.Rhistory

enrichment_method = "correlation_enrichment",
datamatrix=protdata[,shortlist])
knitr::kable(head(protdata.enrichment.correlation.short[order(protdata.enrichment.correlation.short[,"pvalue"]),]))
protdata.enrichment.correlation.long = leapR(geneset=ncipid,
enrichment_method = "correlation_enrichment",
datamatrix=protdata[,longlist])
knitr::kable(head(protdata.enrichment.correlation.long[order(protdata.enrichment.correlation.long[,"pvalue"]),]))
# load phosphoproteomics data for 69 tumors
data("phosphodata")
data("kinasesubstrates")
# for an individual tumor calculate the Kinase-Substrate Enrichment (similar to KSEA)
#     This uses the site-specific phosphorylation data to determine which kinases
#     might be active by assessing the enrichment of the phosphorylation of their known substrates
phosphodata.ksea.order = leapR(geneset=kinasesubstrates,
enrichment_method="enrichment_in_order",
datamatrix=phosphodata,
primary_columns="TCGA-13-1484")
knitr::kable(phosphodata.ksea.order[order(phosphodata.ksea.order[,"pvalue"]),][1:10,])
# now do the same thing but use a threshold
phosphodata.sets.order = leapR(geneset=kinasesubstrates,
enrichment_method="enrichment_in_sets",
datamatrix=phosphodata, threshold=1.0,
primary_columns="TCGA-13-1484")
knitr::kable(phosphodata.sets.order[order(phosphodata.sets.order[,"pvalue"]),][1:10,])
#load the single omic and multi-omic pathway databases
data("krbpaths")
data("mo_krbpaths")
# comparison enrichment in transcriptional data
transdata.comp.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_comparison',
datamatrix=transdata, primary_columns=shortlist,
secondary_columns=longlist)
# comparison enrichment in proteomics data
# this is the same code used above, just repeated here for clarity
protdata.comp.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_comparison',
datamatrix=protdata, primary_columns=shortlist,
secondary_columns=longlist)
# comparison enrichment in phosphoproteomics data
phosphodata.comp.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_comparison',
datamatrix=phosphodata, primary_columns=shortlist,
secondary_columns=longlist, id_column=1)
# set enrichment in transcriptomics data
# perform the comparison t test
transdata.svl.ttest = t(sapply(rownames(transdata), function (r) {
res=try(t.test(transdata[r,shortlist], transdata[r,longlist]));
if (class(res) == 'try-error') return(c(NA, NA));
return(c(res$p.value, res$estimate[[1]]-res$estimate[[2]]))}))
colnames(transdata.svl.ttest) = c('p-value','difference')
transdata.set.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_in_sets',
datamatrix=transdata.svl.ttest, primary_columns="p-value",
greaterthan=FALSE, threshold=0.05)
# set enrichment in proteomics data
# perform the comparison t test
protdata.svl.ttest = t(sapply(rownames(protdata), function (r) {
res=try(t.test(protdata[r,shortlist], protdata[r,longlist]));
if (class(res) == 'try-error') return(c(NA, NA));
return(c(res$p.value, res$estimate[[1]]-res$estimate[[2]]))}))
colnames(protdata.svl.ttest) = c('p-value','difference')
protdata.set.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_in_sets',
datamatrix=protdata.svl.ttest, primary_columns="p-value",
greaterthan=FALSE, threshold=0.05)
# set enrichment in phosphoproteomics data
# perform the comparison t test
phosphodata.svl.ttest = t(sapply(rownames(phosphodata), function (r) {
res=try(t.test(phosphodata[r,shortlist], phosphodata[r,longlist]));
if (class(res) == 'try-error') return(c(NA, NA));
return(c(res$p.value, res$estimate[[1]]-res$estimate[[2]]))}))
phosphodata.svl.ttest = data.frame(protein=phosphodata[,1], pvalue=phosphodata.svl.ttest[,1],
difference=phosphodata.svl.ttest[,2])
colnames(phosphodata.svl.ttest) = c('protein', 'p-value','difference')
phosphodata.set.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_in_sets',
id_column="protein",
datamatrix=phosphodata.svl.ttest, primary_columns="p-value",
greaterthan=FALSE, threshold=0.05)
