In the following, we will use the Egambia data set included in the package. The data set has been generated by Maertzdorf et al. [-@maertzdorf2011functional] and has the GEO ID GSE28623. The data is already background corrected and normalized, so we can proceed with a differential gene expression analysis. Note that only a bit over 5000 genes from the original set of over 45000 probes is included.
library(limma)
library(tmod)
data(Egambia)
design <- cbind(Intercept=rep(1, 30), TB=rep(c(0,1), each= 15))
E <- as.matrix(Egambia[,-c(1:3)])
fit <- eBayes( lmFit(E, design))
tt <- topTable(fit, coef=2, number=Inf,
genelist=Egambia[,1:3] )
head(tt, 10)
## GENE_SYMBOL
## 4178 FAM20A
## 20799 FCGR1B
## 4122 BATF2
## 23567 ANKRD22
## 20498 SEPT4
## 20360 CD274
## 2513 AIM2
## 24032 GOLSYN
## 1337 ETV7
## 467 SERPING1
## GENE_NAME EG
## 4178 family with sequence similarity 20, member A" 54757
## 20799 Fc fragment of IgG, high affinity Ib, receptor (CD64)" 2210
## 4122 basic leucine zipper transcription factor, ATF-like 2 116071
## 23567 ankyrin repeat domain 22 118932
## 20498 septin 4 5414
## 20360 CD274 molecule 29126
## 2513 absent in melanoma 2 9447
## 24032 Golgi-localized protein 55638
## 1337 ets variant 7 51513
## 467 serpin peptidase inhibitor, clade G (C1 inhibitor), member 1" 710
## logFC AveExpr t P.Value adj.P.Val B
## 4178 2.955829 4.007327 6.200637 3.423267e-07 0.001898886 6.457171
## 20799 2.391490 13.401207 5.946113 7.552423e-07 0.002094665 5.741043
## 4122 2.680837 10.398520 5.797752 1.198442e-06 0.002215920 5.322491
## 23567 2.763908 8.651749 5.624092 2.057601e-06 0.002692116 4.832003
## 20498 3.286528 4.223270 5.480564 3.215558e-06 0.002692116 4.426508
## 20360 2.377399 7.334747 5.463149 3.394453e-06 0.002692116 4.377314
## 2513 1.966342 9.933621 5.462879 3.397298e-06 0.002692116 4.376553
## 24032 -2.534812 2.221666 -5.362575 4.639596e-06 0.003018586 4.093323
## 1337 2.844012 8.075046 5.345142 4.897651e-06 0.003018586 4.044119
## 467 2.639069 7.708228 5.150375 8.958000e-06 0.004969002 3.495088
OK, we see some of the genes known to be prominent in the human host response to TB. We can display one of these using tmod's showGene function (it's just a boxplot combined with a beeswarm, nothing special):
group <- rep( c("CTRL", "TB"), each=15)
showGene(E["20799",], group,
main=Egambia["20799", "GENE_SYMBOL"])
Fine, but what about the modules?
There are two main functions in tmod to understand which modules are significantly enriched[If you work with limma, there are other, more efficient and simpler to use functions. See “Working with limma” below. ]
The first one, tmodHGtest, is simply a hypergeometric test on two groups of genes: 'foreground' (fg), or the list of differentially expressed genes, and 'background' (bg) – the gene universe, i.e., all genes present in the analysis. The gene identifiers used currently by tmod are HGNC identifiers, and we will use the GENE_SYMBOL field from the Egambia data set.
In this particular example, however, we have almost no genes which are significantly differentially expressed after correction for multiple testing: the power of the test with 10 individuals in each group is too low. For the sake of the example, we will therefore relax our selection. Normally, I'd use a q-value threshold of at least 0.001.
fg <- tt$GENE_SYMBOL[tt$adj.P.Val < 0.05 & abs( tt$logFC ) > 1]
res <- tmodHGtest(fg=fg, bg=tt$GENE_SYMBOL)
res
## ID Title b B
## LI.M112.0 LI.M112.0 complement activation (I) 4 11
## LI.M11.0 LI.M11.0 enriched in monocytes (II) 4 20
## LI.M75 LI.M75 antiviral IFN signature 3 10
## LI.S4 LI.S4 Monocyte surface signature 3 10
## LI.S5 LI.S5 DC surface signature 4 34
## LI.M165 LI.M165 enriched in activated dendritic cells (II) 3 19
## LI.M4.3 LI.M4.3 myeloid cell enriched receptors and transporters 2 5
## LI.M16 LI.M16 TLR and inflammatory signaling 2 5
## n N E P.Value adj.P.Val
## LI.M112.0 47 4826 37.33849 2.480096e-06 0.0008581134
## LI.M11.0 47 4826 20.53617 3.414323e-05 0.0059067783
## LI.M75 47 4826 30.80426 9.906126e-05 0.0085687989
## LI.S4 47 4826 30.80426 9.906126e-05 0.0085687989
## LI.S5 47 4826 12.08010 2.957367e-04 0.0204649814
## LI.M165 47 4826 16.21277 7.521410e-04 0.0394125446
## LI.M4.3 47 4826 41.07234 9.112727e-04 0.0394125446
## LI.M16 47 4826 41.07234 9.112727e-04 0.0394125446
The columns in the above table contain the following:
Well, IFN signature in TB is well known. However, the numbers of genes are not high: n is the size of the foreground, and b the number of genes in fg that belong to the given module. N and B are the respective totals – size of bg+fg and number of genes that belong to the module that are found in this totality of the analysed genes. If we were using the full Gambia data set (with all its genes), we would have a different situation.
Another approach is to sort all the genes (for example, by the respective p-value) and perform a U-test on the ranks of (i) genes belonging to the module and (ii) genes that do not belong to the module. This is a bit slower, but often works even in the case if the power of the statistical test for differential expression is low. That is, even if only a few genes or none at all are significant at acceptable thresholds, sorting them by the p-value or another similar metric can nonetheless allow to get meaningful enrichments[The rationale is that the non-significant p-values are not associated with the test that we are actually performing, but merely used to sort the gene list. Thus, it does not matter whether they are significant or not.].
