Question

Tutorial: Analysing Microarray Data In Bioconductor

109

5.7 years ago by

Washington University, St Louis, USA

Obi Griffith ♦ 16k wrote:

I was thinking about creating a tutorial on how to do a simple microarray analysis in Bioconductor. But, I realized this has already been done quite nicely at the Bioinformatics Knowledgeblog. Their first tutorial on the subject covers installation of necessary packages, downloading of cel files, describing the experiment, loading and normalizing data, quality controls, probe set filtering, finding differentially expressed probe sets, and finally annotating those probe sets to gene symbols. They have follow-up tutorials on visualizing microarray data with volcano plots and assessing the biological significance of results. All three seem quite excellent after a brief perusal.

I might have also included: filtering with 'genefilter', multiple testing correction with 'multtest', and visualizations with 'heatmap.2'. Let me know if you would like to see tutorials specifically on any of those topics.

UPDATE: Since this seems to be coming up with some regularity I decided to add some sample code for importing CEL files, normalizing, and annotating to Entrez Gene ID and Symbol, even though this overlaps largely with the tutorial linked above. It illustrates a few slightly different things and also gives a concrete example for the 'Affymetrix Human Gene 1.0 ST Array'.

#install the core bioconductor packages, if not already installed
source("http://bioconductor.org/biocLite.R")
biocLite()

# install additional bioconductor libraries, if not already installed
biocLite("GEOquery")
biocLite("affy")
biocLite("gcrma")
biocLite("hugene10stv1cdf")
biocLite("hugene10stv1probe")
biocLite("hugene10stprobeset.db")
biocLite("hugene10sttranscriptcluster.db")

#Load the necessary libraries
library(GEOquery)
library(affy)
library(gcrma)
library(hugene10stv1cdf)
library(hugene10stv1probe)
library(hugene10stprobeset.db)
library(hugene10sttranscriptcluster.db)

#Set working directory for download
setwd("/Users/ogriffit/Dropbox/BioStars")

#Download the CEL file package for this dataset (by GSE - Geo series id)
getGEOSuppFiles("GSE27447")

#Unpack the CEL files
setwd("/Users/ogriffit/Dropbox/BioStars/GSE27447")
untar("GSE27447_RAW.tar", exdir="data")
cels = list.files("data/", pattern = "CEL")
sapply(paste("data", cels, sep="/"), gunzip)
cels = list.files("data/", pattern = "CEL")

setwd("/Users/ogriffit/Dropbox/BioStars/GSE27447/data")
raw.data=ReadAffy(verbose=TRUE, filenames=cels, cdfname="hugene10stv1") #From bioconductor

#perform RMA normalization (I would normally use GCRMA but it did not work with this chip)
data.rma.norm=rma(raw.data)

#Get the important stuff out of the data - the expression estimates for each array
rma=exprs(data.rma.norm)

#Format values to 5 decimal places
rma=format(rma, digits=5)

#Map probe sets to gene symbols or other annotations
#To see all available mappings for this platform
ls("package:hugene10stprobeset.db") #Annotations at the exon probeset level
ls("package:hugene10sttranscriptcluster.db") #Annotations at the transcript-cluster level (more gene-centric view)

#Extract probe ids, entrez symbols, and entrez ids
probes=row.names(rma)
Symbols = unlist(mget(probes, hugene10sttranscriptclusterSYMBOL, ifnotfound=NA))
Entrez_IDs = unlist(mget(probes, hugene10sttranscriptclusterENTREZID, ifnotfound=NA))

#Combine gene annotations with raw data
rma=cbind(probes,Symbols,Entrez_IDs,rma)

#Write RMA-normalized, mapped data to file
write.table(rma, file = "rma.txt", quote = FALSE, sep = "\t", row.names = FALSE, col.names = TRUE)

This produces a tab-delimited text file of the following format. Note that many probes will have "NA" for gene symbol and Entrez ID.

probes Symbols Entrez_IDs GSM678364_B2.CEL GSM678365_B4.CEL GSM678366_B5.CEL ...

7897441 H6PD 9563 6.5943 7.0552 7.5201 ...

7897449 SPSB1 80176 6.9727 7.0281 7.2285 ...

7897460 SLC25A33 84275 7.6659 7.4289 7.9707 ...

tutorial analysis bioconductor microarray • 49k views

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modified 7 months ago by Kevin Blighe ♦ 21k • written 5.7 years ago by Obi Griffith ♦ 16k

4

I wanted to perform WGCNA analysis on Affy cell files. So according to this tutorial, the "rma.txt" could be used as the input for WGCNA ?

ADD REPLY • link written 5.1 years ago by pixie@bioinfo • 1.3k

4

I've not used the WGCNA R package. But, if it starts with a gene expression matrix as input, then yes, it seems like you could use the RMA normalized gene expression values for your analysis.

ADD REPLY • link written 5.1 years ago by Obi Griffith ♦ 16k

3

Thanks..yea, the first column has the list of identifiers followed by the expression profiles of all the samples.

ADD REPLY • link written 5.1 years ago by pixie@bioinfo • 1.3k

2

HI, my computer gets stuck every time i use the annotation package( the cbind command). Is there anyway to overcome that? I have also tried allotting heavy memory on our computing cluster and yet the problem is unsolved

ADD REPLY • link written 4.4 years ago by swe.srini • 20

3

This so helpful! I've been trying to figure out all these for months.. Thank you!

