", "antiquewhite2", "blue4", "chocolate1", "deeppink2", "cyan4")

# You could also get and store all distinct colors in the cl object and use the sample function to select colors at random
cl <- colors(distinct = TRUE)
set.seed(15887) # to set random generator seed
mycols2 <- sample(cl, 7)

You can also create color palettes with hex color codes. A great example of this is to work with popular color palettes available on the website. This website has various palettes you can choose from and even derive color palettes from your favorite websites. For example, let’s grab the color palette from the website at .

Screen Shot 2016-07-09 at 5.36.02 PM

After entering the URL of our website, we will receive the hex codes for the color scheme used on the website.

Screen Shot 2016-07-09 at 5.38.34 PM

We can even export the colors as little pencils 🙂


You can also choose from hundred of color schemes based on your color of choice. For example, we will also create a color palette based on the color olive – ColorCombo382.


# For the color palette
mycols3 <- c("#c6d4e1", "#2f2016", "#fcfaea", "#456789")

# For ColorCombos382 palette
mycols4 <- c("#C3D938", "#772877", "#7C821E", "#D8B98B", "#7A4012")

# Now to get the pie charts for the last four palettes
pie3D(sliceValues,explode=0, theta=1.2, col=mycols, main="colors()")
pie3D(sliceValues,explode=0, theta=1.2, col=mycols2, main="sample(colors(distinct=TRUE)")
pie3D(sliceValues[1:4],explode=0, theta=1.2, col=mycols3, main=" color grab")
pie3D(sliceValues[1:5],explode=0, theta=1.2, col=mycols4, main="ColorCombos382")

Screen Shot 2016-07-10 at 9.01.56 AM

We can also create a color palette with the colorRampPalette() to use for heatmaps and other plots. For this example, we will use the leukemia dataset available in the GSVAdata package, which corresponds to microarray data from 37 human acute leukemias where 20 of these cases are Acute lymphoblastic leukemia (ALL) and the other 17 are ALL’s with Mixed leukemia gene rearrangements. For more information on the study please see Armstrong et al. Nat Genet 30:41-47, 2002.

data(leukemia) # loads leukemia_eset

# Create a matrix from the gene expression eset object
M1 <- exprs(leukemia_eset)

# Get a matrix of the top 50 most variable probes accros the samples
topVarGenes <- head(order(-rowVars(M1)), 50)
mat <- M1[ topVarGenes, ]
mat <- mat - rowMeans(mat)

# For sample annotation information

# Get sample group as a factor the ColSideColors
ALLgroup <- as.factor(pData(leukemia_eset)[colnames(M1), 1])

# Get the colors for the ALL subtype
sidecols <- c("#4FD5D6", "#FF0000")

# Here is a fancy color palette inspired by
cool = rainbow(50, start=rgb2hsv(col2rgb('cyan'))[1], end=rgb2hsv(col2rgb('blue'))[1])
warm = rainbow(50, start=rgb2hsv(col2rgb('red'))[1], end=rgb2hsv(col2rgb('yellow'))[1])
cols = c(rev(cool), rev(warm))
mypalette <- colorRampPalette(cols)(255)

library("gplots") # for the heatmap.2 function

png(filename="Heatmap_Example.png", width=12, height=10, units = 'in', res = 300)
heatmap.2(mat, trace="none", col=mypalette, ColSideColors=sidecols[ALLgroup],
labRow=FALSE, labCol=FALSE, mar=c(6,12), scale="row", key.title="")
legend("topright", legend=levels(ALLgroup), fill=sidecols, title="", cex=1.2)


Now you are all set to work with and create your own awesome color palettes! Happy R programing 🙂


Pre-processing .CEL files in R

This post shows you how to compare data from two separate studies without the hassle of tackling batch effects, etc. By scaling and centring the data in both studies, you can look for trends in the data and look for gene expression changes that go in a similar direction.

One of the most efficient ways to pre-process microarray data in R, is to use the oligo R/Bioconductor package. In a few lines of code you can go from raw .CEL files to a normalized data matrix you can work with for downstream analysis. It is particularly useful, if you wish to reanalyse a subset of .CEL files from a previously published dataset.

For example, say we are interested in comparing relative gene expression levels of Atf1, Atf3, Brca1 i in the lung, liver, and bone marrow of CB17 mice to previously published mouse tissue expression data on GEO. From our study, we have a data matrix of normalized expression values we obtained from our microarray study stored in CB17mat object.

First, we will scale all gene expression values by row to obtain center and scale these values to have an idea which organ expresses the highest levels of Atf1, Atf3, and Brca1 relative to the others in our study. To do this we will use the scale() with the default settings center=TRUE and scale=TRUE. Since the scale default function scales and centers columns we will need to transpose our matrix before proceeding.