Moreover, we do not need to set arbitrary thresholds, like p-value or logFC cutoff.
l <- tt$GENE_SYMBOL
res2 <- tmodUtest(l)
head(res2)
## ID Title U N1 AUC
## LI.M37.0 LI.M37.0 immune activation - generic cluster 352659 100 0.7462103
## LI.M37.1 LI.M37.1 enriched in neutrophils (I) 50280 12 0.8703781
## LI.S4 LI.S4 Monocyte surface signature 43220 10 0.8974252
## LI.M75 LI.M75 antiviral IFN signature 42996 10 0.8927741
## LI.M11.0 LI.M11.0 enriched in monocytes (II) 74652 20 0.7766542
## LI.M67 LI.M67 activated dendritic cells 28095 6 0.9714730
## P.Value adj.P.Val
## LI.M37.0 1.597067e-17 5.525852e-15
## LI.M37.1 4.530577e-06 6.569127e-04
## LI.S4 6.853638e-06 6.569127e-04
## LI.M75 8.632649e-06 6.569127e-04
## LI.M11.0 9.492958e-06 6.569127e-04
## LI.M67 3.200305e-05 1.811391e-03
nrow(res2)
## [1] 25
This list makes a lot of sense, and also is more stable than the other one: it does not depend on modules that contain just a few genes. Since the statistics is different, the b, B, n, N and E columns in the output have been replaced by the following:
There are two tests in tmod which both operate on an ordered list of genes:
tmodUtest and tmodCERNOtest. The U test is simple, however
has two main issues. Firstly, it detects enrichments as well as depletions
– that is, modules which are enriched at the bottom of the list (e.g.
modules which are never, ever regulated in a particular comparison) will be
detected as well. This is often undesirable. Secondly, large modules will
be reported as significant even if the actual effect size (i.e., AUC) is
modest or very small, just because of the sheer number of genes in a
module. Unfortunately, also the reverse is true: modules with a small
number of genes, even if they consist of highly up- or down-regulated genes
from the top of the list will not be detected.
The CERNO test, described by Yamaguchi et al. [@yamaguchi2008ifn], is based on Fisher's method of combining probabilities. In summary, for a given module, the ranks of genes from the module are logarithmized, summed and multiplied by -2:
\[f_{CERNO}=-2 \cdot \sum_{i = 1}^{N} \ln{\frac{R_i}{N_{tot}}}\]
This statitic has the \(\chi^2\) distribution with \(2\cdot N\) degrees of freedom, where \(N\) is the number of genes in a given module and \(N_{tot}\) is the total number of genes [@yamaguchi2008ifn].
The CERNO test is actually much more practical than the U test for most purposes.
l <- tt$GENE_SYMBOL
res2 <- tmodCERNOtest(l)
head( res2 )
## ID Title cerno N1
## LI.M37.0 LI.M37.0 immune activation - generic cluster 426.35781 100
## LI.M11.0 LI.M11.0 enriched in monocytes (II) 113.80864 20
## LI.S4 LI.S4 Monocyte surface signature 76.37298 10
## LI.M112.0 LI.M112.0 complement activation (I) 73.67987 11
## LI.M75 LI.M75 antiviral IFN signature 65.29854 10
## LI.M16 LI.M16 TLR and inflammatory signaling 46.33475 5
## AUC cES P.Value adj.P.Val
## LI.M37.0 0.7462103 2.131789 1.824844e-18 6.313962e-16
## LI.M11.0 0.7766542 2.845216 5.255069e-09 9.091270e-07
## LI.S4 0.8974252 3.818649 1.606057e-08 1.852319e-06
## LI.M112.0 0.8455773 3.349085 1.722322e-07 1.489809e-05
## LI.M75 0.8927741 3.264927 1.045914e-06 7.192190e-05
## LI.M16 0.9790500 4.633475 1.247201e-06 7.192190e-05
Here, the results are similar, however CERNO test was able to detect another module – “TLR and inflammatory signaling”. Although only 5 genes are in this module (which is why U test could not detect it), the genes are all on the top of the list of the differentially regulated genes.
Let us now investigate in more detail the module LI.M75, the antiviral
interferon signature. We can use the evidencePlot function to see
how the module is enriched in the list l.
evidencePlot(l, "LI.M75")
In essence, this is a receiver-operator characteristic (ROC) curve, and the area under the curve (AUC) is related to the U-statistic, from which the P-value in the tmodUtest is calculated, as \(\text{AUC}=\frac{U}{n_1\cdot n_2}\). Both the U statistic and the AUC are reported. Moreover, the AUC can be used to calculate effect size according to the Wendt's formula[@wendt1972dealing] for rank-biserial correlation coefficient:
\[r=1-\frac{2\cdot U}{n_1\cdot n_2} = 1 - 2\cdot\text{AUC}\]
In the above diagram, we see that nine out of the 10 genes that belong to the LI.M75 module and which are present in the Egambia data set are ranked among the top 1000 genes (as sorted by p-value).
Given the popularity of the limma package, tmod includes functions to easily integrate with limma. In fact, if you fit a design / contrast with limma function lmFit and calculate the p-values with eBayes(), you can directly use the resulting object in tmodLimmaTest and tmodLimmaDecideTests[The function tmodLimmaDecideTests is described in the next section].
res.l <- tmodLimmaTest(fit, Egambia$GENE_SYMBOL)
length(res.l)
## [1] 2
names(res.l)
## [1] "Intercept" "TB"
head(res.l$TB)
## ID Title cerno N1
## LI.M37.0 LI.M37.0 immune activation - generic cluster 414.27395 100
## LI.M11.0 LI.M11.0 enriched in monocytes (II) 105.61794 20
## LI.M112.0 LI.M112.0 complement activation (I) 75.62229 11
## LI.S4 LI.S4 Monocyte surface signature 69.97439 10
## LI.M75 LI.M75 antiviral IFN signature 66.10214 10
## LI.M67 LI.M67 activated dendritic cells 50.35750 6
## AUC cES P.Value adj.P.Val
## LI.M37.0 0.7255121 2.071370 4.568772e-17 1.580795e-14
## LI.M11.0 0.7862464 2.640449 7.921155e-08 9.671792e-06
## LI.M112.0 0.8667988 3.437377 8.385947e-08 9.671792e-06
## LI.S4 0.8836794 3.498719 1.838992e-07 1.590728e-05
## LI.M75 0.8645349 3.305107 7.780282e-07 5.383955e-05
## LI.M67 0.9712310 4.196458 1.208877e-06 6.971189e-05
The tmodLimmaTest function uses coefficients and p-values from the limma object to order the genes. By default, the genes are ordered by MSD (Minimum Significant Difference), rather than p-value or log fold change.
The MSD is defined as follows:
\[ \text{MSD} = \begin{cases} CI.L & \text{if logFC} > 0\ -CI.R & \text{if logFC} < 0\\ \end{cases} \]
Where logFC is the log fold change, CI.L is the left boundary of the 95\% confidence interval of logFC and CI.R is the right boundary. MSD is always greater than zero and is equivalent to the absolute distance between the confidence interval and the x axis. For example, if the logFC is 0.7 with 95% CI = [0.5, 0.9], then MSD=0.5; if logFC is -2.5 with 95% CI = [-3.0, -2.0], then MSD = 2.0.
