Creating Annotated Data Frames from GEO with the GEOquery package

In this post, we will go over how to use the GEOquery package to download a data matrix (or eset object) directly into R and append specific probe annotation information to this matrix for it to be exported as a csv file for easy manipulation in Excel or spreadsheet tools. This is especially useful for sharing data with collaborators who are not familiar with R and would rather look up there favorite genes in a spreadsheet format.

Mining gene expression data from publicly available databases is a great way to find evidence to support you working hypothesis that gene X is relevant in condition Y. You may also want to mine publicly available data to build on an existing hypothesis or simply to find additional support for your favorite gene in a different animal model or experimental condition. In this post, we will go over how to use the GEOquery package to download a data matrix (or eset object) directly into R and append specific probe annotation information to this matrix for it to be exported as a csv file for easy manipulation in Excel or spreadsheet tools. This is especially useful for sharing data with collaborators who are not familiar with R and would rather look up there favorite genes in a spreadsheet format.

First, let’s start by opening an R session and creating a function to return the eset (ExpressionSet) object or the original list object downloaded by the getGEO() function in R.

getGEOdataObjects <- function(x, getGSEobject=FALSE){
# Make sure the GEOquery package is installed
# Use the getGEO() function to download the GEO data for the id stored in x
GSEDATA <- getGEO(x, GSEMatrix=T, AnnotGPL=FALSE)
# Inspect the object by printing a summary of the expression values for the first 2 columns
print(summary(exprs(GSEDATA[[1]])[, 1:2]))

# Get the eset object
eset <- GSEDATA[[1]]
# Save the objects generated for future use in the current working directory
save(GSEDATA, eset, file=paste(x, ".RData", sep=""))

# check whether we want to return the list object we downloaded on GEO or
# just the eset object with the getGSEobject argument
if(getGSEobject) return(GSEDATA) else return(eset)

We can test this function on a GEO dataset such as GSE73835 as follows:

# Store the dataset ids in a vector GEO_DATASETS just in case you want to loop through several GEO ids
GEO_DATASETS <- c("GSE73835")

# Use the function we created to return the eset object
eset <- getGEOdataObjects(GEO_DATASETS[1])
# Inspect the eset object to get the annotation GPL id

You will see the following output:

ExpressionSet (storageMode: lockedEnvironment)
assayData: 45281 features, 6 samples
element names: exprs
protocolData: none
sampleNames: GSM1904293 GSM1904294 … GSM1904298 (6 total)
varLabels: title geo_accession … data_row_count (35 total)
varMetadata: labelDescription
featureNames: ILMN_1212602 ILMN_1212603 … ILMN_3163582 (45281 total)
fvarLabels: ID Species … ORF (30 total)
fvarMetadata: Column Description labelDescription
experimentData: use ‘experimentData(object)’
Annotation: GPL6887

We will first need to download the annotation file for GPL6887. Then we can create a data frame with the probe annotation categories we are most interested in as follows:

# Get the annotation GPL id (see Annotation: GPL10558)
gpl <- getGEO('GPL6887', destdir=".")

# Inspect the table of the gpl annotation object

# Get the gene symbol and entrez ids to be used for annotations
Table(gpl)[1:10, c(1, 6, 9, 12)]

# Get the gene expression data for all the probes with a gene symbol
geneProbes <- which(!$Symbol))
probeids <- as.character(Table(gpl)$ID[geneProbes])

probes <- intersect(probeids, rownames(exprs(eset)))

geneMatrix <- exprs(eset)[probes, ]

inds <- which(Table(gpl)$ID %in% probes)
# Check you get the same probes

# Create the expression matrix with gene ids
geneMatTable <- cbind(geneMatrix, Table(gpl)[inds, c(1, 6, 9, 12)])

# Save a copy of the expression matrix as a csv file
write.csv(geneMatTable, paste(GEO_DATASETS[1], "_DataMatrix.csv", sep=""), row.names=T)

Let’s take a look at the first 6 lines of the data frame we just created with the head() function.

example1As you can see once we export this data frame as a csv file, it is much easier for others to open this file as a spreadsheet and get useful information such as the gene symbol or entrez id with the expression values across the samples.

Hope this helps and happy collaborations!

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.

Feel free to leave comments or email me at