# From your gene expression matrix stored in CB17mat
genes = c("Atf1", "Atf3", "Brca1")
CB17scal <- t(apply(CB17mat[genes, ], 1, scale))

# We will also add the missing column names to our scaled matrix
colnames(CB17scal) <- colnames(CB17mat)

# You can also plot a heatmap to look at the effects of the scaling on the expression levels across the tissues using the gplots package

# Clusters the rows (and potentially columns) by Pearson correlation as distance method
corrdist = function(x) as.dist(1-cor(t(x), method="pearson"))

# and Ward method as the agglomeration method
hclust.avl = function(x) hclust(x, method="ward.D2")

# with dendrogram="row" and Colv=NA, we are only clustering the rows i.e. genes
png(filename="example1.png", width=10, height=10, units = 'in', res = 300)
heatmap.2(CB17scal, dendrogram="row", Colv=NA, scale="none", key.title="", col=rev(redblue(250)), trace='none', cexCol=1.2, cexRow=1.5, hclustfun=hclust.avl, distfun=corrdist, margins=c(8, 12))


As you can see Atf1 and Brca1 are relatively higher in bone marrow, whereas Atf3 is relatively higher in the lung compared to the other organs.

Now let’s analyze the liver, lung and bone marrow data from the Large-scale analysis of the human and mouse transcriptomes study from Su et al, 2002, PNAS Apr 2;99(7):4465-70. The individual files from the liver, lung and bone marrow were downloaded from GSE97 and the data was normalized with the oligo package as follows:

# Move the CEL files to ~/MY_WORKING_DIRECTORY/filesToAnalyse
# Set the working directory to the folder you would like to save your results

# Load the libraries needed for the analysis

# Load the packages needed for the analysis
# You can choose to save the CEL files for your tissues of interest
geneCELs <- list.celfiles("~/MY_WORKING_DIRECTORY/filesToAnalyse", full.names=TRUE)

affyGeneFS <- read.celfiles(geneCELs)

# RMA at the probet level
geneCore <- rma(affyGeneFS)

# Inspect the eset object

# For the featureData info for the array used in this study

probeids <- featureNames(geneCore)
geneAnnotation <- select(mgu74a.db, probeids, c("SYMBOL", "ENTREZID", "GENENAME"), multiVals="first")

# Save the ESET data and annotation
save(geneCore, geneAnnotation, file="geneCoreTissueV2.RData")
saveRDS(geneCore, "geneCore.rds")
saveRDS(geneAnnotation, "geneAnnotation.rds")

# Convert the eset object to a matrix to get the gene expression values
# for Atf1, Atf3, and Brca1
M1 <- exprs(geneCore)

Gene_Symbols <- sapply(rownames(M1), getGeneSymbol, df=geneAnnotation)
DM1 <- data.frame(M1, Gene_Symbols=Gene_Symbols[rownames(M1)], stringsAsFactors=FALSE)

# Use can create a function to select the probe with the top interquantile range with IQR()
# to represent the gene expression value or use the TopIqrSymbolMatrix()
# in the "Useful Functions to Work with Microarrays" post (coming soon!)

tissueM1 <- TopIqrSymbolMatrix(DM1)
tissueExprs <- tissueM1[, 1:ncol(M1)]
rownames(tissueExprs) <- tissueM1$Gene_Symbols
head(tissueExprs[, 1:4])

# To get the geo file ids
geoColnames <- gsub(".CEL", "", colnames(tissueExprs))

# Get the new colnames based on tissue
# Create an excel sheet with the GEO .CEL file id and tissue sample names you would like to use
# Load the excel sheet as a data frame using the gdata package
TissueNames <- read.xls("GSE97_FileSampleIdentifier.xlsx", sheet=1, stringsAsFactors=FALSE, row.names=1)
TissueSamples <- TissueNames[geoColnames, 2]

# Replace the tissue exprs with the tissue names
colnames(tissueExprs) <- TissueSamples
saveRDS(tissueExprs, "tissueExprs.rds")

# Now create a matrix for our 3 genes of interest
genes = c("Atf1", "Atf3", "Brca1")
genesMat <- as.matrix(M1[genes, ])

# Let's scale and center the expression values
genesMatScal <- t(apply(genesMat, 1, scale))

# Add the column names
colnames(genesMatScal) <- rep(c("liver", "lung", "bone marrow"), each=2)

png(filename="example1b.png", width=10, height=10, units = 'in', res = 300)
heatmap.2(genesMatScal, dendrogram="row", Colv=NA, scale="none", key.title="", col=rev(redblue(250)), trace='none', cexCol=1.2, cexRow=1.5, hclustfun=hclust.avl, distfun=corrdist, margins=c(8, 12))


By comparing both heatmaps, we can see that Atf1 and Brca1 have more similar expression patterns across the tissues than Atf3 in these mice compared to our CB17 mice tissue data.
Therefore, Brca1 and Atf1 tissue difference might be more consistent among the tissues from different mice.

Alternatively, it could just be an artifact of the small sample size and/or how the samples were processed before running the arrays. That being said, it’s a good thing to reanalyse studies and compare with your arrays to get an idea of sample variability and how the experimental design, pre- and post- processing affects the overall interpretation of the results.

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