[med-svn] [r-bioc-mergeomics] 02/03: Imported Upstream version 1.4.0
Dylan Aïssi
bob.dybian-guest at moszumanska.debian.org
Mon Jul 24 19:49:46 UTC 2017
This is an automated email from the git hooks/post-receive script.
bob.dybian-guest pushed a commit to branch master
in repository r-bioc-mergeomics.
commit 34a5e2d0446607858d15421fb7e74ee97a6a34c4
Author: Dylan Aïssi <bob.dybian at gmail.com>
Date: Mon Jul 24 21:49:01 2017 +0200
Imported Upstream version 1.4.0
---
DESCRIPTION | 4 +-
R/cle.LS.R | 498 ++++++++++++++++++++-----------------
build/vignette.rds | Bin 233 -> 231 bytes
inst/doc/Mergeomics.Rnw | 8 +-
inst/doc/Mergeomics.pdf | Bin 265347 -> 265168 bytes
inst/unitTests/test_ssea.analyze.R | 4 +-
man/MSEA.KDA.onestep.Rd | 8 +-
man/Mergeomics-package.Rd | 8 +-
man/kda.analyze.Rd | 8 +-
man/kda.analyze.exec.Rd | 16 +-
man/kda.analyze.simulate.Rd | 16 +-
man/kda.analyze.test.Rd | 16 +-
man/kda.configure.Rd | 8 +-
man/kda.finish.Rd | 8 +-
man/kda.finish.estimate.Rd | 8 +-
man/kda.finish.save.Rd | 8 +-
man/kda.finish.summarize.Rd | 8 +-
man/kda.finish.trim.Rd | 8 +-
man/kda.prepare.Rd | 16 +-
man/kda.prepare.overlap.Rd | 8 +-
man/kda.prepare.screen.Rd | 8 +-
man/kda.start.Rd | 16 +-
man/kda.start.edges.Rd | 8 +-
man/kda.start.identify.Rd | 8 +-
man/kda.start.modules.Rd | 8 +-
man/kda2cytoscape.Rd | 8 +-
man/kda2cytoscape.colorize.Rd | 8 +-
man/kda2cytoscape.colormap.Rd | 8 +-
man/kda2cytoscape.drivers.Rd | 8 +-
man/kda2cytoscape.edges.Rd | 8 +-
man/kda2cytoscape.exec.Rd | 8 +-
man/kda2cytoscape.identify.Rd | 8 +-
man/kda2himmeli.Rd | 8 +-
man/kda2himmeli.colorize.Rd | 8 +-
man/kda2himmeli.colormap.Rd | 8 +-
man/kda2himmeli.drivers.Rd | 8 +-
man/kda2himmeli.edges.Rd | 8 +-
man/kda2himmeli.exec.Rd | 8 +-
man/kda2himmeli.identify.Rd | 8 +-
man/ssea.analyze.Rd | 25 +-
man/ssea.analyze.observe.Rd | 10 +-
man/ssea.analyze.randgenes.Rd | 51 +++-
man/ssea.analyze.randloci.Rd | 10 +-
man/ssea.analyze.simulate.Rd | 29 ++-
man/ssea.analyze.statistic.Rd | 8 +-
man/ssea.control.Rd | 10 +-
man/ssea.finish.Rd | 10 +-
man/ssea.finish.details.Rd | 10 +-
man/ssea.finish.fdr.Rd | 10 +-
man/ssea.finish.genes.Rd | 10 +-
man/ssea.meta.Rd | 8 +-
man/ssea.prepare.Rd | 10 +-
man/ssea.prepare.counts.Rd | 10 +-
man/ssea.prepare.structure.Rd | 10 +-
man/ssea.start.Rd | 8 +-
man/ssea.start.configure.Rd | 10 +-
man/ssea.start.identify.Rd | 8 +-
man/ssea.start.relabel.Rd | 10 +-
man/ssea2kda.Rd | 8 +-
man/ssea2kda.analyze.Rd | 10 +-
man/ssea2kda.import.Rd | 10 +-
man/tool.aggregate.Rd | 8 +-
man/tool.cluster.Rd | 10 +-
man/tool.cluster.static.Rd | 8 +-
man/tool.coalesce.Rd | 10 +-
man/tool.coalesce.exec.Rd | 8 +-
man/tool.coalesce.find.Rd | 8 +-
man/tool.coalesce.merge.Rd | 8 +-
vignettes/Mergeomics.Rnw | 8 +-
69 files changed, 634 insertions(+), 537 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index 5ca0d32..5accb61 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
Package: Mergeomics
Type: Package
Title: Integrative network analysis of omics data
-Version: 1.2.0
+Version: 1.4.0
Date: 2016-01-04
Author: Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang,
Xia Yang
@@ -16,5 +16,5 @@ biocViews: Software
Suggests: RUnit, BiocGenerics
License: GPL (>= 2)
Depends: R (>= 3.0.1)
-Packaged: 2016-10-18 00:16:52 UTC; biocbuild
+Packaged: 2017-04-25 00:37:08 UTC; biocbuild
NeedsCompilation: no
diff --git a/R/cle.LS.R b/R/cle.LS.R
index c1dde9f..4081566 100644
--- a/R/cle.LS.R
+++ b/R/cle.LS.R
@@ -1685,252 +1685,290 @@ ssea2kda.analyze <- function(job, moddata) {
# P enrichment P-values
# FREQ enrichment P-values (raw frequencies)
#
-# Written by Ville-Petteri Makinen 2013
-#
-ssea.analyze <- function(job) {
- cat("\nEstimating enrichment...\n")
- set.seed(job$seed)
-
- # Observed enrichment scores.
- db <- job$database
- scores <- ssea.analyze.observe(db)
- nmods <- length(scores)
-
- # Simulated scores.
- nperm <- job$nperm
- nullsets <- ssea.analyze.simulate(db, scores, nperm, job$permtype)
-
- # Estimate scores based on Gaussian distribution.
- cat("\nNormalizing scores...\n")
- znull <- double()
- zscores <- NA*scores
- for(i in which(0*scores == 0)) {
- x <- nullsets[[i]]
- x <- x[which(0*x == 0)]
- param <- tool.normalize(x)
- z <- tool.normalize(x, param)
- zscores[i] <- tool.normalize(scores[i], param)
- znull <- c(znull, z)
- }
-
- # Estimate hit frequencies.
- freq <- NA*scores
- nnull <- length(znull)
- for(i in which(0*scores == 0))
- freq[i] <- sum(zscores[i] <= znull)/nnull
-
- # Estimate scores based on frequencies.
- hscores <- NA*freq
- rows <- which(freq > 5.0/nnull)
- hscores[rows] <- qnorm(freq[rows], lower.tail=FALSE)
-
- # Fill in scores for low frequencies.