# order enrichment in transcriptomics data
transdata.order.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_in_order',
datamatrix=transdata.svl.ttest, primary_columns="difference")
# order enrichment in proteomics data
protdata.order.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_in_order',
datamatrix=protdata.svl.ttest, primary_columns="difference")
# order enrichment in phosphoproteomics data
phosphodata.order.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_in_order',
id_column=1,
datamatrix=phosphodata.svl.ttest, primary_columns="difference")
# correlation difference in transcriptomics data
transdata.corr.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method="correlation_comparison",
datamatrix=transdata, primary_columns=shortlist,
secondary_columns=longlist)
source("R/leapR.R")
# correlation difference in transcriptomics data
transdata.corr.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method="correlation_comparison",
datamatrix=transdata, primary_columns=shortlist,
secondary_columns=longlist)
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
devtools::install_github("biodataganache/leapR",build_vignette=TRUE)
data(krbpaths)
data("mo_krbpaths")
unlink('vignettes/leapr_cache', recursive = TRUE)
devtools::install()
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(leapr)
data(msigdb)
data(ncipid)
data(protdata)
data(transdata)
data(shortlist)
data(longlist)
# in this example we lump a bunch of patients together (the 'short survivors') and compare
#   them to another group (the 'long survivors')
###using enrichment_wrapper function
protdata.enrichment.svl = leapR(geneset=ncipid,
enrichment_method='enrichment_comparison',
datamatrix=protdata, primary_columns=shortlist,
secondary_columns=longlist)
knitr::kable(protdata.enrichment.svl[order(protdata.enrichment.svl[,"pvalue"]),][1:10,])
# another application is to compare just one patient against another (this would be the
#     equivalent of comparing one time point to another)
###using enrichment_wrapper function
protdata.enrichment.svl.ovo = leapR(geneset=ncipid,
enrichment_method='enrichment_comparison',
datamatrix=protdata,
primary_columns=shortlist[1],
secondary_columns=longlist[1])
knitr::kable(protdata.enrichment.svl.ovo[order(protdata.enrichment.svl.ovo[,"pvalue"]),][1:10,])
# for this example we will construct a list of genes from the expression data
#     to emulate what you might be inputting
genelist = rownames(protdata)[which(protdata[,1]>0.5)]
background = rownames(protdata)
###using enrichment_wrapper function
protdata.enrichment.order = leapR(geneset=ncipid,
enrichment_method="enrichment_in_order",
datamatrix=protdata, threshold=.5,
primary_columns="TCGA-13-1484")
knitr::kable(protdata.enrichment.order[order(protdata.enrichment.order[,"pvalue"]),][1:10,])
# in this example we construct some modules from the hierarchical clustering of the
#     data
protdata_naf = as.matrix(protdata)
# hierarchical clustering is not too happy with lots of missing values
#    so we'll do a zero fill on this to get the modules
protdata_naf[which(is.na(protdata_naf))] = 0
# construct the hierarchical clustering using the 'wardD' method, which
#    seems to give more even sized modules
protdata_hc = hclust(dist(protdata_naf), method="ward.D2")
# arbitrarily we'll chop the clusters into 5 modules
modules = cutree(protdata_hc, k=5)
##sara: created list
clusters = lapply(unique(modules),function(x) names(which(modules==x)))
# modules is a named list of values where each value is a module
#         number and the name is the gene name
# To do enrichment for one module (module 1 in this case) do this
protdata.enrichment.sets.module_1 = leapR(geneset=ncipid, enrichment_method="enrichment_in_sets",
background=names(modules),
targets=names(modules[which(modules==1)]))
# To do enrichment on all modules and return the list of enrichment results tables do this
protdata.enrichment.sets.modules = cluster_enrichment(geneset=ncipid, clusters=clusters, background=names(modules), sigfilter=0.5)