ADD REPLY • link written 5.7 years ago by Ane • 180

1

No problem. Although, thanks should go to the good people at the Bioinformatics Knowledgeblog who created these tutorials. ps. For future, comments/thanks like this would be better as a "comment" to the main post rather than as an "answer".

ADD REPLY • link written 5.7 years ago by Obi Griffith ♦ 16k

Dear Obi,

I have normalized my affymetrix microarray data and did gene filter to remove probes that do not have entrez id's.

Now I want to annotate my data for gene symbols. I've followed your steps to retrieve gene symbols. After writing the data in text file I found a column mentioning as <S4 object of class "ExpressionSet"> instead of expression values of the sample. can you please help me how to get expression values

ADD REPLY • link written 2.9 years ago by rohit • 30

3

The limma user manual also goes into linear models and is very useful for analysis of illumina microarray data

ADD REPLY • link written 5.7 years ago by Ying W • 3.8k

3

Very useful. Thanks.

ADD REPLY • link written 5.1 years ago by Suren • 90

2

Hi, this was very good tutorial. You said you might do a heat map. 2, which would be great. I am analysing data on this kind of chip and need to do heat map.2. I would be very grateful to see how you would incorporate heatmap.2 into this tutorial. As I am having difficulty with it. Thanks for your time

ADD REPLY • link written 4.8 years ago by martinkara7 • 20

2

Very good tutorial, thank you

ADD REPLY • link written 4.0 years ago by sana.mystudies • 60

2

Very Good tutorial. Thank you

ADD REPLY • link written 4.0 years ago by sana.mystudies • 60

2

very good tutorial. thanks for sharing

ADD REPLY • link written 3.4 years ago by ju.luan • 20

1

Hi Obi,

i followed the steps you mentioned today and i have a text file now, how can i visualize the data to interpretation please?

ADD REPLY • link written 3.1 years ago by F • 3.0k

1

Thanks for such a nice tutorial

ADD REPLY • link modified 2.8 years ago • written 2.9 years ago by Bioinformatist Newbie • 220

1

I found also this It may be helpful http://learn.genetics.utah.edu/content/labs/microarray/

ADD REPLY • link written 2.4 years ago by Medhat • 7.6k

1

to avoid converting the entire data frame to a "character" matrix, I would use cbind.data.frame() instead of cbind():

#Combine gene annotations with raw data
rma=cbind.data.frame(probes,Symbols,Entrez_IDs,rma)

ADD REPLY • link written 2.2 years ago by informatics bot • 530

1

Can you please tell me how to find the best probeset in microarray analysis?

ADD REPLY • link written 2.2 years ago by Bioinfo • 200

1

Dear all,

I tried to use below code to normalize my data before using combat to deal with batch effects:

library(affy)
norm <- ReadAffy()
norm.rma <- rma(norm)

Unluckily, my data include 3 data sets, 2 Affymetrix Human Genome U133 Plus 2.0 Array and 1 Affymetrix Human Gene 1.0 ST Array. I received this error message "Error in affyio::read_abatch(filenames, rm.mask, rm.outliers, rm.extra,: Cel file D:////batchdemo/GSMxxxx.CEL.gz does not seem to have the correct dimensions".

I tried to use dchip instead but again, I failed. I even couldn't input the data to dchip.

Any advice and suggestions will be greatly appreciated.

Thank you.

ADD REPLY • link modified 2.1 years ago • written 2.1 years ago by BioMed • 40

1

@BioMed: Please create a new post with this question, if you feel this is not directly related to Obi's tutorial. It is not appropriate to use Submit Answers on this post to ask a new question.

ADD REPLY • link modified 2.1 years ago • written 2.1 years ago by genomax ♦ 49k

Please delete the above comment. I created a new post. Thank you.

ADD REPLY • link written 2.1 years ago by BioMed • 40

this is very useful,but I have a problem in Describing the experiment step,when I am using command.

$ ls data/*.CEL > data/phenodata.txt

this returns an error

$ ls data/*.CEL > data/phenodata.txt Error: unexpected '$' in "$" what should I do now?

ADD REPLY • link written 17 months ago by Edalat • 30

$ is your terminal's command prompt end. Don't use $ and try.

ADD REPLY • link written 17 months ago by venu ♦ 5.3k

thank you for a reply. actually I do not work with linux. do you know same of this tutorial for windows?

ADD REPLY • link written 17 months ago by Edalat • 30

hi, i tried to run this command data.rma.norm=rma(raw.data) but Unfortunately I faced this error, would you please help me. my platform is GPL96

Error in getCdfInfo(object) : Could not obtain CDF environment, problems encountered: Specified environment does not contain HGU133A_HS_ENTREZG,HGU133B_HS_ENTREZG Library - package hgu133ahsentrezg,hgu133bhsentrezgcdf not installed Bioconductor - hgu133ahsentrezg,hgu133bhsentrezgcdf not available

ADD REPLY • link written 17 months ago by arezoo.yazdani • 0

for RMA normalization

library(affy)

# To read all CEL files in the working directory:
Data<-ReadAffy()

eset<-rma(Data)
norm.data<-exprs(eset)

ADD REPLY • link modified 17 months ago by WouterDeCoster ♦ 29k • written 17 months ago by F • 3.0k

Hello Dear Obi Griffith, I am beginner using bioconductor, I like so much your workflow is very explanatory, however in the end based on the data that are generated the file "rma.txt" how do I analyze using the limma pack? Would you have some workflow as good as yours that I can follow?

Note: I am working with two normal states and tumor, how to compare the exprssão between them?

ADD REPLY • link written 7 months ago by Leite • 240

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