diff --git a/.Rbuildignore b/.Rbuildignore new file mode 100644 index 0000000..91114bf --- /dev/null +++ b/.Rbuildignore @@ -0,0 +1,2 @@ +^.*\.Rproj$ +^\.Rproj\.user$ diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..807ea25 --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +.Rproj.user +.Rhistory +.RData diff --git a/DESCRIPTION b/DESCRIPTION new file mode 100644 index 0000000..906f22c --- /dev/null +++ b/DESCRIPTION @@ -0,0 +1,25 @@ +Package: directPA +Type: Package +Title: Direction Analysis for Pathways and Kinases +Version: 1.5.1 +Date: 2023-11-14 +Author: Pengyi Yang & Ellis Patrick +Maintainer: Pengyi Yang +Description: Direction analysis is a set of tools designed to identify + combinatorial effects of multiple treatments/conditions on pathways + and kinases profiled by microarray, RNA-seq, proteomics, or phosphoproteomics + data. See Yang P et al (2014) ; and Yang P et al. (2016) . +License: GPL-3 +Depends: + R (>= 3.10.0), +Imports: + grDevices, + graphics, + stats, + plotly, + calibrate +Packaged: 2016-03-22 13:08:32 UTC; Pengyi +NeedsCompilation: no +Repository: CRAN +Date/Publication: 2016-03-22 18:00:22 +RoxygenNote: 7.1.1 diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 6b156fe..0000000 --- a/LICENSE +++ /dev/null @@ -1,675 +0,0 @@ -GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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But first, please read -. - diff --git a/MD5 b/MD5 new file mode 100644 index 0000000..3c8c935 --- /dev/null +++ b/MD5 @@ -0,0 +1,30 @@ +e05721202d78707adb25b5dadb9a905c *DESCRIPTION +32f7448fafd8f272e98f8f3053cc3f51 *NAMESPACE +61911534dedf36247467a549fbf57ec1 *R/directEnrichPlot2d.R +76cad031899e402b3ab2c8ee3bad588f *R/directExplorer2d.R +c193001c14dbc241fd64be4f11202254 *R/directPA.R +016bae5eb003dc005c4dd37ccc50f58e *R/geneStats.R +c94800bbd93527de53b09fc6bbce19a3 *R/pathwayStats.R +0fb8763a7e2602a1004522f451bbea85 *R/rotate2d.R +989f12754c30a855e238dd4a160a9806 *R/rotate3d.R +83648a7a500619337f5f19913ad14f71 *data/HEK.rda +079cc541e6cfd73ded8ab5b20a8687e6 *data/PM.rda +d8b015f9de0c68ca738fa61b8604add3 *data/Pathways.rda +a4829f119c5d601a1663ff68b6f033c9 *data/PhosphoELM.rda +48bf3aaf03017c2519d62984ea9587a2 *data/PhosphoSite.rda +a8abd01dded8fe74481dfc5d6ee6284a *man/HEK.Rd +c02262b58e37b1fefd695441707b8809 *man/PM.Rd +19d4e0276d10cd41f6d577ed5245ea12 *man/Pathways.KEGG.Rd +ffd89f58f9af47d0ff556011a5c3521c *man/Pathways.reactome.Rd +50f5b7321b62385a4af7e5c1ee1ee901 *man/PhosphoELM.human.Rd +8e9a9b800cbdb9e5e9764143c54ef6fb *man/PhosphoELM.mouse.Rd +9224f661163899dbf13b3b3a8775a167 *man/PhosphoSite.human.Rd +02df716ef54fb50fdecd8648c6883235 *man/PhosphoSite.mouse.Rd +3d1ba4de7361b2947991bf8b6f1de90b *man/directEnrichPlot2d.Rd +29b9f292ce686fa8368ab99a2a527a74 *man/directExplorer2d.Rd +8809fdbeedb03dd0c8672be14ef11383 *man/directPA-package.Rd +02069d7456d6905cc361baab683de7f8 *man/directPA.Rd +fcc77f5e75cdbce83340ec66f2777f47 *man/geneStats.Rd +aefc1a7e9de81ead1d8f8d1911002812 *man/pathwayStats.Rd +3e13753f1c19b7fb239f048db2376b20 *man/rotate2d.Rd +574ba8d8e806479f12b078b52b6de344 *man/rotate3d.Rd diff --git a/NAMESPACE b/NAMESPACE new file mode 100644 index 0000000..8152451 --- /dev/null +++ b/NAMESPACE @@ -0,0 +1,15 @@ +# Generated by roxygen2: do not edit by hand + +export(directExplorer2d) +export(directPA) +export(geneStats) +export(kinasePA) +export(pathwayStats) +export(perturbPlot2d) +export(perturbPlot3d) +export(rotate2d) +export(rotate3d) +import(calibrate) +import(grDevices) +import(graphics) +import(stats) diff --git a/source/directPA_1.1/R/directExplorer2d.R b/R/directExplorer2d.R similarity index 75% rename from source/directPA_1.1/R/directExplorer2d.R rename to R/directExplorer2d.R index 41135d9..d8ef077 100644 --- a/source/directPA_1.1/R/directExplorer2d.R +++ b/R/directExplorer2d.R @@ -1,7 +1,8 @@ #' Batch Direction Analysis in 2-dimentional space #' #' Rotate to the direction of interest in polar coordinates by degree (e.g. pi/4). -#' @usage directExplorer2d(Tc, annotation=NULL, gene.method="OSP", path.method="Stouffer", top=10, ...) +#' @usage directExplorer2d(Tc, annotation=NULL, gene.method="OSP", +#' path.method="Stouffer", top=10, nd=8, ...) #' @param Tc a numeric matrix with 2 columns. The rows are genes or phosphorylation sites and the columns #' are treatments vs control statistics. #' @param annotation a list with names correspond to pathways or kinases and elements correspond to @@ -13,6 +14,7 @@ #' sites that belongs to a pathway or kinase. Available methods are Stouffer, OSP, Fisher, and maxP. #' Default method is Stouffer. #' @param top the number of entries to be highlighted in the plot. +#' @param nd the number of directions to plot (4 or 8) #' @param ... parameters for controlling the plot. #' @return The the list of enrichment analysis in tables. #' @export @@ -41,26 +43,44 @@ #' bda$gene.tab[order(bda$gene.tab[,"--"]),][1:10,] #' bda$path.tab[order(bda$path.tab[,"--"]),][1:10,] #' -directExplorer2d <- function(Tc, annotation=NULL, gene.method="OSP", path.method="Stouffer", top=10, ...) { +directExplorer2d <- function(Tc, annotation=NULL, gene.method="OSP", path.method="Stouffer", top=10, nd=8, ...) { directionCode <- c('++','+*','+-','*-','--','-*','-+','*+') - gene.tab <- matrix(NA, nrow(Tc), 8) - colnames(gene.tab) <- directionCode + gene.tab <- matrix(NA, nrow(Tc), nd) rownames(gene.tab) <- rownames(Tc) - + if(nd != 8) { + colnames(gene.tab) <- directionCode[c(1,3,5,7)] + } else { + colnames(gene.tab) <- directionCode + } + path.tab <- NULL if (!is.null(annotation)) { - path.tab <- matrix(NA, length(annotation), 9) - colnames(path.tab) <- c("size", directionCode) - rownames(path.tab) <- names(annotation) + path.tab <- matrix(NA, length(annotation), nd+1) + rownames(path.tab) <- names(annotation) + if(nd != 8) { + colnames(path.tab) <- c("size", directionCode[c(1,3,5,7)]) + } else { + colnames(path.tab) <- c("size", directionCode) + } + } + + ds <- c() + if (nd != 8) { + ds <- c(0, 2, 4, 6) + } else { + ds <- 0:7 } + count <- 0 - for(i in 0:7){ + for(i in ds){ + count <- count + 1 + # gene or phosphorylation site level Tc.zscores <- apply(Tc, 2, function(x){qnorm(rank(x)/(nrow(Tc)+1))}) Tc.rotated <- rotate2d(Tc.zscores, pi/4*i) gene.pvalues <- apply(Tc.rotated, 1, geneStats, gene.method) - gene.tab[,(i+1)] <- gene.pvalues + gene.tab[,count] <- gene.pvalues # pathway or kinase level if (!is.null(annotation)) { @@ -69,7 +89,7 @@ directExplorer2d <- function(Tc, annotation=NULL, gene.method="OSP", path.method if (i == 0) { path.tab[,1] <- unlist(gst[,"size"]) } - path.tab[,(i+2)] <- unlist(gst[,"pvalue"]) + path.tab[,(count+1)] <- unlist(gst[,"pvalue"]) } } @@ -77,11 +97,13 @@ directExplorer2d <- function(Tc, annotation=NULL, gene.method="OSP", path.method plot(Tc, col="gray", pch=16, ...) abline(h=0, v=0, col="gold", lty=2) abline(a=0, b=1, lty = 2, col="darkgreen") - color <- rainbow(8) - for (i in 1:8) { - ids <- names(sort(gene.tab[,i])[1:top]) - points(Tc[ids,], col=color[i], pch=16) - textxy(Tc[ids,1], Tc[ids,2], ids, col = color[i]) + color <- c("red", "orange3", "green4", "#169B48", "blue4", "#0F8AB5", "purple", "gray50") + count <- 0 + for (i in ds) { + count <- count + 1 + ids <- names(sort(gene.tab[,count])[1:top]) + points(Tc[ids,], col=color[i+1], pch=16) + textxy(Tc[ids,1], Tc[ids,2], ids, col = color[i+1]) } results <- list() diff --git a/R/directPA.R b/R/directPA.R new file mode 100644 index 0000000..5fe62b0 --- /dev/null +++ b/R/directPA.R @@ -0,0 +1,129 @@ +#' Direction Analysis for Pathways +#' +#' The main function of direction Analysis. This function takes in a matrix of test statistics with +#' two (2-dimensional space) or three (3-dimensional space) columns, the direction of interests, and +#' the annotation list such as pathway annotation, and test for enrichment of pathways on the specified +#' direction. +#' +#' @usage directPA(Tc, direction, annotation, minSize=5, gene.method="OSP", +#' path.method="Stouffer", visualize=TRUE, ...) +#' +#' @param Tc a numeric matrix. Rows are genes and columns are treatments vs control statistics. +#' @param direction the direction to be tested for enrichment. Either specified as a degree for +#' two-dimensional analysis or as contrast (in a triplet) for three-dimensional analysis. +#' @param annotation a list with names correspond to pathways and elements correspond to genes belong to +#' each pathway, respectively. +#' @param minSize the size of annotation groups to be considered for calculating enrichment. Groups +#' that are smaller than the minSize will be removed from the analysis. +#' @param gene.method the method to be used for integrating statistics across treatments for each gene. +#' Available methods are Stouffer, OSP, Fisher, and maxP. +#' Default method is OSP. +#' @param path.method the method to be used for integrating statistics of all genes that belongs to a +#' pathway. Available methods are Stouffer, OSP, Fisher, and maxP. +#' Default method is Stouffer. +#' @param visualize whether to visualize the plot. +#' @param ... other visualization parameters to pass on. +#' @return a list that contains directional p-values for each gene and directional enrichment for each pathway. +#' @export +#' @examples +#' +#' # load the proteomics dataset +#' data(PM) +#' +#' # load pathway annotations +#' data(Pathways) +#' +#' # display reactome pathways. Could be replaced by any other pathway databases +#' Pathways.reactome[1:5] +#' +#' # direction pathway analysis in 3-dimensional space. Implemnted as rotating by contrast +#' # (1) test combined effect of all 3 treatments (stimulation and inhibitions) vs control (basal) +#' # on the original direction. +#' dPA <- directPA(Tc=PM, direction=c(1,1,1), annotation=Pathways.reactome) +#' dPA$gst[order(unlist(dPA$gst[,1])),][1:20,] +#' # rank