Demo Series 2: using the XGR package to interpret GWAS summary data
Mon Aug 1 15:44:18 2016
1 Web-app (linkto)
2 Summary
This demo showcases the analytic power of using XGR to make sense of GWAS SNPs, including network analysis, enrichment analysis, and annotation analysis. Network analysis supported in XGR is unique in its power to identify SNP-modulated gene networks (via SNP scoring, gene scoring and network scoring) and to perform cross-disease analysis. When coupled with annotation analysis (via looking at nearby gene annotations by ontologies or via looking at co-localised functional genomic annotations), XGR is able to perform in-depth interpretations of the underlying genetic landscape of immunological diseases based on GWAS summary data.
3 Packages
library(XGR)
# Specify the locations of built-in data
RData.location <- "http://galahad.well.ox.ac.uk/bigdata"4 Data
The dataset ImmunoBase is GWAS lead SNPs associated with immunologically related human diseases, obtained from ImmunoBase.
ImmunoBase <- xRDataLoader(RData.customised='ImmunoBase', RData.location=RData.location)
# get info about diseases
disease_list <- lapply(ImmunoBase, function(x) x$disease)
# get the number of disease associated variants/SNPs
variants_list <- lapply(ImmunoBase, function(x) length(unique(names(x$variants))))
# get the number of genes that are located within 50kb distance window of SNPs
genes_list <- lapply(ImmunoBase, function(x) length(x$genes_variants<=50000))
# create a data frame
df_ib <- data.frame(Code=names(ImmunoBase), Disease=unlist(disease_list), num_SNPs=unlist(variants_list), num_nearby_genes=unlist(genes_list), stringsAsFactors=F)We restrict common diseases and each having at least 30 GWAS lead SNPs. As such we have 11 common diseases for cross-disease analysis below.
ind <- match(c('IBD','CRO','UC','PSO','AS','RA','CEL','T1D','MS','SLE','ATD'), names(ImmunoBase))
IB <- ImmunoBase[ind]A summary of 11 diseases (along with GWAS lead SNPs and their nearby genes):
| Code | Disease | num_SNPs | num_nearby_genes |
|---|---|---|---|
| IBD | Inflammatory Bowel Disease (IBD) | 148 | 2280 |
| CRO | Crohn’s Disease (CRO) | 174 | 1945 |
| UC | Ulcerative Colitis (UC) | 120 | 1651 |
| PSO | Psoriasis (PSO) | 68 | 624 |
| AS | Ankylosing Spondylitis (AS) | 32 | 437 |
| RA | Rheumatoid Arthritis (RA) | 165 | 1070 |
| CEL | Celiac Disease (CEL) | 144 | 734 |
| T1D | Type 1 Diabetes (T1D) | 180 | 1046 |
| MS | Multiple Sclerosis (MS) | 237 | 1725 |
| SLE | Systemic Lupus Erythematosus (SLE) | 61 | 742 |
| ATD | Autoimmune Thyroid Disease (ATD) | 33 | 233 |
5 Showcase
5.1 Scoring from SNPs to genes
Do SNP scoring and gene scoring for each disease
disease_ls <- lapply(IB, function(x){
gr <- x$variant
data <- GenomicRanges::mcols(gr)[, c('Variant','Pvalue')]
xSNP2GeneScores(data=data, include.LD='EUR', LD.r2=0.8, significance.threshold=5e-8, distance.max=50000, decay.kernel="rapid", decay.exponent=2, GR.SNP="dbSNP_GWAS", GR.Gene="UCSC_knownCanonical", scoring.scheme="max", verbose=T, RData.location=RData.location)
})Get all diseases and all genes
## all diseases
allDiseases <- names(IB)
## all Genes
res_Gene <- lapply(disease_ls, function(x) x$Gene$Gene)
allGenes <- unique(unlist(res_Gene))A matrix of Genes X Diseases:
mat_Gene <- 0
for(i in 1:length(disease_ls)){
df <- disease_ls[[i]]$Gene
df$Disease <- rep(allDiseases[i], nrow(df))
mat <- xSparseMatrix(df[,c("Gene","Disease","Score")], rows=allGenes, columns=allDiseases)
mat_Gene <- mat_Gene + mat
}
mat_Gene <- as.matrix(mat_Gene)A neighbor-joining tree (with bootstrap confidence values) based on the matrix of Genes X Diseases. The distance between two diseases is defined as the sum of their difference in gene scores (that is, manhattan/cityblock distance).
data <- mat_Gene
tree_bs <- visTreeBootstrap(data=t(data), algorithm="nj", metric="manhattan", num.bootstrap=10000, reroot="min.bootstrap", visTree=T, nodelabels.arg=list(cex=0.6))
The corresponding consensus tree of the above bootstrapped tree is (based on the majority voting):
data <- mat_Gene
tree_cons <- visTreeBootstrap(data=t(data), algorithm="nj", metric="manhattan", num.bootstrap=10000, consensus=T, reroot="min.bootstrap", visTree=T)
g <- igraph::as.igraph(tree_cons)
vertex.label <- V(g)$name
vertex.label <- gsub("Node.*", "", vertex.label)
vertex.size <- rep(12, vcount(g))
vertex.size[vertex.label==''] <- 2
xVisNet(g=g, glayout=layout_with_kk, vertex.shape="sphere", vertex.size=vertex.size, vertex.color="white", vertex.label=vertex.label, vertex.label.dist=0.9, vertex.label.font=2, , newpage=F)
Heatmap of the 1311 Genes X 11 Diseases matrix:
tree_bs_ind <- match(tree_bs$tip.label, colnames(mat_Gene))
data <- mat_Gene[, rev(tree_bs_ind)]
visHeatmapAdv(data=data, Rowv=T, Colv=F, dendrogram="none", dist.metric="manhattan", colormap="lightblue-blue", labRow=NA, KeyValueName="Gene scores", zlim=c(0,8), add.expr=abline(v=(1:(ncol(data)+1))-0.5,col="white"), lmat=rbind(c(4,3), c(2,1)), lhei=c(1,4), lwid=c(1,3), margins=c(12,2), srtCol=30, cexCol=0.8)
5.2 SNP-modulated gene networks underlying disease categories
Neighbour-joining tree of above 11 common diseases (based on scored genes under the genetic influence of GWAS SNPs) nicely captures the current disease classification/taxonomy according to the genetic and cellular basis fo autoinflammation and autoimmunity, against the traditional classification by organ or system affected.
