Simple differential expression analysis for 2 groups
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@christiangriffioen-11642
Last seen 5.1 years ago

Hello,

I have 1 csv file containing 200, 100 of 'group A' and 100 of 'group B'

looking like this:

                sample1   sample2   sample3   samepl4   sample5 etc...  

                groupA     groupA      groupA     groupA     groupA  etc...  groupB    groupB     groupB

probe1     7.823      3.45645     4.2349      2.2349       5.23498         3.45645   2.2139     3.4534  

probe2     9.53        3.2340       3.0980      3.8858       3.83493         3.85673   2.2399     3.2340 

probe3     8.823      9.45645     3.2349      2.2349       5.23498         8.823       3.2349     9.59

etc...

Using Limma in R, I want to know which genes are differentially expressed between the 2 groups. Seems very simple to me, but when I look up tutorials everything seems way to complicated for what I want. How should I approach this?

differential gene expression agilent microarrays microarray DEA Limma • 1.7k views
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This manual right, where is the data they are working with? They have all these examples but the data is nowhere nowhere to be found. Where is "targets.txt" for example or "eset"?

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It is quite straightforward to do w/o the example data, you just need to keep trying.

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Really? From the first two examples alone:

Example 1:

In this example we assume that the data is provided as a GAL file called fish.gal and raw SPOT output files and that these files are in the current working directory. The data used for this case study can be downloaded from http://bioinf.wehi.edu.au/limmaGUI/DataSets.html

Example 2:

In this section we consider a case study where two RNA sources are compared through a common reference RNA. The analysis of the log-ratios involves a two-sample comparison of means for each gene.

In this example we assume that the data is available as an RGList in the data file Apoa1.RData. The data used for this case study can be downloaded from http://bioinf.wehi.edu.au/limma

And the example starting on page 40 should be instructive. You shouldn't actually need any data to follow the example, as it's pretty straightforward.

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