knitr::kable(head(protdata.enrichment.sets.modules))
# This is how you calculate enrichment in a ranked list (for example from topology)
###using enrichment_wrapper function
protdata.enrichment.sets = leapR(geneset=ncipid, "enrichment_in_order",
datamatrix=protdata,
primary_columns=shortlist[1])
knitr::kable(protdata.enrichment.sets[order(protdata.enrichment.sets[,"pvalue"]),][1:10,])
###using enrichment_wrapper function
protdata.enrichment.correlation = leapR(geneset=ncipid,
enrichment_method = "correlation_enrichment",
datamatrix=protdata)
knitr::kable(head(protdata.enrichment.correlation[order(protdata.enrichment.correlation[,"pvalue"]),]))
protdata.enrichment.correlation.short = leapR(geneset=ncipid,
enrichment_method = "correlation_enrichment",
datamatrix=protdata[,shortlist])
knitr::kable(head(protdata.enrichment.correlation.short[order(protdata.enrichment.correlation.short[,"pvalue"]),]))
protdata.enrichment.correlation.long = leapR(geneset=ncipid,
enrichment_method = "correlation_enrichment",
datamatrix=protdata[,longlist])
knitr::kable(head(protdata.enrichment.correlation.long[order(protdata.enrichment.correlation.long[,"pvalue"]),]))
# load phosphoproteomics data for 69 tumors
data("phosphodata")
data("kinasesubstrates")
# for an individual tumor calculate the Kinase-Substrate Enrichment (similar to KSEA)
#     This uses the site-specific phosphorylation data to determine which kinases
#     might be active by assessing the enrichment of the phosphorylation of their known substrates
phosphodata.ksea.order = leapR(geneset=kinasesubstrates,
enrichment_method="enrichment_in_order",
datamatrix=phosphodata,
primary_columns="TCGA-13-1484")
knitr::kable(phosphodata.ksea.order[order(phosphodata.ksea.order[,"pvalue"]),][1:10,])
# now do the same thing but use a threshold
phosphodata.sets.order = leapR(geneset=kinasesubstrates,
enrichment_method="enrichment_in_sets",
datamatrix=phosphodata, threshold=1.0,
primary_columns="TCGA-13-1484")
knitr::kable(phosphodata.sets.order[order(phosphodata.sets.order[,"pvalue"]),][1:10,])
#load the single omic and multi-omic pathway databases
data("krbpaths")
data("mo_krbpaths")
# comparison enrichment in transcriptional data
transdata.comp.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_comparison',
datamatrix=transdata, primary_columns=shortlist,
secondary_columns=longlist)
# comparison enrichment in proteomics data
# this is the same code used above, just repeated here for clarity
protdata.comp.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_comparison',
datamatrix=protdata, primary_columns=shortlist,
secondary_columns=longlist)
# comparison enrichment in phosphoproteomics data
phosphodata.comp.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_comparison',
datamatrix=phosphodata, primary_columns=shortlist,
secondary_columns=longlist, id_column=1)
# set enrichment in transcriptomics data
# perform the comparison t test
transdata.svl.ttest = t(sapply(rownames(transdata), function (r) {
res=try(t.test(transdata[r,shortlist], transdata[r,longlist]));
if (class(res) == 'try-error') return(c(NA, NA));
return(c(res$p.value, res$estimate[[1]]-res$estimate[[2]]))}))
colnames(transdata.svl.ttest) = c('p-value','difference')
transdata.set.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_in_sets',
datamatrix=transdata.svl.ttest, primary_columns="p-value",
greaterthan=FALSE, threshold=0.05)
# set enrichment in proteomics data
# perform the comparison t test
protdata.svl.ttest = t(sapply(rownames(protdata), function (r) {
res=try(t.test(protdata[r,shortlist], protdata[r,longlist]));
if (class(res) == 'try-error') return(c(NA, NA));
return(c(res$p.value, res$estimate[[1]]-res$estimate[[2]]))}))
colnames(protdata.svl.ttest) = c('p-value','difference')
protdata.set.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_in_sets',