The tree shows the analysable diseases span the autoinflammatory-autoimmne continuum (a classification system reflecting the relative role of innate immune responses versus adaptive immune responses played in the disease development), including three categories:
Classic polygenic autoinflammatory diseases, that is, polygenic diseases with a prominent autoinflammatory component, including
IBD,CROandUC;classic polygenic autoimmune diseases, that is, polygenic diseases with a prominent autoimmune component, including
SLE,ATD,CEL,MS,T1DandRA;mixed diseases having both components, including
PSOandAS.
Our analysis also shows that mixed diseases have more the autoinflammatory component than the autoimmune component. Polygenic autoinflammatory diseases may be subdivided into two subtypes, the one for SLE and ATD, the other for CEL, MS, T1D and RA.
To understand the molecular basis of these three disease categories, we next aim to identify the top SNP-modulated gene networks based on pooled GWAS SNPs for each category.
5.2.1 Polygenic autoinflammatory diseases
# pool together GWAS SNPs
autoinfl <- c('IBD','CRO','UC')
ind <- match(autoinfl, names(ImmunoBase))
ls_gr <- lapply(ImmunoBase[ind], function(x){
gr <- x$variant
})
GRL <- GenomicRanges::GRangesList(ls_gr)
inGR <- GenomicRanges::unlist(GRL)
# find maximum-scoring gene subnetwork with the desired node number=23
data <- GenomicRanges::mcols(inGR)[, c('Variant','Pvalue')]
subnet_in <- xSubneterSNPs(data=data, significance.threshold=5e-8, distance.max=50000, network="STRING_high", decay.kernel="rapid", decay.exponent=2, GR.SNP="dbSNP_GWAS", GR.Gene="UCSC_knownCanonical", scoring.scheme="max", seed.genes=T, subnet.size=23, RData.location=RData.location)The identified gene network with 23 nodes colored according to gene scores (the higher the more signficant) is shown below. Notably, if nodes appear abnormally, please remove vertex.shape="sphere" when running the function xVisNet.
subnet <- subnet_in
pattern <- -log10(as.numeric(V(subnet)$significance))
xVisNet(g=subnet, pattern=pattern, glayout=layout_with_kk, vertex.shape="sphere", colormap="white-yellow-orange", zlim=c(0,8), newpage=F, vertex.size=10, vertex.label.color="blue", vertex.label.dist=0.45, vertex.label.font=3)
5.2.2 Mixed diseases
# pool together GWAS SNPs
mix <- c('AS','PSO')
ind <- match(mix, names(ImmunoBase))
ls_gr <- lapply(ImmunoBase[ind], function(x){
gr <- x$variant
})
GRL <- GenomicRanges::GRangesList(ls_gr)
mxGR <- GenomicRanges::unlist(GRL)
# find maximum-scoring gene subnetwork with the desired node number=23
data <- GenomicRanges::mcols(mxGR)[, c('Variant','Pvalue')]
subnet_mx <- xSubneterSNPs(data=data, significance.threshold=5e-8, distance.max=50000, network="STRING_high", decay.kernel="rapid", decay.exponent=2, GR.SNP="dbSNP_GWAS", GR.Gene="UCSC_knownCanonical", scoring.scheme="max", seed.genes=T, subnet.size=23, RData.location=RData.location)The identified gene network with 23 nodes colored according to gene scores (the higher the more signficant) is shown below. Notably, if nodes appear abnormally, please remove vertex.shape="sphere" when running the function xVisNet.
subnet <- subnet_mx
pattern <- -log10(as.numeric(V(subnet)$significance))
xVisNet(g=subnet, pattern=pattern, glayout=layout_with_kk, vertex.shape="sphere", colormap="white-lightcyan-cyan", zlim=c(0,8), newpage=F, vertex.size=10, vertex.label.color="blue", vertex.label.dist=0.45, vertex.label.font=3)
5.2.3 Polygenic autoimmune diseases
# pool together GWAS SNPs
autoimmu <- c('SLE','ATD','T1D','RA','MS','CEL')
ind <- match(autoimmu, names(ImmunoBase))
ls_gr <- lapply(ImmunoBase[ind], function(x){
gr <- x$variant
})
GRL <- GenomicRanges::GRangesList(ls_gr)
imGR <- GenomicRanges::unlist(GRL)
# find maximum-scoring gene subnetwork with the desired node number=23
data <- GenomicRanges::mcols(imGR)[, c('Variant','Pvalue')]
subnet_im <- xSubneterSNPs(data=data, significance.threshold=5e-8, distance.max=50000, network="STRING_high", decay.kernel="rapid", decay.exponent=2, GR.SNP="dbSNP_GWAS", GR.Gene="UCSC_knownCanonical", scoring.scheme="max", seed.genes=T, subnet.size=23, RData.location=RData.location)The identified gene network with 23 nodes colored according to gene scores (the higher the more signficant) is shown below. Notably, if nodes appear abnormally, please remove vertex.shape="sphere" when running the function xVisNet.
subnet <- subnet_im
pattern <- -log10(as.numeric(V(subnet)$significance))
xVisNet(g=subnet, pattern=pattern, glayout=layout_with_kk, vertex.shape="sphere", colormap="white-red-darkred", zlim=c(0,8), newpage=F, vertex.size=10, vertex.label.color="blue", vertex.label.dist=0.45, vertex.label.font=3)
5.2.4 Comparing network genes across disease categories
# Combine into an igraph object called "net_merged"
list_igraph <- list(subnet_in, subnet_mx, subnet_im)
## edges
ls_edges <- lapply(list_igraph, function(x){
igraph::get.data.frame(x, what="edges")
})
relations <- do.call(rbind, ls_edges)
relations <- relations[!duplicated(relations), ]
## nodes
ls_nodes <- lapply(list_igraph, function(x){
igraph::get.data.frame(x, what="vertices")
})
nodes <- do.call(rbind, ls_nodes)
nodes <- nodes[,c('name','description')]
nodes <- nodes[!duplicated(nodes), ]
## net_merged
net_merged <- igraph::graph.data.frame(d=relations, directed=T, vertices=nodes)
# combined into a data frame called 'df_merged'
## indicator matrix
df_nodes <- igraph::get.data.frame(net_merged, what="vertices")
mat_merged <- matrix(0, nrow=nrow(df_nodes), ncol=3)
colnames(mat_merged) <- c('Autoinflammatory', 'Mixed', 'Autoimmune')
rownames(mat_merged) <- rownames(df_nodes)
mat_merged[V(subnet_in)$name, 1] <- 1
mat_merged[V(subnet_mx)$name, 2] <- 1
mat_merged[V(subnet_im)$name, 3] <- 1
## number of genes being shared
nSharings <- apply(mat_merged, 1, sum)
df_merged <- data.frame(df_nodes, mat_merged, nSharings=nSharings, stringsAsFactors=F)
# reorder
ind <- with(df_merged, order(-nSharings, -Autoinflammatory, -Mixed, -Autoimmune, rownames(df_merged)))
df_merged <- df_merged[ind, ]
#write.table(df_merged, file="network_genes_merged.txt", sep="\t", row.names=F)A table listing network genes across 3 disease categories is shown below. The 0-1 codings indicate whether a gene is present or absent.