The idea behind MSD is as follows. Ordering genes by decreasing absolute log fold change will include on the top of the list some genes close to background, for which log fold changes are grand, but so are the errors and confidence intervals, just because measuring genes with low expression is loaded with errors. Ordering genes by decreasing absolute log fold change should be avoided.
On the other hand, in a list ordered by p-values, many of the genes on the top of the list will have strong signals and high expression, which results in better statistical power and ultimately with lower p-values – even though the actual fold changes might not be very impressive.
However, by using MSD and using the boundary of the confidence interval to order the genes, the genes on the top of the list are those for which we can confidently that the actual log fold change is large. That is because the 95% confidence intervals tells us that in 95% cases, the real log fold change will be anywhere within that interval. Using its bountary closer to the x-axis (zero log fold change), we say that in 95% of the cases the log fold change will have this or larger magnitude (hence, “minimal significant difference”).
This can be visualized as follows, using the drop-in replacement for limma's topTable function, tmodLimmaTopTable, which calculates msd as well as confidence intervals. We will consider only genes with positive log fold changes and we will show top 50 genes as ordered by the three different measures:
plotCI <- function(x, ci.l, ci.r, title="") {
n <- length(x)
plot(x,
ylab="logFC", xlab="Index",
pch=19, ylim=c( min(x-ci.l), max(x+ci.r)),
main=title)
segments(1:n, ci.l, 1:n, ci.r, lwd=5, col="#33333333")
}
par(mfrow=c(1,3))
x <- tmodLimmaTopTable(fit, coef="TB")
print(head(x))
## logFC.TB msd.TB ciL.TB ciR.TB qval.TB
## 1 0.02819016 -0.7277810 -0.7277810 0.7841613 0.99538447
## 2 1.52416640 0.7280616 0.7280616 2.3202712 0.04393162
## 3 0.07888294 -0.8174289 -0.8174289 0.9751948 0.99504430
## 4 0.15321399 -0.8055162 -0.8055162 1.1119442 0.99504430
## 5 -0.23501607 -0.5368429 -1.0068750 0.5368429 0.99504430
## 6 -0.31952987 -0.8399144 -1.4789741 0.8399144 0.99504430
x <- x[ x$logFC.TB > 0, ] # only to simplify the output!
x2 <- x[ order(abs(x$logFC.TB), decreasing=T),][1:50,]
plotCI(x2$logFC.TB, x2$ciL.TB, x2$ciR.TB, "logFC")
x2 <- x[ order(x$qval.TB),][1:50,]
plotCI(x2$logFC.TB, x2$ciL.TB, x2$ciR.TB, "q-value")
x2 <- x[ order(x$msd.TB, decreasing=T),][1:50,]
plotCI(x2$logFC.TB, x2$ciL.TB, x2$ciR.TB, "MSD")
Black dots are logFCs, and grey bars denote 95% confidence intervals. On the left panel, the top 50 genes ordered by the fold change include several genes with broad confidence intervals, which, despite having a large log fold change, are not significantly up- or down-regulated.
On the middle panel the genes are ordered by p-value. It is clear that the log fold changes of the genes vary considerably, and that the list includes genes which are more and less strongly regulated in TB.
The third panel shows genes ordered by decreasing MSD. There is less variation in the logFC than on the second panel, but at the same time the fallacy of the first panel is avoided. MSD is a compromise between considering the effect size and the statistical significance.
What about enrichments?
x <- tmodLimmaTopTable(fit, coef="TB", genelist=Egambia[,1:3])
x.lfc <- x[ order(abs(x$logFC.TB), decreasing=T),]
x.qval <- x[ order(x$qval.TB),]
x.msd <- x[ order(x$msd.TB, decreasing=T),]
head(tmodCERNOtest(x.lfc$GENE_SYMBOL))
## ID Title cerno
## LI.M37.0 LI.M37.0 immune activation - generic cluster 381.20058
## LI.M112.0 LI.M112.0 complement activation (I) 67.57425
## LI.M75 LI.M75 antiviral IFN signature 59.49036
## LI.S4 LI.S4 Monocyte surface signature 58.93059
## LI.M67 LI.M67 activated dendritic cells 44.54543
## LI.M165 LI.M165 enriched in activated dendritic cells (II) 84.06285
## N1 AUC cES P.Value adj.P.Val
## LI.M37.0 100 0.7345239 1.906003 1.990299e-13 6.886434e-11
## LI.M112.0 11 0.8340036 3.071557 1.583945e-06 2.740225e-04
## LI.M75 10 0.8714493 2.974518 8.537502e-06 8.529686e-04
## LI.S4 10 0.8759759 2.946529 1.041171e-05 8.529686e-04
## LI.M67 6 0.9406639 3.712119 1.232614e-05 8.529686e-04
## LI.M165 19 0.7057362 2.212180 2.482939e-05 1.431828e-03
head(tmodCERNOtest(x.qval$GENE_SYMBOL))
## ID Title cerno N1
## LI.M37.0 LI.M37.0 immune activation - generic cluster 427.03180 100
## LI.M11.0 LI.M11.0 enriched in monocytes (II) 114.85395 20
## LI.S4 LI.S4 Monocyte surface signature 77.27812 10
## LI.M112.0 LI.M112.0 complement activation (I) 74.30410 11
## LI.M75 LI.M75 antiviral IFN signature 65.41723 10
## LI.M16 LI.M16 TLR and inflammatory signaling 46.32281 5
## AUC cES P.Value adj.P.Val
## LI.M37.0 0.7523212 2.135159 1.521279e-18 5.263625e-16
## LI.M11.0 0.7910112 2.871349 3.691442e-09 6.386194e-07
## LI.S4 0.9164037 3.863906 1.131929e-08 1.305491e-06
## LI.M112.0 0.8595676 3.377459 1.367620e-07 1.182991e-05
## LI.M75 0.8932932 3.270861 1.001270e-06 6.928788e-05
## LI.M16 0.9790085 4.632281 1.253436e-06 7.228150e-05
head(tmodCERNOtest(x.msd$GENE_SYMBOL))
## ID Title cerno N1
## LI.M37.0 LI.M37.0 immune activation - generic cluster 414.27395 100
## LI.M11.0 LI.M11.0 enriched in monocytes (II) 105.61794 20
## LI.M112.0 LI.M112.0 complement activation (I) 75.62229 11
## LI.S4 LI.S4 Monocyte surface signature 69.97439 10
## LI.M75 LI.M75 antiviral IFN signature 66.10214 10
## LI.M67 LI.M67 activated dendritic cells 50.35750 6
## AUC cES P.Value adj.P.Val
## LI.M37.0 0.7255121 2.071370 4.568772e-17 1.580795e-14
## LI.M11.0 0.7862464 2.640449 7.921155e-08 9.671792e-06
## LI.M112.0 0.8667988 3.437377 8.385947e-08 9.671792e-06
## LI.S4 0.8836794 3.498719 1.838992e-07 1.590728e-05
## LI.M75 0.8645349 3.305107 7.780282e-07 5.383955e-05
## LI.M67 0.9712310 4.196458 1.208877e-06 6.971189e-05
In this case, the results of p-value and msd-ordering are very similar.