- if(length(rows) < length(freq)) {
- omega <- which.max(zscores)
- hscores[omega] <- zscores[omega]
- rows <- c(rows, omega)
- pt <- approx(x=zscores[rows], y=hscores[rows], xout=zscores)
- hscores <- pt$y
- }
-
- # Estimate statistical significance.
- z <- 0.5*(zscores + hscores)
- pvalues <- pnorm(z, lower.tail=FALSE)
-
- # Collect results.
- res <- data.frame(MODULE=(1:nmods), stringsAsFactors=FALSE)
- res$P <- pvalues
- res$FREQ <- freq
-
- # Remove missing scores.
- targets <- which(0*scores == 0)
- job$results <- res[targets,]
- return(job)
+# Written by Ville-Petteri Makinen 2013, Modified by Le Shu 2016
+#
+ssea.analyze <- function(job, trim_start=0.002, trim_end=0.998) {
+ cat("\nEstimating enrichment...\n")
+ set.seed(job$seed)
+
+ # Observed enrichment scores.
+ db <- job$database
+ scores <- ssea.analyze.observe(db)
+ nmods <- length(scores)
+
+ # Simulated scores.
+ nperm <- job$nperm
+ nullsets <- ssea.analyze.simulate(db, scores, nperm, job$permtype, trim_start, trim_end)
+
+ # Estimate scores based on Gaussian distribution.
+ cat("\nNormalizing scores...\n")
+ znull <- double()
+ zscores <- NA*scores
+ for(i in which(0*scores == 0)) {
+ x <- nullsets[[i]]
+ x <- x[which(0*x == 0)]
+ param <- tool.normalize(x)
+ z <- tool.normalize(x, param)
+ zscores[i] <- tool.normalize(scores[i], param)
+ znull <- c(znull, z)
+ }
+
+ # Estimate hit frequencies.
+ freq <- NA*scores
+ nnull <- length(znull)
+ for(i in which(0*scores == 0))
+ freq[i] <- sum(zscores[i] <= znull)/nnull
+
+ # Estimate scores based on frequencies.
+ hscores <- NA*freq
+ rows <- which(freq > 5.0/nnull)
+ hscores[rows] <- qnorm(freq[rows], lower.tail=FALSE)
+
+ # Fill in scores for low frequencies.
+ if(length(rows) < length(freq)) {
+ omega <- which.max(zscores)
+ hscores[omega] <- zscores[omega]
+ rows <- c(rows, omega)
+ pt <- approx(x=zscores[rows], y=hscores[rows], xout=zscores)
+ hscores <- pt$y
+ }
+
+ # Estimate statistical significance.
+ z <- 0.5*(zscores + hscores)
+ pvalues <- pnorm(z, lower.tail=FALSE)
+
+ # Collect results.
+ res <- data.frame(MODULE=(1:nmods), stringsAsFactors=FALSE)
+ res$P <- pvalues
+ res$FREQ <- freq
+
+ # Remove missing scores.
+ targets <- which(0*scores == 0)
+ job$results <- res[targets,]
+ return(job)
}
#----------------------------------------------------------------------------
-ssea.analyze.simulate <- function(db, observ, nperm, permtype) {
-
- # Include only non-empty modules for simulation.
- nmods <- length(db$modulesizes)
- targets <- which(db$modulesizes > 0)
- hits <- rep(NA, nmods)
- hits[targets] <- 0
-
- # Prepare data structures to hold null samples.
- keys <- rep(0, nperm)
- scores <- rep(NA, nperm)
- scoresets <- list()
- for(i in 1:nmods)
- scoresets[[i]] <- double()
-
- # Simulate random scores.
- nelem <- 0
- snull <- double()
- stamp <- Sys.time()
- for(k in 1:nperm) {
- if(permtype == "gene") snull <- ssea.analyze.randgenes(db, targets)
- if(permtype == "locus") snull <- ssea.analyze.randloci(db, targets)
- if(length(snull) < 1) stop("Unknown permutation type.")
-
- # Check data capacity.
- ntarg <- length(targets)
- if((nelem + ntarg) >= length(keys)) {
- keys <- c(keys, rep(0, nelem))
- scores <- c(scores, rep(NA, nelem))
- }
-
- # Collect scores.
- for(i in 1:ntarg) {
- nelem <- (nelem + 1)
- t <- targets[i]
- keys[nelem] <- t
- scores[nelem] <- snull[i]
- hits[t] <- (hits[t] + (snull[i] > observ[t]))
- }
-
- # Drop less significant modules.
- hmax <- min(sqrt(nperm/k), 10)
- targets <- which(hits < hmax)
- if(length(targets) < 1) break
-
- # Progress report.
- delta <- as.double(Sys.time() - stamp)
- if((delta < 10.0) & (k < nperm)) next
- cat(sprintf("%d/%d cycles\n", k, nperm))
- stamp <- Sys.time()
- }
-
- # Remove missing entries.
- scores <- scores[1:nelem]
- keys <- keys[1:nelem]
-
- # Organize null scores into lists.
- st <- tool.aggregate(keys)
- labels <- as.integer(st$labels)
- blocks <- st$blocks
- for(i in 1:length(blocks)) {
- key <- labels[i]
- rows <- blocks[[i]]
- scoresets[[key]] <- scores[rows]
- }
- return(scoresets)
+ssea.analyze.simulate <- function(db, observ, nperm, permtype, trim_start, trim_end) {
+
+ #############################################################################
+ #####This is an additional process to trim genes with exceptionally high value####
+ ###################################################################################
+ gene2loci <- db$gene2loci
+ locus2row <- db$locus2row
+ observed <- db$observed
+ #Calcuate individual gene enrichment score
+ trim_scores <- rep(NA, length(gene2loci))
+
+ for(k in 1:length(trim_scores)) {
+ genes <- k
+
+ # Collect markers.
+ loci <- integer()
+ for(i in genes)
+ loci <- c(loci, gene2loci[[i]])
+
+ # Determine data rows.
+ loci <- unique(loci)
+ rows <- locus2row[loci]
+ nloci <- length(rows)
+
+ # Calculate total counts.
+ e <- (nloci/length(locus2row))*colSums(observed)
+ o <- observed[rows,]
+ if(nloci > 1) o <- colSums(o)
+
+ # Estimate enrichment.
+ trim_scores[k] <- ssea.analyze.statistic(o, e)
+ }
+ cutoff=as.numeric(quantile(trim_scores,probs=c(trim_start,trim_end)))
+ gene_sel=which(trim_scores>cutoff[1]&trim_scores<cutoff[2])
+
+
+ # Include only non-empty modules for simulation.
