examples/FinalCombination.R
In Albluca/rpgraph: r interface to VDAOEngine to build elastic principal graphs
library(readr)
library(Pricecycle)
library(GO.db)
library(biomaRt)
AllTerms <- GOBPOFFSPRING[["GO:0007049"]]
AllTerms <- sort(c("GO:0007049", AllTerms))
MouseGenes_GOCellCycle <- getBM(attributes = c("external_gene_name"),
filters = "go",
values = AllTerms,
mart = biomaRt::useMart("ensembl", dataset = "mmusculus_gene_ensembl"))
MouseGenes_GOCellCycle <- MouseGenes_GOCellCycle[,1]
Human_GOCellCycle <- getBM(attributes = c("external_gene_name"),
filters = "go",
values = AllTerms,
mart = biomaRt::useMart("ensembl", dataset = "hsapiens_gene_ensembl"))
Human_GOCellCycle <- Human_GOCellCycle[,1]
# Load data
Data.Sasa <- read_rds("~/Google Drive/Datasets/Sasagawa et al - Murine Stem Cells (EB5 cell line)/rPG_Last.rds")
Data.Kowa <- read_rds("~/Google Drive/Datasets/Kowalczyk et al - Murine hematopoietic stem cells/rPG_Last.rds")
Data.Buet <- read_rds("~/Google Drive/Datasets/Buettner et al. - Murie embrionic stem cells/rPG_Last.rds")
Data.Sasa.Norm <- read_rds("~/Google Drive/Datasets/Sasagawa et al - Murine Stem Cells (EB5 cell line)/rPG_Last-Filtered.rds")
Data.Kowa.Norm <- read_rds("~/Google Drive/Datasets/Kowalczyk et al - Murine hematopoietic stem cells/rPG_Last-Filtered.rds")
Data.Buet.Norm <- read_rds("~/Google Drive/Datasets/Buettner et al. - Murie embrionic stem cells/rPG_Last-Filtered.rds")
# Plot data and construct auxiliary structures
TargetStruct <- Data.Kowa$Analysis$PGStructs[[length(Data.Kowa$Analysis$PGStructs)]]
Proc.Exp.Kowa <- PlotOnStages(Structure = "Circle",
Categories = TargetStruct$Categories,
nGenes = 2,
TaxonList = TargetStruct$TaxonList[[length(TargetStruct$TaxonList)]],
PrinGraph = TargetStruct$PrinGraph,
Net = TargetStruct$Net[[length(TargetStruct$Net)]],
SelThr = .3,
ComputeOverlaps = TRUE,
ExpData = TargetStruct$FiltExp,
RotatioMatrix = TargetStruct$PCAData$rotation[,1:TargetStruct$nDims],
PCACenter = TargetStruct$PCAData$center,
PointProjections = TargetStruct$ProjPoints[[length(TargetStruct$ProjPoints)]],
OrderOnCat = TRUE,
SmoothPoints = 2,
MinCellPerNode = 2,
Title = 'Kowalczyk et al')
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Kowalczyk et al.",
ExpValues = NULL, PlotPC3 = FALSE,
PointAlpha = .5
)
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Kowalczyk et al.",
ExpValues = NULL, PlotPC3 = TRUE,
PointAlpha = .5
)
TargetStruct <- Data.Buet$Analysis$PGStructs[[length(Data.Buet$Analysis$PGStructs)]]
Proc.Exp.Buet <- PlotOnStages(Structure = "Circle",
Categories = TargetStruct$Categories,
nGenes = 2,
TaxonList = TargetStruct$TaxonList[[length(TargetStruct$TaxonList)]],
PrinGraph = TargetStruct$PrinGraph,
Net = TargetStruct$Net[[length(TargetStruct$Net)]],
SelThr = .34,
ComputeOverlaps = TRUE,
ExpData = TargetStruct$FiltExp,
RotatioMatrix = TargetStruct$PCAData$rotation[,1:TargetStruct$nDims],