substrates on the direciton of interest +#' sort(dPA$gene.pvalues)[1:20] +#' +#' # (2) test combined effect of all 3 treatments vs controls on direction c(1,-1, 0) +#' # this rotates Ins by 0 degree, Wmn by 90 degree, and MK by 45 degree. +#' dPA <- directPA(Tc=PM, direction=c(1,-1,0), annotation=Pathways.reactome) +#' dPA$gst[order(unlist(dPA$gst[,1])),][1:20,] +#' +#' # (3) test combined effect of all 3 perturbations vs controls on direction c(1,-1, 1) +#' # this rotates Ins by 0 degree, Wmn by 90 degree, and MK by 0 degree. +#' dPA <- directPA(Tc=PM, direction=c(1,-1,1), annotation=Pathways.reactome) +#' dPA$gst[order(unlist(dPA$gst[,1])),][1:20,] +#' +directPA <- function(Tc, direction, annotation, minSize=5, gene.method="OSP", path.method="Stouffer", visualize=TRUE, ...){ + ## spherical coordinates for three-dimensional rotation + if (length(direction) == 3) { + + # step 1. convert statistics into z-scores + Tc.zscores <- apply(Tc, 2, function(x){qnorm(rank(x)/(nrow(Tc)+1))}) + + # step 2. rotate z-scores + Tc.rotated <- rotate3d(Tc.zscores, direction) + + # step 3. integrate statistics across treatments + gene.pvalues <- apply(Tc.rotated, 1, geneStats, gene.method) + + if (visualize == TRUE) { + #HC = rainbow(length(gene.pvalues)*1.2) + #plot3d(Tc, col=HC[rank(gene.pvalues)], size=5, ...) + #abclines3d(x=0, y=0, z=0, a=diag(3), col="black", lwd=3) + #abclines3d(x=0, a=direction, col="pink", lwd=5) + + df <- data.frame(Tc) + colnames(df) <- c("x", "y", "z") + df$pvalue <- gene.pvalues[rownames(df)] + + my_col = colorRampPalette(rainbow(12))(100) + + p <- plotly::plot_ly(df, x=~x, y=~y, z=~z, color=~pvalue, colors=my_col, size=5) + p <- plotly::add_markers(p) + p <- plotly::layout(p, scene = list(xaxis = list(title = colnames(Tc)[[1]]), + yaxis = list(title = colnames(Tc)[[2]]), + zaxis = list(title = colnames(Tc)[[3]]))) + print(p) + } + + # step 4. integrate statistics for pathways + gene.zscores <- qnorm(gene.pvalues, lower.tail = FALSE) + gst <- t(sapply(annotation, pathwayStats, gene.zscores, minSize=5, path.method)) + + result <- list() + result$gene.pvalues <- gene.pvalues + result$gst <- gst + return(result) + } + + ## polar coordinates for two-dimensional rotation + if (length(direction) == 1) { + + # step 1. convert statistics into z-scores + Tc.zscores <- apply(Tc, 2, function(x){qnorm(rank(x)/(nrow(Tc)+1))}) + + # step 2. rotate z-scores + Tc.rotated <- rotate2d(Tc.zscores, direction) + + # step 3. integrate statistics across treatments + gene.pvalues <- apply(Tc.rotated, 1, geneStats, gene.method) + + if (visualize == TRUE) { + HC = rainbow(length(gene.pvalues)*1.2) + plot(Tc, col=HC[rank(gene.pvalues)], pch=16, ...) + abline(v = 0,h = 0, lty=2, col="gold") + abline(a=0, b=1, col="darkgreen", lty=2) + abline(a=0, b=-1, col="darkgreen", lty=2) + } + + # step 4. integrate statistics for pathways + gene.zscores <- qnorm(gene.pvalues, lower.tail = FALSE) + gst <- t(sapply(annotation, pathwayStats, gene.zscores, minSize=5, path.method)) + + result <- list() + result$gene.pvalues <- sort(gene.pvalues) + result$pathways <- gst[order(as.numeric(gst[,1])),] + return(result) + } +} diff --git a/source/directPA_1.1/R/geneStats.R b/R/geneStats.R similarity index 98% rename from source/directPA_1.1/R/geneStats.R rename to R/geneStats.R index 2d296dc..c821f85 100644 --- a/source/directPA_1.1/R/geneStats.R +++ b/R/geneStats.R @@ -1,4 +1,4 @@ -#' Gene Level Statistics +#' Molecule Level Statistics #' #' Takes a vector of statistics with each element corresponds to a treatment vs control comparison, #' and calculates a combined statistics accross multiple treatments. diff --git a/R/kinasePA.R b/R/kinasePA.R new file mode 100644 index 0000000..e2503bf --- /dev/null +++ b/R/kinasePA.R @@ -0,0 +1,59 @@ +#' Direction Analysis for Kinases +#' +#' This is a wrapper for runing directPA for kinase perturbation analysis (kinasePA) +#' +#' @usage kinasePA(Tc, direction, annotation, minSize=5, substrate.method="OSP", +#' kinase.method="Stouffer", visualize=TRUE, ...) +#' +#' @param Tc a numeric matrix. The columns are phosphorylation sites and the columns are treatments vs +#' control statistics. +#' @param direction the direction to be tested for enrichment. Either specified as a degree for +#' two-dimensional analysis or as contrast (in a triplet) for three-dimensional analysis. +#' @param annotation a list with names correspond to kinases and elements correspond to substrates belong +#' to each kinase, respectively. +#' @param minSize the size of annotation groups to be considered for calculating enrichment. Groups +#' that are smaller than the minSize will be removed from the analysis. +#' @param substrate.method the method to be used for integrating statistics across treatments for each +#' substrate (phosphorylation site). Available methods are Stouffer, OSP, Fisher, and maxP. +#' Default method is OSP. +#' @param kinase.method the method to be used for integrating statistics of all phosphorylation +#' sites that belongs to a kinase. Available methods are Stouffer, OSP, Fisher, and maxP. +#' Default method is Stouffer. +#' @param visualize whether to visualize the plot. +#' @param ... other visualization parameters to pass on. +#' @return a list that contains directional p-values for each substrate and directional enrichment for +#' each kinase. +#' @export +#' @examples +#' +#' # load the phosphoproteomics dataset +#' data(HEK) +#' +#' # load the kinase-substrate annoations +#' data(PhosphoSite) +#' +#' # direction pathway analysis in 2-dimensional space. Implemented as rotating by degree +#' # (1) test combined effect of Torin1 and Rapamycin vs insul both on "down-regulation" +#' # (180 degree to original direction) +#' kPA <- kinasePA(Tc=HEK, direction=pi, annotation=PhosphoSite.mouse) +#' kPA$kinase[order(unlist(kPA$kinase[,1])),][1:20,] +#' # rank substrates on the direciton of interest +#' sort(kPA$substrate.pvalues)[1:20] +#' +#' # (2) test combined effect of Torin1 and Rapamycin vs insul on "no change and down-regulation" +#' # (135 degree to the original direction) +#' kPA <- kinasePA(Tc=HEK, direction=pi*3/4, annotation=PhosphoSite.mouse) +#' kPA$kinase[order(unlist(kPA$kinase[,1])),][1:20,] +#' +#' # (3) test combined effect of Torin1 and Rapamycin vs insul on "down-regulation and no change" +#' # (225 degree to the original direction) +#' kPA <- kinasePA(Tc=HEK, direction=pi*5/4, annotation=PhosphoSite.mouse) +#' kPA$kinase[order(unlist(kPA$kinase[,1])),][1:20,] +#' +kinasePA <- function(Tc, direction, annotation, minSize=5, substrate.method="OSP", kinase.method="Stouffer", visualize=TRUE, ...){ + dPA <- directPA(Tc, direction, annotation, minSize, gene.method=substrate.method, path.method=kinase.method, visualize, ...) + kPA <- list() + kPA$substrate.pvalues <- dPA$gene.pvalues + kPA$kinase <- dPA$pathways + return(kPA) +} diff --git a/source/directPA_1.1/R/pathwayStats.R b/R/pathwayStats.R similarity index 99% rename from source/directPA_1.1/R/pathwayStats.R rename to R/pathwayStats.R index 0b6f177..107581f 100644 --- a/source/directPA_1.1/R/pathwayStats.R +++ b/R/pathwayStats.R @@ -28,7 +28,7 @@ #' #' # Rotate the matrix by contrast 1, -1, -1 (i.e. up-regulation, down-regulation, dow-regulation). #' PM.rotated <- rotate3d(PM.zscores, contrast = c(1, -1, -1)) -#' +#' #' # combine rotated statistics across treatments #' gene.pvalues <- apply(PM.rotated, 1, geneStats) #' diff --git a/source/directPA_1.1/R/directEnrichPlot2d.R b/R/perturbPlot2d.R similarity index 85% rename from source/directPA_1.1/R/directEnrichPlot2d.R rename to R/perturbPlot2d.R index 44b660a..528a35f 100644 --- a/source/directPA_1.1/R/directEnrichPlot2d.R +++ b/R/perturbPlot2d.R @@ -1,10 +1,10 @@ -#' Direction Enrichment Plot for Pathways and Kinases +#' Perturbation Plot #' #' This function takes in a matrix of test statistics with two columns (2-dimensional space) and the #' annotation list such as pathway annotation or kinase-substrate annotation, and visualize the enrichment #' of pathways or kinases in direction specific manner. #' -#' @usage directEnrichPlot2d(Tc, annotation, minSize=5, ...) +#' @usage perturbPlot2d(Tc, annotation, minSize=5, ...) #' @param Tc a numeric matrix. The columns are genes or phosphorylation sites and the columns are treatments #' vs control statistics. #' @param annotation a list with names correspond to pathways or kinases and elements correspond to genes or @@ -13,6 +13,10 @@ #' that are smaller than the minSize will be removed from the analysis. #' @param ... parameters for controling the plot. #' @return a list of coordinates for pathways or kinases +#' @import graphics +#' @import grDevices +#' @import stats +#' @import calibrate #' @export #' @examples #' # load the phosphoproteomics dataset @@ -21,9 +25,9 @@ #' # load the kinase-substrate annoations #' data(PhosphoSite) #' -#' directEnrichPlot2d(Tc=HEK, annotation=PhosphoSite.mouse, cex=3, xlim=c(-8, 3), ylim=c(-6, 3)) +#' perturbPlot2d(Tc=HEK, annotation=PhosphoSite.mouse, cex=3) #' -directEnrichPlot2d <- function(Tc, annotation, minSize=5, ...) { +perturbPlot2d <- function(Tc, annotation, minSize=5, ...) { # step 1. convert statistics into z-scores Tc.zscores <- apply(Tc, 2, function(x){qnorm(rank(x)/(nrow(Tc)+1))}) @@ -45,7 +49,7 @@ directEnrichPlot2d <- function(Tc, annotation, minSize=5, ...) { Z2 <- unlist(sapply(DE, function(x){x[2]})) # visualization - plot(Z1, Z2, col="darkblue", pch=16, ...) + plot(Z1, Z2, col="darkblue", pch=16, xlab=colnames(Tc)[1], ylab=colnames(Tc)[2], ...) textxy(Z1,Z2, names(DE), col="black", cex=1) abline(v=0, h=0, col="gold", lty=2) abline(a=0, b=1, col="darkgreen", lty=2) diff --git a/R/perturbPlot3d.R b/R/perturbPlot3d.R new file mode 100644 index 0000000..715f4e5 --- /dev/null +++ b/R/perturbPlot3d.R @@ -0,0 +1,61 @@ +#' Perturbation Plot 3D +#' +#' This function takes in a matrix of test statistics with two columns (3-dimensional space) and the +#' annotation list such as pathway annotation or kinase-substrate annotation, and visualize the enrichment +#' of pathways or kinases in direction specific manner. +#' +#' @usage perturbPlot3d(Tc, annotation, minSize=5, ...) +#' @param Tc a numeric matrix. The columns are genes or phosphorylation sites and the columns are treatments +#' vs control statistics. +#' @param annotation a list with names correspond to pathways or kinases and elements correspond to genes or +#' substrates belong to each pathway or kinase, respectively. +#' @param minSize the size of annotation groups to be considered for calculating enrichment. Groups +#' that are smaller than the minSize will be removed from the analysis. +#' @param ... parameters for controling the plot. +#' @return a list of coordinates for pathways or kinases +#' @export +#' +perturbPlot3d <- function(Tc, annotation, minSize=5, ...) { + + # step 1. convert statistics into z-scores + Tc.zscores <- apply(Tc, 2, function(x){qnorm(rank(x)/(nrow(Tc)+1))}) + + # step 2. filter the groups that are smaller than the minimun cutoff + DE = lapply(annotation, function(x){ + if(sum(rownames(Tc.zscores) %in% x) >= minSize) { + X <- Tc.zscores[rownames(Tc.zscores)%in%x,] + n = nrow(X) + Z1 = sum(X[,1])/sqrt(n) + Z2 = sum(X[,2])/sqrt(n) + Z3 = sum(X[,3])/sqrt(n) + list(Z1=Z1, Z2=Z2, Z3=Z3) + } + }) + + # step3. filter DE that has 0 element + DE <- DE[which(sapply(DE, length) != 0)] + + # step4. visualise + df <- data.frame(do.call(rbind, lapply(DE, function(x) unlist(unname(x))))) + colnames(df) <- c("Z1", "Z2", "Z3") + df$pathway <- rownames(df) + + my_col = colorRampPalette(rainbow(12))(100) + t <- list(family = "sans serif", size = 14, color = "black") + + p <- plotly::plot_ly(df, x=~Z1, y=~Z2, z=~Z3, size=5, text=df$pathway) + p <- plotly::add_markers(p) + p <- plotly::add_text(p, textfont = t, textposition = "top right") + p <- plotly::layout(p, scene = list(xaxis = list(title = colnames(df)[[2]]), + yaxis = list(title = colnames(df)[[3]]), + zaxis = list(title = colnames(df)[[4]]))) + print(p) + + + ## return the results + result <- list() + result$Z1 <- df$Z1 + result$Z2 <- df$Z2 + result$Z3 <- df$Z3 + return(result) +} \ No newline at end of file diff --git a/source/directPA_1.1/R/rotate2d.R b/R/rotate2d.R similarity index 100% rename from source/directPA_1.1/R/rotate2d.R rename to R/rotate2d.R diff --git a/source/directPA_1.1/R/rotate3d.R b/R/rotate3d.R similarity index 100% rename from source/directPA_1.1/R/rotate3d.R rename to R/rotate3d.R diff --git a/README.md b/README.md index 91273e2..55bd398 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,38 @@ -directPA +### Direction analysis for pathways and kinases ======== -A package for directional pathway analysis in experiments with multiple perturbation designs. +#### Description +`directPA' is a package designed to identify combinatorial effects of multiple treatments and/or perturbations on pathways and kinases profiled by microarray, RNA-seq, proteomics, or phosphoproteomics data. + +#### Download and install +The relsease version can be downloaded from CRAN [link](https://cran.r-project.org/package=directPA); + +Install the release version from CRAN with `install.packages("directPA")` + +#### Examples +I. for kinase anlaysis on a phosphoproteomics dataset (Humphrey et al. Cell Metab., 2013) +```r +# load the phosphoproteomics dataset +data(HEK) + +# load the kinase-substrate annoations +data(PhosphoSite) + +# direction pathway analysis in 2-dimensional space. Implemented as rotating by degree +# (1) test combined effect of Torin1 and Rapamycin vs insul both on "down-regulation" +# (180 degree to original direction) +kPA <- kinasePA(Tc=HEK, direction=pi, annotation=PhosphoSite.mouse) +kPA$kinase[order(unlist(kPA$kinase[,1])),][1:20,] +# rank substrates on the direciton of interest +sort(kPA$substrate.pvalues)[1:20] + +# (2) test combined effect of Torin1 and Rapamycin vs insul on "no change and down-regulation" +# (135 degree to the original direction) +kPA <- kinasePA(Tc=HEK, direction=pi*3/4, annotation=PhosphoSite.mouse) +kPA$kinase[order(unlist(kPA$kinase[,1])),][1:20,] + +# (3) test combined effect of Torin1 and Rapamycin vs insul on "down-regulation and no change" +# (225 degree to the original direction) +kPA <- kinasePA(Tc=HEK, direction=pi*5/4, annotation=PhosphoSite.mouse) +kPA$kinase[order(unlist(kPA$kinase[,1])),][1:20,] +``` diff --git a/source/directPA_1.1/data/HEK.rda b/data/HEK.rda similarity index 100% rename from source/directPA_1.1/data/HEK.rda rename to data/HEK.rda diff --git a/source/directPA_1.1/data/PM.rda b/data/PM.rda similarity index 100% rename from source/directPA_1.1/data/PM.rda rename to data/PM.rda diff --git a/source/directPA_1.1/data/Pathways.rda b/data/Pathways.rda similarity index 100% rename from source/directPA_1.1/data/Pathways.rda rename to data/Pathways.rda diff --git a/data/PhosphoELM.rda b/data/PhosphoELM.rda new file mode 100644 index 0000000..a2e52fd Binary files /dev/null and b/data/PhosphoELM.rda differ diff --git a/source/directPA_1.1/data/PhosphoSite.rda b/data/PhosphoSite.rda similarity index 100% rename from source/directPA_1.1/data/PhosphoSite.rda rename to data/PhosphoSite.rda diff --git a/directPA.Rproj b/directPA.Rproj new file mode 100644 index 0000000..bf73ff4 --- /dev/null +++ b/directPA.Rproj @@ -0,0 +1,19 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX + +BuildType: Package +PackageUseDevtools: Yes +PackageInstallArgs: --no-multiarch --with-keep.source +PackageCheckArgs: --as-cran +PackageRoxygenize: rd,collate,namespace,vignette diff --git a/directPA_1.0.tar.gz b/directPA_1.0.tar.gz deleted file mode 100644 index 245ec98..0000000 Binary files a/directPA_1.0.tar.gz and /dev/null differ diff --git a/directPA_1.0.zip b/directPA_1.0.zip deleted file mode 100644 index bc6a612..0000000 Binary files a/directPA_1.0.zip and /dev/null differ diff --git a/source/directPA_1.1/man/HEK.Rd b/man/HEK.Rd similarity index 100% rename from source/directPA_1.1/man/HEK.Rd rename to man/HEK.Rd diff --git a/source/directPA_1.1/man/PM.Rd b/man/PM.Rd similarity index 100% rename from source/directPA_1.1/man/PM.Rd rename to man/PM.Rd diff --git a/source/directPA_1.1/man/Pathways.KEGG.Rd b/man/Pathways.KEGG.Rd similarity index 100% rename from source/directPA_1.1/man/Pathways.KEGG.Rd rename to man/Pathways.KEGG.Rd diff --git a/source/directPA_1.1/man/Pathways.reactome.Rd b/man/Pathways.reactome.Rd similarity index 100% rename from source/directPA_1.1/man/Pathways.reactome.Rd rename to man/Pathways.reactome.Rd diff --git a/man/PhosphoELM.human.Rd b/man/PhosphoELM.human.Rd new file mode 100644 index 0000000..ac84afe --- /dev/null +++ b/man/PhosphoELM.human.Rd @@ -0,0 +1,9 @@ +\name{PhosphoELM.human} +\docType{data} +\alias{PhosphoELM.human} +\title{PhosphoELM annotations for human} +\description{ +The data object contains the annotations of kinases and their conrresponding substrates as phosphorylation sites in human. It is extracted +from the PhosphoELM database. For details of PhosphoELM, please refer to the article: Phospho.ELM: a database of phosphorylation sites--update 2008. +Diella, F., Gould, C.M., Chica, C., Via, A. & Gibson, T.J. Nucleic Acids Res. 2008 Jan;36(Database issue):D240-4. +} diff --git a/man/PhosphoELM.mouse.Rd b/man/PhosphoELM.mouse.Rd new file mode 100644 index 0000000..cb96ca5 --- /dev/null +++ b/man/PhosphoELM.mouse.Rd @@ -0,0 +1,9 @@ +\name{PhosphoELM.mouse} +\docType{data} +\alias{PhosphoELM.mouse} +\title{PhosphoELM annotations for mouse} +\description{ +The data object contains the annotations of kinases and their conrresponding substrates as phosphorylation sites in mouse. It is extracted +from the PhosphoELM database. For details of PhosphoELM, please refer to the article: Phospho.ELM: a database of phosphorylation sites--update 2008. +Diella, F., Gould, C.M., Chica, C., Via, A. & Gibson, T.J. Nucleic Acids Res. 2008 Jan;36(Database issue):D240-4. +} diff --git a/source/directPA_1.1/man/PhosphoSite.human.Rd b/man/PhosphoSite.human.Rd similarity index 100% rename from source/directPA_1.1/man/PhosphoSite.human.Rd rename to man/PhosphoSite.human.Rd diff --git a/source/directPA_1.1/man/PhosphoSite.mouse.Rd b/man/PhosphoSite.mouse.Rd similarity index 100% rename from source/directPA_1.1/man/PhosphoSite.mouse.Rd rename to man/PhosphoSite.mouse.Rd diff --git a/source/directPA_1.1/man/directExplorer2d.Rd b/man/directExplorer2d.Rd similarity index 86% rename from source/directPA_1.1/man/directExplorer2d.Rd rename to man/directExplorer2d.Rd index 9a10491..8abfaf8 100644 --- a/source/directPA_1.1/man/directExplorer2d.Rd +++ b/man/directExplorer2d.Rd @@ -1,28 +1,31 @@ -% Generated by roxygen2 (4.1.0): do not edit by hand +% Generated by roxygen2: do not edit by hand % Please edit documentation in R/directExplorer2d.R \name{directExplorer2d} \alias{directExplorer2d} \title{Batch Direction Analysis in 2-dimentional space} \usage{ -directExplorer2d(Tc, annotation=NULL, gene.method="OSP", path.method="Stouffer", top=10, ...) +directExplorer2d(Tc, annotation=NULL, gene.method="OSP", +path.method="Stouffer", top=10, nd=8, ...) } \arguments{ -\item{Tc}{a numeric matrix with 2 columns. The rows are genes or phosphorylation sites and the columns +\item{Tc}{a numeric matrix with 2 columns. The rows are genes or phosphorylation sites and the columns are treatments vs control statistics.} -\item{annotation}{a list with names correspond to pathways or kinases and elements correspond to +\item{annotation}{a list with names correspond to pathways or kinases and elements correspond to genes or substrates belong to each pathway or kinase, respectively.