+ nmods <- length(db$modulesizes)
+ targets <- which(db$modulesizes > 0)
+ hits <- rep(NA, nmods)
+ hits[targets] <- 0
+
+ # Prepare data structures to hold null samples.
+ keys <- rep(0, nperm)
+ scores <- rep(NA, nperm)
+ scoresets <- list()
+ for(i in 1:nmods)
+ scoresets[[i]] <- double()
+
+ # Simulate random scores.
+ nelem <- 0
+ snull <- double()
+ stamp <- Sys.time()
+ for(k in 1:nperm) {
+ if(permtype == "gene") snull <- ssea.analyze.randgenes(db, targets, gene_sel)
+ if(permtype == "locus") snull <- ssea.analyze.randloci(db, targets)
+ if(length(snull) < 1) stop("Unknown permutation type.")
+
+ # Check data capacity.
+ ntarg <- length(targets)
+ if((nelem + ntarg) >= length(keys)) {
+ keys <- c(keys, rep(0, nelem))
+ scores <- c(scores, rep(NA, nelem))
+ }
+
+ # Collect scores.
+ for(i in 1:ntarg) {
+ nelem <- (nelem + 1)
+ t <- targets[i]
+ keys[nelem] <- t
+ scores[nelem] <- snull[i]
+ hits[t] <- (hits[t] + (snull[i] > observ[t]))
+ }
+
+ # Drop less significant modules.
+ hmax <- min(sqrt(nperm/k), 10)
+ targets <- which(hits < hmax)
+ if(length(targets) < 1) break
+
+ # Progress report.
+ delta <- as.double(Sys.time() - stamp)
+ if((delta < 10.0) & (k < nperm)) next
+ cat(sprintf("%d/%d cycles\n", k, nperm))
+ stamp <- Sys.time()
+ }
+
+ # Remove missing entries.
+ scores <- scores[1:nelem]
+ keys <- keys[1:nelem]
+
+ # Organize null scores into lists.
+ st <- tool.aggregate(keys)
+ labels <- as.integer(st$labels)
+ blocks <- st$blocks
+ for(i in 1:length(blocks)) {
+ key <- labels[i]
+ rows <- blocks[[i]]
+ scoresets[[key]] <- scores[rows]
+ }
+ return(scoresets)
}
#----------------------------------------------------------------------------
-ssea.analyze.observe <- function(db) {
- mod2gen <- db$module2genes
- gene2loci <- db$gene2loci
- locus2row <- db$locus2row
- observed <- db$observed
- expected <- db$expected
- nmods <- length(mod2gen)
-
- # Test every module.
- scores <- rep(NA, nmods)
- for(k in 1:nmods) {
- genes <- mod2gen[[k]]
-
- # Collect markers.
- loci <- integer()
- for(i in genes)
- loci <- c(loci, gene2loci[[i]])
-
- # Determine data rows.
- loci <- unique(loci)
- rows <- locus2row[loci]
- nloci <- length(rows)
-
- # Calculate total counts.
- e <- nloci*expected
- o <- observed[rows,]
- if(nloci > 1) o <- colSums(o)
-
- # Estimate enrichment.
- scores[k] <- ssea.analyze.statistic(o, e)
- }
- return(scores)
+ssea.analyze.observe <- function(db) {
+ mod2gen <- db$module2genes
+ gene2loci <- db$gene2loci
+ locus2row <- db$locus2row
+ observed <- db$observed
+ expected <- db$expected
+ nmods <- length(mod2gen)
+
+ # Test every module.
+ scores <- rep(NA, nmods)
+ for(k in 1:nmods) {
+ genes <- mod2gen[[k]]
+
+ # Collect markers.
+ loci <- integer()
+ for(i in genes)
+ loci <- c(loci, gene2loci[[i]])
+
+ # Determine data rows.
+ loci <- unique(loci)
+ rows <- locus2row[loci]
+ nloci <- length(rows)
+
+ # Calculate total counts.
+ #e <- nloci*expected
+ e <- (nloci/length(locus2row))*colSums(observed)
+ o <- observed[rows,]
+ if(nloci > 1) o <- colSums(o)
+
+ # Estimate enrichment.
+ scores[k] <- ssea.analyze.statistic(o, e)
+ }
+ return(scores)
}
#----------------------------------------------------------------------------
-ssea.analyze.randgenes <- function(db, targets) {
- mod2gen <- db$module2genes
- modsizes <- db$modulesizes
- modlengths <- db$modulelengths
- gene2loci <- db$gene2loci
- locus2row <- db$locus2row
- observed <- db$observed
- expected <- db$expected
-
- # Test target modules.
- scores <- double()
- nrows <- length(locus2row)
- npool <- length(gene2loci)
- for(k in targets) {
- msize <- modsizes[[k]]
- nloci <- modlengths[[k]]
-
- # Collect pre-defined number of markers from random genes.
- loci <- integer()
- genes <- sample.int(npool, (msize + 10))
- while(length(loci) < nloci) {
- for(i in genes) {
- tmp <- gene2loci[[i]]
- loci <- c(loci, tmp)
- }
- loci <- unique(loci)
- genes <- sample.int(npool, msize)
- }
-
- # Determine data rows.
- loci <- loci[1:nloci]
- rows <- locus2row[loci]
-
- # Calculate total counts.
- e <- nloci*expected
- o <- observed[rows,]
- if(nloci > 1) o <- colSums(o)
-
- # Estimate enrichment.
- z <- ssea.analyze.statistic(o, e)
- scores <- c(scores, z)
- }
- return(scores)
+ssea.analyze.randgenes <- function(db, targets, gene_sel) {
+ mod2gen <- db$module2genes
+ modsizes <- db$modulesizes
+ modlengths <- db$modulelengths
+ gene2loci <- db$gene2loci
+ locus2row <- db$locus2row
+ observed <- db$observed
+ expected <- db$expected
+
+ # Test target modules.
+ scores <- double()
+ nrows <- length(locus2row)
+ #npool <- length(gene2loci)
+ for(k in targets) {
+ msize <- modsizes[[k]]
+ nloci <- modlengths[[k]]
+
+ # Collect pre-defined number of markers from random genes.
+ loci <- integer()
+ #genes <- sample.int(npool, (msize + 10))
+ genes <- sample(gene_sel, (msize + 10))
+ while(length(loci) < nloci) {
+ for(i in genes) {
+ tmp <- gene2loci[[i]]
+ loci <- c(loci, tmp)
+ }
+ loci <- unique(loci)
+ genes <- sample(gene_sel, (msize + 10))
+ }
+
+ # Determine data rows.
+ loci <- loci[1:nloci]
+ rows <- locus2row[loci]
+
+ # Calculate total counts.