PCACenter = TargetStruct$PCAData$center,
PointProjections = TargetStruct$ProjPoints[[length(TargetStruct$ProjPoints)]],
OrderOnCat = TRUE,
SmoothPoints = 2,
MinCellPerNode = 2,
Title = 'Buettner et al')
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Buettner et al.",
ExpValues = NULL, PlotPC3 = FALSE,
PointAlpha = .5
)
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Buettner et al.",
ExpValues = NULL, PlotPC3 = TRUE,
PointAlpha = .5
)
TargetStruct <- Data.Sasa$Analysis$PGStructs[[length(Data.Sasa$Analysis$PGStructs)]]
Proc.Exp.Sasa <- PlotOnStages(Structure = "Circle",
Categories = TargetStruct$Categories,
nGenes = 2,
TaxonList = TargetStruct$TaxonList[[length(TargetStruct$TaxonList)]],
PrinGraph = TargetStruct$PrinGraph,
Net = TargetStruct$Net[[length(TargetStruct$Net)]],
SelThr = .35,
ComputeOverlaps = TRUE,
ExpData = TargetStruct$FiltExp,
RotatioMatrix = TargetStruct$PCAData$rotation[,1:TargetStruct$nDims],
PCACenter = TargetStruct$PCAData$center,
PointProjections = TargetStruct$ProjPoints[[length(TargetStruct$ProjPoints)]],
OrderOnCat = TRUE,
SmoothPoints = 0,
MinCellPerNode = 1,
Title = 'Sasagawa et al')
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Sasagawa et al.",
ExpValues = NULL, PlotPC3 = FALSE,
PointAlpha = .5
)
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Sasagawa et al.",
ExpValues = NULL, PlotPC3 = TRUE,
PointAlpha = .5
)
TargetStruct <- Data.Kowa.Norm$Analysis$PGStructs[[length(Data.Kowa.Norm$Analysis$PGStructs)]]
Proc.Exp.Kowa.Norm <- PlotOnStages(Structure = "Circle",
Categories = TargetStruct$Categories,
nGenes = 2,
TaxonList = TargetStruct$TaxonList[[length(TargetStruct$TaxonList)]],
PrinGraph = TargetStruct$PrinGraph,
Net = TargetStruct$Net[[length(TargetStruct$Net)]],
SelThr = .292,
ComputeOverlaps = TRUE,
ExpData = TargetStruct$FiltExp,
RotatioMatrix = TargetStruct$PCAData$rotation[,1:TargetStruct$nDims],
PCACenter = TargetStruct$PCAData$center,
PointProjections = TargetStruct$ProjPoints[[length(TargetStruct$ProjPoints)]],
OrderOnCat = TRUE,
SmoothPoints = 1,
MinCellPerNode = 2,
Title = 'Kowalczyk et al')
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Kowalczyk et al. (Norm)",
ExpValues = NULL, PlotPC3 = FALSE,
PointAlpha = .5
)
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Kowalczyk et al. (Norm)",
ExpValues = NULL, PlotPC3 = TRUE,
PointAlpha = .5
)
TargetStruct <- Data.Buet.Norm$Analysis$PGStructs[[length(Data.Buet.Norm$Analysis$PGStructs)]]
Proc.Exp.Buet.Norm <- PlotOnStages(Structure = "Circle",
Categories = TargetStruct$Categories,
nGenes = 2,
TaxonList = TargetStruct$TaxonList[[length(TargetStruct$TaxonList)]],
PrinGraph = TargetStruct$PrinGraph,
Net = TargetStruct$Net[[length(TargetStruct$Net)]],
SelThr = .34,
ComputeOverlaps = TRUE,
ExpData = TargetStruct$FiltExp,
RotatioMatrix = TargetStruct$PCAData$rotation[,1:TargetStruct$nDims],