datamatrix=protdata.svl.ttest, primary_columns="p-value",
greaterthan=FALSE, threshold=0.05)
# set enrichment in phosphoproteomics data
# perform the comparison t test
phosphodata.svl.ttest = t(sapply(rownames(phosphodata), function (r) {
res=try(t.test(phosphodata[r,shortlist], phosphodata[r,longlist]));
if (class(res) == 'try-error') return(c(NA, NA));
return(c(res$p.value, res$estimate[[1]]-res$estimate[[2]]))}))
phosphodata.svl.ttest = data.frame(protein=phosphodata[,1], pvalue=phosphodata.svl.ttest[,1],
difference=phosphodata.svl.ttest[,2])
colnames(phosphodata.svl.ttest) = c('protein', 'p-value','difference')
phosphodata.set.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_in_sets',
id_column="protein",
datamatrix=phosphodata.svl.ttest, primary_columns="p-value",
greaterthan=FALSE, threshold=0.05)
# order enrichment in transcriptomics data
transdata.order.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_in_order',
datamatrix=transdata.svl.ttest, primary_columns="difference")
# order enrichment in proteomics data
protdata.order.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_in_order',
datamatrix=protdata.svl.ttest, primary_columns="difference")
# order enrichment in phosphoproteomics data
phosphodata.order.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method='enrichment_in_order',
id_column=1,
datamatrix=phosphodata.svl.ttest, primary_columns="difference")
# correlation difference in transcriptomics data
transdata.corr.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method="correlation_comparison",
datamatrix=transdata, primary_columns=shortlist,
secondary_columns=longlist)
# correlation difference in proteomics data
protdata.corr.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method="correlation_comparison",
datamatrix=protdata, primary_columns=shortlist,
secondary_columns=longlist)
# correlation difference in phosphoproteomics data
phosphodata.corr.enrichment.svl = leapR(geneset=krbpaths,
enrichment_method="correlation_comparison",
datamatrix=phosphodata, primary_columns=shortlist,
secondary_columns=longlist, id_column=1)
#combine the omics data into one with prefix tags
combodata = combine_omics(proteomics=protdata, transcriptomics=transdata, phospho=phosphodata, id_column=1)
# comparison enrichment for combodata
combodata.enrichment.svl = leapR(geneset=mo_krbpaths,
enrichment_method='enrichment_comparison',
datamatrix=combodata, primary_columns=shortlist,
secondary_columns=longlist, id_column=1)
# set enrichment in combo data
# perform the comparison t test
combodata.svl.ttest = t(sapply(rownames(combodata), function (r) {
res=try(t.test(combodata[r,shortlist], combodata[r,longlist]));
if (class(res) == 'try-error') return(c(NA, NA));
return(c(res$p.value, res$estimate[[1]]-res$estimate[[2]]))}))
combodata.svl.ttest = data.frame(protein=combodata[,1], pvalue=combodata.svl.ttest[,1],
difference=combodata.svl.ttest[,2])
colnames(combodata.svl.ttest) = c('protein', 'p-value','difference')
combodata.set.enrichment.svl = leapR(geneset=mo_krbpaths,
enrichment_method='enrichment_in_sets',
datamatrix=combodata.svl.ttest, primary_columns="p-value",
greaterthan=FALSE, threshold=0.05)
# order enrichment in transcriptomics data
combodata.order.enrichment.svl = leapR(geneset=mo_krbpaths,
enrichment_method='enrichment_in_order',
datamatrix=combodata.svl.ttest, primary_columns="difference")
# correlation difference in combo data
combodata.corr.enrichment.svl = leapR(geneset=mo_krbpaths,
enrichment_method="correlation_comparison",
datamatrix=combodata, primary_columns=shortlist,
secondary_columns=longlist)
# now take all these results and combine them into one figure
all_results = list(transdata.comp.enrichment.svl,
protdata.comp.enrichment.svl,
phosphodata.comp.enrichment.svl,
combodata.enrichment.svl,
transdata.set.enrichment.svl,
protdata.set.enrichment.svl,
phosphodata.set.enrichment.svl,
combodata.set.enrichment.svl,
transdata.order.enrichment.svl,
