As a beginner, you can use the geneXplain platform. Registration for free account is here:

http://www.genexplain.com/genexplain-platform-registration

More information you can find here:

http://genexplain.com/genexplain-platform-1

We have ready-to-use workflows to analyse microarray experiments. You need just to upload .CEL files (directly from GEO).

The normalization of the raw data and estimation of differential expressed genes can be done very easily. For any question please contact: [email protected]

Hope to see you on the platform!

A SOFT file ("Simple Omnibus Format in Text ")is just a text file. It will be zipped with gzip, but once you uncompress it you can open it in any ASCII text editor, or excel or some other text-parsing spreadsheet program.

The SOFT file may or may not include actual values for analyzed data - if the submitter included their results tables. Sometimes, the SOFT file will only contain the meta-data associated with the study. You will need to g'unzip that file, open it and see if it contains the data or results you wish to extract.

However, if you want to actually evaluate the data yourself, you need to download the TAR archive of the corresponding data files (e.g. CEL files if an affymetrix experiment) and then analyze those to get fold changes and statistical values. You can analyze any microarray data (Affy, Agilent, Illumina, GE BioChips, etc) with free tools available in the BioConductor software suite (http://www.bioconductor.org/) or commercial packages like Partek GS, SAS's JMP Genomics, and others.

All submitters are required to include their raw data (usually CEL files for Affy, text expression values for Agilent, or whatever platform appropriate files they have) and those should be available from the study accession as supplemental files, in the form of a g'zipped TAR archive (linked to at the very bottom of the study's accession page in GEO).

The R package 'limma' is useful if you are happy to program in R, or if you prefer a point and click solution, then you can try MeV.

As a beginner, you can use the geneXplain platform. Registration for free account is here:

http://www.genexplain.com/genexplain-platform-registration

More information you can find here:

http://genexplain.com/genexplain-platform-1

We have ready-to-use workflows to analyse microarray experiments. You need just to upload .CEL files (directly from GEO).

The normalization of the raw data and estimation of differential expressed genes can be done very easily. For any question please contact: [email protected]

Hope to see you on the platform!

The simplest way to do is to use the R package getGEO. You can do the following

(Assuming the values are already transformed and corrected for background)

source("http://bioconductor.org/biocLite.R")

biocLite("affy")

biocLite("gcrma")

biocLite("limma")

biocLite("R.utils")

biocLite("affycoretools")

biocLite("Biobase")

biocLite("GEOquery")

library(affy)

library(gcrma)

library(limma)

library(R.utils)

library(affycoretools)

library(Biobase)

library(GEOquery)

geoNumber = "GSEXXXXX"

gse

if you are a beginner to Microarray, and have no ideas about how to upload .cel files to software such as ArrayTrack, I think the TXT files (Series Matrix File) provided by GEO is the best choice for you. it is directly provided as a sample-probe matrix with label information. You can easily open it with excel.

To find the differential expressed genes (probes), p-value and fold change seems to be the most common criteria, you can use Matlab, R or any analysis software (even excel itself) to find genes which p1.4 (threshold depends on dataset) to define the DEGs.

to obtain a reproducible result of DEG analysis, you may need to see papers about MAQC II (microarray quality control) and carefully read the dataset provider's original paper.

If you don't need a deep analysis and you just want a list of DEG, you could use the software integrated in GEO, it is called GEO2R and it uses some basic packages from Bioconductor.

https://www.ncbi.nlm.nih.gov/geo/geo2r/

Yes, with the default settings data are quantile-normalized. You can see the used code in the "R script" section of the tool.

it may be helpfull

https://www.youtube.com/watch?v=FYIcCDUhsHA&t=28s

1) http://igc1.salk.edu:3838/bart/

BART is an R shiny web application for microarray analysis

2) https://www.geodiver.co.uk/