} \item{gene.method}{the method to be used for integrating statistics across treatments for each gene -or phosphorylation site. Available methods are Stouffer, OSP, Fisher, and maxP. +or phosphorylation site. Available methods are Stouffer, OSP, Fisher, and maxP. Default method is OSP.} \item{path.method}{the method to be used for integrating statistics of all genes or phosphorylation -sites that belongs to a pathway or kinase. Available methods are Stouffer, OSP, Fisher, and maxP. +sites that belongs to a pathway or kinase. Available methods are Stouffer, OSP, Fisher, and maxP. Default method is Stouffer.} \item{top}{the number of entries to be highlighted in the plot.} +\item{nd}{the number of directions to plot (4 or 8)} + \item{...}{parameters for controlling the plot.} } \value{ @@ -51,9 +54,9 @@ bda <- directExplorer2d(Tc=HEK, annotation=PhosphoSite.mouse) # -+: down-regulated in the first treatment and up-regulated in the second treatment # *+: unchanged in the first treatment and up-regulated in the second treatment -# sort the most enriched phosphorylation sites and kinases on down-regulaiton from both +# sort the most enriched phosphorylation sites and kinases on down-regulaiton from both # treatments (i.e. "--") and displa the top-10 entries bda$gene.tab[order(bda$gene.tab[,"--"]),][1:10,] bda$path.tab[order(bda$path.tab[,"--"]),][1:10,] -} +} diff --git a/source/directPA_1.0/man/directPA-package.Rd b/man/directPA-package.Rd similarity index 92% rename from source/directPA_1.0/man/directPA-package.Rd rename to man/directPA-package.Rd index f24a827..719046c 100644 --- a/source/directPA_1.0/man/directPA-package.Rd +++ b/man/directPA-package.Rd @@ -14,8 +14,7 @@ corresponding controls. \tabular{ll}{ Package: \tab directPA\cr Type: \tab Package\cr -Version: \tab 1.0\cr -Date: \tab 2013-10-21\cr +Version: \tab 1.5.1\cr License: \tab GPL-3\cr } @@ -25,5 +24,5 @@ Pengyi Yang & Ellis Patrick } \references{ Pengyi Yang, Ellis Patrick, Shi-Xiong Tan, Daniel J. Fazakerley, James Burchfield, Christopher Gribben, Matthew J. Prior, -David E. James, Yee Hwa Yang, Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway, submitted. +David E. James, Yee Hwa Yang, Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway, Bioinformatics, 30(6), 808-814, 2014. } diff --git a/man/directPA.Rd b/man/directPA.Rd new file mode 100644 index 0000000..d49b20d --- /dev/null +++ b/man/directPA.Rd @@ -0,0 +1,72 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/directPA.R +\name{directPA} +\alias{directPA} +\title{Direction Analysis for Pathways} +\usage{ +directPA(Tc, direction, annotation, minSize=5, gene.method="OSP", +path.method="Stouffer", visualize=TRUE, ...) +} +\arguments{ +\item{Tc}{a numeric matrix. Rows are genes and columns are treatments vs control statistics.} + +\item{direction}{the direction to be tested for enrichment. Either specified as a degree for +two-dimensional analysis or as contrast (in a triplet) for three-dimensional analysis.} + +\item{annotation}{a list with names correspond to pathways and elements correspond to genes belong to +each pathway, respectively.} + +\item{minSize}{the size of annotation groups to be considered for calculating enrichment. Groups +that are smaller than the minSize will be removed from the analysis.} + +\item{gene.method}{the method to be used for integrating statistics across treatments for each gene. +Available methods are Stouffer, OSP, Fisher, and maxP. +Default method is OSP.} + +\item{path.method}{the method to be used for integrating statistics of all genes that belongs to a +pathway. Available methods are Stouffer, OSP, Fisher, and maxP. +Default method is Stouffer.} + +\item{visualize}{whether to visualize the plot.} + +\item{...}{other visualization parameters to pass on.} +} +\value{ +a list that contains directional p-values for each gene and directional enrichment for each pathway. +} +\description{ +The main function of direction Analysis. This function takes in a matrix of test statistics with +two (2-dimensional space) or three (3-dimensional space) columns, the direction of interests, and +the annotation list such as pathway annotation, and test for enrichment of pathways on the specified +direction. +} +\examples{ + +# load the proteomics dataset +data(PM) + +# load pathway annotations +data(Pathways) + +# display reactome pathways. Could be replaced by any other pathway databases +Pathways.reactome[1:5] + +# direction pathway analysis in 3-dimensional space. Implemnted as rotating by contrast +# (1) test combined effect of all 3 treatments (stimulation and inhibitions) vs control (basal) +# on the original direction. +dPA <- directPA(Tc=PM, direction=c(1,1,1), annotation=Pathways.reactome) +dPA$gst[order(unlist(dPA$gst[,1])),][1:20,] +# rank substrates on the direciton of interest +sort(dPA$gene.pvalues)[1:20] + +# (2) test combined effect of all 3 treatments vs controls on direction c(1,-1, 0) +# this rotates Ins by 0 degree, Wmn by 90 degree, and MK by 45 degree. +dPA <- directPA(Tc=PM, direction=c(1,-1,0), annotation=Pathways.reactome) +dPA$gst[order(unlist(dPA$gst[,1])),][1:20,] + +# (3) test combined effect of all 3 perturbations vs controls on direction c(1,-1, 1) +# this rotates Ins by 0 degree, Wmn by 90 degree, and MK by 0 degree. +dPA <- directPA(Tc=PM, direction=c(1,-1,1), annotation=Pathways.reactome) +dPA$gst[order(unlist(dPA$gst[,1])),][1:20,] + +} diff --git a/source/directPA_1.1/man/geneStats.Rd b/man/geneStats.Rd similarity index 82% rename from source/directPA_1.1/man/geneStats.Rd rename to man/geneStats.Rd index 455e3ef..6944190 100644 --- a/source/directPA_1.1/man/geneStats.Rd +++ b/man/geneStats.Rd @@ -1,23 +1,23 @@ -% Generated by roxygen2 (4.1.0): do not edit by hand +% Generated by roxygen2: do not edit by hand % Please edit documentation in R/geneStats.R \name{geneStats} \alias{geneStats} -\title{Gene Level Statistics} +\title{Molecule Level Statistics} \usage{ geneStats(T, method="OSP") } \arguments{ -\item{T}{a vector of statistics (z-scores converted) with each element correspond to a treatment vs control +\item{T}{a vector of statistics (z-scores converted) with each element correspond to a treatment vs control comparison.} -\item{method}{the p-value integration method for combining accross multiple treatments. Available methods +\item{method}{the p-value integration method for combining accross multiple treatments. Available methods are Stouffer, OSP, Fisher, and maxP. The default method is OSP.} } \value{ a p-value after integration across treatments. } \description{ -Takes a vector of statistics with each element corresponds to a treatment vs control comparison, +Takes a vector of statistics with each element corresponds to a treatment vs control comparison, and calculates a combined statistics accross multiple treatments. } \examples{ @@ -32,5 +32,5 @@ PM.rotated <- rotate3d(PM.zscores, contrast = c(1, -1, -1)) # combine rotated statistics across treatments gene.pvalues <- apply(PM.rotated, 1, geneStats) -} +} diff --git a/man/kinasePA.Rd b/man/kinasePA.Rd new file mode 100644 index 0000000..3f10dd0 --- /dev/null +++ b/man/kinasePA.Rd @@ -0,0 +1,68 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/kinasePA.R +\name{kinasePA} +\alias{kinasePA} +\title{Direction Analysis for Kinases} +\usage{ +kinasePA(Tc, direction, annotation, minSize=5, substrate.method="OSP", +kinase.method="Stouffer", visualize=TRUE, ...) +} +\arguments{ +\item{Tc}{a numeric matrix. The columns are phosphorylation sites and the columns are treatments vs +control statistics.} + +\item{direction}{the direction to be tested for enrichment. Either specified as a degree for +two-dimensional analysis or as contrast (in a triplet) for three-dimensional analysis.} + +\item{annotation}{a list with names correspond to kinases and elements correspond to substrates belong +to each kinase, respectively.} + +\item{minSize}{the size of annotation groups to be considered for calculating enrichment. Groups +that are smaller than the minSize will be removed from the analysis.} + +\item{substrate.method}{the method to be used for integrating statistics across treatments for each +substrate (phosphorylation site). Available methods are Stouffer, OSP, Fisher, and maxP. +Default method is OSP.} + +\item{kinase.method}{the method to be used for integrating statistics of all phosphorylation +sites that belongs to a kinase. Available methods are Stouffer, OSP, Fisher, and maxP. +Default method is Stouffer.} + +\item{visualize}{whether to visualize the plot.} + +\item{...}{other visualization parameters to pass on.} +} +\value{ +a list that contains directional p-values for each substrate and directional enrichment for +each kinase. +} +\description{ +This is a wrapper for runing directPA for kinase perturbation analysis (kinasePA) +} +\examples{ + +# load the phosphoproteomics dataset +data(HEK) + +# load the kinase-substrate annoations +data(PhosphoSite) + +# direction pathway analysis in 2-dimensional space. Implemented as rotating by degree +# (1) test combined effect of Torin1 and Rapamycin vs insul both on "down-regulation" +# (180 degree to original direction) +kPA <- kinasePA(Tc=HEK, direction=pi, annotation=PhosphoSite.mouse) +kPA$kinase[order(unlist(kPA$kinase[,1])),][1:20,] +# rank substrates on the direciton of interest +sort(kPA$substrate.pvalues)[1:20] + +# (2) test combined effect of Torin1 and Rapamycin vs insul on "no change and down-regulation" +# (135 degree to the original direction) +kPA <- kinasePA(Tc=HEK, direction=pi*3/4, annotation=PhosphoSite.mouse) +kPA$kinase[order(unlist(kPA$kinase[,1])),][1:20,] + +# (3) test combined effect of Torin1 and Rapamycin vs insul on "down-regulation and no change" +# (225 degree to the original direction) +kPA <- kinasePA(Tc=HEK, direction=pi*5/4, annotation=PhosphoSite.mouse) +kPA$kinase[order(unlist(kPA$kinase[,1])),][1:20,] + +} diff --git a/source/directPA_1.1/man/pathwayStats.Rd b/man/pathwayStats.Rd similarity index 88% rename from source/directPA_1.1/man/pathwayStats.Rd rename to man/pathwayStats.Rd index fec4ea5..e5ce2ae 100644 --- a/source/directPA_1.1/man/pathwayStats.Rd +++ b/man/pathwayStats.Rd @@ -1,4 +1,4 @@ -% Generated by roxygen2 (4.1.0): do not edit by hand +% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pathwayStats.R \name{pathwayStats} \alias{pathwayStats} @@ -9,22 +9,22 @@ pathwayStats(PGs, T, minSize=5, method="Stouffer") \arguments{ \item{PGs}{an array of names indicating genes or substrates that belong to a given pathway or kinase.