+ #e <- nloci*expected
+ e <- (nloci/length(locus2row))*colSums(observed)
+ o <- observed[rows,]
+ if(nloci > 1) o <- colSums(o)
+
+ # Estimate enrichment.
+ z <- ssea.analyze.statistic(o, e)
+ scores <- c(scores, z)
+ }
+ return(scores)
}
#----------------------------------------------------------------------------
-ssea.analyze.randloci <- function(db, targets) {
- modlengths <- db$modulelengths
- locus2row <- db$locus2row
- observed <- db$observed
- expected <- db$expected
-
- # Test target modules.
- scores <- double()
- nrows <- length(locus2row)
- for(k in targets) {
- nloci <- modlengths[[k]]
-
- # Determine data rows.
- loci <- sample.int(nrows, nloci)
- rows <- locus2row[loci]
-
- # Calculate total counts.
- e <- nloci*expected
- o <- observed[rows,]
- if(nloci > 1) o <- colSums(o)
-
- # Estimate enrichment.
- z <- ssea.analyze.statistic(o, e)
- scores <- c(scores, z)
- }
-
- # Return results.
- return(scores)
+ssea.analyze.randloci <- function(db, targets) {
+ modlengths <- db$modulelengths
+ locus2row <- db$locus2row
+ observed <- db$observed
+ expected <- db$expected
+
+ # Test target modules.
+ scores <- double()
+ nrows <- length(locus2row)
+ for(k in targets) {
+ nloci <- modlengths[[k]]
+
+ # Determine data rows.
+ loci <- sample.int(nrows, nloci)
+ rows <- locus2row[loci]
+
+ # Calculate total counts.
+ #e <- nloci*expected
+ e <- (nloci/length(locus2row))*colSums(observed)
+ o <- observed[rows,]
+ if(nloci > 1) o <- colSums(o)
+
+ # Estimate enrichment.
+ z <- ssea.analyze.statistic(o, e)
+ scores <- c(scores, z)
+ }
+
+ # Return results.
+ return(scores)
}
#----------------------------------------------------------------------------
diff --git a/build/vignette.rds b/build/vignette.rds
index 3652ad0..9c13842 100644
Binary files a/build/vignette.rds and b/build/vignette.rds differ
diff --git a/inst/doc/Mergeomics.Rnw b/inst/doc/Mergeomics.Rnw
index 294180d..3426f00 100644
--- a/inst/doc/Mergeomics.Rnw
+++ b/inst/doc/Mergeomics.Rnw
@@ -44,10 +44,10 @@ xyang123 at ucla.edu
If you use mergeomics in published research, please cite:
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
\tableofcontents
diff --git a/inst/doc/Mergeomics.pdf b/inst/doc/Mergeomics.pdf
index 90dd8e8..e521779 100644
Binary files a/inst/doc/Mergeomics.pdf and b/inst/doc/Mergeomics.pdf differ
diff --git a/inst/unitTests/test_ssea.analyze.R b/inst/unitTests/test_ssea.analyze.R
index 061c1d0..3c6593e 100644
--- a/inst/unitTests/test_ssea.analyze.R
+++ b/inst/unitTests/test_ssea.analyze.R
@@ -23,7 +23,7 @@ test_ssea.analyze <- function() {
## compare the pvals with the expected ones:
## since we set the seed for random # generation, we know the exact
## results for our input sets:
- checkEqualsNumeric(sort(as.numeric(job.msea$results$P))[1],
- 3.67e-33, tolerance=1.0e-4)
+ checkEqualsNumeric(sort(as.numeric(job.msea$results$P))[2],
+ 2.25e-60, tolerance=1.0e-4)
}
diff --git a/man/MSEA.KDA.onestep.Rd b/man/MSEA.KDA.onestep.Rd
index 158778d..79dd583 100644
--- a/man/MSEA.KDA.onestep.Rd
+++ b/man/MSEA.KDA.onestep.Rd
@@ -81,10 +81,10 @@ plan$nperm <- 100 ## default value is 20000
\donttest{plan <- MSEA.KDA.onestep(plan, apply.MSEA=TRUE)}
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Zeyneb Kurt
diff --git a/man/Mergeomics-package.Rd b/man/Mergeomics-package.Rd
index 095d661..e25b2b4 100644
--- a/man/Mergeomics-package.Rd
+++ b/man/Mergeomics-package.Rd
@@ -58,10 +58,10 @@ Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang, Xia Yang
Maintainer: <zeyneb at ucla.edu>
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\keyword{
diff --git a/man/kda.analyze.Rd b/man/kda.analyze.Rd
index 5a8ecac..cc7257e 100644
--- a/man/kda.analyze.Rd
+++ b/man/kda.analyze.Rd
@@ -83,10 +83,10 @@ file.remove("subsetof.supersets.txt")
unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.analyze.exec.Rd b/man/kda.analyze.exec.Rd
index 1f5441c..71c452d 100644
--- a/man/kda.analyze.exec.Rd
+++ b/man/kda.analyze.exec.Rd
@@ -57,13 +57,13 @@ job.kda$depth<-1
## 0 means we do not consider the directions of the regulatory interactions
## while 1 is opposite.
job.kda$direction<-1
-job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
-
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+
## kda.start() process takes long time while seeking hubs in the given net
## Here, we used a very small subset of the module list (1st 10 mods
## from the original module file):
moddata <- tool.read(job.kda$modfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
## save this to a temporary file and set its path as new job.kda$modfile:
@@ -81,17 +81,17 @@ graph <- job.kda$graph ## we need to import a network
nsim <- job.kda$nperm ## number of simulations
## calculate p-vals of KDs for the specified module:
# p <- kda.analyze.exec(memb, graph, nsim) ## see kda.analyze() for details
-
+
## Remove the temporary files used for the test:
file.remove("subsetof.supersets.txt")
## remove the results folder
unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.analyze.simulate.Rd b/man/kda.analyze.simulate.Rd
index 18b1922..eddbdea 100644
--- a/man/kda.analyze.simulate.Rd
+++ b/man/kda.analyze.simulate.Rd
@@ -71,13 +71,13 @@ job.kda$depth<-1
## 0 means we do not consider the directions of the regulatory interactions
## while 1 is opposite.
job.kda$direction<-1
-job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
-
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+
## kda.start() process takes long time while seeking hubs in the given net
## Here, we used a very small subset of the module list (1st 10 mods
## from the original module file):
moddata <- tool.read(job.kda$modfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
## save this to a temporary file and set its path as new job.kda$modfile:
@@ -116,7 +116,7 @@ nmemb <- length(memb)