| name | description | Autoinflammatory | Mixed | Autoimmune | nSharings |
|---|---|---|---|---|---|
| STAT3 | signal transducer and activator of transcription 3 (acute-phase response factor) | 1 | 1 | 1 | 3 |
| IL23R | interleukin 23 receptor | 1 | 1 | 0 | 2 |
| IL2RA | interleukin 2 receptor, alpha | 1 | 0 | 1 | 2 |
| TNFRSF1A | tumor necrosis factor receptor superfamily, member 1A | 0 | 1 | 1 | 2 |
| TNIP1 | TNFAIP3 interacting protein 1 | 0 | 1 | 1 | 2 |
| ATG16L1 | autophagy related 16-like 1 | 1 | 0 | 0 | 1 |
| CCL2 | chemokine (C-C motif) ligand 2 | 1 | 0 | 0 | 1 |
| CCL7 | chemokine (C-C motif) ligand 7 | 1 | 0 | 0 | 1 |
| CUL2 | cullin 2 | 1 | 0 | 0 | 1 |
| CYLD | cylindromatosis (turban tumor syndrome) | 1 | 0 | 0 | 1 |
| FCGR2A | Fc fragment of IgG, low affinity IIa, receptor (CD32) | 1 | 0 | 0 | 1 |
| IFNG | interferon, gamma | 1 | 0 | 0 | 1 |
| IFNGR2 | interferon gamma receptor 2 (interferon gamma transducer 1) | 1 | 0 | 0 | 1 |
| IKZF3 | IKAROS family zinc finger 3 (Aiolos) | 1 | 0 | 0 | 1 |
| IL10 | interleukin 10 | 1 | 0 | 0 | 1 |
| IL18RAP | interleukin 18 receptor accessory protein | 1 | 0 | 0 | 1 |
| IL27 | interleukin 27 | 1 | 0 | 0 | 1 |
| JAK2 | Janus kinase 2 | 1 | 0 | 0 | 1 |
| NOD2 | nucleotide-binding oligomerization domain containing 2 | 1 | 0 | 0 | 1 |
| PDGFB | platelet-derived growth factor beta polypeptide | 1 | 0 | 0 | 1 |
| PTPN2 | protein tyrosine phosphatase, non-receptor type 2 | 1 | 0 | 0 | 1 |
| SMAD3 | SMAD family member 3 | 1 | 0 | 0 | 1 |
| SOCS1 | suppressor of cytokine signaling 1 | 1 | 0 | 0 | 1 |
| TNFSF15 | tumor necrosis factor (ligand) superfamily, member 15 | 1 | 0 | 0 | 1 |
| ZMIZ1 | zinc finger, MIZ-type containing 1 | 1 | 0 | 0 | 1 |
| DDX58 | DEAD (Asp-Glu-Ala-Asp) box polypeptide 58 | 0 | 1 | 0 | 1 |
| ERAP1 | endoplasmic reticulum aminopeptidase 1 | 0 | 1 | 0 | 1 |
| ERAP2 | endoplasmic reticulum aminopeptidase 2 | 0 | 1 | 0 | 1 |
| ETS1 | v-ets avian erythroblastosis virus E26 oncogene homolog 1 | 0 | 1 | 0 | 1 |
| HLA-C | major histocompatibility complex, class I, C | 0 | 1 | 0 | 1 |
| ICAM3 | intercellular adhesion molecule 3 | 0 | 1 | 0 | 1 |
| IFNLR1 | interferon, lambda receptor 1 | 0 | 1 | 0 | 1 |
| IL12B | interleukin 12B | 0 | 1 | 0 | 1 |
| IL13 | interleukin 13 | 0 | 1 | 0 | 1 |
| IL23A | interleukin 23, alpha subunit p19 | 0 | 1 | 0 | 1 |
| IL4 | interleukin 4 | 0 | 1 | 0 | 1 |
| IL6R | interleukin 6 receptor | 0 | 1 | 0 | 1 |
| NOS2 | nitric oxide synthase 2, inducible | 0 | 1 | 0 | 1 |
| REL | v-rel avian reticuloendotheliosis viral oncogene homolog | 0 | 1 | 0 | 1 |
| RUNX3 | runt-related transcription factor 3 | 0 | 1 | 0 | 1 |
| SPATA2 | spermatogenesis associated 2 | 0 | 1 | 0 | 1 |
| STAT2 | signal transducer and activator of transcription 2, 113kDa | 0 | 1 | 0 | 1 |
| TNFAIP3 | tumor necrosis factor, alpha-induced protein 3 | 0 | 1 | 0 | 1 |
| TYK2 | tyrosine kinase 2 | 0 | 1 | 0 | 1 |
| CIITA | class II, major histocompatibility complex, transactivator | 0 | 0 | 1 | 1 |
| CLEC16A | C-type lectin domain family 16, member A | 0 | 0 | 1 | 1 |
| ERBB3 | erb-b2 receptor tyrosine kinase 3 | 0 | 0 | 1 | 1 |
| HLA-DQA1 | major histocompatibility complex, class II, DQ alpha 1 | 0 | 0 | 1 | 1 |
| HLA-DQB1 | major histocompatibility complex, class II, DQ beta 1 | 0 | 0 | 1 | 1 |
| IFI30 | interferon, gamma-inducible protein 30 | 0 | 0 | 1 | 1 |
| IL21 | interleukin 21 | 0 | 0 | 1 | 1 |
| IRF5 | interferon regulatory factor 5 | 0 | 0 | 1 | 1 |
| ITGAM | integrin, alpha M (complement component 3 receptor 3 subunit) | 0 | 0 | 1 | 1 |
| MAPK1 | mitogen-activated protein kinase 1 | 0 | 0 | 1 | 1 |
| NCF2 | neutrophil cytosolic factor 2 | 0 | 0 | 1 | 1 |
| NFKB1 | nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 | 0 | 0 | 1 | 1 |
| PTPN22 | protein tyrosine phosphatase, non-receptor type 22 (lymphoid) | 0 | 0 | 1 | 1 |
| SH2B3 | SH2B adaptor protein 3 | 0 | 0 | 1 | 1 |
| STAT4 | signal transducer and activator of transcription 4 | 0 | 0 | 1 | 1 |
| TH | tyrosine hydroxylase | 0 | 0 | 1 | 1 |
| TSHR | thyroid stimulating hormone receptor | 0 | 0 | 1 | 1 |
| VCAM1 | vascular cell adhesion molecule 1 | 0 | 0 | 1 | 1 |
| VMP1 | vacuole membrane protein 1 | 0 | 0 | 1 | 1 |
Heatmap of the indicator matrix of Genes X Disease Categories:
data <- df_merged[,3:5]
visHeatmapAdv(t(data), Rowv=F, Colv=F, dendrogram="none", key=F, zlim=c(0,1), colormap="grey-steelblue", add.expr=abline(v=(1:(nrow(data)+1))-0.5,h=(1:(ncol(data)+1))-0.5,,col="white"), lhei=c(1,4), lwid=c(1,5), margins=c(4,10), cexCol=0.8, cexRow=1)
