GAGE vs other GO analysis tools
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@january-weiner-4252
Last seen 4.8 years ago
European Union
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 --------------------------------------
Microarray Pathways GO gage Microarray Pathways GO gage • 1.5k views
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Luo Weijun ★ 1.6k
@luo-weijun-1783
Last seen 9 months ago
United States
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 what’s 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. [[alternative HTML version deleted]]
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