Dear all,
I am trying to use GAGE for GO and pathway analysis, but the results
of the GAGE analysis applied to data sets that I know rather well are
strange.
The samples come from patients suffering of an infectious disease, and
if compared with controls, they normally show an enrichment in GO
terms and KEGG pathways related to immune answer.
In GAGE, the same data also show a significant enrichment, but to
ribosomal functions, for example:
p.geomean stat.mean p.val q.val set.size
name
GO:0005840 1.610041e-10 5.917761 1.614836e-58 4.925249e-55 185
GO:0005840 ribosome
GO:0003735 3.190154e-10 5.893759 9.135429e-57 1.393153e-53 141
GO:0003735 structural constituent of ribosome
GO:0006412 1.051080e-09 5.581083 1.619059e-54 1.646043e-51 351
GO:0006412 translation
GO:0030529 1.412392e-09 5.339373 3.149454e-50 2.401458e-47 408
GO:0030529 ribonucleoprotein complex
GO:0033279 3.177217e-08 5.093234 7.376959e-43 4.499945e-40 109
GO:0033279 ribosomal subunit
GO:0006414 2.438856e-07 4.695261 1.070581e-36 5.442121e-34 98
GO:0006414 translational elongation
I can't believe the above; not only these results are not confirmed by
any other analysis (topGO, GOrilla, online GO analysis tools, GSEA,
SPIA for comparison with kegg.gs), but furthermore if one is to plot
the microarray intensities of the genes by group and by GO term for
the above GO terms, it becomes apparent that there is little
difference in the analysed genes.
I know that I am giving but few details in my e-mail, but I hope that
maybe some other person had similar troubles with GAGE.
Kind regards,
j.
--
-------- Dr. January Weiner 3 --------------------------------------
Hi January,
I am not sure what happened based on what you described. I need
more information, can you provide the code you run GAGE analysis with
and your
input data? If you input data file is too big, give me a smaller file
with a
few representative samples on each group (disease vs control). I will
try to
see whatâs the problem. Thanks for your interest in GAGE!
Weijun
Dear all,
I am trying to use GAGE for GO and pathway analysis, but the results
of the GAGE analysis applied to data sets that I know rather well are
strange.
The samples come from patients suffering of an infectious disease, and
if compared with controls, they normally show an enrichment in GO
terms and KEGG pathways related to immune answer.
In GAGE, the same data also show a significant enrichment, but to
ribosomal functions, for example:
p.geomean stat.mean p.val q.val set.size
name
GO:0005840 1.610041e-10 5.917761 1.614836e-58 4.925249e-55 185
GO:0005840 ribosome
GO:0003735 3.190154e-10 5.893759 9.135429e-57 1.393153e-53 141
GO:0003735 structural constituent of ribosome
GO:0006412 1.051080e-09 5.581083 1.619059e-54 1.646043e-51 351
GO:0006412 translation
GO:0030529 1.412392e-09 5.339373 3.149454e-50 2.401458e-47 408
GO:0030529 ribonucleoprotein complex
GO:0033279 3.177217e-08 5.093234 7.376959e-43 4.499945e-40 109
GO:0033279 ribosomal subunit
GO:0006414 2.438856e-07 4.695261 1.070581e-36 5.442121e-34 98
GO:0006414 translational elongation
I can't believe the above; not only these results are not confirmed by
any other analysis (topGO, GOrilla, online GO analysis tools, GSEA,
SPIA for comparison with kegg.gs), but furthermore if one is to plot
the microarray intensities of the genes by group and by GO term for
the above GO terms, it becomes apparent that there is little
difference in the analysed genes.
I know that I am giving but few details in my e-mail, but I hope that
maybe some other person had similar troubles with GAGE.
Kind regards,
j.
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