5.2.5 Pathway analysis of network genes across disease categories
Pathway enrichment analysis for network genes is using Reactome pathways:
ontology <- "MsigdbC2CPall"Pathway enrichments for genes in autoimmune network
data <- V(subnet_im)$name
eTerm <- xEnricherGenes(data=data, ontology=ontology, test="fisher", RData.location=RData.location)
eTerm_im <- xEnrichConciser(eTerm)
xEnrichViewer(eTerm_im, 10)Barplot of enriched terms
bp <- xEnrichBarplot(eTerm_im, top_num='auto', displayBy="fdr")
bp
| name | nAnno | nOverlap | fc | zscore | pvalue | adjp | members |
|---|---|---|---|---|---|---|---|
| Leishmania infection | 72 | 6 | 38.50 | 14.90 | 0.0e+00 | 2.0e-07 | NFKB1, MAPK1, NCF2, ITGAM, HLA-DQB1, HLA-DQA1 |
| Cytokine Signaling in Immune system | 268 | 7 | 12.10 | 8.57 | 8.0e-07 | 1.5e-05 | MAPK1, STAT3, IL2RA, VCAM1, HLA-DQA1, CIITA, IRF5 |
| Ceramide Signaling Pathway | 22 | 3 | 62.90 | 13.60 | 1.3e-05 | 7.8e-05 | NFKB1, MAPK1, TNFRSF1A |
| Role of ERBB2 in Signal Transduction and Oncology | 22 | 3 | 62.90 | 13.60 | 1.3e-05 | 7.8e-05 | MAPK1, STAT3, ERBB3 |
| IL12-mediated signaling events | 63 | 4 | 29.30 | 10.50 | 8.6e-06 | 7.8e-05 | NFKB1, STAT3, STAT4, IL2RA |
| Antigen processing and presentation | 88 | 4 | 21.00 | 8.78 | 3.3e-05 | 1.7e-04 | HLA-DQB1, HLA-DQA1, IFI30, CIITA |
| Bioactive Peptide Induced Signaling Pathway | 43 | 3 | 32.20 | 9.56 | 1.0e-04 | 3.2e-04 | MAPK1, STAT3, STAT4 |
| Cell adhesion molecules (CAMs) | 133 | 4 | 13.90 | 6.98 | 1.6e-04 | 3.9e-04 | ITGAM, VCAM1, HLA-DQB1, HLA-DQA1 |
| Autoimmune thyroid disease | 52 | 3 | 26.60 | 8.64 | 1.8e-04 | 4.0e-04 | HLA-DQB1, HLA-DQA1, TSHR |
| IL2-mediated signaling events | 55 | 3 | 25.20 | 8.38 | 2.1e-04 | 4.5e-04 | MAPK1, STAT3, IL2RA |
| Acute myeloid leukemia | 57 | 3 | 24.30 | 8.22 | 2.3e-04 | 4.7e-04 | NFKB1, MAPK1, STAT3 |
| Jak-STAT signaling pathway | 155 | 4 | 11.90 | 6.39 | 3.0e-04 | 5.6e-04 | STAT3, STAT4, IL2RA, IL21 |
| Downstream signaling in naive CD8+ T cells | 65 | 3 | 21.30 | 7.66 | 3.5e-04 | 5.7e-04 | MAPK1, STAT4, IL2RA |
| Signaling events mediated by HDAC Class I | 66 | 3 | 21.00 | 7.59 | 3.6e-04 | 5.7e-04 | NFKB1, STAT3, TNFRSF1A |
| Immune System | 912 | 8 | 4.05 | 4.53 | 3.5e-04 | 5.7e-04 | MAPK1, STAT3, NCF2, IL2RA, VCAM1, HLA-DQA1, CIITA, IRF5 |
| Signaling by SCF-KIT | 75 | 3 | 18.50 | 7.08 | 5.3e-04 | 7.3e-04 | MAPK1, STAT3, SH2B3 |
| Toll-like receptor signaling pathway | 102 | 3 | 13.60 | 5.95 | 1.3e-03 | 1.7e-03 | NFKB1, MAPK1, IRF5 |
| Leukocyte transendothelial migration | 117 | 3 | 11.80 | 5.50 | 1.9e-03 | 2.4e-03 | NCF2, ITGAM, VCAM1 |
| Neurotrophin signaling pathway | 126 | 3 | 11.00 | 5.26 | 2.4e-03 | 2.8e-03 | NFKB1, MAPK1, SH2B3 |
| Cytokine-cytokine receptor interaction | 266 | 3 | 5.21 | 3.25 | 1.9e-02 | 2.1e-02 | TNFRSF1A, IL2RA, IL21 |
Pathway enrichments for genes in mixed network
data <- V(subnet_mx)$name
eTerm <- xEnricherGenes(data=data, ontology=ontology, test="fisher", RData.location=RData.location)
eTerm_mx <- xEnrichConciser(eTerm)
xEnrichViewer(eTerm_mx, 10)Barplot of enriched terms
bp <- xEnrichBarplot(eTerm_mx, top_num='auto', displayBy="fdr")
bp
| name | nAnno | nOverlap | fc | zscore | pvalue | adjp | members |
|---|---|---|---|---|---|---|---|
| Jak-STAT signaling pathway | 155 | 10 | 28.30 | 16.40 | 0.0e+00 | 0.0e+00 | IL4, STAT2, STAT3, IL12B, IL13, IL6R, TYK2, IFNLR1, IL23A, IL23R |
| IL23-mediated signaling events | 37 | 6 | 71.10 | 20.40 | 0.0e+00 | 0.0e+00 | STAT3, IL12B, TYK2, NOS2, IL23A, IL23R |
| Cytokine-cytokine receptor interaction | 266 | 8 | 13.20 | 9.65 | 1.0e-07 | 5.0e-07 | IL4, TNFRSF1A, IL12B, IL13, IL6R, IFNLR1, IL23A, IL23R |
| IL27-mediated signaling events | 26 | 4 | 67.50 | 16.20 | 3.0e-07 | 1.4e-06 | STAT2, STAT3, IL12B, TYK2 |
| IL12-mediated signaling events | 63 | 5 | 34.80 | 12.90 | 2.0e-07 | 1.4e-06 | IL4, STAT3, IL12B, TYK2, NOS2 |
| Interleukin-6 signaling | 10 | 3 | 132.00 | 19.70 | 1.2e-06 | 5.0e-06 | STAT3, IL6R, TYK2 |
| Immune System | 912 | 10 | 4.81 | 5.81 | 9.8e-06 | 2.7e-05 | STAT2, STAT3, TNFAIP3, IL6R, TYK2, HLA-C, ICAM3, DDX58, REL, ERAP1 |
| Cytokine Network | 21 | 3 | 62.60 | 13.50 | 1.3e-05 | 3.2e-05 | IL4, IL12B, IL13 |
| Allograft rejection | 37 | 3 | 35.60 | 10.10 | 7.5e-05 | 1.3e-04 | IL4, IL12B, HLA-C |
| Adaptive Immune System | 524 | 4 | 3.35 | 2.65 | 2.8e-02 | 3.2e-02 | HLA-C, ICAM3, REL, ERAP1 |
| Pathways in cancer | 325 | 3 | 4.05 | 2.68 | 3.6e-02 | 3.9e-02 | STAT3, ETS1, NOS2 |
Pathway enrichments for genes in autoinflammatory network
data <- V(subnet_in)$name