In the above example with the Gambian data set there were only two coefficients calculated in limma, the intercept and the TB. However, often there are several coefficients or contrasts which are analysed simultaneously, for example different experimental conditions or different time points. tmod includes several functions which make it easy to visualize such sets of enrichments.
The object res.l created above using the tmod function tmodLimmaTest is a list of tmod results. Any such list can be directly passed on to functions tmodSummary and tmodPanelPlot, as long as each element of the list has been created with tmodCERNOtest or a similar function. tmodSummary creates a table summarizing module information in each of the comparisons made. The values for modules which are not found in a result object (i.e., which were not found to be significantly enriched in a given comparison) are shown as NA's:
head(tmodSummary(res.l), 5)
## ID Title AUC.Intercept
## LI.M11.0 LI.M11.0 enriched in monocytes (II) 0.8145651
## LI.M112.0 LI.M112.0 complement activation (I) NA
## LI.M118.0 LI.M118.0 enriched in monocytes (IV) NA
## LI.M124 LI.M124 enriched in membrane proteins 0.8807517
## LI.M127 LI.M127 type I interferon response NA
## q.Intercept AUC.TB q.TB
## LI.M11.0 0.0001137611 0.7862464 9.671792e-06
## LI.M112.0 NA 0.8667988 9.671792e-06
## LI.M118.0 NA 0.8377967 2.850219e-03
## LI.M124 0.0114869572 NA NA
## LI.M127 NA 0.9448247 1.043621e-02
We can neatly visualize the above information on a heatmap-like representation:
tmodPanelPlot(res.l, text.cex=0.8)
The sizes of the red blobs on the figure correspond to the effect size, that is, the AUC, while the intensity of the color reflects the q-value from the module enrichment test. We can see that also the intercept term is enriched for genes found in monocytes and neutrophils. Note that by default, tmodPanelPlot only shows enrichments with p < 0.01, hence a slight difference from the tmodSummary output.
The function tmodPanelPlot has many optional arguments for customization, including options for label sizes, p value thresholds and custom functions for plotting the test results instead of just red blobs.
It is often of interest to see which enriched modules go up, and which go down? Specifically, we would like to see, for each module, how many genes are up-, and how many genes are down-regulated. tmodPanelPlot takes an optional argument, pie, which contains information on significantly regulated genes in modules. We can conveniently generate it from a limma linear fit object with the tmodLimmaDecideTests function:
pie <- tmodLimmaDecideTests(fit, genes=Egambia$GENE_SYMBOL)
head(pie$TB[ order( pie$TB[,"Up"], decreasing=T), ])
## Down Zero Up
## DC.M3.4 0 11 9
## DC.M4.2 0 16 7
## LI.M11.0 0 16 4
## LI.M37.0 0 110 4
## LI.M112.0 0 9 4
## LI.M165 0 24 4
data(tmod)
tmod$MODULES["DC.M3.4",]
## ID Title Category Annotated
## DC.M3.4 DC.M3.4 Interferon DC.M3 Yes
## URL
## DC.M3.4 http://www.biir.net/public_wikis/module_annotation/V2_Trial_8_Modules_M3.4
## Source SourceID original.ID B
## DC.M3.4 http://www.biir.net/ DC M3.4 53
The pie object is a list. Each element of the list corresponds to one coefficient and is a data frame with the columns “Down”, “Zero” and “Up” (in that order). We can use this information in tmodPanelPlot:
tmodPanelPlot(res.l, pie=pie, text.cex=0.8)
A rug-like plot can be also generated:
tmodPanelPlot(res.l,
pie=pie, pie.style="rug",
grid="between")
There is also a more general function, tmodDecideTests that also produces a tmodPanelPlot-compatible object, a list of data frames with gene counts. However, instead of taking a limma object, it requires (i) a gene name, (ii) a vector or a matrix of log fold changes, and (iii) a vector or a matrix of p-values. We can replicate the result of tmodLimmaDecideTests above with the following commands:
tt.I <-
topTable(fit, coef="Intercept", number=Inf, sort.by="n")
tt.TB <- topTable(fit, coef="TB", number=Inf, sort.by="n")
pie2 <- tmodDecideTests(Egambia$GENE_SYMBOL,
lfc=cbind(tt.I$logFC, tt.TB$logFC),
pval=cbind(tt.I$adj.P.Val, tt.TB$adj.P.Val))
identical(pie[[1]], pie2[[1]])
## [1] TRUE
By default, tmod uses the modules published by Li et al. @li2013molecular. A second set of modules was published by Chaussabel et al. @chaussabel2008modular; new module definitions were described by Banchereau et al. [@banchereau2012host] and can be found on a public website[http://www.biir.net/public_wikis/module_annotation/G2_Trial_8_Modules].
Depending on the mset parameter to the test functions, either the LI
or DC sets are used, or both, if the mset=all has been specified.
l <- tt$GENE_SYMBOL
res2 <- tmodUtest(l, mset="all")
head( res2 )
## ID Title U N1 AUC
## LI.M37.0 LI.M37.0 immune activation - generic cluster 352659 100 0.7462103
## DC.M4.2 DC.M4.2 Inflammation 91352 20 0.9503953
## DC.M1.2 DC.M1.2 Interferon 73612 17 0.9004196
## DC.M3.2 DC.M3.2 Inflammation 96366 24 0.8361620
## DC.M5.15 DC.M5.15 Neutrophils 65289 16 0.8483498
## DC.M7.29 DC.M7.29 Undetermined 77738 20 0.8087599
## P.Value adj.P.Val
## LI.M37.0 1.597067e-17 9.678227e-15
## DC.M4.2 1.674762e-12 5.074530e-10
## DC.M1.2 5.703006e-09 9.623646e-07
## DC.M3.2 6.352241e-09 9.623646e-07
## DC.M5.15 7.240084e-07 8.774982e-05
## DC.M7.29 9.084521e-07 9.175366e-05
As you can see, the information contained in both module sets is partially redundant.