PCACenter = TargetStruct$PCAData$center,
PointProjections = TargetStruct$ProjPoints[[length(TargetStruct$ProjPoints)]],
OrderOnCat = TRUE,
SmoothPoints = 2,
MinCellPerNode = 2,
Title = 'Buettner et al')
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Buettner et al. (Norm)",
ExpValues = NULL, PlotPC3 = FALSE,
PointAlpha = .5
)
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Buettner et al. (Norm)",
ExpValues = NULL, PlotPC3 = TRUE,
PointAlpha = .5
)
TargetStruct <- Data.Sasa.Norm$Analysis$PGStructs[[length(Data.Sasa.Norm$Analysis$PGStructs)]]
Proc.Exp.Sasa.Norm <- PlotOnStages(Structure = "Circle",
Categories = TargetStruct$Categories,
nGenes = 2,
TaxonList = TargetStruct$TaxonList[[length(TargetStruct$TaxonList)]],
PrinGraph = TargetStruct$PrinGraph,
Net = TargetStruct$Net[[length(TargetStruct$Net)]],
SelThr = .35,
ComputeOverlaps = TRUE,
ExpData = TargetStruct$FiltExp,
RotatioMatrix = TargetStruct$PCAData$rotation[,1:TargetStruct$nDims],
PCACenter = TargetStruct$PCAData$center,
PointProjections = TargetStruct$ProjPoints[[length(TargetStruct$ProjPoints)]],
OrderOnCat = TRUE,
SmoothPoints = 0,
MinCellPerNode = 1,
Title = 'Sasagawa et al')
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Sasagawa et al. (Norm)",
ExpValues = NULL, PlotPC3 = FALSE,
PointAlpha = .5
)
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Sasagawa et al. (Norm)",
ExpValues = NULL, PlotPC3 = TRUE,
PointAlpha = .5
)
# Create structure to use thge dashboard
InputList <- list(
list(Name = "Buettner et al", Expression = Data.Buet$ExpMat,
Categories = Data.Buet$Cats,
OrderedData = Proc.Exp.Buet,
PGStruct = Data.Buet$Analysis$PGStructs[[length(Data.Buet$Analysis$PGStructs)]],
TaxonList = Data.Buet$Analysis$PGStructs[[length(Data.Buet$Analysis$PGStructs)]]$TaxonList
),
list(Name = "Buettner et al (Norm)", Expression = Data.Buet.Norm$ExpMat,
Categories = Data.Buet.Norm$Cats,
OrderedData = Proc.Exp.Buet.Norm,
PGStruct = Data.Buet.Norm$Analysis$PGStructs[[length(Data.Buet.Norm$Analysis$PGStructs)]],
TaxonList = Data.Buet.Norm$Analysis$PGStructs[[length(Data.Buet.Norm$Analysis$PGStructs)]]$TaxonList
),
list(Name = "Kowalczyk et al", Expression = Data.Kowa$ExpMat,
Categories = Data.Kowa$Cats,
OrderedData = Proc.Exp.Kowa,
PGStruct = Data.Kowa$Analysis$PGStructs[[length(Data.Kowa$Analysis$PGStructs)]],
TaxonList = Data.Kowa$Analysis$PGStructs[[length(Data.Kowa$Analysis$PGStructs)]]$TaxonList
),
list(Name = "Kowalczyk et al (Norm)", Expression = Data.Kowa.Norm$ExpMat,
Categories = Data.Kowa.Norm$Cats,
OrderedData = Proc.Exp.Kowa.Norm,
PGStruct = Data.Kowa.Norm$Analysis$PGStructs[[length(Data.Kowa.Norm$Analysis$PGStructs)]],
TaxonList = Data.Kowa.Norm$Analysis$PGStructs[[length(Data.Kowa.Norm$Analysis$PGStructs)]]$TaxonList
),
list(Name = "Sasagawa et al", Expression = Data.Sasa$ExpMat,
Categories = Data.Sasa$Cats,
OrderedData = Proc.Exp.Sasa,