eTerm <- xEnricherGenes(data=data, ontology=ontology, test="fisher", RData.location=RData.location)
eTerm_in <- xEnrichConciser(eTerm)
xEnrichViewer(eTerm_in, 10)Barplot of enriched terms
bp <- xEnrichBarplot(eTerm_in, top_num='auto', displayBy="fdr")
bp
| name | nAnno | nOverlap | fc | zscore | pvalue | adjp | members |
|---|---|---|---|---|---|---|---|
| Regulation of IFNG signaling | 14 | 5 | 142.00 | 26.50 | 0.0e+00 | 0.0e+00 | IFNG, JAK2, SOCS1, IFNGR2, PTPN2 |
| IL23-mediated signaling events | 37 | 6 | 64.60 | 19.50 | 0.0e+00 | 0.0e+00 | IFNG, JAK2, STAT3, CCL2, IL18RAP, IL23R |
| Cytokine-cytokine receptor interaction | 266 | 10 | 15.00 | 11.60 | 0.0e+00 | 0.0e+00 | IFNG, IL10, CCL2, IL2RA, IFNGR2, CCL7, TNFSF15, IL18RAP, IL23R, PDGFB |
| Jak-STAT signaling pathway | 155 | 8 | 20.60 | 12.30 | 0.0e+00 | 0.0e+00 | IFNG, IL10, JAK2, STAT3, SOCS1, IL2RA, IFNGR2, IL23R |
| IL12-mediated signaling events | 63 | 6 | 38.00 | 14.80 | 0.0e+00 | 1.0e-07 | IFNG, JAK2, STAT3, SOCS1, IL2RA, IL18RAP |
| IFN-gamma pathway | 40 | 5 | 49.80 | 15.50 | 0.0e+00 | 2.0e-07 | IFNG, JAK2, STAT3, SOCS1, PTPN2 |
| Cytokine Signaling in Immune system | 268 | 8 | 11.90 | 9.09 | 2.0e-07 | 8.0e-07 | IFNG, JAK2, STAT3, SOCS1, IL2RA, IFNGR2, NOD2, PTPN2 |
| IL2-mediated signaling events | 55 | 5 | 36.20 | 13.10 | 2.0e-07 | 9.0e-07 | IFNG, STAT3, SOCS1, IL2RA, IKZF3 |
| IL27-mediated signaling events | 26 | 4 | 61.30 | 15.40 | 4.0e-07 | 1.5e-06 | IFNG, JAK2, STAT3, IL27 |
| Leishmania infection | 72 | 5 | 27.70 | 11.40 | 8.0e-07 | 2.5e-06 | IFNG, IL10, JAK2, IFNGR2, FCGR2A |
| Signaling events mediated by PTP1B | 52 | 4 | 30.70 | 10.80 | 7.4e-06 | 2.1e-05 | JAK2, STAT3, PDGFB, FCGR2A |
| Th1/Th2 Differentiation | 19 | 3 | 62.90 | 13.60 | 1.3e-05 | 2.9e-05 | IFNG, IL2RA, IFNGR2 |
| SHP2 signaling | 58 | 4 | 27.50 | 10.20 | 1.2e-05 | 2.9e-05 | IFNG, JAK2, IL2RA, PDGFB |
| Role of Tob in T-cell activation | 21 | 3 | 56.90 | 12.90 | 1.8e-05 | 3.7e-05 | IFNG, SMAD3, IL2RA |
| Immune System | 912 | 10 | 4.37 | 5.39 | 2.8e-05 | 5.4e-05 | IFNG, JAK2, STAT3, SOCS1, IL2RA, IFNGR2, NOD2, CUL2, CYLD, PTPN2 |
| Signaling events mediated by TCPTP | 43 | 3 | 27.80 | 8.84 | 1.6e-04 | 2.3e-04 | STAT3, PDGFB, PTPN2 |
| PDGFR-beta signaling pathway | 129 | 4 | 12.40 | 6.52 | 2.7e-04 | 3.7e-04 | JAK2, STAT3, PDGFB, PTPN2 |
| NOD-like receptor signaling pathway | 62 | 3 | 19.30 | 7.25 | 4.7e-04 | 6.1e-04 | CCL2, CCL7, NOD2 |
| IL4-mediated signaling events | 65 | 3 | 18.40 | 7.06 | 5.4e-04 | 6.7e-04 | IL10, JAK2, SOCS1 |
| Chemokine signaling pathway | 190 | 4 | 8.39 | 5.17 | 1.1e-03 | 1.3e-03 | JAK2, STAT3, CCL2, CCL7 |
| Pathways in cancer | 325 | 4 | 4.91 | 3.60 | 8.0e-03 | 8.4e-03 | STAT3, SMAD3, PDGFB, CUL2 |
Comparing pathway enrichments across networks
list_eTerm <- list(eTerm_in, eTerm_mx, eTerm_im)
names(list_eTerm) <- c('Autoinflammatory', 'Mixed', 'Autoimmune')
bp_Pathway <- xEnrichCompare(list_eTerm, displayBy="fdr", FDR.cutoff=1e-3, wrap.width=50, bar.label=F)
bp_Pathway + theme(axis.text.y=element_text(size=11,color="black"), axis.title.x=element_text(size=16,color="black")) + scale_fill_manual(values=c("orange","cyan","red"))
5.3 Annotation analysis of SNPs across disease categories
# pool together GWAS SNPs for polygenic autoimmune diseases
autoimmu <- c('SLE','ATD','T1D','RA','MS','CEL')
ind <- match(autoimmu, names(ImmunoBase))
ls_gr <- lapply(ImmunoBase[ind], function(x){
gr <- x$variant
})
GRL <- GenomicRanges::GRangesList(ls_gr)
imGR <- GenomicRanges::unlist(GRL)
# pool together GWAS SNPs for mixed diseases
mix <- c('AS','PSO')
ind <- match(mix, names(ImmunoBase))
ls_gr <- lapply(ImmunoBase[ind], function(x){
gr <- x$variant
})
GRL <- GenomicRanges::GRangesList(ls_gr)
mxGR <- GenomicRanges::unlist(GRL)
# pool together GWAS SNPs for polygenic autoinflammatory diseases
autoinfl <- c('IBD','CRO','UC')
ind <- match(autoinfl, names(ImmunoBase))
ls_gr <- lapply(ImmunoBase[ind], function(x){
gr <- x$variant
})
GRL <- GenomicRanges::GRangesList(ls_gr)
inGR <- GenomicRanges::unlist(GRL)
# pool together GWAS SNPs for all diseases
ind <- match(c('SLE','ATD','T1D','RA','MS','CEL','PSO','AS','IBD','CRO','UC'), names(ImmunoBase))
ls_gr <- lapply(ImmunoBase[ind], function(x){
gr <- x$variant
})
GRL <- GenomicRanges::GRangesList(ls_gr)
allGR <- GenomicRanges::unlist(GRL)GWAS SNPs are used as the test background
background.file <- xRDataLoader(RData.customised='dbSNP_GWAS', RData.location=RData.location)5.3.1 Via gene annotations
