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antoinefelden
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10
@antoinefelden-16088
Last seen 6.2 years ago
I ran a DGE analysis with 4 different contrasts with lmFit() + contrasts.fit() in limma, and I'm interested in the overlap of the four different contrasts. I identified the genes that are indeed differentially expressed in all four contrasts, and coded that in tfit$genes$test ("yes" if differentially expressed, "no" if not). The MArrayLM object is pasted below.
I did manage to run a Kegg pathway enrichment analysis for each of the contrasts individually, but I'm after a way to run a single analysis for the set of DE genes in all contrasts. Is that feasible?
An object of class "MArrayLM"
$coefficients
Contrasts
ARvsCA ARvsEU ARvsAU ARvsNZ
1 -0.18067896 -0.2044603 -0.22881771 -0.1833862
2 -0.04079345 -1.1859285 -0.39206763 -0.3653143
3 -0.12733594 0.1763288 -0.07934863 -0.1252855
4 0.07648264 0.6875827 0.13266024 0.3442508
5 0.09678434 0.4514540 0.13137207 0.2943875
10387 more rows ...
$stdev.unscaled
Contrasts
ARvsCA ARvsEU ARvsAU ARvsNZ
1 0.1820675 0.2300422 0.1936519 0.1780754
2 0.1499697 0.1868217 0.1574127 0.1458230
3 0.1480576 0.1898012 0.1567854 0.1442379
4 0.1883755 0.2695961 0.2030905 0.1910940
5 0.1406236 0.1774499 0.1479352 0.1374007
10387 more rows ...
$sigma
[1] 1.2656682 1.3338325 0.5922579 1.9853202 0.9379173
10387 more elements ...
$df.residual
[1] 21 21 21 21 21
10387 more elements ...
$cov.coefficients
Contrasts
Contrasts ARvsCA ARvsEU ARvsAU ARvsNZ
ARvsCA 0.3666667 0.2000000 0.2 0.2000000
ARvsEU 0.2000000 0.5333333 0.2 0.2000000
ARvsAU 0.2000000 0.2000000 0.4 0.2000000
ARvsNZ 0.2000000 0.2000000 0.2 0.3428571
$rank
[1] 5
$genes
gene_id line test
1 gene12245 1 no
2 gene12244 2 no
3 gene12247 3 no
4 gene12246 4 no
5 gene12241 5 no
10387 more rows ...
$Amean
1 2 3 4 5
4.749884 8.665232 5.995541 4.329715 6.534481
10387 more elements ...
$method
[1] "ls"
$design
AR_heads AU_heads CA_heads EU_heads NZ_heads
1 1 0 0 0 0
2 1 0 0 0 0
3 1 0 0 0 0
4 1 0 0 0 0
5 1 0 0 0 0
21 more rows ...
$contrasts
Contrasts
Levels ARvsCA ARvsEU ARvsAU ARvsNZ
AR_heads 1 1 1 1
AU_heads 0 0 -1 0
CA_heads -1 0 0 0
EU_heads 0 -1 0 0
NZ_heads 0 0 0 -1
$df.prior
[1] 2.659777
$s2.prior
[1] 0.6719199
$s2.post
[1] 1.4973681 1.6546414 0.3868724 3.5739382 0.8563319
10387 more elements ...
$df.total
[1] 23.65978 23.65978 23.65978 23.65978 23.65978
10387 more elements ...
$t
Contrasts
ARvsCA ARvsEU ARvsAU ARvsNZ
1 -0.1937939 -0.2378606 -0.3853473 -0.2105622
2 0.0000000 -4.3627269 -1.2572034 -1.2144966
3 0.0000000 0.3288759 0.0000000 0.0000000
4 0.0000000 1.0792898 0.0000000 0.5722939
5 0.0000000 1.9118965 0.0000000 1.2338677
10387 more rows ...
$p.value
Contrasts
ARvsCA ARvsEU ARvsAU ARvsNZ
1 0.5071515 0.5252303689 0.4194142 0.4945336
2 0.8719553 0.0001139932 0.1181080 0.1248538
3 0.5476869 0.3794963643 0.7396859 0.5572903
4 0.8441133 0.2050344009 0.7492352 0.3837871
5 0.6637349 0.0347921880 0.5483615 0.1158894
10387 more rows ...
$treat.lfc
[1] 0.1375035
Yes, it's easy enough. But you need to have generally recognized gene IDs (usually Entrez Gene Ids) order to run kegga(). Your gene_ids don't seem to be Entrez Ids. Have you just anonymized them?
Hi Gordon,
Sorry I reported the wrong MArrayLM fit. Before using it as input for kegga(), I did change the gene_id into RefSeq identifiers as below:
$genesgene_id line test
1 105678280 1 no
2 105678292 2 no
3 105678279 3 no
4 105678278 4 no
5 105678296 5 no
10387 more rows ...