Gene Symbols – Let's talk about science with R

Converting mouse to human gene names with biomaRt package

Converting mouse gene names to the human equivalent and vice versa is not always as straightforward as it seems, so I wrote a function to simplify the task. The function takes advantage of the getLDS() function from the biomaRt to get the hgnc symbol equivalent from the mgi symbol. For example, let’s convert the following mouse gene symbols, Hmmr, Tlx3, and Cpeb4, to their human equivalent.

musGenes <- c("Hmmr", "Tlx3", "Cpeb4")

# Basic function to convert mouse to human gene names
convertMouseGeneList <- function(x){

require("biomaRt")
human = useMart("ensembl", dataset = "hsapiens_gene_ensembl")
mouse = useMart("ensembl", dataset = "mmusculus_gene_ensembl")

genesV2 = getLDS(attributes = c("mgi_symbol"), filters = "mgi_symbol", values = x , mart = mouse, attributesL = c("hgnc_symbol"), martL = human, uniqueRows=T)
humanx <- unique(genesV2[, 2])

# Print the first 6 genes found to the screen
print(head(humanx))
return(humanx)
}

We can just as easily write a function to go from human to mouse genes.

# Basic function to convert human to mouse gene names
convertHumanGeneList <- function(x){

require("biomaRt")
human = useMart("ensembl", dataset = "hsapiens_gene_ensembl")
mouse = useMart("ensembl", dataset = "mmusculus_gene_ensembl")

genesV2 = getLDS(attributes = c("hgnc_symbol"), filters = "hgnc_symbol", values = x , mart = human, attributesL = c("mgi_symbol"), martL = mouse, uniqueRows=T)

humanx <- unique(genesV2[, 2])

# Print the first 6 genes found to the screen
print(head(humanx))
return(humanx)
}

genes <- convertHumanGeneList(humGenes)

If you have any other suggestions on how to convert mouse to human gene names in R, I would love to hear them just email me at info@rjbioinformatics.com.

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
require("GEOquery")
# 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
eset

You will see the following output:

ExpressionSet (storageMode: lockedEnvironment)
assayData: 45281 features, 6 samples
element names: exprs
protocolData: none
phenoData
sampleNames: GSM1904293 GSM1904294 … GSM1904298 (6 total)
varLabels: title geo_accession … data_row_count (35 total)
varMetadata: labelDescription
featureData
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=".")
Meta(gpl)$title

# Inspect the table of the gpl annotation object
colnames(Table(gpl))

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

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

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

geneMatrix <- exprs(eset)[probes, ]

inds <- which(Table(gpl)$ID %in% probes)
# Check you get the same probes
head(probes)
head(as.character(Table(gpl)$ID[inds]))

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

# 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.

example1 As 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!

Converting Gene Names in R with AnnotationDbi

There are many ways to convert gene accession numbers or ids to gene symbols or other types of ids in R and several R/Bioconductor packages to facilitate this process including the AnnotationDbi, annotate, and biomaRt packages. In this post, we are going to learn how to convert gene ids with the AnnotationDbi and org.Hs.eg.db package.

For example, say we have a gene expression matrix stored in M1 created from an eset object you downloaded from GEO. The study I will be using for this example is A Leukemic Stem Cell Expression Signature is Associated with Clinical Outcomes in Acute Myeloid Leukemia deposited on GEO with the accession id GSE24006. To view the script on how to generate the expression set (eset) object see the post – Retrieving Gene Expression Data Objects & Matrices From GEO.

# Convert you eset object to a matrix with the exprs() function
library(Biobase)
M1 <- exprs(eset)

# Convert the row names to entrez ids
library("AnnotationDbi")
library("org.Hs.eg.db")
columns(org.Hs.eg.db)

geneSymbols <- mapIds(org.Hs.eg.db, keys=rownames(M1), column="SYMBOL", keytype="ENTREZID", multiVals="first")
head(geneSymbols)

The mapIds() function from the AnnotationDbi package returns a named vector making it simple to retrieve entrez id for a given gene as follows:

gene.to.search <- c("658", "1360")
geneSymbols[gene.to.search]

# returns the gene symbols of the entrez
# "BMPR1B" "CPB1"

We can create a function to return a matrix with gene symbols instead of entrez ids as follows:

getMatrixWithSymbols <- function(df){
require("AnnotationDbi")
require("org.Hs.eg.db")

geneSymbols <- mapIds(org.Hs.eg.db, keys=rownames(df), column="SYMBOL", keytype="ENTREZID", multiVals="first")

# get the entrez ids with gene symbols i.e. remove those with NA's for gene symbols
inds <- which(!is.na(geneSymbols))
found_genes <- geneSymbols[inds]

# subset your data frame based on the found_genes
df2 <- df[names(found_genes), ]
rownames(df2) <- found_genes
return(df2)
}

# Now, let's use the function to create a matrix for the genes with gene symbols
M1symb <- getMatrixWithSymbols(M1)

We can generalize this function to go back and forth between gene symbols and entrez ids (or other ids) as follows:

We can generalize this function to go back and forth between gene symbols and entrez ids (or other ids) as follows:


# This function can take any of the columns(org.Hs.eg.db) as type and keys as long as the row names are in the format of the keys argument
getMatrixWithSelectedIds <- function(df, type, keys){
require("AnnotationDbi")
require("org.Hs.eg.db")

geneSymbols <- mapIds(org.Hs.eg.db, keys=rownames(df), column=type, keytype=keys, multiVals="first")

# get the entrez ids with gene symbols i.e. remove those with NA's for gene symbols
inds <- which(!is.na(geneSymbols))
found_genes <- geneSymbols[inds]

# subset your data frame based on the found_genes
df2 <- df[names(found_genes), ]
rownames(df2) <- found_genes
return(df2)
}

# for example, going from SYMBOL to ENTREZID
M1entrez <- getMatrixWithSelectedIds(M1symb, type="ENTREZID", keys="SYMBOL")

Stay tuned for more posts on Converting Gene Names in R with the annotation and biomaRt package.