We use gene annotations to interpret pooled GWAS SNPs for each of the three categories by looking directly at genes harbouring these SNPs, that is, functional and phenotypic annotation of genes harbouring GWAS SNPs for each of three disease categories.
5.3.1.1 Using Gene Ontology Molecular Function (GOMF)
ontology <- "GOMF"GOMF enrichments for SNPs in autoimmune diseases
eTerm_gomf_im <- xGRviaGeneAnno(data.file=imGR, background.file=background.file, format.file="GRanges", ontology=ontology, ontology.algorithm="lea", RData.location=RData.location)
xEnrichViewer(eTerm_gomf_im, 10)GOMF enrichments for SNPs in autoinflammatory diseases
eTerm_gomf_in <- xGRviaGeneAnno(data.file=inGR, background.file=background.file, format.file="GRanges", ontology=ontology, ontology.algorithm="lea", RData.location=RData.location)
xEnrichViewer(eTerm_gomf_in, 10)GOMF enrichments for SNPs in mixed diseases
eTerm_gomf_mx <- xGRviaGeneAnno(data.file=mxGR, background.file=background.file, format.file="GRanges", ontology=ontology, ontology.algorithm="lea", RData.location=RData.location)
xEnrichViewer(eTerm_gomf_mx, 10)Comparing GOMF enrichments across disease categories
list_eTerm <- list(eTerm_gomf_in, eTerm_gomf_mx, eTerm_gomf_im)
names(list_eTerm) <- c('Autoinflammatory', 'Mixed', 'Autoimmune')
bp_gomf <- xEnrichCompare(list_eTerm, displayBy="fdr", FDR.cutoff=5e-2, wrap.width=100, bar.label=F, sharings=c(2,3))
bp_gomf + theme(axis.text.y=element_text(size=9,color="black"), axis.title.x=element_text(size=16,color="black")) + scale_fill_manual(values=c("orange","cyan","red"))
DAGplot of terms enriched in any analyses above, nodes/terms colored according to how many times being called significant enrichment.
xEnrichDAGplotAdv(bp_gomf, displayBy="nSig", colormap="white-lightgreen-green", zlim=c(1,3), layout.orientation="left_right", path.mode=c("all_paths","shortest_paths","all_shortest_paths")[1], node.info=c("term_name"), graph.node.attrs=list(fontsize=25), newpage=F)
5.3.1.2 Using Gene Ontology Biological Process (GOBP)
ontology <- "GOBP"GOBP enrichments for SNPs in autoimmune diseases
eTerm_GOBP_im <- xGRviaGeneAnno(data.file=imGR, background.file=background.file, format.file="GRanges", ontology=ontology, ontology.algorithm="lea", RData.location=RData.location)
xEnrichViewer(eTerm_GOBP_im, 10)GOBP enrichments for SNPs in autoinflammatory diseases
eTerm_GOBP_in <- xGRviaGeneAnno(data.file=inGR, background.file=background.file, format.file="GRanges", ontology=ontology, ontology.algorithm="lea", RData.location=RData.location)
xEnrichViewer(eTerm_GOBP_in, 10)GOBP enrichments for SNPs in mixed diseases
eTerm_GOBP_mx <- xGRviaGeneAnno(data.file=mxGR, background.file=background.file, format.file="GRanges", ontology=ontology, ontology.algorithm="lea", RData.location=RData.location)
xEnrichViewer(eTerm_GOBP_mx, 10)Comparing GOBP enrichments across disease categories
list_eTerm <- list(eTerm_GOBP_in, eTerm_GOBP_mx, eTerm_GOBP_im)
names(list_eTerm) <- c('Autoinflammatory', 'Mixed', 'Autoimmune')
bp_GOBP <- xEnrichCompare(list_eTerm, displayBy="fdr", FDR.cutoff=1e-2, wrap.width=100, bar.label=F, sharings=c(2,3))
bp_GOBP + theme(axis.text.y=element_text(size=9,color="black"), axis.title.x=element_text(size=16,color="black")) + scale_fill_manual(values=c("orange","cyan","red"))
5.3.1.3 Using Human Phenotype Phenotypic Abnormality (HPPA)
ontology <- "HPPA"HPPA enrichments for SNPs in autoimmune diseases
eTerm_hppa_im <- xGRviaGeneAnno(data.file=imGR, background.file=background.file, format.file="GRanges", ontology=ontology, ontology.algorithm="lea", RData.location=RData.location)
xEnrichViewer(eTerm_hppa_im, 10)HPPA enrichments for SNPs in autoinflammatory diseases
eTerm_hppa_in <- xGRviaGeneAnno(data.file=inGR, background.file=background.file, format.file="GRanges", ontology=ontology, ontology.algorithm="lea", RData.location=RData.location)
xEnrichViewer(eTerm_hppa_in, 10)HPPA enrichments for SNPs in mixed diseases
eTerm_hppa_mx <- xGRviaGeneAnno(data.file=mxGR, background.file=background.file, format.file="GRanges", ontology=ontology, ontology.algorithm="lea", RData.location=RData.location)
xEnrichViewer(eTerm_hppa_mx, 10)Comparing HPPA enrichments across disease categories
list_eTerm <- list(eTerm_hppa_in, eTerm_hppa_mx, eTerm_hppa_im)
names(list_eTerm) <- c('Autoinflammatory', 'Mixed', 'Autoimmune')
bp_hppa <- xEnrichCompare(list_eTerm, displayBy="fdr", FDR.cutoff=1e-2, wrap.width=50, bar.label=F, sharings=c(2,3))
bp_hppa + theme(axis.text.y=element_text(size=9,color="black"), axis.title.x=element_text(size=16,color="black")) + scale_fill_manual(values=c("orange","cyan","red"))