# x <- kda.analyze.simulate(obs[k], g, nmemb, nnodes, 200)
## Then, use x to estimate preliminary and final P-values.
## See kda.analyze() for more detail
-
+
## Remove the temporary files used for the test:
file.remove("subsetof.supersets.txt")
## remove the results folder
@@ -124,10 +124,10 @@ unlink("Results", recursive = TRUE)
# } ## finishing for loop
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.analyze.test.Rd b/man/kda.analyze.test.Rd
index 49c0414..0594af3 100644
--- a/man/kda.analyze.test.Rd
+++ b/man/kda.analyze.test.Rd
@@ -66,13 +66,13 @@ job.kda$depth<-1
## 0 means we do not consider the directions of the regulatory interactions
## while 1 is opposite.
job.kda$direction<-1
-job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
-
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+
## kda.start() process takes long time while seeking hubs in the given net
## Here, we used a very small subset of the module list (1st 10 mods
## from the original module file):
moddata <- tool.read(job.kda$modfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
## save this to a temporary file and set its path as new job.kda$modfile:
@@ -105,17 +105,17 @@ obs[k] <- kda.analyze.test(g$RANK, g$STRENG, memb, nnodes)
## Then, estimate preliminary and final P-values by kda.analyze.simulate()
## See kda.analyze() for more details
-
+
## Remove the temporary files used for the test:
file.remove("subsetof.supersets.txt")
## remove the results folder
unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.configure.Rd b/man/kda.configure.Rd
index 6c505cc..764395e 100644
--- a/man/kda.configure.Rd
+++ b/man/kda.configure.Rd
@@ -84,10 +84,10 @@ if(is.null(plan$seed)) plan$seed <- 1
## these are the main parameters needed to be assigned default values.
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.finish.Rd b/man/kda.finish.Rd
index 683d0d0..9a33cfb 100644
--- a/man/kda.finish.Rd
+++ b/man/kda.finish.Rd
@@ -64,10 +64,10 @@ job.kda <- kda.finish(job.kda)
unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.finish.estimate.Rd b/man/kda.finish.estimate.Rd
index 85de67a..a88a8f6 100644
--- a/man/kda.finish.estimate.Rd
+++ b/man/kda.finish.estimate.Rd
@@ -40,10 +40,10 @@ data(job_kda_analyze)
# }
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.finish.save.Rd b/man/kda.finish.save.Rd
index b47c56a..42d49a9 100644
--- a/man/kda.finish.save.Rd
+++ b/man/kda.finish.save.Rd
@@ -46,10 +46,10 @@ data(job_kda_analyze)
# }
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.finish.summarize.Rd b/man/kda.finish.summarize.Rd
index 5849c6c..dfd3d3e 100644
--- a/man/kda.finish.summarize.Rd
+++ b/man/kda.finish.summarize.Rd
@@ -50,10 +50,10 @@ data(job_kda_analyze)
## See kda.analyze() and kda.finish() for details
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.finish.trim.Rd b/man/kda.finish.trim.Rd
index 7b103c5..7b247c3 100644
--- a/man/kda.finish.trim.Rd
+++ b/man/kda.finish.trim.Rd
@@ -44,10 +44,10 @@ data(job_kda_analyze)
## See kda.analyze() and kda.finish() for details
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.prepare.Rd b/man/kda.prepare.Rd
index 45922f0..df63d8c 100644
--- a/man/kda.prepare.Rd
+++ b/man/kda.prepare.Rd
@@ -64,13 +64,13 @@ job.kda$depth<-1
## 0 means we do not consider the directions of the regulatory interactions
## while 1 is opposite.
job.kda$direction <- 1
-job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
-
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+
## kda.start() process takes long time while seeking hubs in the given net
## Here, we used a very small subset of the module list (1st 10 mods
## from the original module file):
moddata <- tool.read(job.kda$modfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
## save this to a temporary file and set its path as new job.kda$modfile:
@@ -84,17 +84,17 @@ job.kda <- kda.start(job.kda)
## Find the hubs, co-hubs, and hub neighborhoods (hubnets), etc.:
job.kda <- kda.prepare(job.kda)
## After that, we need to call kda.analyze() and kda.finish()
-
+
## Remove the temporary files used for the test:
file.remove("subsetof.supersets.txt")
## remove the results folder
unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.prepare.overlap.Rd b/man/kda.prepare.overlap.Rd
index 9b91bde..79ee090 100644
--- a/man/kda.prepare.overlap.Rd
+++ b/man/kda.prepare.overlap.Rd
@@ -73,10 +73,10 @@ job.kda$direction <- 1
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.prepare.screen.Rd b/man/kda.prepare.screen.Rd
index 0af1d76..179c40d 100644
--- a/man/kda.prepare.screen.Rd
+++ b/man/kda.prepare.screen.Rd
@@ -81,10 +81,10 @@ job.kda$direction <- 1
## Then, extract overlapping co-hubs by kda.prepare.overlap()
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.start.Rd b/man/kda.start.Rd
