Question: How to identify significant differentially expressed genes and gene regulatory networks from microarray data.
1
gravatar for morteza.mahmoudisaber
2.2 years ago by
Japan
morteza.mahmoudisaber60 wrote:

I have two sets of gene expression data from agilent microarray (SurePrint G3 Human Gene Expression v3 8x60K Microarray), one for a gene perturbation sample and the other as control. And now I need to identify the significantly differentiated expressed genes and the regulatory networks affected by the gene perturbation. I have good knowledge on Python and intermediate on R. Online search I found lots of suggestions. But I wanted to know If anyone has experience and suggestions for a good point to start. Thank you

ADD COMMENTlink modified 2.2 years ago • written 2.2 years ago by morteza.mahmoudisaber60
3

I have recently done such a job so.

1- by http://mapman.gabipd.org/web/guest/robin I extracted DE genes and by R implementation of GENIE3 algorithm inferred a robust GRN with the most true edges specially if you normalize arrays by RMA method (robin software will do that for you). on the other hand when you are ready with DE genes list you can use ARACNE algorithm embedded in Cytoscape to infer a GRN.

or

2- R minet package has these simple functions to infer a GRN

**data(your normalized gene expression list in which genes are in columns and samples are in row)

mim <- build.mim(syn.data,estimator="spearman")

net <- aracne(mim)**

however might be you want to evaluate the predicted edges in your GRN, I don't know if there is gold standard for your desired organism to be used as a reference network for evaluating your inferred network. in brief you can find modules in your GRN for example by Glay app in Cytoscape and finally classify genes in each modules in GO terms.

3- http://dream.broadinstitute.org/gp/pages/index.jsf has some modules to infer GRNs the input file should be expression file in which genes are in columns and no need any sample name in rows and rows should leave empty

4- finally this is very good and simple step wise tutorial to construct a GRN without need to any bioinformatics skills

http://virtualplant.bio.puc.cl/Lab/doc/Moyano.etal-2014.pdf

ADD REPLYlink modified 2.2 years ago • written 2.2 years ago by F3.1k

Thank you @Angel for the information. The gene expression analysis was done using Agilent microarray: "SurePrint G3 Human Gene Expression v3 8x60K Microarray " which is not supported by "RobiNA" for DEG analysis. Do you have suggestions for agilent array DGE analysis?

ADD REPLYlink written 2.2 years ago by morteza.mahmoudisaber60

actually I never did Agilent data analysis but I did Robin supports that

http://mapman.gabipd.org/web/guest/forum/-/message_boards/message/21978

ADD REPLYlink written 2.2 years ago by F3.1k

Dear Angel hi,

Is this link about Agilent data analysis ?

Take care

ADD REPLYlink written 2.2 years ago by Farbod3.2k

hi Farbod,

I checked Robin and supports Agilent the link is a forum about Agilent in Robin software.

ADD REPLYlink written 2.2 years ago by F3.1k
2

use limma or rankprod bioconductor packages for identifying DGEs

ADD REPLYlink written 2.2 years ago by rajasekargutha20
1

Dear Morteza, Hi

maybe this post and this and DESeq2 will help you.

~ Best

ADD REPLYlink modified 2.2 years ago • written 2.2 years ago by Farbod3.2k
1

DESeq2 is for count data as in RNA-seq fragment counts, there will be more appropriate tools for microarray data (intensities), such as limma. The two (counts and intensities) are not equivalent since the distribution is different.

ADD REPLYlink written 2.2 years ago by WouterDeCoster35k

Hi my friend, WouterDeCoster

you are right,

I just mentioned that PDF as a post to observing some graphs not using exactly that package.

~ Take care

ADD REPLYlink modified 2.2 years ago • written 2.2 years ago by Farbod3.2k
2
gravatar for WouterDeCoster
2.2 years ago by
Belgium
WouterDeCoster35k wrote:

This workflow will definitely put you in the right direction: https://www.bioconductor.org/help/workflows/arrays/

ADD COMMENTlink written 2.2 years ago by WouterDeCoster35k

Thank you @Wouter. This workflow is exactly what I need but the only problem is that it is just for Affymetrix array. I have used agilent microarray: "SurePrint G3 Human Gene Expression v3 8x60K Microarray " I found the package agilp for agilent but it has only the options of:

AALoess Normalises a set of gene expression data files using LOESS
AAProcess   Extracts raw expression data from Agilent expression array scanner files.
Baseline    Constructs a file with the mean of each probe from a set of raw expression array data files
Equaliser   Trims a set of gene expression data files to include only the set of identifiers common to all files
filenamex   A file name listing utility
IDswop  Mapping expression data across bioinformatic identifiers
Loader         A file choser utility file

is there any package conducting similar analysis for agilent microarray results as that of affymetrix?

ADD REPLYlink written 2.2 years ago by morteza.mahmoudisaber60
2

It would have been useful if your initial question also included that it's an Agilent array, since that's not the most common type. I assume (but have never done it myself) that if extract raw expression data from the Agilent array you can continue with the limma package for normalisation and differential expression analysis. In addition, using google I found this: http://matticklab.com/index.php?title=Single_channel_analysis_of_Agilent_microarray_data_with_Limma

ADD REPLYlink written 2.2 years ago by WouterDeCoster35k
2
gravatar for Marge
2.2 years ago by
Marge280
Italy
Marge280 wrote:

One option I would recommend to investigate co-expression networks changing between the two conditions is the R package WGCNA (Weighted Gene Co-expression Network Analysis). Several tutorials and published application examples are available and there is also very active and informative support.

ADD COMMENTlink written 2.2 years ago by Marge280
1
gravatar for Farbod
2.2 years ago by
Farbod3.2k
Toronto
Farbod3.2k wrote:

Hi,

Analyze your own microarray data in R/Bioconductor (R-code to identify DE genes based on Affymetrix microarray)

Using R/Bioconductor for Microarray Analysis

Take care

ADD COMMENTlink written 2.2 years ago by Farbod3.2k

Hi Farbod, Thank you for the editing and answers. Actually your answer work perfectly for Affymetrix arrays. But I have used agilent microarray that is more comprehensive and accurate than affymetrix but seemingly less popular.

ADD REPLYlink written 2.2 years ago by morteza.mahmoudisaber60
1

Is there any chance for you to use GeneSpring in your Lab, Morteza?

ADD REPLYlink written 2.2 years ago by Farbod3.2k

Hi Farbod San, Unfortunately we do not have access to GeneSpring at the moment. I will try the solutions suggested here. Thank you all.

ADD REPLYlink written 2.2 years ago by morteza.mahmoudisaber60

Hi Morteza Jan,

Is the workflow of DEG analysis really different from "Affymetrix arrays" to "agilent microarray" ?

Take care

ADD REPLYlink written 2.2 years ago by Farbod3.2k
1

Farbod I think the first step in both Agilent and microarray is normalization. output of robin is a file exactly the same output of limma R package

ADD REPLYlink written 2.2 years ago by F3.1k
1

Hi there,

it seems that you are right and I think that the micro-array DEG analysis has it's standard pipeline and i think the @Wouter suggestion about "if extract raw expression data from the Agilent array you can continue with the limma package", would be a good place to start.

~ Best

ADD REPLYlink written 2.2 years ago by Farbod3.2k
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