protdata.order.enrichment.svl,
phosphodata.order.enrichment.svl,
combodata.order.enrichment.svl,
transdata.corr.enrichment.svl,
protdata.corr.enrichment.svl,
phosphodata.corr.enrichment.svl,
combodata.corr.enrichment.svl)
pathways_of_interest = c("KEGG_APOPTOSIS",
"KEGG_CELL_CYCLE",
"KEGG_ERBB_SIGNALING_PATHWAY",
"KEGG_FOCAL_ADHESION",
"KEGG_INSULIN_SIGNALING_PATHWAY",
"KEGG_MAPK_SIGNALING_PATHWAY",
"KEGG_MISMATCH_REPAIR",
"KEGG_MTOR_SIGNALING_PATHWAY" ,
"KEGG_OXIDATIVE_PHOSPHORYLATION",
"KEGG_P53_SIGNALING_PATHWAY",
"KEGG_PATHWAYS_IN_CANCER",
"KEGG_PROTEASOME",
"KEGG_RIBOSOME",
"KEGG_VEGF_SIGNALING_PATHWAY",
"KEGG_WNT_SIGNALING_PATHWAY")
results.frame = data.frame(pathway=pathways_of_interest,
td.comp=all_results[[1]][pathways_of_interest,"BH_pvalue"]<0.05,
pd.comp=all_results[[2]][pathways_of_interest,"BH_pvalue"]<0.05,
fd.comp=all_results[[3]][pathways_of_interest,"BH_pvalue"]<0.05,
cd.comp=all_results[[4]][pathways_of_interest,"BH_pvalue"]<0.05,
td.set=all_results[[5]][pathways_of_interest,"BH_pvalue"]<0.05,
pd.set=all_results[[6]][pathways_of_interest,"BH_pvalue"]<0.05,
fd.set=all_results[[7]][pathways_of_interest,"BH_pvalue"]<0.05,
cd.set=all_results[[8]][pathways_of_interest,"BH_pvalue"]<0.05,
td.order=all_results[[9]][pathways_of_interest,"BH_pvalue"]<0.05,
pd.order=all_results[[10]][pathways_of_interest,"BH_pvalue"]<0.05,
fd.order=all_results[[11]][pathways_of_interest,"BH_pvalue"]<0.05,
cd.order=all_results[[12]][pathways_of_interest,"BH_pvalue"]<0.05,
td.corr=all_results[[13]][pathways_of_interest,"BH_pvalue"]<0.05,
pd.corr=all_results[[14]][pathways_of_interest,"BH_pvalue"]<0.05,
fd.corr=all_results[[15]][pathways_of_interest,"BH_pvalue"]<0.05,
cd.corr=all_results[[16]][pathways_of_interest,"BH_pvalue"]<0.05)
results.frame.or = data.frame(pathway=pathways_of_interest,
td.comp=all_results[[1]][pathways_of_interest,"oddsratio"],
pd.comp=all_results[[2]][pathways_of_interest,"oddsratio"],
fd.comp=all_results[[3]][pathways_of_interest,"oddsratio"],
cd.comp=all_results[[4]][pathways_of_interest,"oddsratio"],
td.set=log(all_results[[5]][pathways_of_interest,"oddsratio"],2),
pd.set=log(all_results[[6]][pathways_of_interest,"oddsratio"],2),
fd.set=log(all_results[[7]][pathways_of_interest,"oddsratio"],2),
cd.set=log(all_results[[8]][pathways_of_interest,"oddsratio"],2),
td.order=all_results[[9]][pathways_of_interest,"oddsratio"],
pd.order=all_results[[10]][pathways_of_interest,"oddsratio"],
fd.order=all_results[[11]][pathways_of_interest,"oddsratio"],
cd.order=all_results[[12]][pathways_of_interest,"oddsratio"],
td.corr=all_results[[13]][pathways_of_interest,"oddsratio"],
pd.corr=all_results[[14]][pathways_of_interest,"oddsratio"],
fd.corr=all_results[[15]][pathways_of_interest,"oddsratio"],
cd.corr=all_results[[16]][pathways_of_interest,"oddsratio"])
rownames(results.frame) = results.frame[,1]
rownames(results.frame.or) = results.frame.or[,1]
results.frame.sig = results.frame[,2:17]*results.frame.or[,2:17]
library(gplots)
heatmap.2(as.matrix(results.frame.sig[,c(1:4,9:16)]), Colv=NA, trace="none", breaks=c(-1,-.1,-0.0001,0,0.1,1), col=c("blue","lightblue", "grey","pink", "red"), dendrogram="none")
# this comparison of abundance in substrates between case and control
#     is lopsided in the sense that phosphorylation levels were previously
#     reported to be overall higher in the short survivors. Thus the
#     results are not terribly interesting (all kinases are in the same direction)
phosphodata.ksea.comp.svl = leapR(geneset=kinasesubstrates,
enrichment_method="enrichment_comparison",
datamatrix=phosphodata,
primary_columns=shortlist, secondary_columns=longlist)
# thus for the example we'll look at correlation between known substrates in the
#      case v control conditions
phosphodata.ksea.corr.svl = leapR(geneset=kinasesubstrates,
enrichment_method="correlation_comparison",
datamatrix=phosphodata,
primary_columns=shortlist,
secondary_columns=longlist)