PGStruct = Data.Sasa$Analysis$PGStructs[[length(Data.Sasa$Analysis$PGStructs)]],
TaxonList = Data.Sasa$Analysis$PGStructs[[length(Data.Sasa$Analysis$PGStructs)]]$TaxonList
),
list(Name = "Sasagawa et al (Norm)", Expression = Data.Sasa.Norm$ExpMat,
Categories = Data.Sasa.Norm$Cats,
OrderedData = Proc.Exp.Sasa.Norm,
PGStruct = Data.Sasa.Norm$Analysis$PGStructs[[length(Data.Sasa.Norm$Analysis$PGStructs)]],
TaxonList = Data.Sasa.Norm$Analysis$PGStructs[[length(Data.Sasa.Norm$Analysis$PGStructs)]]$TaxonList
)
)
SpanVect <- c(.1, .1, .05, .05, .3, .3)
for(i in 1:length(InputList)){
p <- PlotOnPseudotime(WorkStruct = InputList[[i]]$OrderedData,
Expression = InputList[[i]]$Expression,
Name = InputList[[i]]$Name,
gName = "Cdk1", SpanVal = SpanVect[i], CatOrder = NULL)
print(p)
}
for(i in 1:length(InputList)){
p <- PlotOnPseudotime(WorkStruct = InputList[[i]]$OrderedData,
Expression = InputList[[i]]$Expression,
Name = InputList[[i]]$Name,
gName = "Cdk2", SpanVal = SpanVect[i], CatOrder = NULL)
print(p)
}
for(i in 1:length(InputList)){
p <- PlotOnPseudotime(WorkStruct = InputList[[i]]$OrderedData,
Expression = InputList[[i]]$Expression,
Name = InputList[[i]]$Name,
gName = "Cdk4", SpanVal = SpanVect[i], CatOrder = NULL)
print(p)
}
# Launch dashboard
# CompareAcrossData(InputList, NULL)
# AllFreman_Mouse
#
#
Genes.Buett <- Data.Buet$Analysis$Genes[[length(Data.Buet$Analysis$Genes)]]
Genes.Sasa <- Data.Sasa$Analysis$Genes[[length(Data.Sasa$Analysis$Genes)]]
Genes.Kowa <- Data.Kowa$Analysis$Genes[[length(Data.Kowa$Analysis$Genes)]]
Genes.Buett.Norm <- Data.Buet.Norm$Analysis$Genes[[length(Data.Buet.Norm$Analysis$Genes)]]
Genes.Sasa.Norm <- Data.Sasa.Norm$Analysis$Genes[[length(Data.Sasa.Norm$Analysis$Genes)]]
Genes.Kowa.Norm <- Data.Kowa.Norm$Analysis$Genes[[length(Data.Kowa.Norm$Analysis$Genes)]]
length(Genes.Buett)
length(Genes.Kowa)
length(Genes.Sasa)
length(Genes.Buett.Norm)
length(Genes.Kowa.Norm)
length(Genes.Sasa.Norm)
FreemanData <- read_delim("~/Google Drive/Datasets/Gene List/Freeman.gmt",
"\t", escape_double = FALSE, col_names = FALSE,
trim_ws = TRUE)
Freeman_CC1 <- unlist(FreemanData[1,-c(1,2)], use.names = FALSE)
Freeman_CC2 <- unlist(FreemanData[2,-c(1,2)], use.names = FALSE)
Freeman_G1S_CC4 <- unlist(FreemanData[3,-c(1,2)], use.names = FALSE)
Freeman_G2M_CC6 <- unlist(FreemanData[4,-c(1,2)], use.names = FALSE)
Freeman_CC9 <- unlist(FreemanData[5,-c(1,2)], use.names = FALSE)
Freeman_G1S_CC4A <- unlist(FreemanData[6,-c(1,2)], use.names = FALSE)
Freeman_S_CC4B <- unlist(FreemanData[7,-c(1,2)], use.names = FALSE)
Freeman_G2_CC6A <- unlist(FreemanData[8,-c(1,2)], use.names = FALSE)
Freeman_M_CC6B <- unlist(FreemanData[9,-c(1,2)], use.names = FALSE)
Freeman_CC1_Mouse <- ConvertNames("human", "mouse", Freeman_CC1)
Freeman_CC2_Mouse <- ConvertNames("human", "mouse", Freeman_CC2)
Freeman_G1S_CC4_Mouse <- ConvertNames("human", "mouse", Freeman_G1S_CC4)