Transcriptional modules can help to understand the biological meaning of the calculated multivariate transformations. For example, consider a principal component analysis (PCA), visualised using the pca3d package [@pca3d]:
library(pca3d)
mypal <- c("#E69F00", "#56B4E9")
pca <- prcomp(t(E), scale.=TRUE)
par(mfrow=c(1, 2))
l<-pca2d(pca, group=group,
palette=mypal)
##
## Legend:
## -------------------------
## group: color, shape
## -------------------------
## CTRL: #56B4E9, 16
## TB: #E69F00, 17
cols <- as.character(l$colors)
legend("topleft", as.character(l$groups),
pch=l$shapes,
col=cols, bty="n")
l<-pca2d(pca, group=group, components=3:4,
palette=mypal)
##
## Legend:
## -------------------------
## group: color, shape
## -------------------------
## CTRL: #56B4E9, 16
## TB: #E69F00, 17
legend("topleft", as.character(l$groups),
pch=l$shapes,
col=cols, bty="n")
The fourth component looks really interesting. Does it correspond to the modules which we have found before? Each principal component is, after all, a linear combination of gene expression values multiplied by weights (or scores) which are constant for a given component. The i-th principal component for sample j is given by
\[PC_{i,j} = \sum_{k} w_{i,k} \cdot x_{k,j}\]
where \(k\) is the index of the variables (genes in our case), \(w_{i,k}\) is the weight associated with the \(i\)-th component and the \(k\)-th variable (gene), and \(x_{k,j}\) is the value of the variable \(k\) for the sample \(j\); that is, the gene expression of gene \(k\) in the sample \(j\). Genes influence the position of a sample along a given component the more the larger their absolute weight for that component.
For example, on the right-hand figure above, we see that samples which were taken from TB patients have a high value of the principal component 4; the opposite is true for the healthy controls. The genes that allow us to differentiate between these two groups will have very large, positive weights for genes highly expressed in TB patients, and very large, negative weights for genes which are highly expressed in NID, but not TB.
We can sort the genes by their weight in the given component, since the weights are stored in the pca object in the “rotation” slot, and use the tmodUtest function to test for enrichment of the modules.
o <- order(abs(pca$rotation[,4]), decreasing=TRUE)
l <- Egambia$GENE_SYMBOL[o]
res <- tmodUtest(l)
head(res)
## ID Title U N1 AUC
## LI.M37.0 LI.M37.0 immune activation - generic cluster 339742 100 0.7188785
## LI.M37.1 LI.M37.1 enriched in neutrophils (I) 50096 12 0.8671929
## LI.M75 LI.M75 antiviral IFN signature 43379 10 0.9007267
## LI.M11.0 LI.M11.0 enriched in monocytes (II) 74343 20 0.7734395
## LI.S5 LI.S5 DC surface signature 115007 34 0.7058762
## LI.M67 LI.M67 activated dendritic cells 28291 6 0.9782503
## P.Value adj.P.Val
## LI.M37.0 3.133111e-14 1.084056e-11
## LI.M37.1 5.405722e-06 6.700097e-04
## LI.M75 5.809333e-06 6.700097e-04
## LI.M11.0 1.185187e-05 1.025187e-03
## LI.S5 1.711493e-05 1.184353e-03
## LI.M67 2.506730e-05 1.445548e-03
Perfect, this is what we expected: we see that the neutrophil / interferon signature which is the hallmark of the TB biosignature. What about other components? We can run the enrichment for each component and visualise the results using tmod's functions tmodSummary and tmodPanelPlot. Below, we use the filter.empty option to omit the principal components which show no enrichment at all.
# Calculate enrichment for each component
gs <- Egambia$GENE_SYMBOL
# function calculating the enrichment of a PC
gn.f <- function(r) {
tmodCERNOtest(gs[order(abs(r), decreasing=T)],
qval=0.01)
}
x <- apply(pca$rotation, 2, gn.f)
tmodSummary(x, filter.empty=TRUE)[1:5,]
## ID Title AUC.PC3 q.PC3
## LI.M11.0 LI.M11.0 enriched in monocytes (II) NA NA
## LI.M112.0 LI.M112.0 complement activation (I) NA NA
## LI.M118.0 LI.M118.0 enriched in monocytes (IV) NA NA
## LI.M127 LI.M127 type I interferon response NA NA
## LI.M144 LI.M144 cell cycle, ATP binding 0.9894257 0.006051848
## AUC.PC4 q.PC4 AUC.PC9 q.PC9 AUC.PC14 q.PC14 AUC.PC30
## LI.M11.0 0.7734395 2.136524e-07 NA NA NA NA NA
## LI.M112.0 0.7509865 4.910746e-05 NA NA NA NA NA
## LI.M118.0 0.8528591 5.027869e-05 NA NA NA NA NA
## LI.M127 0.9593030 3.706095e-03 NA NA NA NA NA
## LI.M144 NA NA NA NA NA NA NA
## q.PC30
## LI.M11.0 NA
## LI.M112.0 NA
## LI.M118.0 NA
## LI.M127 NA
## LI.M144 NA
The following plot shows the same information in a visual form. The size of the blobs corresponds to the effect size (AUC value), and their color – to the q-value.
tmodPanelPlot(x)
However, we might want to ask, for each module, how many of the genes in that module have a negative, and how many have a positive weight? We can use the function tmodDecideTests for that. For each principal component shown, we want to know how many genes have very large (in absolute terms) weights – we can use the “lfc” parameter of tmodDecideTests for that. We define here “large” as being in the top 25% of all weights in the given component. For this, we need first to calculate the 3rd quartile (top 25% threshold). We will show only 10 components:
qfnc <- function(r) quantile(r, 0.75)
qqs <- apply(pca$rotation[,1:10], 2, qfnc)
pie <- tmodDecideTests(gs, lfc=pca$rotation[,1:10], lfc.thr=qqs)
tmodPanelPlot(x[1:10], pie=pie,
pie.style="rug", grid="between")
For another way of visualizing enrichment, we can use the tagcloud package [@tagcloud]. P-Values will be represented by the size of the tags, while AUC – which is a proxy for the effect size – will be shown by the color of the tag, from grey (AUC=0.5, random) to black (1):
library(tagcloud)
## Loading required package: Rcpp
w <- -log10(res$P.Value)
c <- smoothPalette(res$AUC, min=0.5)
tags <- strmultline(res$Title)
tagcloud(tags, weights=w, col=c)
We can now annotate the PCA axes using the tag clouds; however, see below for a shortcut in tmod.
