Running the package
Input data
Load test genes (geneDouble) and pathways annotations
(pathwaysGMT):
Take a look at the data structure:
str(geneDouble)
#> List of 3
#> $ background : chr [1:17804] "SLCO4A1" "PGRMC2" "LDLR" "RABL3" ...
#> $ FASN_negative: chr [1:379] "SLCO4A1" "PGRMC2" "LDLR" "RABL3" ...
#> $ FASN_positive: chr [1:298] "CDC34" "PRKCE" "SMARCC2" "EIF3A" ...
str(head(pathwaysGMT))
#> List of 6
#> $ REGULATION OF PLK1 ACTIVITY AT G2 M TRANSITION%REACTOME%R-HSA-2565942.1 : chr [1:84] "CSNK1E" "DYNLL1" "TUBG1" "CKAP5" ...
#> $ GLYCEROPHOSPHOLIPID BIOSYNTHESIS%REACTOME%R-HSA-1483206.4 : chr [1:126] "PCYT1B" "PCYT1A" "PLA2G4D" "PLA2G4B" ...
#> $ MITOTIC PROPHASE%REACTOME DATABASE ID RELEASE 74%68875 : chr [1:134] "SETD8" "NUMA1" "NCAPG2" "LMNB1" ...
#> $ ACTIVATION OF NF-KAPPAB IN B CELLS%REACTOME%R-HSA-1169091.1 : chr [1:67] "PSMA6" "PSMA3" "PSMA4" "PSMA1" ...
#> $ CD28 DEPENDENT PI3K AKT SIGNALING%REACTOME DATABASE ID RELEASE 74%389357 : chr [1:22] "CD28" "THEM4" "AKT1" "TRIB3" ...
#> $ UBIQUITIN-DEPENDENT DEGRADATION OF CYCLIN D%REACTOME DATABASE ID RELEASE 74%75815: chr [1:52] "PSMA6" "PSMA3" "PSMA4" "PSMA1" ...To see more info on this data, run ?geneDouble or
?pathwaysGMT. You could also run
example("prepInput", package = "fedup") or
example("readPathways", package = "fedup") to see exactly
how the data was generated using the prepInput() and
readPathways() functions. ? and
example() can be used on any other functions mentioned here
to see their documentation and run examples.
The sample geneDouble list object contains three vector
elements: background , FASN_negative, and
FASN_positive. The background consists of all
genes that the test sets (in this case FASN_negative and
FASN_positive) will be compared against.
FASN_negative consists of genes that form negative
genetic interactions with the FASN gene after CRISPR-Cas9
knockout. FASN_positive consists of genes that form
positve genetic interactions with FASN. If
you’re interested in seeing how this data set was constructed, check out
the code.
Also, the paper the data was taken from is found here.
Given that FASN is a fatty acid synthase, we would expect to see enrichment of the negative interactions for pathways associated with sensitization of fatty acid synthesis, as well as enrichment of the positive interactions for pathways associated with suppression of the function. Conversely, we expect to find depletion for pathways not at all involved with FASN biology. Let’s see!
Pathway analysis
Now use runFedup on the sample data:
fedupRes <- runFedup(geneDouble, pathwaysGMT)
#> Running fedup with:
#> => 2 test set(s)
#> + FASN_negative: 379 genes
#> + FASN_positive: 298 genes
#> => 17804 background genes
#> => 1437 pathway annotations
#> All done!The fedupRes output is a list of length
length(which(names(geneDouble) != "background")),
corresponding to the number of test sets in geneDouble
(i.e., 2).