index de7d76c..e6ba5d7 100644
--- a/man/kda.start.Rd
+++ b/man/kda.start.Rd
@@ -57,13 +57,13 @@ job.kda$depth<-1
## 0 means we do not consider the directions of the regulatory interactions
## while 1 is opposite.
job.kda$direction <- 1
-job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
-
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+
## kda.start() process takes long time while seeking hubs in the given net
## Here, we used a very small subset of the module list (1st 10 mods
## from the original module file):
moddata <- tool.read(job.kda$modfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
## save this to a temporary file and set its path as new job.kda$modfile:
@@ -75,15 +75,15 @@ job.kda <- kda.configure(job.kda)
job.kda <- kda.start(job.kda)
## Remove the temporary files used for the test:
-file.remove("subsetof.supersets.txt")
+file.remove("subsetof.supersets.txt")
## remove the results folder
unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.start.edges.Rd b/man/kda.start.edges.Rd
index 8d0309a..549e563 100644
--- a/man/kda.start.edges.Rd
+++ b/man/kda.start.edges.Rd
@@ -23,10 +23,10 @@ maximum allowed node degree, edge direction (\code{job$netfile},
is smaller than the maximum allowed node degree}
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\examples{
job.kda <- list()
diff --git a/man/kda.start.identify.Rd b/man/kda.start.identify.Rd
index 3ef2bc3..43efe61 100644
--- a/man/kda.start.identify.Rd
+++ b/man/kda.start.identify.Rd
@@ -39,10 +39,10 @@ dd <- kda.start.identify(aa, "NODE", c("GeneA"))
dd
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda.start.modules.Rd b/man/kda.start.modules.Rd
index ffa5ff6..ac46941 100644
--- a/man/kda.start.modules.Rd
+++ b/man/kda.start.modules.Rd
@@ -56,10 +56,10 @@ job.kda <- kda.configure(job.kda)
unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda2cytoscape.Rd b/man/kda2cytoscape.Rd
index 7fdaf0f..2df2749 100644
--- a/man/kda2cytoscape.Rd
+++ b/man/kda2cytoscape.Rd
@@ -88,10 +88,10 @@ job.kda <- kda2cytoscape(job.kda)
unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Zeyneb Kurt
diff --git a/man/kda2cytoscape.colorize.Rd b/man/kda2cytoscape.colorize.Rd
index 515720a..f98d927 100644
--- a/man/kda2cytoscape.colorize.Rd
+++ b/man/kda2cytoscape.colorize.Rd
@@ -33,10 +33,10 @@ colors will be assigned to that node (one color for each of these modules)
}
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\examples{
## Trace module memberships for each KD and its neighbors
diff --git a/man/kda2cytoscape.colormap.Rd b/man/kda2cytoscape.colormap.Rd
index b3f9908..3ac3851 100644
--- a/man/kda2cytoscape.colormap.Rd
+++ b/man/kda2cytoscape.colormap.Rd
@@ -23,10 +23,10 @@ color.number = 5 ## let us assume we have 5 modules, assign 1 color to each:
palette <- kda2cytoscape.colormap(color.number)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Zeyneb Kurt
diff --git a/man/kda2cytoscape.drivers.Rd b/man/kda2cytoscape.drivers.Rd
index 21487e8..4b7a50e 100644
--- a/man/kda2cytoscape.drivers.Rd
+++ b/man/kda2cytoscape.drivers.Rd
@@ -61,10 +61,10 @@ drivers <- kda2cytoscape.drivers(job.kda$results, modules, ndriv=2)
unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Zeyneb Kurt
diff --git a/man/kda2cytoscape.edges.Rd b/man/kda2cytoscape.edges.Rd
index 87bd269..f8018a5 100644
--- a/man/kda2cytoscape.edges.Rd
+++ b/man/kda2cytoscape.edges.Rd
@@ -65,10 +65,10 @@ job.kda$depth, job.kda$direction)
unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Zeyneb Kurt
diff --git a/man/kda2cytoscape.exec.Rd b/man/kda2cytoscape.exec.Rd
index c566d2f..54d7ced 100644
--- a/man/kda2cytoscape.exec.Rd
+++ b/man/kda2cytoscape.exec.Rd
@@ -89,10 +89,10 @@ job.kda$depth)
unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Zeyneb Kurt
diff --git a/man/kda2cytoscape.identify.Rd b/man/kda2cytoscape.identify.Rd
index 05aa05e..a0fa9ae 100644
--- a/man/kda2cytoscape.identify.Rd
+++ b/man/kda2cytoscape.identify.Rd
@@ -45,10 +45,10 @@ dd <- kda2cytoscape.identify(aa, "NODE", c("GeneA"))
dd
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Zeyneb Kurt
diff --git a/man/kda2himmeli.Rd b/man/kda2himmeli.Rd
index cae9d65..e3dbde6 100644
--- a/man/kda2himmeli.Rd
+++ b/man/kda2himmeli.Rd
@@ -70,10 +70,10 @@ unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda2himmeli.colorize.Rd b/man/kda2himmeli.colorize.Rd
index c3522bc..6761426 100644
--- a/man/kda2himmeli.colorize.Rd
+++ b/man/kda2himmeli.colorize.Rd
@@ -34,10 +34,10 @@ modules)
}
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\examples{
## Trace module memberships for each KD
diff --git a/man/kda2himmeli.colormap.Rd b/man/kda2himmeli.colormap.Rd
index bef93e8..604437b 100644
--- a/man/kda2himmeli.colormap.Rd
+++ b/man/kda2himmeli.colormap.Rd
@@ -23,10 +23,10 @@ color.number = 5 ## let us assume we have 5 modules, assign 1 color to each:
palette <- kda2himmeli.colormap(color.number)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda2himmeli.drivers.Rd b/man/kda2himmeli.drivers.Rd
index ae5c402..9cbd154 100644
--- a/man/kda2himmeli.drivers.Rd
+++ b/man/kda2himmeli.drivers.Rd
@@ -63,10 +63,10 @@ drivers <- kda2himmeli.drivers(job.kda$results, modules, ndriv=2)
unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda2himmeli.edges.Rd b/man/kda2himmeli.edges.Rd
index a4e405d..8f171f9 100644
--- a/man/kda2himmeli.edges.Rd
+++ b/man/kda2himmeli.edges.Rd
@@ -67,10 +67,10 @@ job.kda$depth, job.kda$direction)
unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda2himmeli.exec.Rd b/man/kda2himmeli.exec.Rd
index 1ba62d4..e03fbf2 100644
--- a/man/kda2himmeli.exec.Rd
+++ b/man/kda2himmeli.exec.Rd
@@ -101,10 +101,10 @@ tmp <- kda2himmeli.exec(job.kda, valdata, drivers[rows,], mods, palette)
unlink("Results", recursive = TRUE)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/kda2himmeli.identify.Rd b/man/kda2himmeli.identify.Rd
index e234908..96581b4 100644
--- a/man/kda2himmeli.identify.Rd
+++ b/man/kda2himmeli.identify.Rd
@@ -45,10 +45,10 @@ dd <- kda2himmeli.identify(aa, "NODE", c("GeneA"))
dd
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.analyze.Rd b/man/ssea.analyze.Rd
index ed48676..5a1d0e5 100644
--- a/man/ssea.analyze.Rd
+++ b/man/ssea.analyze.Rd
@@ -17,7 +17,7 @@ MSEA is performed with either gene-level or marker-level permutations
based on Gaussian distribution.
}
\usage{
-ssea.analyze(job)
+ssea.analyze(job, trim_start, trim_end)
}
\arguments{
\item{job}{
@@ -29,6 +29,16 @@ of permutations \code{(job$nperm)} for the permutation test, and the
database that uses indexed identities for modules, genes, and markers
(e.g. loci) \code{(job$database)}.
}
+\item{trim_start}{percentile taken from the beginning for
+trimming away a defined proportion of genes with significant trait
+association to avoid signal inflation of null background in gene permutation.
+Default value is 0.002.
+}
+\item{trim_end}{percentile taken from the ending point for
+trimming away a defined proportion of genes with significant trait
+association to avoid signal inflation of null background in gene permutation.
+Default value is 0.998.
+}
}
\details{
\code{\link{ssea.analyze}} associates the gene sets (pathways or
@@ -41,6 +51,9 @@ loci) are calculated. Then, a Gaussian distribution based simulation is
performed, by using the statistics of the observed scores (mean, std.dev.,
etc.), to obtain the estimated enrichment scores, enrichment frequencies,
and other statistics e.g. p-values for the pathways.
+\code{ssea.analyze} trims away a defined proportion of genes with
+significant trait association to avoid signal inflation of null background
+in gene permutation by using \code{trim_start} and \code{trim_end}.