par(mar=c(1,1,1,1))
o3 <- order(abs(pca$rotation[,3]), decreasing=TRUE)
l3 <- Egambia$GENE_SYMBOL[o3]
res3 <- tmodUtest(l3)
layout(matrix(c(3,1,0,2),2,2,byrow=TRUE),
widths=c(0.3, 0.7), heights=c(0.7, 0.3))
# note -- PC4 is now x axis!!
l<-pca2d(pca, group=group, components=4:3,
palette=mypal)
cols <- as.character(l$colors)
legend("topleft",
as.character(l$groups),
pch=l$shapes,
col=cols, bty="n")
tagcloud(tags, weights=w, col=c, fvert= 0)
tagcloud(strmultline(res3$Title),
weights=-log10(res3$P.Value),
col=smoothPalette(res3$AUC, min=0.5),
fvert=1)
As mentioned previously, there is a way of doing it all with tmod much more
quickly. Note that plot.params are just parameters which will be passed to
the pca2d function.
tmodPCA(pca,
genes=Egambia$GENE_SYMBOL,
components=3:4,
plot.params=list(group=group,
palette=mypal
))
##
## Legend:
## -------------------------
## group: color, shape
## -------------------------
## CTRL: #56B4E9, 16
## TB: #E69F00, 17
The GSEA approach [@subramanian2005gene] is based on similar premises as the other approaches described here. In principle, GSEA is a combination of an arbitrary scoring of a sorted list of genes and a permutation test. Although the GSEA approach has been criticized from statistical standpoint [@damian2004statistical], it remains one of the most popular tools to analyze gene sets amongst biologists. In the following, it will be shown how to use a permutation-based test with tmod.
A permutation test is based on a simple principle. The labels of observations (that is, their group assignments) are permutated and a statistic \(s_i\) is calculated for each \(i\)-th permutation. Then, the same statistic \(s_o\) is calculated for the original data set. The proportion of the permutated sets that yielded a statistic \(s_i\) equal to or higher than \(s_o\) is the p-value for a statistical hypothesis test.
First, we will set up a function that creates a permutation of the Egambia data set and repeats the limma procedure for this permutation, returning the ordering of the genes.
permset <- function(data, design) {
require(limma)
data <- data[, sample(1:ncol(data)) ]
fit <- eBayes(lmFit(data, design))
tt <- topTable(fit, coef=2, number=Inf, sort.by="n")
order(tt$P.Value)
}
In the next step, we will generate 100 random permutations. The
sapply function will return a matrix with a column for each
permutation and a row for each gene. The values indicate the order of the
genes in each permutation. We then use the tmod function tmodAUC to
calculate the enrichment of each module for each permutation.
# same design as before
design <- cbind(Intercept=rep(1, 30),
TB=rep(c(0,1), each= 15))
E <- as.matrix(Egambia[,-c(1:3)])
N <- 250 # small number for the sake of example
set.seed(54321)
perms <- sapply(1:N, function(x) permset(E, design))
pauc <- tmodAUC(Egambia$GENE_SYMBOL, perms)
dim(perms)
## [1] 5547 250
We can now calculate the true values of the AUC for each module and compare them to the results of the permutation. The parameters “order.by” and “qval” ensure that we will calculate the values for all the modules (even those without any genes in our gene list!) and in the same order as in the perms variable.
fit <- eBayes(lmFit(E, design))
tt <- topTable(fit, coef=2, number=Inf,
genelist=Egambia[,1:3])
res <- tmodCERNOtest(tt$GENE_SYMBOL, qval=Inf, order.by="n")
all(res$ID == rownames(perms))
## [1] TRUE
fnsum <- function(m) sum(pauc[m,] >= res[m,"AUC"])
sums <- sapply(res$ID, fnsum)
res$perm.P.Val <- sums / N
res$perm.P.Val.adj <- p.adjust(res$perm.P.Val)
res <- res[order(res$AUC, decreasing=T),]
head(res[order(res$perm.P.Val),
c("ID", "Title", "AUC", "adj.P.Val", "perm.P.Val.adj") ])
## ID Title AUC adj.P.Val
## LI.M16 LI.M16 TLR and inflammatory signaling 0.9790500 7.192190e-05
## LI.M59 LI.M59 CCR1, 7 and cell signaling 0.9771973 5.751429e-02
## LI.M67 LI.M67 activated dendritic cells 0.9714730 8.363690e-05
## LI.M150 LI.M150 innate antiviral response 0.9498859 9.956972e-03
## LI.M127 LI.M127 type I interferon response 0.9455715 1.163487e-02
## LI.S4 LI.S4 Monocyte surface signature 0.8974252 1.852319e-06
## perm.P.Val.adj
## LI.M16 0
## LI.M59 0
## LI.M67 0
## LI.M150 0
## LI.M127 0
## LI.S4 0
Although the results are based on a small number of permutations, the results are nonetheless strikingly similar. For more permutations, they improve further. The table below is a result of calculating 100,000 permutations.
ID Title AUC adj.P.Val
LI.M37.0 immune activation - generic cluster 0.7462103 0.00000
LI.M11.0 enriched in monocytes (II) 0.7766542 0.00000
LI.M112.0 complement activation (I) 0.8455773 0.00000
LI.M37.1 enriched in neutrophils (I) 0.8703781 0.00000
LI.M105 TBA 0.8949512 0.00000
LI.S4 Monocyte surface signature 0.8974252 0.00000
LI.M150 innate antiviral response 0.9498859 0.00000
LI.M67 activated dendritic cells 0.9714730 0.00000
LI.M16 TLR and inflammatory signaling 0.9790500 0.00000
LI.M118.0 enriched in monocytes (IV) 0.8774710 0.00295
LI.M75 antiviral IFN signature 0.8927741 0.00295
LI.M127 type I interferon response 0.9455715 0.00295
LI.S5 DC surface signature 0.6833387 0.02336
LI.M188 TBA 0.8684647 0.09894
LI.M165 enriched in activated dendritic cells (II) 0.7197180 0.11600
LI.M240 chromosome Y linked 0.8157171 0.11849
LI.M20 AP-1 transcription factor network 0.8763327 0.12672
LI.M81 enriched in myeloid cells and monocytes 0.7562851 0.13202
LI.M3 regulation of signal transduction 0.7763995 0.14872
LI.M4.3 myeloid cell enriched receptors and transporters 0.8859573 0.15675
Unfortunately, the permutation approach has two main drawbacks. Firstly, it requires a sufficient number of samples – for example, with three samples in each group there are only \(6!=720\) possible permutations. Secondly, the computational load is substantial.
The tmod package stores its data in two data frames and two lists.
This object is contained in a list called tmod, which is loaded
with data("tmod"). The names mimick the various environments from
Annotation.dbi packages, but currently the objects are just two lists and
two data frames.