View fedup results for FASN_negative sorted
by pvalue:
set <- "FASN_negative"
print(head(fedupRes[[set]][which(fedupRes[[set]]$status == "enriched"),]))
#> pathway
#> <char>
#> 1: ASPARAGINE N-LINKED GLYCOSYLATION%REACTOME DATABASE ID RELEASE 74%446203
#> 2: BIOSYNTHESIS OF THE N-GLYCAN PRECURSOR (DOLICHOL LIPID-LINKED OLIGOSACCHARIDE, LLO) AND TRANSFER TO A NASCENT PROTEIN%REACTOME%R-HSA-446193.1
#> 3: DISEASES ASSOCIATED WITH N-GLYCOSYLATION OF PROTEINS%REACTOME DATABASE ID RELEASE 74%3781860
#> 4: INTRA-GOLGI AND RETROGRADE GOLGI-TO-ER TRAFFIC%REACTOME DATABASE ID RELEASE 74%6811442
#> 5: RAB REGULATION OF TRAFFICKING%REACTOME DATABASE ID RELEASE 74%9007101
#> 6: DISEASES OF GLYCOSYLATION%REACTOME%R-HSA-3781865.1
#> size real_frac expected_frac fold_enrichment status real_gene
#> <int> <num> <num> <num> <char> <list>
#> 1: 286 8.179420 1.53336329 5.334300 enriched MOGS, DO....
#> 2: 78 3.693931 0.42125365 8.768901 enriched DOLPP1, ....
#> 3: 17 2.110818 0.09548416 22.106472 enriched MOGS, AL....
#> 4: 183 4.749340 0.99415862 4.777246 enriched ARL1, RA....
#> 5: 120 3.693931 0.62345540 5.924933 enriched RAB18, T....
#> 6: 139 3.957784 0.74702314 5.298074 enriched MOGS, AL....
#> pvalue qvalue
#> <num> <num>
#> 1: 1.596605e-12 2.294321e-09
#> 2: 6.358461e-09 4.568554e-06
#> 3: 3.054616e-08 1.463161e-05
#> 4: 2.516179e-07 9.039372e-05
#> 5: 4.945154e-07 1.421237e-04
#> 6: 7.240716e-07 1.734151e-04
print(head(fedupRes[[set]][which(fedupRes[[set]]$status == "depleted"),]))
#> pathway size
#> <char> <int>
#> 1: GPCR LIGAND BINDING%REACTOME%R-HSA-500792.3 454
#> 2: OLFACTORY SIGNALING PATHWAY%REACTOME DATABASE ID RELEASE 74%381753 396
#> 3: CLASS A 1 (RHODOPSIN-LIKE RECEPTORS)%REACTOME%R-HSA-373076.7 323
#> 4: NEURONAL SYSTEM%REACTOME DATABASE ID RELEASE 74%112316 379
#> 5: PEPTIDE LIGAND-BINDING RECEPTORS%REACTOME%R-HSA-375276.5 195
#> 6: KERATINIZATION%REACTOME DATABASE ID RELEASE 74%6805567 217
#> real_frac expected_frac fold_enrichment status real_gene pvalue
#> <num> <num> <num> <char> <list> <num>
#> 1: 0.0000000 2.3702539 0.0000000 depleted 0.000318537
#> 2: 0.0000000 1.9096832 0.0000000 depleted 0.001508862
#> 3: 0.0000000 1.6906313 0.0000000 depleted 0.003316944
#> 4: 0.5277045 2.0950348 0.2518834 depleted KCNK2, P.... 0.026904721
#> 5: 0.0000000 1.0166255 0.0000000 depleted 0.057057149
#> 6: 0.0000000 0.8425073 0.0000000 depleted 0.079543380
#> qvalue
#> <num>
#> 1: 0.01760530
#> 2: 0.05420587
#> 3: 0.10361845
#> 4: 0.42024004
#> 5: 0.57670567
#> 6: 0.67813171Here we see the strongest enrichment for the
ASPARAGINE N-LINKED GLYCOSYLATION pathway. Given that
FASN mutant cells show a strong dependence on lipid uptake,
this enrichment for negative interactions with genes involved in
glycosylation is expected. We also see significant enrichment for other
related pathways, including
DISEASES ASSOCIATED WITH N-GLYCOSYLATION OF PROTEINS and
DISEASES OF GLYCOSYLATION. Conversely, we see significant
depletion for functions not associated with these processes, such as
OLFACTORY SIGNALING PATHWAY,
GPCR LIGAND BINDING and KERATINIZATION.
