Question

Camera gene set interpretation

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Entering edit mode

natalia.pietrosemoli • 0

@nataliapietrosemoli-10587

Last seen 7.8 years ago

Dear all,

After some careful reading, I've settled for CAMERA as my favaorite gene set enrichment test.

However, in one of my datasets (condition 3 vs condition 1; 3 replicates, microarray data), I get a very confusing error:

I get virtually the same enriched gene sets (and direction!) when I perform the test on the first or on the second condition. When I look at the genes, they are also the same! How can this be possible?

Here's some snipets:

Mi data are in this data.frame:

colnames(final.table)

'data.frame': 22518 obs. of 9 variables:
$ Probe.Set.ID: Factor w/ 22518 levels "1415676_a_at",..: 1 2 3 4 5 6 7 8 9 10 ...
$ SYMBOL : Factor w/ 12142 levels "0610006L08Rik",..: 8523 3533 8506 1316 6878 1654 6973 8500 12091 6863 ...
$ Entrez_IDs : Factor w/ 12142 levels "100017","100019",..: 3274 7450 8495 2718 11955 1075 11391 3264 10576 11953 ...
$ Cond1_1.CEL : num 11.57 8.29 10.63 9.87 8.32 ...
$ Cond1_2.CEL : num 11.48 8.16 10.47 9.63 8.06 ...
$ Cond1_3.CEL : num 11.39 8.41 11.01 10.18 8.63 ...
$ Cond3_1.CEL : num 11.11 7.93 10.58 8.89 7.98 ...
$ Cond3_2.CEL : num 11.13 8.16 10.51 9.07 7.67 ...
$ Cond3_3.CEL : num 11.04 8.62 11.04 9.1 7.82 ...

And then I construct my test:

grp = c( "Cond1", "Cond3")

Group=as.factor(c(rep(grp[1], length(grep(grp[1], colnames(final.table),ignore.case = F ))),
rep(grp[2], length(grep(grp[2], colnames(final.table),ignore.case = F )))))
des = model.matrix(~0+Group)
colnames(des) <- levels(Group)

contr <- makeContrasts(Cond3-Cond1, levels = des)

str_split(rownames(final.table), pattern = "_") ->k
unlist(lapply(k,function(x) x[1]))->k

ids2indices(iset,k)-> indices

camera(final.table, indices, des, contrast=2)-> current.cam.up.Cond3
camera(final.table,, indices, des, contrast=1)-> current.cam.up.Cond1

Here I'm showing the first 6 gene sets, but you can trust me that they are pretty much the same ones...

current.cam.up.Cond3

                                   NGenes Direction       PValue          FDR
HALLMARK_MYC_TARGETS_V1               452        Up 3.867097e-12 1.933549e-10
HALLMARK_OXIDATIVE_PHOSPHORYLATION    379        Up 1.868544e-10 4.671361e-09
HALLMARK_UNFOLDED_PROTEIN_RESPONSE    262        Up 1.813248e-05 3.022080e-04
HALLMARK_MTORC1_SIGNALING             492        Up 4.983641e-05 6.229551e-04
HALLMARK_TNFA_SIGNALING_VIA_NFKB      419        Up 4.852641e-04 4.852641e-03
HALLMARK_MYC_TARGETS_V2               138        Up 7.818134e-04 6.515112e-03

> head(res.fil.up.A)
                                   NGenes Direction       PValue          FDR
HALLMARK_MYC_TARGETS_V1               452        Up 6.978126e-14 3.489063e-12
HALLMARK_OXIDATIVE_PHOSPHORYLATION    379        Up 1.145075e-10 2.862686e-09
HALLMARK_E2F_TARGETS                  509        Up 1.303369e-08 2.172282e-07
HALLMARK_G2M_CHECKPOINT               592        Up 4.696686e-05 5.870858e-04
HALLMARK_UNFOLDED_PROTEIN_RESPONSE    262        Up 6.785470e-05 6.785470e-04
HALLMARK_MTORC1_SIGNALING             492        Up 2.174479e-04 1.812066e-03

I would expect that if the genes sets are significicant and the same in the two conditions, the direction should change..