}
\value{
\item{job }{the updated data frame including results: indexed module
@@ -71,7 +84,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -98,10 +111,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.analyze.observe.Rd b/man/ssea.analyze.observe.Rd
index 14b7587..ca255e0 100644
--- a/man/ssea.analyze.observe.Rd
+++ b/man/ssea.analyze.observe.Rd
@@ -56,7 +56,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -88,10 +88,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.analyze.randgenes.Rd b/man/ssea.analyze.randgenes.Rd
index 2d9c05a..88e4173 100644
--- a/man/ssea.analyze.randgenes.Rd
+++ b/man/ssea.analyze.randgenes.Rd
@@ -8,7 +8,7 @@ Estimate enrichment from randomized genes
randomizing the genes from all modules (from database - db)
}
\usage{
-ssea.analyze.randgenes(db, targets)
+ssea.analyze.randgenes(db, targets, gene_sel)
}
\arguments{
\item{db}{
@@ -28,7 +28,10 @@ each quantile point for each marker.
expected: 1.0 - quantile points.
}
}
-\item{targets}{all modules}
+\item{targets}{all modules}
+\item{gene_sel}{selected genes to be trimmed away to avoid signal inflation
+ of null background in gene permutation.
+}
}
\value{
\item{scores }{randomly simulated enrichment scores}
@@ -55,7 +58,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -76,7 +79,37 @@ job.msea <- ssea.prepare(job.msea)
job.msea <- ssea.control(job.msea)
## Observed enrichment scores.
-db <- job.msea$database
+db <- job.msea$database
+gene2loci <- db$gene2loci
+locus2row <- db$locus2row
+observed <- db$observed
+#Calcuate individual gene enrichment score
+trim_scores <- rep(NA, length(gene2loci))
+for(k in 1:length(trim_scores)) {
+ genes <- k
+ # Collect markers.
+ loci <- integer()
+ for(i in genes)
+ loci <- c(loci, gene2loci[[i]])
+
+ # Determine data rows.
+ loci <- unique(loci)
+ rows <- locus2row[loci]
+ nloci <- length(rows)
+
+ # Calculate total counts.
+ e <- (nloci/length(locus2row))*colSums(observed)
+ o <- observed[rows,]
+ if(nloci > 1) o <- colSums(o)
+
+ # Estimate enrichment.
+ trim_scores[k] <- ssea.analyze.statistic(o, e)
+}
+trim_start=0.002 # default
+trim_end=1-trim_start
+cutoff=as.numeric(quantile(trim_scores,probs=c(trim_start,trim_end)))
+gene_sel=which(trim_scores>cutoff[1]&trim_scores<cutoff[2])
+
scores <- ssea.analyze.observe(db)
nmods <- length(scores)
@@ -97,7 +130,7 @@ for(i in 1:nmods) scoresets[[i]] <- double()
## Simulate random scores.
## within a for loop: check capacity, find new statistics, update snull
## distribution (simulated null distr.) by permuting genes
-snull <- ssea.analyze.randgenes(db, targets)
+snull <- ssea.analyze.randgenes(db, targets, gene_sel)
## Remove the temporary files used for the test:
file.remove("subsetof.coexpr.modules.txt")
@@ -105,10 +138,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.analyze.randloci.Rd b/man/ssea.analyze.randloci.Rd
index 3504d38..77cab69 100644
--- a/man/ssea.analyze.randloci.Rd
+++ b/man/ssea.analyze.randloci.Rd
@@ -56,7 +56,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -106,10 +106,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.analyze.simulate.Rd b/man/ssea.analyze.simulate.Rd
index 87addb9..0957f4e 100644
--- a/man/ssea.analyze.simulate.Rd
+++ b/man/ssea.analyze.simulate.Rd
@@ -9,7 +9,7 @@ randomly permuting database with respect to the specified permutation
type (either gene-level or marker-level).
}
\usage{
-ssea.analyze.simulate(db, observ, nperm, permtype)
+ssea.analyze.simulate(db, observ, nperm, permtype, trim_start, trim_end)
}
\arguments{
\item{db}{
@@ -38,6 +38,16 @@ maximum nubmer of permutations (for simulation)
\item{permtype}{
permutation type (either gene or locus)
}
+\item{trim_start}{percentile taken from the beginning for
+trimming away a defined proportion of genes with significant trait
+association to avoid signal inflation of null background in gene permutation.
+Default value is 0.002.
+}
+\item{trim_end}{percentile taken from the ending point for
+trimming away a defined proportion of genes with significant trait
+association to avoid signal inflation of null background in gene permutation.
+Default value is 0.998.
+}
}
\value{
\item{scoresets }{simulated score lists for the statistically significant
@@ -65,7 +75,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -91,8 +101,11 @@ scores <- ssea.analyze.observe(db)
nmods <- length(scores)