# thus for the example we'll look at correlation between known substrates in the
#      case v control conditions
phosphodata.ksea.corr.svl = leapR(geneset=kinasesubstrates,
enrichment_method="correlation_comparison",
datamatrix=phosphodata,
primary_columns=shortlist,
secondary_columns=longlist)
phosphodata[,shortlist]
phosphodata[,longlist]
source("R/correlation_comparison_enrichment.R")
phosphodata.ksea.corr.svl = leapR(geneset=kinasesubstrates,
enrichment_method="correlation_comparison",
datamatrix=phosphodata,
primary_columns=shortlist,
secondary_columns=longlist)
source("R/correlation_comparison_enrichment.R")
phosphodata.ksea.corr.svl = leapR(geneset=kinasesubstrates,
enrichment_method="correlation_comparison",
datamatrix=phosphodata,
primary_columns=shortlist,
secondary_columns=longlist)
devtools::install()
# this comparison of abundance in substrates between case and control
#     is lopsided in the sense that phosphorylation levels were previously
#     reported to be overall higher in the short survivors. Thus the
#     results are not terribly interesting (all kinases are in the same direction)
phosphodata.ksea.comp.svl = leapR(geneset=kinasesubstrates,
enrichment_method="enrichment_comparison",
datamatrix=phosphodata,
primary_columns=shortlist, secondary_columns=longlist)
# thus for the example we'll look at correlation between known substrates in the
#      case v control conditions
phosphodata.ksea.corr.svl = leapR(geneset=kinasesubstrates,
enrichment_method="correlation_comparison",
datamatrix=phosphodata,
primary_columns=shortlist,
secondary_columns=longlist)
source("R/correlation_comparison_enrichment.R")
phosphodata.ksea.corr.svl = leapR(geneset=kinasesubstrates,
enrichment_method="correlation_comparison",
datamatrix=phosphodata,
primary_columns=shortlist,
secondary_columns=longlist)
devtools::install()
phosphodata.ksea.corr.svl = leapR(geneset=kinasesubstrates,
enrichment_method="correlation_comparison",
datamatrix=phosphodata,
primary_columns=shortlist,
secondary_columns=longlist)
cols1
cols2
abcols1
allallgenes_present
allgenes_present
phosphodata.ksea.corr.svl = leapR(geneset=kinasesubstrates,
enrichment_method="correlation_comparison",
datamatrix=phosphodata,
primary_columns=shortlist,
secondary_columns=longlist)
allgenes
abundance[1:5,1:5]
ids
which(allgenes %in% ids)
allgenes
ids
allgenes
ids
allgenes
which(ids == "ADD1-S12")
which(ids == "CLK4S339")
which(ids == "CLK4-S339")
which(ids == "CLK4-S339")
ids[1]
which(ids == "AAAS-S495s")
ids
which(ids == "ANKS1A-S663s")
allgenes
kinasesubstrates
kinasesubstrates$matrix[1:10,1:10]
phosphodata[1:5,1:5]
sapply(rownames(phosphodata), function (n) substring(n, 1, length(n)-1)[1:5]
)
version()
sapply(rownames(phosphodata), function (n) substring(n, 1, length(n)-1))
sapply(rownames(phosphodata), function (n) substring(n, 1, len(n)-1))
sapply(rownames(phosphodata), function (n) substring(n, 1, nchar(n)-1)[1:5]
)
sapply(rownames(phosphodata), function (n) substring(n, 1, nchar(n)-1))[1:5]
rownames(phosphodata) = sapply(rownames(phosphodata), function (n) substring(n, 1, nchar(n)-1))[1:5]
rownames(phosphodata) = sapply(rownames(phosphodata), function (n) substring(n, 1, nchar(n)-1))
save(phosphodata, file="data/phosphodata.rda")
devtools::install()
# thus for the example we'll look at correlation between known substrates in the
#      case v control conditions
phosphodata.ksea.corr.svl = leapR(geneset=kinasesubstrates,
enrichment_method="correlation_comparison",
datamatrix=phosphodata,
primary_columns=shortlist,
secondary_columns=longlist)
devtools::install_github("biodataganache/leapR")
devtools::install_github("biodataganache/leapR")
devtools::install_github("biodataganache/leapR")
devtools::install_github("biodataganache/leapR", force=TRUE)
devtools::install_github("biodataganache/leapR")
devtools::install_github("biodataganache/leapR")
devtools::install_github("biodataganache/leapR")
devtools::install_github("biodataganache/leapR")