Freeman_G2M_CC6_Mouse <- ConvertNames("human", "mouse", Freeman_G2M_CC6)
Freeman_CC9_Mouse <- ConvertNames("human", "mouse", Freeman_CC9)
Freeman_G1S_CC4A_Mouse <- ConvertNames("human", "mouse", Freeman_G1S_CC4A)
Freeman_S_CC4B_Mouse <- ConvertNames("human", "mouse", Freeman_S_CC4B)
Freeman_G2_CC6A_Mouse <- ConvertNames("human", "mouse", Freeman_G2_CC6A)
Freeman_M_CC6B_Mouse <- ConvertNames("human", "mouse", Freeman_M_CC6B)
AllFreman_Mouse <- c(Freeman_CC1_Mouse, Freeman_CC2_Mouse, Freeman_CC9_Mouse,
Freeman_G1S_CC4_Mouse, Freeman_G1S_CC4A_Mouse, Freeman_G2_CC6A_Mouse,
Freeman_G2M_CC6_Mouse, Freeman_M_CC6B_Mouse, Freeman_S_CC4B_Mouse)
AllFreman_Mouse <- unique(AllFreman_Mouse)
AllFreman_Mouse_CCP <- c(Freeman_G1S_CC4_Mouse, Freeman_G1S_CC4A_Mouse, Freeman_G2_CC6A_Mouse,
Freeman_G2M_CC6_Mouse, Freeman_M_CC6B_Mouse, Freeman_S_CC4B_Mouse)
AllFreman_Mouse_CCP <- unique(AllFreman_Mouse_CCP)
AllWit_Mouse <- ConvertNames("human", "mouse", unlist(StageAssociation_Whit_Ext[-c(1,2)], use.names = FALSE))
WitAndFree <- intersect(AllFreman_Mouse, AllWit_Mouse)
library(VennDiagram)
#
# grid.newpage()
# draw.pairwise.venn(area1 = length(Genes.Buett), area2 = length(AllFreman_Mouse),
# cross.area = length(intersect(Genes.Buett, AllFreman_Mouse)),
# category = c("Buettner et al.", "Freeman"),
# fill = c("blue", "green"), cex = 3)
#
# grid.newpage()
# draw.pairwise.venn(area1 = length(Genes.Buett), area2 = length(AllWit_Mouse),
# cross.area = length(intersect(Genes.Buett, AllWit_Mouse)),
# category = c("Buettner et al.", "Withfield"),
# fill = c("blue", "green"), cex = 3)
#
#
# grid.newpage()
# draw.pairwise.venn(area1 = length(Genes.Buett), area2 = length(WitAndFree),
# cross.area = length(intersect(Genes.Buett, WitAndFree)),
# category = c("Buettner et al.", "Withfield"),
# fill = c("blue", "green"), cex = 3)
#
#
#
#
#
# grid.newpage()
# draw.quad.venn(area1 = length(Genes.Buett), area2 = length(Genes.Kowa),
# area3 = length(Genes.Sasa), area4 = length(AllFreman_Mouse),
# n12 = length(intersect(Genes.Buett, Genes.Kowa)),
# n23 = length(intersect(Genes.Sasa, Genes.Kowa)),
# n13 = length(intersect(Genes.Buett, Genes.Sasa)),
# n14 = length(intersect(Genes.Buett, AllFreman_Mouse)),
# n24 = length(intersect(AllFreman_Mouse, Genes.Kowa)),
# n34 = length(intersect(Genes.Sasa, AllFreman_Mouse)),
# n123 = length(intersect(Genes.Buett, intersect(Genes.Kowa, Genes.Sasa))),
# n124 = length(intersect(Genes.Buett, intersect(Genes.Kowa, AllFreman_Mouse))),
# n134 = length(intersect(Genes.Buett, intersect(Genes.Sasa, AllFreman_Mouse))),
# n234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, AllFreman_Mouse))),
# n1234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, intersect(Genes.Buett, AllFreman_Mouse)))),
# category = c("Buettner et al.", "Kowalczyk et al.", "Sasagawa et al.", "Freeman"),
# fill = c("red", "green", "blue", "white"), cex = 3)
# grid.newpage()
#
#
#
#
#
#
# grid.newpage()
# draw.quad.venn(area1 = length(Genes.Buett), area2 = length(Genes.Kowa),
# area3 = length(Genes.Sasa), area4 = length(AllWit_Mouse),
# n12 = length(intersect(Genes.Buett, Genes.Kowa)),
# n23 = length(intersect(Genes.Sasa, Genes.Kowa)),
# n13 = length(intersect(Genes.Buett, Genes.Sasa)),
# n14 = length(intersect(Genes.Buett, AllWit_Mouse)),
# n24 = length(intersect(AllWit_Mouse, Genes.Kowa)),
# n34 = length(intersect(Genes.Sasa, AllWit_Mouse)),
# n123 = length(intersect(Genes.Buett, intersect(Genes.Kowa, Genes.Sasa))),
# n124 = length(intersect(Genes.Buett, intersect(Genes.Kowa, AllWit_Mouse))),
# n134 = length(intersect(Genes.Buett, intersect(Genes.Sasa, AllWit_Mouse))),
# n234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, AllWit_Mouse))),
# n1234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, intersect(Genes.Buett, AllWit_Mouse)))),
# category = c("Buettner et al.", "Kowalczyk et al.", "Sasagawa et al.", "Freeman"),
# fill = c("red", "green", "blue", "white"), cex = 3)
# grid.newpage()
#
#
#
#
#
#
#
#
#
#
#
# grid.newpage()
# draw.quad.venn(area1 = length(Genes.Buett), area2 = length(Genes.Kowa),
# area3 = length(Genes.Sasa), area4 = length(MouseGenes_GOCellCycle),
# n12 = length(intersect(Genes.Buett, Genes.Kowa)),
# n23 = length(intersect(Genes.Sasa, Genes.Kowa)),
# n13 = length(intersect(Genes.Buett, Genes.Sasa)),
# n14 = length(intersect(Genes.Buett, MouseGenes_GOCellCycle)),
# n24 = length(intersect(MouseGenes_GOCellCycle, Genes.Kowa)),
# n34 = length(intersect(Genes.Sasa, MouseGenes_GOCellCycle)),
# n123 = length(intersect(Genes.Buett, intersect(Genes.Kowa, Genes.Sasa))),
# n124 = length(intersect(Genes.Buett, intersect(Genes.Kowa, MouseGenes_GOCellCycle))),
# n134 = length(intersect(Genes.Buett, intersect(Genes.Sasa, MouseGenes_GOCellCycle))),
# n234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, MouseGenes_GOCellCycle))),
# n1234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, intersect(Genes.Buett, MouseGenes_GOCellCycle)))),
# category = c("Buettner et al.", "Kowalczyk et al.", "Sasagawa et al.", "GO"),
# fill = c("red", "green", "blue", "white"), cex = 3)
# grid.newpage()
grid.newpage()
draw.triple.venn(area1 = length(Genes.Buett), area2 = length(Genes.Kowa),
area3 = length(Genes.Sasa),
n12 = length(intersect(Genes.Buett, Genes.Kowa)),
n23 = length(intersect(Genes.Sasa, Genes.Kowa)),
n13 = length(intersect(Genes.Buett, Genes.Sasa)),
n123 = length(intersect(Genes.Buett, intersect(Genes.Kowa, Genes.Sasa))),
category = c("Buettner et al.", "Kowalczyk et al.", "Sasagawa et al."),
fill = c("red", "green", "blue"), cex = 3)
grid.newpage()
draw.triple.venn(area1 = length(Genes.Buett.Norm), area2 = length(Genes.Kowa.Norm),
area3 = length(Genes.Sasa.Norm),
n12 = length(intersect(Genes.Buett.Norm, Genes.Kowa.Norm)),