5.3.1.4 Using Mammalian Phenotypes (MP)
ontology <- "MP"MP enrichments for SNPs in autoimmune diseases
eTerm_MP_im <- xGRviaGeneAnno(data.file=imGR, background.file=background.file, format.file="GRanges", ontology=ontology, ontology.algorithm="lea", RData.location=RData.location)
xEnrichViewer(eTerm_MP_im, 10)MP enrichments for SNPs in autoinflammatory diseases
eTerm_MP_in <- xGRviaGeneAnno(data.file=inGR, background.file=background.file, format.file="GRanges", ontology=ontology, ontology.algorithm="lea", RData.location=RData.location)
xEnrichViewer(eTerm_MP_in, 10)MP enrichments for SNPs in mixed diseases
eTerm_MP_mx <- xGRviaGeneAnno(data.file=mxGR, background.file=background.file, format.file="GRanges", ontology=ontology, ontology.algorithm="lea", RData.location=RData.location)
xEnrichViewer(eTerm_MP_mx, 10)Comparing MP enrichments across disease categories
list_eTerm <- list(eTerm_MP_in, eTerm_MP_mx, eTerm_MP_im)
names(list_eTerm) <- c('Autoinflammatory', 'Mixed', 'Autoimmune')
bp_MP <- xEnrichCompare(list_eTerm, displayBy="fdr", FDR.cutoff=1e-6, wrap.width=100, bar.label=F, sharings=c(2,3))
bp_MP + theme(axis.text.y=element_text(size=9,color="black"), axis.title.x=element_text(size=16,color="black")) + scale_fill_manual(values=c("orange","cyan","red"))
5.3.2 Via genomic annotations
Using functional genomic annotations supported in XGR, we are also able to compare and define genetic and epigenetic characteristics underlying each of the three categories.
5.3.2.1 FANTOM5 enhancer analysis
Enhancer enrichment analysis is using cell-type-specific expressed enhancers:
GR.annotation <- "FANTOM5_Enhancer_Cell"Enhancer enrichments for SNPs in autoimmune diseases
eTerm_enhancer_im <- xGRviaGenomicAnno(data.file=imGR, background.file=background.file, format.file="GRanges", GR.annotation=GR.annotation, RData.location=RData.location)
xEnrichViewer(eTerm_enhancer_im, 10)Enhancer enrichments for SNPs in autoinflammatory diseases
eTerm_enhancer_in <- xGRviaGenomicAnno(data.file=inGR, background.file=background.file, format.file="GRanges", GR.annotation=GR.annotation, RData.location=RData.location)
xEnrichViewer(eTerm_enhancer_in, 10)Enhancer enrichments for SNPs in mixed diseases
eTerm_enhancer_mx <- xGRviaGenomicAnno(data.file=mxGR, background.file=background.file, format.file="GRanges", GR.annotation=GR.annotation, RData.location=RData.location)
xEnrichViewer(eTerm_enhancer_mx, 10)Comparing enhancer enrichments across disease categories
list_eTerm <- list(eTerm_enhancer_in, eTerm_enhancer_mx, eTerm_enhancer_im)
names(list_eTerm) <- c('Autoinflammatory', 'Mixed', 'Autoimmune')
bp_enhancer <- xEnrichCompare(list_eTerm, displayBy="fdr", FDR.cutoff=5e-2, wrap.width=50, bar.label=F)
bp_enhancer + theme(axis.text.y=element_text(size=9,color="black"), axis.title.x=element_text(size=16,color="black")) + scale_fill_manual(values=c("orange","cyan","red"))
5.3.2.2 ENCODE TFBS analysis
ChIP-seq TFBS enrichment analysis is using uniformly identified TFBSs assayed in the cell line Gm12878 (human lymphoblastoid cell line):
# load all cell-type-specific TFBSs
all_gr <- xRDataLoader(RData.customised="Uniform_TFBS", RData.location=RData.location)
# extract TFBSs in Gm12878, stored in a list of GR objects called 'anno_gr'
ind <- grep('Gm12878', names(all_gr))
anno_gr <- all_gr[ind]TFBS enrichments for SNPs in autoimmune diseases
eTerm_tfbs_im <- xGRviaGenomicAnno(data.file=imGR, annotation.file=anno_gr, background.file=background.file, format.file="GRanges", RData.location=RData.location)
xEnrichViewer(eTerm_tfbs_im, 10)TFBS enrichments for SNPs in autoinflammatory diseases
eTerm_tfbs_in <- xGRviaGenomicAnno(data.file=inGR, annotation.file=anno_gr, background.file=background.file, format.file="GRanges", RData.location=RData.location)
xEnrichViewer(eTerm_tfbs_in, 10)TFBS enrichments for SNPs in mixed diseases
eTerm_tfbs_mx <- xGRviaGenomicAnno(data.file=mxGR, annotation.file=anno_gr, background.file=background.file, format.file="GRanges", RData.location=RData.location)
xEnrichViewer(eTerm_tfbs_mx, 10)Comparing TFBS enrichments across disease categories
list_eTerm <- list(eTerm_tfbs_in, eTerm_tfbs_mx, eTerm_tfbs_im)
names(list_eTerm) <- c('Autoinflammatory', 'Mixed', 'Autoimmune')
bp_tfbs <- xEnrichCompare(list_eTerm, displayBy="fdr", FDR.cutoff=5e-2, wrap.width=50, bar.label=F, sharings=c(2,3))
bp_tfbs + theme(axis.text.y=element_text(size=9,color="black"), axis.title.x=element_text(size=16,color="black")) + scale_fill_manual(values=c("orange","cyan","red"))
5.3.2.3 ENCODE histone analysis