} -\item{T}{a vector of statistics (z-scores converted) with each element correspond to a gene or +\item{T}{a vector of statistics (z-scores converted) with each element correspond to a gene or phosphorylation site that belong to the same pathway or kinase.} -\item{minSize}{the size of annotation groups to be considered for calculating enrichment. Groups +\item{minSize}{the size of annotation groups to be considered for calculating enrichment. Groups that are smaller than the minSize will be removed from the analysis.} -\item{method}{the p-value integration method for combining accross multiple treatments. Available methods +\item{method}{the p-value integration method for combining accross multiple treatments. Available methods are Stouffer, OSP, Fisher, and maxP. The default method is Stouffer.} } \value{ -a doublet corresponding to the enrichment after integration across all genes or substrates +a doublet corresponding to the enrichment after integration across all genes or substrates that belong to the same pathway or kinase, and the size of the mapped genes or substrates to that pathway or kinase. } \description{ -Takes a vector of statistics with each element corresponds to a gene or phosphorylation site, +Takes a vector of statistics with each element corresponds to a gene or phosphorylation site, and calculates a combined statistics for those that belong to the same pathway or kinase. } \examples{ @@ -46,5 +46,5 @@ gene.pvalues <- apply(PM.rotated, 1, geneStats) # compute statistics for all reactome pathways gene.zscores <- qnorm(gene.pvalues, lower.tail = FALSE) gst <- t(sapply(Pathways.reactome, pathwayStats, gene.zscores)) -} +} diff --git a/source/directPA_1.1/man/directEnrichPlot2d.Rd b/man/perturbPlot2d.Rd similarity index 64% rename from source/directPA_1.1/man/directEnrichPlot2d.Rd rename to man/perturbPlot2d.Rd index b08a67a..d401e10 100644 --- a/source/directPA_1.1/man/directEnrichPlot2d.Rd +++ b/man/perturbPlot2d.Rd @@ -1,19 +1,19 @@ -% Generated by roxygen2 (4.1.0): do not edit by hand -% Please edit documentation in R/directEnrichPlot2d.R -\name{directEnrichPlot2d} -\alias{directEnrichPlot2d} -\title{Direction Enrichment Plot for Pathways and Kinases} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/perturbPlot2d.R +\name{perturbPlot2d} +\alias{perturbPlot2d} +\title{Perturbation Plot} \usage{ -directEnrichPlot2d(Tc, annotation, minSize=5, ...) +perturbPlot2d(Tc, annotation, minSize=5, ...) } \arguments{ -\item{Tc}{a numeric matrix. The columns are genes or phosphorylation sites and the columns are treatments +\item{Tc}{a numeric matrix. The columns are genes or phosphorylation sites and the columns are treatments vs control statistics.} \item{annotation}{a list with names correspond to pathways or kinases and elements correspond to genes or substrates belong to each pathway or kinase, respectively.} -\item{minSize}{the size of annotation groups to be considered for calculating enrichment. Groups +\item{minSize}{the size of annotation groups to be considered for calculating enrichment. Groups that are smaller than the minSize will be removed from the analysis.} \item{...}{parameters for controling the plot.} @@ -22,7 +22,7 @@ that are smaller than the minSize will be removed from the analysis.} a list of coordinates for pathways or kinases } \description{ -This function takes in a matrix of test statistics with two columns (2-dimensional space) and the +This function takes in a matrix of test statistics with two columns (2-dimensional space) and the annotation list such as pathway annotation or kinase-substrate annotation, and visualize the enrichment of pathways or kinases in direction specific manner. } @@ -33,6 +33,6 @@ data(HEK) # load the kinase-substrate annoations data(PhosphoSite) -directEnrichPlot2d(Tc=HEK, annotation=PhosphoSite.mouse, cex=3, xlim=c(-8, 3), ylim=c(-6, 3)) -} +perturbPlot2d(Tc=HEK, annotation=PhosphoSite.mouse, cex=3) +} diff --git a/man/perturbPlot3d.Rd b/man/perturbPlot3d.Rd new file mode 100644 index 0000000..726864d --- /dev/null +++ b/man/perturbPlot3d.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/perturbPlot3d.R +\name{perturbPlot3d} +\alias{perturbPlot3d} +\title{Perturbation Plot 3D} +\usage{ +perturbPlot3d(Tc, annotation, minSize=5, ...) +} +\arguments{ +\item{Tc}{a numeric matrix. The columns are genes or phosphorylation sites and the columns are treatments +vs control statistics.} + +\item{annotation}{a list with names correspond to pathways or kinases and elements correspond to genes or +substrates belong to each pathway or kinase, respectively.} + +\item{minSize}{the size of annotation groups to be considered for calculating enrichment. Groups +that are smaller than the minSize will be removed from the analysis.} + +\item{...}{parameters for controling the plot.} +} +\value{ +a list of coordinates for pathways or kinases +} +\description{ +This function takes in a matrix of test statistics with two columns (3-dimensional space) and the +annotation list such as pathway annotation or kinase-substrate annotation, and visualize the enrichment +of pathways or kinases in direction specific manner. +} diff --git a/source/directPA_1.1/man/rotate2d.Rd b/man/rotate2d.Rd similarity index 94% rename from source/directPA_1.1/man/rotate2d.Rd rename to man/rotate2d.Rd index 940baa2..6dc2047 100644 --- a/source/directPA_1.1/man/rotate2d.Rd +++ b/man/rotate2d.Rd @@ -1,4 +1,4 @@ -% Generated by roxygen2 (4.1.0): do not edit by hand +% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rotate2d.R \name{rotate2d} \alias{rotate2d} @@ -27,5 +27,5 @@ HEK.zscores <- apply(HEK, 2, function(x){qnorm(rank(x)/(nrow(HEK)+1))}) # Rotate the matrix by 1/2 pi (i.e. down-regulation, dow-regulation). HEK.rotated <- rotate2d(HEK.zscores, degree = pi/2) -} +} diff --git a/source/directPA_1.1/man/rotate3d.Rd b/man/rotate3d.Rd similarity index 94% rename from source/directPA_1.1/man/rotate3d.Rd rename to man/rotate3d.Rd index 8367f4c..4d3e964 100644 --- a/source/directPA_1.1/man/rotate3d.Rd +++ b/man/rotate3d.Rd @@ -1,4 +1,4 @@ -% Generated by roxygen2 (4.1.0): do not edit by hand +% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rotate3d.R \name{rotate3d} \alias{rotate3d} @@ -27,5 +27,5 @@ PM.zscores <- apply(PM, 2, function(x){qnorm(rank(x)/(nrow(PM)+1))}) # Rotate the matrix by contrast 1, -1, -1 (i.e. up-regulation, down-regulation, dow-regulation). PM.rotated <- rotate3d(PM.zscores, contrast = c(1, -1, -1)) -} +} diff --git a/source/directPA_1.0/DESCRIPTION b/source/directPA_1.0/DESCRIPTION deleted file mode 100644 index 293a7bd..0000000 --- a/source/directPA_1.0/DESCRIPTION +++ /dev/null @@ -1,14 +0,0 @@ -Package: directPA -Type: Package -Title: Direction Pathway Analysis -Version: 1.0 -Date: 2013-10-21 -Author: Pengyi Yang & Ellis Patrick -Maintainer: Pengyi Yang -Description: A package for pathway analysis in experiments with multiple perturbation designs. -License: GPL-3 -Suggests: rgl -Packaged: 2013-10-22 03:11:35 UTC; Pengyi -NeedsCompilation: no -Repository: CRAN -Date/Publication: 2013-10-22 08:17:12 diff --git a/source/directPA_1.0/MD5 b/source/directPA_1.0/MD5 deleted file mode 100644 index 1601b8f..0000000 --- a/source/directPA_1.0/MD5 +++ /dev/null @@ -1,19 +0,0 @@ -b48988d74be489124b461d34c4fc9165 *DESCRIPTION -9fdfeb11f7ddba6f3aab9aaa6b1ac5dd *NAMESPACE -21bac41fcf79da6b272afa18b1d1b33d *R/directPA.R -8abb176db7b9eb5bdcfa867fc2196738 *R/geneStats.R -a2b6f8c0ab65c54e7b9c5695ecb286c3 *R/pathwayStats.R -fef655e69c8b78cf39a04e35a7d6459c *R/rotate2Sphere.R -830d3d921abb14fdd7b9778d8bebcf13 *R/rotate3Sphere.R -bbbcdf399dacf15ca214c0a349c0f486 *data/PM.RData -b0bdb9ce7aee6bedfcd151b1f54c676d *data/reactome.RData -532f75f4dbdb772ee2d908110ce02183 *man/Ins.Rd -93a06e4a8e6324f0dd8d1bbc7da57bf1 *man/MK.Rd -c7d0d9c1785de143e728c098643302dc *man/Wmn.Rd -ae558be839eb2049322f059f1f1862b5 *man/directPA-package.Rd -e705598a5092397524a7fc32e5efe04a *man/directPA.Rd -90c9920a595f2a1cb0831853102cf3c5 *man/geneStats.Rd -621d2b42e82f8876825d5925802edbd6 *man/pathwayStats.Rd -81b83274a21787bb4934bf9b5a4c1da9 *man/reactome.list.Rd -d037fd6450859e32ebe2951ba1566447 *man/rotate2Sphere.Rd -4854eb6b047d9c27ac5094ad336389ba *man/rotate3Sphere.Rd diff --git a/source/directPA_1.0/NAMESPACE b/source/directPA_1.0/NAMESPACE deleted file mode 100644 index 2328f3c..0000000 --- a/source/directPA_1.0/NAMESPACE +++ /dev/null @@ -1,5 +0,0 @@ -# Default NAMESPACE created by R -# Remove the previous line if you edit this file - -# Export all names -exportPattern(".") diff --git a/source/directPA_1.0/R/directPA.R b/source/directPA_1.0/R/directPA.R deleted file mode 100644 index 703cf8e..0000000 --- a/source/directPA_1.0/R/directPA.R +++ /dev/null @@ -1,37 +0,0 @@ -##### - -directPA <- function(Tc, direction, pathway.list, minSize=5, gene.method="OSP", path.method="Stouffer", visualize=FALSE){ - # spherical coordinates for three-dimensional rotation - if (length(direction) == 3) { - Tc.rotated <- rotate3Sphere(Tc, direction) - gene.pvalues <- apply(Tc.rotated, 1, geneStats, gene.method) - - if (visualize == TRUE) { - library(rgl) - HC = rainbow(length(gene.pvalues)*1.2) - plot3d(Tc, col=HC[rank(gene.pvalues)], size=5) - abclines3d(x=0, y=0, z=0, a=diag(3), col="black", lwd=3) - abclines3d(x=0, a=direction, col="pink", lwd=5) - } - - gene.zscores <- qnorm(gene.pvalues, lower.tail = FALSE) - gst <- t(sapply(pathway.list, pathwayStats, gene.zscores, minSize=5, path.method)) - return(gst) - } - - # polar coordinates for two-dimensional rotation - if (length(direction) == 1) { - Tc.rotated <- rotate2Sphere(Tc, direction) - gene.pvalues <- apply(Tc.rotated, 1, geneStats, gene.method) - - if (visualize == TRUE) { - HC = rainbow(length(gene.pvalues)*1.2) - plot(Tc, col=HC[rank(gene.pvalues)], pch=16) - abline(v = 0,h = 0, lty=2, col="gold") - } - - gene.zscores <- qnorm(gene.pvalues, lower.tail = FALSE) - gst <- t(sapply(pathway.list, pathwayStats, gene.zscores, minSize=5, path.method)) - return(gst) - } -} diff --git a/source/directPA_1.0/R/geneStats.R b/source/directPA_1.0/R/geneStats.R deleted file mode 100644 index 958b67a..0000000 --- a/source/directPA_1.0/R/geneStats.R +++ /dev/null @@ -1,20 +0,0 @@ -# gene level statistics is calculated from here -# the default method for integration gene level information is OSP - -geneStats <- function(T, method="OSP") { - pvalue <- 0 - - if (method == "Stouffer") { - pvalue <- pnorm(sum(T), 0, sqrt(length(T)), lower.tail =FALSE) - } else if (method == "OSP") { - p <- pnorm(T, lower.tail = TRUE) - pvalue <- pchisq(-2*sum(log(p)), 2*length(p), lower.tail = TRUE) - } else if (method == "Fisher") { - p <- pnorm(T, lower.tail = FALSE) - pvalue <- pchisq(-2*sum(log(p)), 2*length(p), lower.tail = FALSE) - } else if (method == "maxP") { - pvalue <- pnorm(max(T), lower.tail = FALSE) - } - - return (pvalue) -} diff --git a/source/directPA_1.0/R/pathwayStats.R b/source/directPA_1.0/R/pathwayStats.R deleted file mode 100644 index 79a7b03..0000000 --- a/source/directPA_1.0/R/pathwayStats.R +++ /dev/null @@ -1,28 +0,0 @@ -# Pathway level statistics is calculated from here -# The default method for integrating pathway level information is Stouffer - -pathwayStats = function(PGs, T, minSize, method="Stouffer"){ - pvalue <- 0 - Z <- T[names(T) %in% PGs] - - if (length(Z) >= minSize) { - if (method == "Stouffer") { - pvalue <- pnorm(sum(Z), 0, sqrt(length(Z)), lower.tail=FALSE) - } else if (method == "OSP") { - p <- pnorm(Z, lower.tail = TRUE) - pvalue <- pchisq(-2*sum(log(p)), 2*length(p), lower.tail = TRUE) - } else if (method == "Fisher") { - p <- pnorm(Z, lower.tail = FALSE) - pvalue <- pchisq(-2*sum(log(p)), 2*length(p), lower.tail = FALSE) - } else if (method == "maxP") { - pvalue <- pnorm(max(Z), lower.tail = FALSE) - } - } else { - pvalue <- NA - } - - result <- list() - result$pvalue <- pvalue - result$size <- length(Z) - return (result) -} diff --git a/source/directPA_1.0/R/rotate2Sphere.R b/source/directPA_1.0/R/rotate2Sphere.R deleted file mode 100644 index 36fa4a3..0000000 --- a/source/directPA_1.0/R/rotate2Sphere.R +++ /dev/null @@ -1,12 +0,0 @@ -# polar coordinates rotation - -rotate2Sphere = function(T, degree = 0){ - # rotate to the direction that you want the test to face ie pi/4 degree - # T is a matrix of test statistics where the rows correspond to genes and the columns treatments - - theta = atan2(T[,2], T[,1]) + degree - r = sqrt(T[,1]^2 + T[,2]^2) - x2 = r*cos(theta) - y2 = r*sin(theta) - return(cbind(x2, y2)) -} diff --git a/source/directPA_1.0/R/rotate3Sphere.R b/source/directPA_1.0/R/rotate3Sphere.R deleted file mode 100644 index e62422b..0000000 --- a/source/directPA_1.0/R/rotate3Sphere.R +++ /dev/null @@ -1,30 +0,0 @@ -# spherical coordinates rotation - -rotate3Sphere = function(T, direction = c(1,1,1)){ - - # rotate to the direction that you want the test to face ie c(1,-1,-1) - # T is a matrix of test statistics where the rows correspond to genes and the columns treatments - - a = direction - b= c(1,1,1) # the original direction is c(1,1,1) - - if(sum(a == b)==3)return(T) - if(sum(a == -b)==3)return(-T) - - a = a/sqrt(sum(a^2)) - b = b/sqrt(sum(b^2)) - u = c((a[2]*b[3] - a[3]*b[2]) , (a[3]*b[1] - a[1]*b[3]) ,(a[1]*b[2] - a[2]*b[1]) ) - u = u/sqrt(sum(u^2)) - R = matrix(0,3,3) - the = acos(sum(a*b)) - R[1,1] = cos(the) + u[1]^2*(1-cos(the)) - R[2,2] = cos(the) + u[2]^2*(1-cos(the)) - R[3,3] = cos(the) + u[3]^2*(1-cos(the)) - R[1,2] = u[1]*u[2]*(1-cos(the)) - u[3]*sin(the) - R[1,3] = u[1]*u[3]*(1-cos(the)) + u[2]*sin(the) - R[2,1] = u[2]*u[1]*(1-cos(the)) + u[3]*sin(the) - R[2,3] = u[2]*u[3]*(1-cos(the)) - u[1]*sin(the) - R[3,1] = u[3]*u[1]*(1-cos(the)) - u[2]*sin(the) - R[3,2] = u[3]*u[2]*(1-cos(the)) + u[1]*sin(the) - return(t(R%*%t(T))) -} diff --git a/source/directPA_1.0/data/PM.RData b/source/directPA_1.0/data/PM.RData deleted file mode 100644 index 3d713c6..0000000 Binary files a/source/directPA_1.0/data/PM.RData and /dev/null differ diff --git a/source/directPA_1.0/data/reactome.RData b/source/directPA_1.0/data/reactome.RData deleted file mode 100644 index 3ea2fe0..0000000 Binary files a/source/directPA_1.0/data/reactome.RData and /dev/null differ diff --git a/source/directPA_1.0/man/Ins.Rd b/source/directPA_1.0/man/Ins.Rd deleted file mode 100644 index 245add9..0000000 --- a/source/directPA_1.0/man/Ins.Rd +++ /dev/null @@ -1,7 +0,0 @@ -\name{Ins} -\docType{data} -\alias{Ins} -\title{Insulin perturbation vesus control} -\description{ -The data object contains all quantified proteins with insulin perturbation vesus control in 3T3L1 cell lines. -} diff --git a/source/directPA_1.0/man/MK.Rd b/source/directPA_1.0/man/MK.Rd deleted file mode 100644 index 21bf9d2..0000000 --- a/source/directPA_1.0/man/MK.Rd +++ /dev/null @@ -1,7 +0,0 @@ -\name{MK} -\docType{data} -\alias{MK} -\title{MK perturbation vesus control} -\description{ -The data object contains all quantified proteins with MK perturbation vesus control in 3T3L1 cell lines. -} diff --git a/source/directPA_1.0/man/Wmn.Rd b/source/directPA_1.0/man/Wmn.Rd deleted file mode 100644 index 153ac6f..0000000 --- a/source/directPA_1.0/man/Wmn.Rd +++ /dev/null @@ -1,7 +0,0 @@ -\name{Wmn} -\docType{data} -\alias{Wmn} -\title{Wortmannin perturbation vesus control} -\description{ -The data object contains all quantified proteins with Wortmannin perturbation vesus control in 3T3L1 cell lines. -} diff --git a/source/directPA_1.0/man/directPA.Rd b/source/directPA_1.0/man/directPA.Rd deleted file mode 100644 index 1cf4153..0000000 --- a/source/directPA_1.0/man/directPA.Rd +++ /dev/null @@ -1,90 +0,0 @@ -\name{directPA} -\alias{directPA} -\title{ -Direction Pathway Analysis -} -\description{ -The main function of direction pathway analysis package. -} -\usage{ -directPA(Tc, direction, pathway.list, minSize = 5, gene.method = "OSP", -path.method = "Stouffer", visualize = FALSE) -} -\arguments{ - \item{Tc}{ -The matrix of test statistics where rows correspond to proteins/genes and columns correspond to perturbations compared to controls. -} - \item{direction}{ -The direction to be tested. For two-dimensional rotation (experiments with two perturbations), directions are specified -as degree. For three-dimensional rotation (experiments with three perturbations), directions are specified as contrast. -See examples below for more details. -} - \item{pathway.list}{ -A pathway database in list format. See example below for details. -} - \item{minSize}{ -The minimum size of the pathway to be included in the report. -} - \item{gene.method}{ -A p-value integration method for protein/gene level combination. That is integrating information for each protein/gene accross -all perturbations (i.e. for each row of matrix Tc, integrating accross the columns). Available methods are Stouffer, OSP, Fisher, and maxP. -Default method is OSP. -} - \item{path.method}{ -A p-value integration method for pathway level combination. That is integrating information of protein/gene included in a pathway, given -a pathway database. Available methods are Stouffer, OSP, Fisher, and maxP. Default method is Stouffer. -} - \item{visualize}{ -A boolean value indicating whether to visualize the protein/gene level integration statistics in a scatter plot. -} -} -\value{ -Return a matrix of test statistics for all given pathway that passed the minimum size cutoff on a specified test direction. -} -\author{ -Pengyi Yang & Ellis Patrick -} -\references{ -Pengyi Yang, Ellis Patrick, Shi-Xiong Tan, Daniel J. Fazakerley, James Burchfield, Christopher Gribben, Matthew J. Prior, David E. James, Yee Hwa Yang, Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway, submitted. -} - -\examples{ - -# Load the example dataset. -data(PM) - -# Load the reactome pathway. -data(reactome) - -# Display reactome pathways. Could be replaced by any other pathway databases. -reactome.list - -# Combine test statistics of each perturbation vs control into a matrix of test statistics. -Tc = cbind(Ins,Wmn,MK) - -## Direction pathway analysis in two-dimensional space. Implemented as rotating by degree. -# (1) Testing combined effect of Ins and Wmn vs controls on original direction. -gst1 <- directPA(Tc[,c(1,2)], direction=0, pathway.list=reactome.list) -# Display the top 20 pathways ranked by integrated p-value. -gst1[order(unlist(gst1[,1])),][1:20,] - -# (2) Testing combined effect of Ins and MK vs controls on the pi/2 direction. -gst2 <- directPA(Tc[,c(1,3)], direction=pi/2, pathway.list=reactome.list) -gst2[order(unlist(gst2[,1])),][1:20,] - -## Direction pathway analysis in three-dimensional space. Implemnted as rotating by contrast. -# (1) Testing combined effect of all 3 perturbations vs controls on the original direction. -gst3 <- directPA(Tc, direction=c(1,1,1), pathway.list=reactome.list) -gst3[order(unlist(gst3[,1])),][1:20,] - -# (2) Testing combined effect of all 3 perturbations vs controls on direction c(1,-1, 