Using these variables, one can apply any other tool for the analysis of
enriched module sets available, for example, the geneSetTest
function from the limma package (Smyth et al. [@limma]). We will first
run tmodUtest setting the qval to Inf to get p-values for
all modules. Then, we apply the geneSetTest function to each module:
data(tmod)
res <- tmodUtest(tt$GENE_SYMBOL, qval=Inf)
gstest <- function(x) {
sel <- tt$GENE_SYMBOL %in% tmod$MODULES2GENES[[x]]
geneSetTest(sel, tt$logFC)
}
gst <- sapply(res$ID, gstest)
Are the results of both statistical approaches similar? tmod uses a very
simple statistical test. The approach from geneSetTest is more complex,
but similar in principle.
plot(res$P.Value, gst,
log="xy", pch=19,
col="#33333366",
xlab="P Values from tmod",
ylab="P Values from geneSetTest")
abline(0,1)
abline(h=0.01, col="grey")
abline(v=0.01, col="grey")
On the plot above, the p-values from tmod are plotted against the p-values from geneSetTest.
As you can see, in this particular example, both methods give very similar results.
It is possible to use any kind of arbitrary or custom gene set definitions.
These custom definition of gene sets takes form of a list which is then
provided as the mset parameter to the test functions. The list in
question must have the following members:
Here is a minimal definition of such a set:
mymset <- new("tmod", list(
MODULES=data.frame(ID=c("A", "B"),
Title=c("A title",
"B title")),
GENES=data.frame(ID=c( "G1", "G2", "G3", "G4" )),
MODULES2GENES=list(
A=c("G1", "G2"),
B=c("G3", "G4")))
)
mymset
## An object of class "tmod"
## 2 modules, 4 genes
Whether the gene IDs are Entrez, or something else entirely does not matter, as long as they matched the provided input to the test functions.
The MSigDB database from the Broad institute is an interesting collection of gene sets (actually, multiple collections). Unfortunately, MSigDB cannot be distributed or even accessed without a free registration, which imposes a heavy limination on third party tools such as tmod. Use the following guide to download and parse the database such that you can use it with R and tmod.
First, you will need to download the MSigDB in XML format[Note that even if you register with MSig, it is not possible to download the database directly from R in the XML format.]. This file can be accessed at the URL http://www.broadinstitute.org/gsea/msigdb/download_file.jsp?filePath=/resources/msigdb/5.0/msigdb_v5.0.xml – follow the link, register and log in, and save the file on your disk (roughly 65MB).
Importing MSigDB is easy – tmod has a function specifically for that purpose. Once you have downloaded the MSigDB file, you can create the tmod-compatible R object with one command[MSigDB gene sets can be also downloaded as “GMT” files. This format contains less information and is therefore less usable.]. However, the tmod function tmodImportMsigDB() can also use this format, look up the manual page:
msig <- tmodImportMSigDB("msigdb_v5.0.xml")
msig
## An object of class "tmod"
## 8430 modules, 32233 genes
That's it – now you can use the full MSigDB for enrichment tests:
res <- tmodCERNOtest(tt$GENE_SYMBOL, mset=msig )
head(res)
## ID Title
## M3408 M3408 GSE1432 ctrl vs ifng 24h microglia dn
## M3010 M3010 Hecker ifnb1 targets
## M3286 M3286 GSE13485 ctrl vs day3 yf17d vaccine pbmc dn
## M3288 M3288 GSE13485 ctrl vs day7 yf17d vaccine pbmc dn
## M3311 M3311 GSE13485 pre vs post yf17d vaccination pbmc dn
## M3347 M3347 GSE14000 unstim vs 4h lps dc dn
## cerno N1 AUC cES P.Value
## M3408 239.0983 39 0.8014227 3.065363 2.967858e-18
## M3010 244.1219 43 0.8459807 2.838626 4.555892e-17
## M3286 247.0915 45 0.7293732 2.745461 1.408943e-16
## M3288 272.2570 54 0.7222067 2.520898 3.626792e-16
## M3311 229.4948 41 0.7272625 2.798718 6.715323e-16
## M3347 272.0698 55 0.7334883 2.473362 9.792737e-16
## adj.P.Val
## M3408 2.501904e-14
## M3010 1.920308e-13
## M3286 3.959129e-13
## M3288 7.643464e-13
## M3311 1.132204e-12
## M3347 1.375880e-12
The results are quite typical for MSigDB, which is quite abundant with similar or overlapping gene sets. As the first results, we see, again, interferon response, as well as sets of genes which are significantly upregulated after yellow fever vaccination – and which are also interferon related. We might want to limit our analysis only to the 50 “hallmark” module categories:
sel <- msig$MODULES$Category == "H"
tmodCERNOtest(tt$GENE_SYMBOL, mset=msig[sel] )
## ID Title
## M5913 M5913 Hallmark interferon gamma response
## M5921 M5921 Hallmark complement
## M5911 M5911 Hallmark interferon alpha response
## M5946 M5946 Hallmark coagulation
## M5890 M5890 Hallmark tnfa signaling via nfkb
## M5930 M5930 Hallmark epithelial mesenchymal transition
## M5932 M5932 Hallmark inflammatory response
## M5953 M5953 Hallmark kras signaling up
## M5892 M5892 Hallmark cholesterol homeostasis
## cerno N1 AUC cES P.Value
## M5913 221.68317 41 0.7786936 2.703453 8.505170e-15
## M5921 217.81028 56 0.6979148 1.944735 8.607634e-09
## M5911 108.39559 20 0.7563566 2.709890 3.192325e-08
## M5946 179.24580 50 0.6779481 1.792458 1.966824e-06
## M5890 148.95123 47 0.6484665 1.584588 2.657694e-04
## M5930 212.53461 73 0.6371808 1.455717 2.701053e-04
## M5932 184.53035 62 0.6206393 1.488148 3.457724e-04
## M5953 221.76208 82 0.6046637 1.352208 1.790956e-03
## M5892 49.14641 14 0.6138968 1.755229 8.040562e-03
## adj.P.Val
## M5913 4.252585e-13
## M5921 2.151909e-07
## M5911 5.320542e-07
## M5946 2.458530e-05
## M5890 2.250878e-03
## M5930 2.250878e-03
## M5932 2.469803e-03
## M5953 1.119347e-02
## M5892 4.466979e-02
We see both – the prominent interferon response and the complement activation. Also, in addition, TNF-\(\alpha\) signalling via NF-\(\kappa\beta\).