Histone enrichment analysis is using uniformly identified histone marks assayed in the cell line Gm12878 (human lymphoblastoid cell line):
# load all cell-type-specific histone marks
all_gr <- xRDataLoader(RData.customised="Broad_Histone", RData.location=RData.location)
# extract histone marks in Gm12878, stored in a list of GR objects called 'anno_gr'
ind <- grep('Gm12878', names(all_gr))
anno_gr <- all_gr[ind]histone enrichments for SNPs in autoimmune diseases
eTerm_histone_im <- xGRviaGenomicAnno(data.file=imGR, annotation.file=anno_gr, background.file=background.file, format.file="GRanges", RData.location=RData.location)
xEnrichViewer(eTerm_histone_im, 10)histone enrichments for SNPs in autoinflammatory diseases
eTerm_histone_in <- xGRviaGenomicAnno(data.file=inGR, annotation.file=anno_gr, background.file=background.file, format.file="GRanges", RData.location=RData.location)
xEnrichViewer(eTerm_histone_in, 10)histone enrichments for SNPs in mixed diseases
eTerm_histone_mx <- xGRviaGenomicAnno(data.file=mxGR, annotation.file=anno_gr, background.file=background.file, format.file="GRanges", RData.location=RData.location)
xEnrichViewer(eTerm_histone_mx, 10)Comparing histone enrichments across disease categories
list_eTerm <- list(eTerm_histone_in, eTerm_histone_mx, eTerm_histone_im)
names(list_eTerm) <- c('Autoinflammatory', 'Mixed', 'Autoimmune')
bp_histone <- xEnrichCompare(list_eTerm, displayBy="fdr", FDR.cutoff=1e-2, wrap.width=50, bar.label=F, sharings=c(2,3))
bp_histone + theme(axis.text.y=element_text(size=9,color="black"), axis.title.x=element_text(size=16,color="black")) + scale_fill_manual(values=c("orange","cyan","red"))
5.3.2.4 ENCODE segment analysis
Genome segmentation enrichment analysis is using 7 category segments in the cell line GM12878:
GR.annotation <- "Segment_Combined_Gm12878"segment enrichments for SNPs in autoimmune diseases
eTerm_segment_im <- xGRviaGenomicAnno(data.file=imGR, background.file=background.file, format.file="GRanges", GR.annotation=GR.annotation, RData.location=RData.location)
xEnrichViewer(eTerm_segment_im, 10)segment enrichments for SNPs in autoinflammatory diseases
eTerm_segment_in <- xGRviaGenomicAnno(data.file=inGR, background.file=background.file, format.file="GRanges", GR.annotation=GR.annotation, RData.location=RData.location)
xEnrichViewer(eTerm_segment_in, 10)segment enrichments for SNPs in mixed diseases
eTerm_segment_mx <- xGRviaGenomicAnno(data.file=mxGR, background.file=background.file, format.file="GRanges", GR.annotation=GR.annotation, RData.location=RData.location)
xEnrichViewer(eTerm_segment_mx, 10)Comparing segment enrichments across disease categories
list_eTerm <- list(eTerm_segment_in, eTerm_segment_mx, eTerm_segment_im)
names(list_eTerm) <- c('Autoinflammatory', 'Mixed', 'Autoimmune')
bp_segment <- xEnrichCompare(list_eTerm, displayBy="fdr", FDR.cutoff=5e-2, wrap.width=50, bar.label=F, sharings=c(2,3))
bp_segment + theme(axis.text.y=element_text(size=9,color="black"), axis.title.x=element_text(size=16,color="black")) + scale_fill_manual(values=c("orange","cyan","red"))
6 Session Info
Here is the output of sessionInfo() on the system on which this user manual was built:
> R version 3.2.4 (2016-03-10)
> Platform: x86_64-apple-darwin13.4.0 (64-bit)
> Running under: OS X 10.11.4 (El Capitan)
>
> locale:
> [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
>
> attached base packages:
> [1] grid stats graphics grDevices utils datasets methods
> [8] base
>
> other attached packages:
> [1] VennDiagram_1.6.16 futile.logger_1.4.1 XGR_1.0.2
> [4] ggplot2_2.1.0 dnet_1.0.9 supraHex_1.11.1
> [7] hexbin_1.27.1 igraph_1.0.1 rmarkdown_0.9.5
>
> loaded via a namespace (and not attached):
> [1] Rcpp_0.12.5 formatR_1.3
> [3] highr_0.5.1 plyr_1.8.3
> [5] GenomeInfoDb_1.4.3 XVector_0.8.0
> [7] futile.options_1.0.0 bitops_1.0-6
> [9] zlibbioc_1.14.0 tools_3.2.4
> [11] digest_0.6.9 evaluate_0.8.3
> [13] nlme_3.1-125 gtable_0.2.0
> [15] lattice_0.20-33 Matrix_1.2-4
> [17] graph_1.46.0 Rgraphviz_2.12.0
> [19] yaml_2.1.13 parallel_3.2.4
> [21] rtracklayer_1.28.10 stringr_1.0.0
> [23] knitr_1.12.3 Biostrings_2.36.4
> [25] RCircos_1.1.3 S4Vectors_0.6.6
> [27] IRanges_2.2.9 stats4_3.2.4
> [29] Biobase_2.28.0 BiocParallel_1.2.22
> [31] XML_3.98-1.4 reshape2_1.4.1
> [33] lambda.r_1.1.7 magrittr_1.5
> [35] GenomicAlignments_1.4.2 Rsamtools_1.20.5
> [37] scales_0.4.0 htmltools_0.3
> [39] BiocGenerics_0.14.0 GenomicRanges_1.20.8
> [41] ape_3.5 colorspace_1.2-6
> [43] labeling_0.3 stringi_1.1.1
> [45] RCurl_1.95-4.8 munsell_0.4.3