0). -# This rotates Ins by 0 degree, Wmn by 90 degree, and MK by 45 degree -gst4 <- directPA(Tc, direction=c(1,-1,0), pathway.list=reactome.list) -gst4[order(unlist(gst4[,1])),][1:20,] - -# (3) Testing combined effect of all 3 perturbations vs controls on direction c(1,-1, 1). -# This rotates Ins by 0 degree, Wmn by 90 degree, and MK by 0 degree -gst5 <- directPA(Tc, direction=c(1,-1,1), pathway.list=reactome.list) -gst5[order(unlist(gst5[,1])),][1:20,] - -} diff --git a/source/directPA_1.0/man/geneStats.Rd b/source/directPA_1.0/man/geneStats.Rd deleted file mode 100644 index cd70c0a..0000000 --- a/source/directPA_1.0/man/geneStats.Rd +++ /dev/null @@ -1,64 +0,0 @@ -\name{geneStats} -\alias{geneStats} -\title{ -Gene Level Statistics -} -\description{ -Calculating protein/gene level statistics accross multiple perturbations. -} -\usage{ -geneStats(T, method = "OSP") -} -%- maybe also 'usage' for other objects documented here. -\arguments{ - \item{T}{ -A matrix of test statistics (normally after rotation), where rows correspond to proteins/genes and columns correspond to perturbations. -} - \item{method}{ -The p-value integration method for combining accross multiple perturbations. -Available methods are Stouffer, OSP, Fisher, and maxP. The default method is OSP. -} -} -\value{ -Return the integration p-value for a protein/gene. -} -\author{ -Pengyi Yang & Ellis Patrick -} - - -\examples{ - -# Load the example data. -data(PM) - - -## (1) For three perturbatins vesus controls, use three dimentional rotation. -Tc <- cbind(Ins, Wmn, MK) - -# Rotate the test statistics matrix to the direction of interest. -Tc.rotated <- rotate3Sphere(Tc, direction=c(1,-1,0)) - -# Integrate protein/gene statistics accross perturmations. -gene.pvalues <- apply(Tc.rotated, 1, geneStats) - -# Visualize the rotation statistics. -library(rgl) -HC <- rainbow(length(gene.pvalues)*1.2) -plot3d(Tc, col=HC[rank(gene.pvalues)], size=5) -abclines3d(x=0, y=0, z=0, a=diag(3), col="black", lwd=3) -abclines3d(x=0, a=c(1,-1,0), col="pink", lwd=5) - - -## (2) For two perturbations vesus controls, use two dimentional rotation. -Tc <- cbind(Ins, Wmn) -Tc.rotated <- rotate2Sphere(Tc, degree=pi/4) -gene.pvalues <- apply(Tc.rotated, 1, geneStats) - -# Visualize the rotation statistics. -HC = rainbow(length(gene.pvalues)*1.2) -plot(Tc, col=HC[rank(gene.pvalues)], pch=16) -abline(v = 0,h = 0, lty=2, col="gold") -} - - diff --git a/source/directPA_1.0/man/pathwayStats.Rd b/source/directPA_1.0/man/pathwayStats.Rd deleted file mode 100644 index 2b6a8eb..0000000 --- a/source/directPA_1.0/man/pathwayStats.Rd +++ /dev/null @@ -1,56 +0,0 @@ -\name{pathwayStats} -\alias{pathwayStats} -\title{ -Pathway Level Statistics -} -\description{ -Calculating pathway level statistics by combining protein/gene belong to this pathway. -} -\usage{ -pathwayStats(PGs, T, minSize, method = "Stouffer") -} -%- maybe also 'usage' for other objects documented here. -\arguments{ - \item{PGs}{ -An array of names indicating proteins/genes belong to a given pathway. -} - \item{T}{ -An array of test statistics of all proteins/genes. This should be the zsore transformed output of geneStats. -} - \item{minSize}{ -The minimum size of a pathway to be reported. -} - \item{method}{ -A p-value integration method for combining proteins/genes in a given pathway. -} -} -\value{ - \item{pvalue }{Integrated pvalue of the given pathway} - \item{size }{size of the given pathway} -} -\author{ -Pengyi Yang & Ellis Patrick -} - -\examples{ - -# Load the example data. -data(PM) -Tc = cbind(Ins, Wmn, MK) - -# Load reactome pathway. -data(reactome) - -# Rotate the test statistics matrix to the direction of interest. -Tc.rotated <- rotate3Sphere(Tc, direction=c(1, -1, 0)) - -# Integrate protein/gene statistics accross perturmations. -gene.pvalues <- apply(Tc.rotated, 1, geneStats) - -# Transform the pvlaue computed from geneStats to zsocres. -gene.zscores <- qnorm(gene.pvalues, lower.tail = FALSE) - -# Compute statistics for all reactome pathway -gst <- t(sapply(reactome.list, pathwayStats, gene.zscores, minSize=5)) - -} diff --git a/source/directPA_1.0/man/reactome.list.Rd b/source/directPA_1.0/man/reactome.list.Rd deleted file mode 100644 index 3cfe4e7..0000000 --- a/source/directPA_1.0/man/reactome.list.Rd +++ /dev/null @@ -1,10 +0,0 @@ -\name{reactome.list} -\docType{data} -\alias{reactome.list} -\title{Reactome Database} -\description{ -Reactome database in list format. Each list element contains an array of gene names corresponding to the genes contains in a pathway. The name of the list element is the pathway name. -} -\references{ -Joshi-Tope, G., et al. Reactome: a knowledgebase of biological pathways. Nucleic Acids Research 33.suppl 1 (2005): D428-D432. -} diff --git a/source/directPA_1.0/man/rotate2Sphere.Rd b/source/directPA_1.0/man/rotate2Sphere.Rd deleted file mode 100644 index 11b7dee..0000000 --- a/source/directPA_1.0/man/rotate2Sphere.Rd +++ /dev/null @@ -1,39 +0,0 @@ -\name{rotate2Sphere} -\alias{rotate2Sphere} -\title{ -Polar Coordinates Rotation -} -\description{ -Rotate to the direction of interest in polar coordinates by degree (i.e pi/4). -} -\usage{ -rotate2Sphere(T, degree = 0) -} -\arguments{ - \item{T}{ -A matrix of test statistics. The rows correspond to protein/genes and the columns correspond to perturbations. The number of colmuns should be 2. -} - \item{degree}{ -The degree to be rotated. -} -} -\value{ -A transformed matrix with respect to the direction of interest. -} -\author{ -Pengyi Yang & Ellis Patrick -} - -\examples{ -# Load the example dataset. -data(PM) - -# Combine two perturbations into a single matrix. -# The matrix contains test statistics of each protein w.r.t each perturbation. -Tc <- cbind(Ins, Wmn) - -# Rotate the matrix by pi/2 in polar coordinates. -Tc.rotated <- rotate2Sphere(Tc, degree = pi/2) -} - - diff --git a/source/directPA_1.0/man/rotate3Sphere.Rd b/source/directPA_1.0/man/rotate3Sphere.Rd deleted file mode 100644 index 93b28dd..0000000 --- a/source/directPA_1.0/man/rotate3Sphere.Rd +++ /dev/null @@ -1,37 +0,0 @@ -\name{rotate3Sphere} -\alias{rotate3Sphere} -\title{ -Spherical Coordinates Rotation -} -\description{ -Rotate to the direction of interest in spherical coordinates by contrast (i.e 1, -1, -1). -} -\usage{ -rotate3Sphere(T, direction = c(1, 1, 1)) -} -\arguments{ - \item{T}{ -A matrix of test statistics. The rows correspond to protein/genes and the columns correspond to perturbations. The number of columns should be 3. -} - \item{direction}{ -The angle to be rotated in contrast format (see example below). -} -} -\value{ -A transformed matrix with respect to the direction of interest. -} -\author{ -Pengyi Yang & Ellis Patrick -} - -\examples{ -# Load the example data. -data(PM) - -# Combine three perturbations into a single matrix. -# The matrix contains test statistics of each protein w.r.t each perturbations. -Tc <- cbind(Ins, Wmn, MK) - -# Rotate the matrix by contrast 1, -1, -1 in spherical coordinates. -Tc.rotated <- rotate3Sphere(Tc, direction = c(1, -1, -1)) -} diff --git a/source/directPA_1.1/DESCRIPTION b/source/directPA_1.1/DESCRIPTION deleted file mode 100644 index 698fb4f..0000000 --- a/source/directPA_1.1/DESCRIPTION +++ /dev/null @@ -1,10 +0,0 @@ -Package: directPA -Type: Package -Title: Direction Pathway Analysis -Version: 1.1 -Date: 2015-03-13 -Author: Pengyi Yang & Ellis Patrick -Maintainer: Pengyi Yang -Description: A package for pathway or kinase analysis in experiments with multiple treatment designs. -License: GPL-3 -Depends: R (>= 2.10.0), rgl, calibrate diff --git a/source/directPA_1.1/NAMESPACE b/source/directPA_1.1/NAMESPACE deleted file mode 100644 index 7fe39f5..0000000 --- a/source/directPA_1.1/NAMESPACE +++ /dev/null @@ -1,11 +0,0 @@ -# Generated by roxygen2 (4.1.0): do not edit by hand - -export(directEnrichPlot2d) -export(directExplorer2d) -export(directPA) -export(geneStats) -export(pathwayStats) -export(rotate2d) -export(rotate3d) -import(rgl) -import(calibrate) diff --git a/source/directPA_1.1/R/directPA.R b/source/directPA_1.1/R/directPA.R deleted file mode 100644 index b365e57..0000000 --- a/source/directPA_1.1/R/directPA.R +++ /dev/null @@ -1,128 +0,0 @@ -#' Direction Analysis for Pathways and Kinases -#' -#' The main function of direction Analysis. This function takes in a matrix of test statistics with -#' two (2-dimensional space) or three (3-dimensional space) columns, the direction of interests, and -#' the annotation list such as pathway annotation or kinase-substrate annotation, and test for -#' enrichment of pathways or kinases on the specified direction. -#' -#' @usage directPA(Tc, direction, annotation, minSize=5, gene.method="OSP", path.method="Stouffer", visualize=TRUE) -#' -#' @param Tc a numeric matrix. The columns are genes or phosphorylation sites and the columns are -#' treatments vs control statistics. -#' @param direction the direction to be tested for enrichment. Either specified as a degree for -#' two-dimensional analysis or as contrast (in a triplet) for three-dimensional analysis. -#' @param annotation a list with names correspond to pathways or kinases and elements correspond to -#' genes or substrates belong to each pathway or kinase, respectively. -#' @param minSize the size of annotation groups to be considered for calculating enrichment. Groups -#' that are smaller than the minSize will be removed from the analysis. -#' @param gene.method the method to be used for integrating statistics across treatments for each gene -#' or phosphorylation site. Available methods are Stouffer, OSP, Fisher, and maxP. -#' Default method is OSP. -#' @param path.method the method to be used for integrating statistics of all genes or phosphorylation -#' sites that belongs to a pathway or kinase. Available methods are