Thanks a lot for helping solve this puzzle,

Natalia

> sessionInfo()
R version 3.3.1 (2016-06-21)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.11.6 (El Capitan)

locale:
[1] C

attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets methods base

other attached packages:
[1] mouse4302cdf_2.18.0   genefilter_1.54.2     affyPLM_1.48.0        preprocessCore_1.34.0
[5] gcrma_2.44.0          affy_1.50.0           mouse4302.db_3.2.3    org.Mm.eg.db_3.3.0
[9] AnnotationDbi_1.34.4 rgl_0.97.0            dplyr_0.5.0           ggrepel_0.6.5
[13] ggplot2_2.2.1         RColorBrewer_1.1-2    stringr_1.1.0         knitr_1.15.1
[17] gplots_3.0.1          calibrate_1.7.2       MASS_7.3-45           FactoMineR_1.34
[21] oligo_1.36.1          Biostrings_2.40.2     XVector_0.12.1        IRanges_2.6.1
[25] S4Vectors_0.10.3      oligoClasses_1.34.0   limma_3.28.21         Biobase_2.32.0
[29] BiocGenerics_0.18.0

loaded via a namespace (and not attached):
[1] jsonlite_1.2               splines_3.3.1              foreach_1.4.3              gtools_3.5.0
[5] shiny_1.0.0                assertthat_0.1             highr_0.6                  RSQLite_1.1-2
[9] lattice_0.20-34            digest_0.6.11              GenomicRanges_1.24.3       colorspace_1.3-2
[13] Matrix_1.2-8               htmltools_0.3.5            httpuv_1.3.3               plyr_1.8.4
[17] XML_3.98-1.5               affxparser_1.44.0          zlibbioc_1.18.0            xtable_1.8-2
[21] scales_0.4.1               gdata_2.17.0               affyio_1.42.0              ff_2.2-13
[25] annotate_1.50.1            tibble_1.2                 SummarizedExperiment_1.2.3 lazyeval_0.2.0
[29] survival_2.40-1            magrittr_1.5               mime_0.5                   evaluate_0.10
[33] memoise_1.0.0              BiocInstaller_1.22.3       tools_3.3.1                munsell_0.4.3
[37] cluster_2.0.5              flashClust_1.01-2          GenomeInfoDb_1.8.7         caTools_1.17.1
[41] RCurl_1.95-4.8             grid_3.3.1                 iterators_1.0.8            htmlwidgets_0.8
[45] leaps_3.0                  labeling_0.3               bitops_1.0-6               gtable_0.2.0
[49] codetools_0.2-15           DBI_0.5-1                  R6_2.2.0                   bit_1.1-12
[53] KernSmooth_2.23-15         stringi_1.1.2              Rcpp_0.12.9                scatterplot3d_0.3-38

limma camera gene set analysis gene set enrichment • 2.7k views

ADD COMMENT • link updated 7.8 years ago by Gordon Smyth 51k • written 7.8 years ago by natalia.pietrosemoli • 0

0

Entering edit mode

I was just trying a shortcut to typing my whole code. Which is here. And it works, my question still remains the same.

runTest <- function(iset,name, final.table, grp){
c(grep(grp[1], colnames(final.table),ignore.case = F ), grep(grp[2], colnames(final.table),ignore.case = F))->curr.ind
exp.mat.0<-final.table[,curr.ind]
dummy_ind <- seq(from=1, to =dim(exp.mat.0)[1])
rownames(exp.mat.0)<- paste0(final.table$Entrez_IDs,"_",dummy_ind) # because we have non-unique values

# remove NA rows
complete.cases(exp.mat.0)->a
exp.mat.0 <- exp.mat.0[a,]

Group=as.factor(c(rep(grp[1], length(grep(grp[1], colnames(final.table),ignore.case = F ))),
rep(grp[2], length(grep(grp[2], colnames(final.table),ignore.case = F )))))
des = model.matrix(~0+Group)
colnames(des) <- levels(Group)

contr <- makeContrasts(Cond3-Cond1, levels = des)
str_split(rownames(exp.mat.0), pattern = "_") ->k
unlist(lapply(k,function(x) x[1]))->k

ids2indices(iset,k)-> indices
camera(exp.mat.0, indices, des, contrast=2)-> current.cam #.up.Q
camera(exp.mat.0, indices, des, contrast=1)-> current.cam

entrez.ids<- lapply(indices, function(x) k[x])
symbol.ids <- lapply(entrez.ids, function(x) match(x, final.table$Entrez_IDs))
col.id <- which(names(final.table) == "SYMBOL")
symbol.names <- lapply(symbol.ids, function(x) paste(as.character(final.table[unlist(x),col.id]), collapse = ";"))

genes.cam <-vector()
match(rownames(current.cam),names(symbol.names))->b
genes.cam <-unlist(symbol.names[b])

cbind(current.cam, genes.cam)->current.camera.tot

res <-list( "camera"= current.camera.tot)

write.table(cbind(rownames(current.camera.tot),current.camera.tot), file=paste0(outdir,name,".",grp[2],"vs",grp[1],".camera.txt"), row.names = FALSE, sep = "\t", col.names =
c("geneSet",colnames(current.camera.tot)))
return(res)
}

load(paste0(gene.sets.dir,"mouse_c2_v5p1.rdata"))
load(paste0(gene.sets.dir,"mouse_H_v5p1.rdata"))
isets <- list()
isets[[1]] <- Mm.H
isets[[2]] <- Mm.c2
names(isets) <- c("H","C2")

