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?
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morteza.mahmoudisaber
use limma or rankprod bioconductor packages for identifying DGEs
Dear Morteza, Hi
maybe this post and this and DESeq2 will help you.
~ Best
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.
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
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
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?
actually I never did Agilent data analysis but I did Robin supports that
http://mapman.gabipd.org/web/guest/forum/-/message_boards/message/21978
Dear Angel hi,
Is this link about Agilent data analysis ?
Take care
hi Farbod,
I checked Robin and supports Agilent the link is a forum about Agilent in Robin software.