Stouffer, OSP, Fisher, and maxP. -#' Default method is Stouffer. -#' @param visualize whether to visualize the -#' @return a list of enrichment for pathways or kinases -#' @export -#' @examples -#' -#' ## I. for kinase anlaysis on a phosphoproteomics dataset (Humphrey et al. Cell Metab., 2013) -#' # load the phosphoproteomics dataset -#' data(HEK) -#' -#' # load the kinase-substrate annoations -#' data(PhosphoSite) -#' -#' # direction pathway analysis in 2-dimensional space. Implemented as rotating by degree -#' # (1) test combined effect of Torin1 and Rapamycin vs insul both on "down-regulation" -#' # (180 degree to original direction) -#' kst1 <- directPA(Tc=HEK, direction=pi, annotation=PhosphoSite.mouse) -#' kst1[order(unlist(kst1[,1])),][1:20,] -#' -#' # (2) test combined effect of Torin1 and Rapamycin vs insul on "no change and down-regulation" -#' # (135 degree to the original direction) -#' kst2 <- directPA(Tc=HEK, direction=pi*3/4, annotation=PhosphoSite.mouse) -#' kst2[order(unlist(kst2[,1])),][1:20,] -#' -#' # (3) test combined effect of Torin1 and Rapamycin vs insul on "down-regulation and no change" -#' # (225 degree to the original direction) -#' kst3 <- directPA(Tc=HEK, direction=pi*5/4, annotation=PhosphoSite.mouse) -#' kst3[order(unlist(kst3[,1])),][1:20,] -#' -#' -#' ## II. for pathway analysis on a proteomics dataset (Yang et al. Bioinformatics, 2014) -#' # load the proteomics dataset -#' data(PM) -#' -#' # load pathway annotations -#' data(Pathways) -#' -#' # display reactome pathways. Could be replaced by any other pathway databases -#' Pathways.reactome[1:5] -#' -#' # direction pathway analysis in 3-dimensional space. Implemnted as rotating by contrast -#' # (1) test combined effect of all 3 treatments (stimulation and inhibitions) vs control (basal) -#' # on the original direction. -#' gst1 <- directPA(Tc=PM, direction=c(1,1,1), annotation=Pathways.reactome) -#' gst1[order(unlist(gst1[,1])),][1:20,] -#' -#' # (2) test combined effect of all 3 treatments vs controls on direction c(1,-1, 0) -#' # this rotates Ins by 0 degree, Wmn by 90 degree, and MK by 45 degree. -#' gst2 <- directPA(Tc=PM, direction=c(1,-1,0), annotation=Pathways.reactome) -#' gst2[order(unlist(gst2[,1])),][1:20,] -#' -#' # (3) test combined effect of all 3 perturbations vs controls on direction c(1,-1, 1) -#' # this rotates Ins by 0 degree, Wmn by 90 degree, and MK by 0 degree. -#' gst3 <- directPA(Tc=PM, direction=c(1,-1,1), annotation=Pathways.reactome) -#' gst3[order(unlist(gst3[,1])),][1:20,] -#' -directPA <- function(Tc, direction, annotation, minSize=5, gene.method="OSP", path.method="Stouffer", visualize=TRUE){ - ## spherical coordinates for three-dimensional rotation - if (length(direction) == 3) { - - # step 1. convert statistics into z-scores - Tc.zscores <- apply(Tc, 2, function(x){qnorm(rank(x)/(nrow(Tc)+1))}) - - # step 2. rotate z-scores - Tc.rotated <- rotate3d(Tc.zscores, direction) - - # step 3. integrate statistics across treatments - gene.pvalues <- apply(Tc.rotated, 1, geneStats, gene.method) - - if (visualize == TRUE) { - HC = rainbow(length(gene.pvalues)*1.2) - plot3d(Tc, col=HC[rank(gene.pvalues)], size=5) - abclines3d(x=0, y=0, z=0, a=diag(3), col="black", lwd=3) - abclines3d(x=0, a=direction, col="pink", lwd=5) - } - - # step 4. integrate statistics for pathways or kinases - gene.zscores <- qnorm(gene.pvalues, lower.tail = FALSE) - gst <- t(sapply(annotation, pathwayStats, gene.zscores, minSize=5, path.method)) - return(gst) - } - - ## polar coordinates for two-dimensional rotation - if (length(direction) == 1) { - - # step 1. convert statistics into z-scores - Tc.zscores <- apply(Tc, 2, function(x){qnorm(rank(x)/(nrow(Tc)+1))}) - - # step 2. rotate z-scores - Tc.rotated <- rotate2d(Tc.zscores, direction) - - # step 3. integrate statistics across treatments - gene.pvalues <- apply(Tc.rotated, 1, geneStats, gene.method) - - if (visualize == TRUE) { - HC = rainbow(length(gene.pvalues)*1.2) - plot(Tc, col=HC[rank(gene.pvalues)], pch=16) - abline(v = 0,h = 0, lty=2, col="gold") - } - - # step 4. integrate statistics for pathways or kinases - gene.zscores <- qnorm(gene.pvalues, lower.tail = FALSE) - gst <- t(sapply(annotation, pathwayStats, gene.zscores, minSize=5, path.method)) - return(gst) - } -} diff --git a/source/directPA_1.1/man/directPA-package.Rd b/source/directPA_1.1/man/directPA-package.Rd deleted file mode 100644 index f24a827..0000000 --- a/source/directPA_1.1/man/directPA-package.Rd +++ /dev/null @@ -1,29 +0,0 @@ -\name{directPA-package} -\alias{directPA-package} -\docType{package} -\title{ -Direction Pathway Analysis Package -} -\description{ -The directPA-package is designed for analysing pathways in experiments with multiple perturbations. The combination effects of -different treatments are tested by rotating polar coordinates in two-dimentional space when the experiment contains two perturbations -and corresponding controls, or spherical coordinates in three-dimensional space when the experiment contains three perturbations and -corresponding controls. -} -\details{ -\tabular{ll}{ -Package: \tab directPA\cr -Type: \tab Package\cr -Version: \tab 1.0\cr -Date: \tab 2013-10-21\cr -License: \tab GPL-3\cr -} - -} -\author{ -Pengyi Yang & Ellis Patrick -} -\references{ -Pengyi Yang, Ellis Patrick, Shi-Xiong Tan, Daniel J. Fazakerley, James Burchfield, Christopher Gribben, Matthew J. Prior, -David E. James, Yee Hwa Yang, Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway, submitted. -} diff --git a/source/directPA_1.1/man/directPA.Rd b/source/directPA_1.1/man/directPA.Rd deleted file mode 100644 index 6fba82e..0000000 --- a/source/directPA_1.1/man/directPA.Rd +++ /dev/null @@ -1,92 +0,0 @@ -% Generated by roxygen2 (4.1.0): do not edit by hand -% Please edit documentation in R/directPA.R -\name{directPA} -\alias{directPA} -\title{Direction Analysis for Pathways and Kinases} -\usage{ -directPA(Tc, direction, annotation, minSize=5, gene.method="OSP", path.method="Stouffer", visualize=TRUE) -} -\arguments{ -\item{Tc}{a numeric matrix. The columns are genes or phosphorylation sites and the columns are -treatments vs control statistics.} - -\item{direction}{the direction to be tested for enrichment. Either specified as a degree for -two-dimensional analysis or as contrast (in a triplet) for three-dimensional analysis.} - -\item{annotation}{a list with names correspond to pathways or kinases and elements correspond to -genes or substrates belong to each pathway or kinase, respectively.} - -\item{minSize}{the size of annotation groups to be considered for calculating enrichment. Groups -that are smaller than the minSize will be removed from the analysis.} - -\item{gene.method}{the method to be used for integrating statistics across treatments for each gene -or phosphorylation site. Available methods are Stouffer, OSP, Fisher, and maxP. -Default method is OSP.} - -\item{path.method}{the method to be used for integrating statistics of all genes or phosphorylation -sites that belongs to a pathway or kinase. Available methods are Stouffer, OSP, Fisher, and maxP. -Default method is Stouffer.} - -\item{visualize}{whether to visualize the} -} -\value{ -a list of enrichment for pathways or kinases -} -\description{ -The main function of direction Analysis. This function takes in a matrix of test statistics with -two (2-dimensional space) or three (3-dimensional space) columns, the direction of interests, and -the annotation list such as pathway annotation or kinase-substrate annotation, and test for -enrichment of pathways or kinases on the specified direction. -} -\examples{ -## I. for kinase anlaysis on a phosphoproteomics dataset (Humphrey et al. Cell Metab., 2013) -# load the phosphoproteomics dataset -data(HEK) - -# load the kinase-substrate annoations -data(PhosphoSite) - -# direction pathway analysis in 2-dimensional space. Implemented as rotating by degree -# (1) test combined effect of Torin1 and Rapamycin vs insul both on "down-regulation" -# (180 degree to original direction) -kst1 <- directPA(Tc=HEK, direction=pi, annotation=PhosphoSite.mouse) -kst1[order(unlist(kst1[,1])),][1:20,] - -# (2) test combined effect of Torin1 and Rapamycin vs insul on "no change and down-regulation" -# (135 degree to the original direction) -kst2 <- directPA(Tc=HEK, direction=pi*3/4, annotation=PhosphoSite.mouse) -kst2[order(unlist(kst2[,1])),][1:20,] - -# (3) test combined effect of Torin1 and Rapamycin vs insul on "down-regulation and no change" -# (225 degree to the original direction) -kst3 <- directPA(Tc=HEK, direction=pi*5/4, annotation=PhosphoSite.mouse) -kst3[order(unlist(kst3[,1])),][1:20,] - - -## II. for pathway analysis on a proteomics dataset (Yang et al. Bioinformatics, 2014) -# load the proteomics dataset -data(PM) - -# load pathway annotations -data(Pathways) - -# display reactome pathways. Could be replaced by any other pathway databases -Pathways.reactome[1:5] - -# direction pathway analysis in 3-dimensional space. Implemnted as rotating by contrast -# (1) test combined effect of all 3 treatments (stimulation and inhibitions) vs control (basal) -# on the original direction. -gst1 <- directPA(Tc=PM, direction=c(1,1,1), annotation=Pathways.reactome) -gst1[order(unlist(gst1[,1])),][1:20,] - -# (2) test combined effect of all 3 treatments vs controls on direction c(1,-1, 0) -# this rotates Ins by 0 degree, Wmn by 90 degree, and MK by 45 degree. -gst2 <- directPA(Tc=PM, direction=c(1,-1,0), annotation=Pathways.reactome) -gst2[order(unlist(gst2[,1])),][1:20,] - -# (3) test combined effect of all 3 perturbations vs controls on direction c(1,-1, 1) -# this rotates Ins by 0 degree, Wmn by 90 degree, and MK by 0 degree. -gst3 <- directPA(Tc=PM, direction=c(1,-1,1), annotation=Pathways.reactome) -gst3[order(unlist(gst3[,1])),][1:20,] -} -