# get enrichments
enrichment.H <- list()
enrichment.C2 <- list()

for (i in 1:dim(comb.all)[2]){

runTest(isets[[1]], "H", mat, c(comb.all[2*i-1],comb.all[2*i]))->enrichment.H[[i]]
#runTest(isets[[2]], "C2", mat, c(comb.all[2*i-1],comb.all[2*i]))->enrichment.C2[[i]]

names(enrichment.H)[[i]] <- paste0(comb.all[2*i],".vs.",comb.all[2*i-1])
#names(enrichment.C2)[[i]] <- paste0(comb.all[2*i-1],".vs.",comb.all[2*i])

}

ADD REPLY • link 7.8 years ago natalia.pietrosemoli • 0

0

Entering edit mode

My answer remains the same. Did you read it?

ADD REPLY • link 7.8 years ago James W. MacDonald 67k

0

Entering edit mode

If I''ve previously defined my design:

> des
Mock_3h Z_3h
1       1    0
2       1    0
3       1    0
4       0    1
5       0    1
6       0    1

wouldn't this instruction:

camera(exp.mat.0, indices, des, contrast=2)

specifiy that I run my test for my condition Z_3h?

this is how I interpret the contrast parameter.

"Can be an integer specifying a column of 'design'..."

ADD REPLY • link updated 7.8 years ago by Gordon Smyth 51k • written 7.8 years ago by natalia.pietrosemoli • 0

0

Entering edit mode

What James is saying is that since your design does not have an intercept term (ie. you are using a "cell means" model), you need to test a contrast among the columns of your design matrix by specifying a vector of coefficients which is as long as the number of columns of your design matrix, and not just a single column (index) into our design matrix.

In your case, this call:

camera(exp.mat.0, indices, des, contrast=2)

is essentially testing something you don't really care to answer, ie. you are asking if the mean expression of the genes is different from zero in your "Z_3h" group.

You probably would rather ask if the mean expression is different between your two groups, in which case you need to pass the vector that specifies your contrast across the columns of your design (as mentioned in the docs), so something like:

camera(exp.mat.0, indices, des, contrast=c(-1, 1))

Which is testing the difference in expression calculated by your Z_3h - Mock_3h groups, given the design you specified in your comment above.

Putting it a third way: when James said camera knows nothing about your contrast matrix, he means that it doesn't know that the "2" in your contrast=2 is referencing the second column of a contrast matrix you built somewhere else (again, beating a dead horse: that contrast matrix has as many rows as you have columns in your design matrix). You need to pass the vector that is the second column of your contrast matrix directly.

Make sense?

ADD REPLY • link 7.8 years ago Steve Lianoglou ★ 13k

score 0 · Answer 1 · 2017-02-13

It's probably more useful if you show the actual code you used, rather than just re-typing things. For example, this line:

camera(final.table,, indices, des, contrast=1)-> current.cam.up.Cond1

won't actually run, due to the double commas after final.table. Plus, while the assignment operator is bi-directional, it's easier for people to decipher your code if you follow the convention of right to left assignment (<-) rather than randomly switching back and forth in your code.

Also do note that camera doesn't use (or even know about) your contrast matrix, so specifying contrast = 1 or contrast = 2 isn't a useful thing to do when you use a cell means model. In that situation the statistic you are using is testing that the expression of each gene is different from zero, rather than different between the two groups. This should be clear from the help page:

contrast: contrast of the linear model coefficients for which the test
          is required. Can be an integer specifying a column of
          'design', or else a numeric vector of same length as the
          number of columns of 'design'.

score 0 · Answer 2 · 2017-02-18

0

Entering edit mode

Gordon Smyth 51k

@gordon-smyth

Last seen 21 minutes ago

WEHI, Melbourne, Australia

You could use

camera(final.table, indices, des, contrast=c(-1,1))

But why not make it even easier:

des <- model.matrix(~group)
camera(final.table, indices, des)