## Simulated scores.
-nperm <- job.msea$nperm
-nullsets <- ssea.analyze.simulate(db, scores, nperm, job.msea$permtype)
+nperm <- job.msea$nperm
+trim_start=0.002 # default
+trim_end=1-trim_start
+nullsets <- ssea.analyze.simulate(db, scores, nperm, job.msea$permtype,
+trim_start, trim_end)
## Remove the temporary files used for the test:
file.remove("subsetof.coexpr.modules.txt")
@@ -100,10 +113,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.analyze.statistic.Rd b/man/ssea.analyze.statistic.Rd
index bc64531..7b5f59f 100644
--- a/man/ssea.analyze.statistic.Rd
+++ b/man/ssea.analyze.statistic.Rd
@@ -32,10 +32,10 @@ e <- rnorm(1)
z <- ssea.analyze.statistic(o, e)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.control.Rd b/man/ssea.control.Rd
index 69b05b0..561008d 100644
--- a/man/ssea.control.Rd
+++ b/man/ssea.control.Rd
@@ -61,7 +61,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -87,10 +87,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.finish.Rd b/man/ssea.finish.Rd
index 4fcf00c..80bc6b0 100644
--- a/man/ssea.finish.Rd
+++ b/man/ssea.finish.Rd
@@ -69,7 +69,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -97,10 +97,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.finish.details.Rd b/man/ssea.finish.details.Rd
index c997486..b65c423 100644
--- a/man/ssea.finish.details.Rd
+++ b/man/ssea.finish.details.Rd
@@ -52,7 +52,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -89,10 +89,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.finish.fdr.Rd b/man/ssea.finish.fdr.Rd
index 4ada3a6..bf442e0 100644
--- a/man/ssea.finish.fdr.Rd
+++ b/man/ssea.finish.fdr.Rd
@@ -58,7 +58,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -89,10 +89,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.finish.genes.Rd b/man/ssea.finish.genes.Rd
index 1a92742..70e85b3 100644
--- a/man/ssea.finish.genes.Rd
+++ b/man/ssea.finish.genes.Rd
@@ -56,7 +56,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -89,10 +89,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.meta.Rd b/man/ssea.meta.Rd
index 2f03dbb..20d05fc 100644
--- a/man/ssea.meta.Rd
+++ b/man/ssea.meta.Rd
@@ -88,10 +88,10 @@ meta.results <- ssea.meta(job.multiple.msea, job.multiple.msea[[1]]$label,
job.multiple.msea[[1]]$folder)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.prepare.Rd b/man/ssea.prepare.Rd
index c418a49..63f08bf 100644
--- a/man/ssea.prepare.Rd
+++ b/man/ssea.prepare.Rd
@@ -78,7 +78,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -103,10 +103,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.prepare.counts.Rd b/man/ssea.prepare.counts.Rd
index 7f5df03..17eedba 100644
--- a/man/ssea.prepare.counts.Rd
+++ b/man/ssea.prepare.counts.Rd
@@ -47,7 +47,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -94,10 +94,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.prepare.structure.Rd b/man/ssea.prepare.structure.Rd
index b4e486d..842463e 100644
--- a/man/ssea.prepare.structure.Rd
+++ b/man/ssea.prepare.structure.Rd
@@ -55,7 +55,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -92,10 +92,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.start.Rd b/man/ssea.start.Rd
index 9093f83..9ab41df 100644
--- a/man/ssea.start.Rd
+++ b/man/ssea.start.Rd
@@ -95,10 +95,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.start.configure.Rd b/man/ssea.start.configure.Rd
index 06da67d..f8848b3 100644
--- a/man/ssea.start.configure.Rd
+++ b/man/ssea.start.configure.Rd
@@ -70,7 +70,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -95,10 +95,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.start.identify.Rd b/man/ssea.start.identify.Rd
index f4ff70b..1c14ba7 100644
--- a/man/ssea.start.identify.Rd
+++ b/man/ssea.start.identify.Rd
@@ -38,10 +38,10 @@ dd <- ssea.start.identify(aa, "NODE", c("GeneA"))
dd
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea.start.relabel.Rd b/man/ssea.start.relabel.Rd
index 57dd51e..65da1d5 100644
--- a/man/ssea.start.relabel.Rd
+++ b/man/ssea.start.relabel.Rd
@@ -44,7 +44,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -101,10 +101,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea2kda.Rd b/man/ssea2kda.Rd
index 95be42b..690852d 100644
--- a/man/ssea2kda.Rd
+++ b/man/ssea2kda.Rd
@@ -120,10 +120,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea2kda.analyze.Rd b/man/ssea2kda.analyze.Rd
index d9ddf10..ed56688 100644
--- a/man/ssea2kda.analyze.Rd
+++ b/man/ssea2kda.analyze.Rd
@@ -52,7 +52,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -120,10 +120,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/ssea2kda.import.Rd b/man/ssea2kda.import.Rd
index 31ef35a..326f3d0 100644
--- a/man/ssea2kda.import.Rd
+++ b/man/ssea2kda.import.Rd
@@ -44,7 +44,7 @@ job.msea$nperm <- 100 ## default value is 20000
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
@@ -80,10 +80,10 @@ file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/tool.aggregate.Rd b/man/tool.aggregate.Rd
index bff8110..998c247 100644
--- a/man/tool.aggregate.Rd
+++ b/man/tool.aggregate.Rd
@@ -40,10 +40,10 @@ cc <- tool.aggregate(aa$GENE)
cc
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/tool.cluster.Rd b/man/tool.cluster.Rd
index f555153..73bfa44 100644
--- a/man/tool.cluster.Rd
+++ b/man/tool.cluster.Rd
@@ -37,7 +37,7 @@ moddata <- tool.read(system.file("extdata",
"modules.mousecoexpr.liver.human.txt", package="Mergeomics"))
## let us cluster the first 10 modules in the module file:
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
## Find clusters.
@@ -49,10 +49,10 @@ nnodes <- length(unique(clustdat$NODE))
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/tool.cluster.static.Rd b/man/tool.cluster.static.Rd
index 9048f30..5dad05e 100644
--- a/man/tool.cluster.static.Rd
+++ b/man/tool.cluster.static.Rd
@@ -31,10 +31,10 @@ hlim <- max(tree$height)
clusters <- tool.cluster.static(tree, hlim)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/tool.coalesce.Rd b/man/tool.coalesce.Rd
index 554cc55..5e77a11 100644
--- a/man/tool.coalesce.Rd
+++ b/man/tool.coalesce.Rd
@@ -38,7 +38,7 @@ moddata <- tool.read(system.file("extdata",
## let us find the overlapping ratio between first 10 modules in the file:
## to merge overlapping modules first collect member genes:
-mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
@@ -54,10 +54,10 @@ moddata <- moddata[,c("MODULE", "GENE", "OVERLAP")]
moddata <- unique(moddata)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/tool.coalesce.exec.Rd b/man/tool.coalesce.exec.Rd
index bf82e2e..b7314b1 100644
--- a/man/tool.coalesce.exec.Rd
+++ b/man/tool.coalesce.exec.Rd
@@ -44,10 +44,10 @@ ncore <- length(members)
res <- tool.coalesce.exec(members, modules, rcutoff, ncore)
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/tool.coalesce.find.Rd b/man/tool.coalesce.find.Rd
index d355c17..ee8c43e 100644
--- a/man/tool.coalesce.find.Rd
+++ b/man/tool.coalesce.find.Rd
@@ -47,10 +47,10 @@ res <- tool.coalesce.merge(clust, ncore)
}
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/man/tool.coalesce.merge.Rd b/man/tool.coalesce.merge.Rd
index 89bfc34..6b18716 100644
--- a/man/tool.coalesce.merge.Rd
+++ b/man/tool.coalesce.merge.Rd
@@ -52,10 +52,10 @@ res <- tool.coalesce.merge(clust, ncore)
}
}
\references{
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
diff --git a/vignettes/Mergeomics.Rnw b/vignettes/Mergeomics.Rnw
index 294180d..3426f00 100644
--- a/vignettes/Mergeomics.Rnw
+++ b/vignettes/Mergeomics.Rnw
@@ -44,10 +44,10 @@ xyang123 at ucla.edu
If you use mergeomics in published research, please cite:
-Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
-Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
-resources to identify pathogenic perturbations to biological systems.
-bioRxiv doi: http://dx.doi.org/10.1101/036012
+Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
+Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
+Mergeomics: multidimensional data integration to identify pathogenic
+perturbations to biological systems. BMC genomics. 2016;17(1):874.
\tableofcontents
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