Nice!
Let’s also view fedup results for
FASN_positive, sorted by pvalue:
set <- "FASN_positive"
print(head(fedupRes[[set]][which(fedupRes[[set]]$status == "enriched"),]))
#> pathway
#> <char>
#> 1: L13A-MEDIATED TRANSLATIONAL SILENCING OF CERULOPLASMIN EXPRESSION%REACTOME%R-HSA-156827.3
#> 2: GTP HYDROLYSIS AND JOINING OF THE 60S RIBOSOMAL SUBUNIT%REACTOME DATABASE ID RELEASE 74%72706
#> 3: CAP-DEPENDENT TRANSLATION INITIATION%REACTOME DATABASE ID RELEASE 74%72737
#> 4: EUKARYOTIC TRANSLATION INITIATION%REACTOME%R-HSA-72613.3
#> 5: TRANSLATION INITIATION COMPLEX FORMATION%REACTOME%R-HSA-72649.3
#> 6: RIBOSOMAL SCANNING AND START CODON RECOGNITION%REACTOME DATABASE ID RELEASE 74%72702
#> size real_frac expected_frac fold_enrichment status real_gene
#> <int> <num> <num> <num> <char> <list>
#> 1: 112 7.382550 0.4718041 15.64749 enriched EIF3A, R....
#> 2: 113 7.382550 0.4774208 15.46340 enriched EIF3A, R....
#> 3: 120 7.382550 0.5167378 14.28684 enriched EIF3A, R....
#> 4: 120 7.382550 0.5167378 14.28684 enriched EIF3A, R....
#> 5: 59 5.369128 0.2583689 20.78086 enriched EIF3A, E....
#> 6: 59 5.369128 0.2583689 20.78086 enriched EIF3A, E....
#> pvalue qvalue
#> <num> <num>
#> 1: 9.628857e-18 8.562503e-15
#> 2: 1.191719e-17 8.562503e-15
#> 3: 4.970934e-17 1.785808e-14
#> 4: 4.970934e-17 1.785808e-14
#> 5: 5.796507e-15 1.388264e-12
#> 6: 5.796507e-15 1.388264e-12
print(head(fedupRes[[set]][which(fedupRes[[set]]$status == "depleted"),]))
#> pathway
#> <char>
#> 1: GPCR LIGAND BINDING%REACTOME%R-HSA-500792.3
#> 2: NEURONAL SYSTEM%REACTOME DATABASE ID RELEASE 74%112316
#> 3: OLFACTORY SIGNALING PATHWAY%REACTOME DATABASE ID RELEASE 74%381753
#> 4: CLASS A 1 (RHODOPSIN-LIKE RECEPTORS)%REACTOME%R-HSA-373076.7
#> 5: G ALPHA (I) SIGNALLING EVENTS%REACTOME%R-HSA-418594.6
#> 6: TRANSMISSION ACROSS CHEMICAL SYNAPSES%REACTOME DATABASE ID RELEASE 74%112315
#> size real_frac expected_frac fold_enrichment status real_gene pvalue
#> <int> <num> <num> <num> <char> <list> <num>
#> 1: 454 0.0000000 2.370254 0.0000000 depleted 0.002390509
#> 2: 379 0.0000000 2.095035 0.0000000 depleted 0.005261657
#> 3: 396 0.0000000 1.909683 0.0000000 depleted 0.007449873
#> 4: 323 0.0000000 1.690631 0.0000000 depleted 0.017309826
#> 5: 396 0.3355705 2.106268 0.1593199 depleted AHCYL1 0.034808044
#> 6: 238 0.0000000 1.314311 0.0000000 depleted 0.035700272
#> qvalue
#> <num>
#> 1: 0.03240718
#> 2: 0.05953545
#> 3: 0.07989155
#> 4: 0.13667154
#> 5: 0.21016453
#> 6: 0.21198880Dot plot
Prepare data for plotting via dplyr and
tidyr:
fedupPlot <- fedupRes %>%
bind_rows(.id = "set") %>%
separate(col = "set", into = c("set", "sign"), sep = "_") %>%
subset(qvalue < 0.05) %>%
mutate(log10qvalue = -log10(qvalue)) %>%
mutate(pathway = gsub("\\%.*", "", pathway)) %>%
as.data.frame()Since we’re dealing with two test sets here, it’s important we create
the sign column in fedupPlot to distinguish
between them. Take a look at ?dplyr::bind_rows for details
on how the output fedup results list
(fedupRes) was bound into a single dataframe and
?tidyr::separate for how the sign column was
created.