n23 = length(intersect(Genes.Sasa.Norm, Genes.Kowa.Norm)),
n13 = length(intersect(Genes.Buett.Norm, Genes.Sasa.Norm)),
n123 = length(intersect(Genes.Buett.Norm, intersect(Genes.Kowa.Norm, Genes.Sasa.Norm))),
category = c("Buettner et al. (Norm)", "Kowalczyk et al. (Norm)", "Sasagawa et al. (Norm)"),
fill = c("red", "green", "blue"), cex = 3)
grid.newpage()
draw.pairwise.venn(area1 = length(Genes.Buett),
area2 = length(Genes.Buett.Norm),
cross.area = length(intersect(Genes.Buett, Genes.Buett.Norm)),
category = c("Buettner et al.", "Buettner et al. (Norm)"),
fill = c("red", "blue"), cex = 2)
grid.newpage()
draw.pairwise.venn(area1 = length(Genes.Kowa),
area2 = length(Genes.Kowa.Norm),
cross.area = length(intersect(Genes.Kowa, Genes.Kowa.Norm)),
category = c("Kowalczyk et al.", "Kowalczyk et al. (Norm)"),
fill = c("red", "blue"), cex = 2)
grid.newpage()
draw.pairwise.venn(area1 = length(Genes.Sasa),
area2 = length(Genes.Sasa.Norm),
cross.area = length(intersect(Genes.Sasa, Genes.Sasa.Norm)),
category = c("Sasagawa et al.", "Sasagawa et al. (Norm)"),
fill = c("red", "blue"), cex = 2)
AllSample <- intersect(Genes.Buett, intersect(Genes.Sasa, Genes.Kowa))
AllSample.Norm <- intersect(Genes.Buett.Norm, intersect(Genes.Sasa.Norm, Genes.Kowa.Norm))
grid.newpage()
draw.pairwise.venn(area1 = length(AllSample),
area2 = length(AllSample.Norm),
cross.area = length(intersect(AllSample, AllSample.Norm)),
category = c("Intersection", "Intersection (Norm)"),
fill = c("red", "blue"), cex = 2)
grid.newpage()
draw.triple.venn(area1 = length(AllSample), area2 = length(AllWit_Mouse),
area3 = length(AllFreman_Mouse),
n12 = length(intersect(AllSample, AllWit_Mouse)),
n23 = length(intersect(AllFreman_Mouse, AllWit_Mouse)),
n13 = length(intersect(AllSample, AllFreman_Mouse)),
n123 = length(intersect(AllSample, intersect(AllWit_Mouse, AllFreman_Mouse))),
category = c("Core module", "Withfield", "Giotti"),
fill = c("red", "green", "blue"), cex = 3)
grid.newpage()
draw.triple.venn(area1 = length(AllSample.Norm), area2 = length(AllWit_Mouse),
area3 = length(AllFreman_Mouse),
n12 = length(intersect(AllSample.Norm, AllWit_Mouse)),
n23 = length(intersect(AllFreman_Mouse, AllWit_Mouse)),
n13 = length(intersect(AllSample.Norm, AllFreman_Mouse)),
n123 = length(intersect(AllSample.Norm, intersect(AllWit_Mouse, AllFreman_Mouse))),
category = c("Core module", "Withfield", "Giotti"),
fill = c("red", "green", "blue"), cex = 3)
grid.newpage()
length(intersect(MouseGenes_GOCellCycle, AllFreman_Mouse)) / length(MouseGenes_GOCellCycle)
length(intersect(AllSample, AllFreman_Mouse)) / length(MouseGenes_GOCellCycle)
length(intersect(MouseGenes_GOCellCycle, AllWit_Mouse)) / length(MouseGenes_GOCellCycle)
length(intersect(AllSample, AllWit_Mouse)) / length(MouseGenes_GOCellCycle)
length(intersect(AllSample, MouseGenes_GOCellCycle))
New <- setdiff(AllSample, MouseGenes_GOCellCycle)
