Running the package
Input data
Load test genes (geneSingle) and pathways annotations (pathwaysGMT):
Take a look at the data structure:
str(geneSingle)
#> List of 2
#> $ background : chr [1:17804] "SLCO4A1" "PGRMC2" "LDLR" "RABL3" ...
#> $ FASN_negative: chr [1:379] "SLCO4A1" "PGRMC2" "LDLR" "RABL3" ...
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 geneSingle list object contains two vector elements: background and FASN_negative. The background consists of all genes that the test sets (in this case FASN_negative) will be compared against. FASN_negative consists of genes that form negative genetic interactions with the FASN gene after CRISPR-Cas9 knockout. 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(geneSingle, pathwaysGMT)
#> Running fedup with:
#> => 1 test set(s)
#> + FASN_negative: 379 genes
#> => 17804 background genes
#> => 1437 pathway annotations
#> All done!The fedupRes output is a list of length length(which(names(geneSingle) != "background")), corresponding to the number of test sets in geneSingle (i.e., 1).
View fedup results for FASN_negative sorted by pvalue:
set <- "FASN_negative"
print(head(fedupRes[[set]][which(fedupRes[[set]]$status == "enriched"),]))
#> pathway
#> 1: ASPARAGINE N-LINKED GLYCOSYLATION%REACTOME DATABASE ID RELEASE 74%446203
#> 2: BIOSYNTHESIS OF THE N-GLYCAN PRECURSOR (DOLICHOL LIPID-LINKED OLIGOSACCHARIDE, LLO) AND TRANSFER TO A NASCENT PROTEIN%REACTOME%R-HSA-446193.1
#> 3: DISEASES ASSOCIATED WITH N-GLYCOSYLATION OF PROTEINS%REACTOME DATABASE ID RELEASE 74%3781860
#> 4: INTRA-GOLGI AND RETROGRADE GOLGI-TO-ER TRAFFIC%REACTOME DATABASE ID RELEASE 74%6811442
#> 5: RAB REGULATION OF TRAFFICKING%REACTOME DATABASE ID RELEASE 74%9007101
#> 6: DISEASES OF GLYCOSYLATION%REACTOME%R-HSA-3781865.1
#> size real_frac expected_frac fold_enrichment status
#> 1: 286 8.179420 1.53336329 5.334300 enriched
#> 2: 78 3.693931 0.42125365 8.768901 enriched
#> 3: 17 2.110818 0.09548416 22.106472 enriched
#> 4: 183 4.749340 0.99415862 4.777246 enriched
#> 5: 120 3.693931 0.62345540 5.924933 enriched
#> 6: 139 3.957784 0.74702314 5.298074 enriched
#> real_gene pvalue qvalue
#> 1: MOGS,DOLPP1,ALG9,ALG12,ALG3,MPDU1,... 1.596605e-12 2.294321e-09
#> 2: DOLPP1,ALG9,ALG12,ALG3,MPDU1,ALG8,... 6.358461e-09 4.568554e-06
#> 3: MOGS,ALG9,ALG12,ALG3,MPDU1,MGAT2,... 3.054616e-08 1.463161e-05
#> 4: ARL1,RAB18,RAB3GAP2,VPS52,NAA35,TMED9,... 2.516179e-07 9.039372e-05
#> 5: RAB18,TSC1,RAB3GAP2,TSC2,TBC1D20,RAB10,... 4.945154e-07 1.421237e-04
#> 6: MOGS,ALG9,ALG12,ALG3,MPDU1,MGAT2,... 7.240716e-07 1.734151e-04
print(head(fedupRes[[set]][which(fedupRes[[set]]$status == "depleted"),]))
#> pathway size
#> 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
#> 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,PRKAB1 0.026904721
#> 5: 0.0000000 1.0166255 0.0000000 depleted 0.057057149
#> 6: 0.0000000 0.8425073 0.0000000 depleted 0.079543380
#> qvalue
#> 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!
Dot 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)) %>%
mutate(status = factor(status, levels = c("enriched", "depleted"))) %>%
as.data.frame()If you’re interested, 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. Facet points by the status column:
p <- plotDotPlot(
df = fedupPlot,
xVar = "log10qvalue",
yVar = "pathway",
xLab = "-log10(qvalue)",
fillVar = "status",
fillLab = "Enrichment\nstatus",
sizeVar = "fold_enrichment",
sizeLab = "Fold enrichment") +
facet_grid("status", scales = "free", space = "free") +
theme(strip.text.y = element_blank())
print(p)
We can also colour in the points via the sign column from fedupPlot, while still faceting by status:
p <- plotDotPlot(
df = fedupPlot,
xVar = "log10qvalue",
yVar = "pathway",
xLab = "-log10(qvalue)",
fillVar = "sign",
fillLab = "Genetic interaction",
fillCol = "#6D90CA",
sizeVar = "fold_enrichment",
sizeLab = "Fold enrichment") +
facet_grid("status", 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 38 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/RtmpBCwLu0/femap_FASN_negative.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/RtmpBCwLu0/pathwaysGMT19c1581d8c3917.gmtCytoscape is open right? If so, run these lines and let the plotFemap magic happen:
netFile <- tempfile("fedupEM_geneSingle", fileext = ".png")
plotFemap(
gmtFile = gmtFile,
resultsFolder = resultsFolder,
qvalue = 0.05,
chartData = "NES_VALUE",
hideNodeLabels = TRUE,
netName = "fedupEM_geneSingle",
netFile = netFile
)
After some manual rearrangement of the annotated pathway clusters, this is the resulting enrichment map we get from our fedup results. Much better!
This has effectively summarized the 36 pathways from our dot plot into 9 unique biological themes (including 4 unclustered pathways). We can now see clear themes in the data pertaining to negative FASN genetic interactions, such as glycan diseases, glycosylation, retrograde golgi transport, and Rab regulation trafficking.