Plot significant results (qvalue < 0.05) in the form of a dot plot
via plotDotPlot. Colour and facet the points by the
sign column:
p <- plotDotPlot(
df = fedupPlot,
xVar = "log10qvalue",
yVar = "pathway",
xLab = "-log10(qvalue)",
fillVar = "sign",
fillLab = "Genetic interaction",
fillCol = c("#6D90CA", "#F6EB13"),
sizeVar = "fold_enrichment",
sizeLab = "Fold enrichment") +
facet_grid("sign", scales = "free", space = "free") +
theme(strip.text.y = element_blank())
print(p)
Look at all those chick… enrichments! This is a bit overwhelming, isn’t it? How do we interpret these 156 fairly redundant pathways in a way that doesn’t hurt our tired brains even more? Oh I know, let’s use an enrichment map!
Enrichment map
First, make sure to have Cytoscape downloaded and and open on your computer. You’ll also need to install the EnrichmentMap (≥ v3.3.0) and AutoAnnotate apps.
Then format results for compatibility with EnrichmentMap using
writeFemap:
resultsFolder <- tempdir()
writeFemap(fedupRes, resultsFolder)
#> Wrote out EM-formatted fedup results file to /tmp/RtmpTZLQ5L/femap_FASN_negative.txt
#> Wrote out EM-formatted fedup results file to /tmp/RtmpTZLQ5L/femap_FASN_positive.txtPrepare a pathway annotation file (gmt format) from the pathway list
you passed to runFedup using the writePathways
function (you don’t need to run this function if your pathway
annotations are already in gmt format, but it doesn’t hurt to make
sure):
gmtFile <- tempfile("pathwaysGMT", fileext = ".gmt")
writePathways(pathwaysGMT, gmtFile)
#> Wrote out pathway gmt file to /tmp/RtmpTZLQ5L/pathwaysGMTbf391357eaae7.gmtCytoscape is open right? If so, run these lines and let the
plotFemap magic happen:
netFile <- tempfile("fedupEM_geneDouble", fileext = ".png")
plotFemap(
gmtFile = gmtFile,
resultsFolder = resultsFolder,
qvalue = 0.05,
chartData = "DATA_SET",
hideNodeLabels = TRUE,
netName = "fedupEM_geneDouble",
netFile = netFile
)
To note here, the EM nodes were coloured manually (by the same
colours passed to plotDotPlot) in Cytoscape via the
Change Colors option in the EM panel. A feature for automated
dataset colouring is set to be released in version
3.3.2 of EnrichmentMap.
This has effectively summarized the 156 pathways from our dot plot
into 21 unique biological themes (including 4 unclustered pathways). We
can now see clear themes in the data pertaining to negative
FASN genetic interactions, such as
diseases glycosylation, proteins,
golgi transport, and
rab regulation trafficking. These can be compared and
constrasted with the enrichment seen for FASN positive
interactions.