For the purposes of an example, the code below shows how to parse the XML MSigDB file using the R package XML. Essentially, this is the same code that tmodImportMsigDB is using:
library(XML)
foo <- xmlParse( "/home/january/Projects/R/pulemodule/vignette/msigdb_v5.0.xml" )
foo2 <- xmlToList(foo)
There are over 10,000 “gene sets” (equivalent to modules in tmod) defined. Each member of foo2 is a named character vector:
path1 <- foo2[[1]]
class(path1)
## [1] "character"
names(path1)
## [1] "STANDARD_NAME" "SYSTEMATIC_NAME"
## [3] "HISTORICAL_NAMES" "ORGANISM"
## [5] "PMID" "AUTHORS"
## [7] "GEOID" "EXACT_SOURCE"
## [9] "GENESET_LISTING_URL" "EXTERNAL_DETAILS_URL"
## [11] "CHIP" "CATEGORY_CODE"
## [13] "SUB_CATEGORY_CODE" "CONTRIBUTOR"
## [15] "CONTRIBUTOR_ORG" "DESCRIPTION_BRIEF"
## [17] "DESCRIPTION_FULL" "TAGS"
## [19] "MEMBERS" "MEMBERS_SYMBOLIZED"
## [21] "MEMBERS_EZID" "MEMBERS_MAPPING"
## [23] "FOUNDER_NAMES" "REFINEMENT_DATASETS"
## [25] "VALIDATION_DATASETS"
For our example analysis, we will use only human gene sets. We further need to make sure there are no NULLs in the list.
orgs <- sapply(foo2, function(x) x["ORGANISM"])
unique(orgs)
foo3 <- foo2[ orgs == "Homo sapiens" ]
foo3 <- foo3[ ! sapply(foo3, is.null) ]
Next, construct the MODULES data frame. We will use four named fields for each vector, which contain the ID (systematic name), description, category and subcategory:
msig <- list()
msig$MODULES <- t(sapply(foo3,
function(x)
x[ c("SYSTEMATIC_NAME", "STANDARD_NAME", "CATEGORY_CODE", "SUBCATEGORY_CODE") ]))
colnames(msig$MODULES) <- c( "ID", "Title", "Category", "Subcategory" )
rownames(msig$MODULES) <- msig$MODULES[,"ID"]
msig$MODULES <- data.frame(msig$MODULES, stringsAsFactors=FALSE)
Then, we create the modules to genes mapping and the GENES data frame. For
this, we use the MEMBERS_SYMBOLIZED field, which is a comma separated
list of gene symbols belonging to a particular module:
msig$MODULES2GENES <- lapply(foo3,
function(x) strsplit( x["MEMBERS_SYMBOLIZED"], "," )[[1]])
names(msig$MODULES2GENES) <- msig$MODULES$ID
msig$GENES <- data.frame( ID=unique(unlist(msig$MODULES2GENES)))
msig <- new("tmod", msig)
From now on, you can use msig with tmod.
Below is an example of how to use the pathway definitions from WikiPathways[http://www.wikipathways.org/]. First, we download the data (human pathways) and clean it up:
human <- tempfile()
download.file(
"http://www.wikipathways.org//wpi/batchDownload.php?species=Homo%20sapiens&fileType=txt",
destfile=human, mode="wb")
files <- unzip(human, list=T)
files$ID <- gsub( ".*_(WP[0-9]*)_.*", "\\1", files$Name )
files$Title <- gsub( "(.*)_WP[0-9]*_.*", "\\1", files$Name )
Since each pathway is in a separate file in the zip archive we downloaded, we
have to read each file separately. Below, we create a list, p2GENES,
which maps the modules to the corresponding genes. To make it consistent, I
decided to use gene symbols rather than the Entrez numbers (just because it
makes the interpretation of results a bit easier), but actually that is not
necessary: tmod does not care what gene symbols are used, as long as the
mappings between genes and modules are consistent, and as long as the same
identifiers are used in the lists of genes.
Furthermore, note that we filter out anything that is not an ENTREZ gene identifier. This gets rid of entities which are not genes (e.g. biochemical compounds), but also of some genes.
suppressMessages(library(org.Hs.eg.db))
p2GENES <- sapply( files$Name, function(fn) {
foo <- read.csv( unz( human,
filename= fn ), sep="\t" )
ids <- foo$Identifier[ foo$Identifier %in% ls( org.Hs.egSYMBOL ) ]
unique(unlist(mget(as.character(ids), org.Hs.egSYMBOL)))
})
names(p2GENES) <- files$ID
p2GENES is the first of three objects that we need to create. The next one is a data frame containing module definitions. We also calculate the number of associated genes and select pathways that have at least 5 associated ENTREZ genes:
pathways <- data.frame( ID=files$ID,
Title=files$Title,
stringsAsFactors=FALSE )
pathways$N <- sapply(p2GENES, length)
pathways$URL <-
paste0("http://www.wikipathways.org/index.php/Pathway:",
pathways$ID )
sel <- pathways$N > 4
pathways <- pathways[ sel, ]
rownames(pathways) <- pathways$ID
Finally, we need a data frame containing the gene IDs[The reason why it is a data frame and not simply a vector of IDs and why it is necessary at all since it can be calculated from the module to gene mapping: it is faster and allows for more than just one gene symbol.] and we are good to go: we can build the list that will be the value of the mset parameter:
GENES <- data.frame( ID=unique(unlist(p2GENES)),
stringsAsFactors=FALSE)
Hspaths <- list( MODULES=pathways,
MODULES2GENES=p2GENES,
GENES=GENES )
Hspaths <- new("tmod", Hspaths)
We can now use the tmodCERNOtest to see whether it works:
tmodCERNOtest(tt$GENE_SYMBOL, mset=Hspaths)
## ID Title cerno N1
## WP558 WP558 Hs_Complement_and_Coagulation_Cascades 107.73082 28
## WP545 WP545 Hs_Complement_Activation,_Classical_Pathway 45.65536 9
## AUC cES P.Value adj.P.Val
## WP558 0.6418746 1.923765 4.008176e-05 0.00865766
## WP545 0.7465689 2.536409 3.330196e-04 0.03596611
Nice – the complement pathway was also found before, when using the default data set. Unfortunately, we don't see anything else: WikiPathways are more oriented on metabolic pathways, while the blood transcriptional modules are particularly good for analyzing immune responses. However, if we were to test a specific hypothesis, we would select modules related to interferon response:
sel <- grep( "Interferon",
Hspaths$MODULES$Title, ignore.case=T )
tmodCERNOtest(tt$GENE_SYMBOL, mset=Hspaths,
modules=Hspaths$MODULES$ID[sel])
## ID Title cerno N1 AUC
## WP619 WP619 Hs_Type_II_interferon_signaling_(IFNG) 42.1566 9 0.7050031
## cES P.Value adj.P.Val
## WP619 2.342033 0.001051527 0.003154582
Since the number of tests is lower, the type-II interferon signalling is now significant.