toupper(New) %in% Human_GOCellCycle
BaseBuett <- Genes.Buett
BaseKowa <- Genes.Kowa
BaseSasa <- Genes.Sasa
Buet.Sasa <- intersect(Genes.Buett, Genes.Sasa)
Buet.Kowa <- intersect(Genes.Buett, Genes.Kowa)
Sasa.Kowa <- intersect(Genes.Sasa, Genes.Kowa)
View(matrix(setdiff(unique(c(Buet.Sasa, Buet.Kowa, Sasa.Kowa)), AllSample), ncol = 1))
All.Buett <- length(AllSample)/length(BaseBuett)
Two.Buett <- length(setdiff(c(Buet.Sasa, Buet.Kowa), AllSample))/length(BaseBuett)
One.Buett <- length(setdiff(BaseBuett, c(BaseKowa, BaseSasa)))/length(BaseBuett)
All.Sasa <- length(AllSample)/length(BaseSasa)
Two.Sasa <- length(setdiff(c(Buet.Sasa, Sasa.Kowa), AllSample))/length(BaseSasa)
One.Sasa <- length(setdiff(BaseSasa, c(BaseBuett, BaseKowa)))/length(BaseSasa)
All.Kowa <- length(AllSample)/length(BaseKowa)
Two.Kowa <- length(setdiff(c(Sasa.Kowa, Buet.Kowa), AllSample))/length(BaseKowa)
One.Kowa <- length(setdiff(BaseKowa, c(BaseBuett, BaseSasa)))/length(BaseKowa)
barplot(c(One.Buett, Two.Buett, All.Buett), main="Buettner")
barplot(c(One.Kowa, Two.Kowa, All.Kowa), main="Kowa")
barplot(c(One.Sasa, Two.Sasa, All.Sasa), main="Sasa")
length(intersect(BaseSasa, MouseGenes_GOCellCycle))/length(BaseSasa)
length(intersect(BaseKowa, MouseGenes_GOCellCycle))/length(BaseKowa)
length(intersect(BaseBuett, MouseGenes_GOCellCycle))/length(BaseBuett)
# CatOrder = c("G0", "G0+G1", "G1", "G1+S", "S", "S+G2M", "G2M", "G2M+G0", "G1M+G1")
# DataStruct = Data.Buet
# ProcStruct = Proc.Exp.Buet
# MinNodes = 2
# FiltMax = .2
# Thr = .9
# QuantSel = .75
# SinglePeack = FALSE
View(matrix(sort(unique(AllSample)), ncol=8))
PeackGenes.Sasa <- GetGenesWithPeaks(DataStruct = Data.Sasa,
ProcStruct = Proc.Exp.Sasa,
FiltMax = .1, Thr = .8,
QuantSel = .5, SinglePeack = FALSE,
AllGenes = TRUE)
PeackGenes.Kowa <- GetGenesWithPeaks(DataStruct = Data.Kowa,
ProcStruct = Proc.Exp.Kowa,
FiltMax = .1, Thr = .8,
QuantSel = .5, SinglePeack = FALSE,
AllGenes = TRUE)
PeackGenes.Buet <- GetGenesWithPeaks(DataStruct = Data.Buet,
ProcStruct = Proc.Exp.Buet,
FiltMax = .1, Thr = .8,
QuantSel = .5, SinglePeack = FALSE,
AllGenes = TRUE)
PeackGenes.Sasa.Norm <- GetGenesWithPeaks(DataStruct = Data.Sasa.Norm,
ProcStruct = Proc.Exp.Sasa.Norm,
FiltMax = .1, Thr = .8,
QuantSel = .5, SinglePeack = FALSE,
AllGenes = TRUE)
PeackGenes.Kowa.Norm <- GetGenesWithPeaks(DataStruct = Data.Kowa.Norm,
ProcStruct = Proc.Exp.Kowa.Norm,
FiltMax = .1, Thr = .8,
QuantSel = .5, SinglePeack = FALSE,
AllGenes = TRUE)
PeackGenes.Buet.Norm <- GetGenesWithPeaks(DataStruct = Data.Buet.Norm,
ProcStruct = Proc.Exp.Buet.Norm,
FiltMax = .1, Thr = .8,
QuantSel = .5, SinglePeack = FALSE,
AllGenes = TRUE)
# DataStruct = Data.Buet
# ProcStruct = Proc.Exp.Buet
# FiltMax = .1
# Thr = .9
# QuantSel = .5
# SinglePeack = FALSE
# MinNodes = 2
names(PeackGenes.Kowa)
names(PeackGenes.Buet)
names(PeackGenes.Sasa)
G1.kowa <- unlist(PeackGenes.Kowa[grep("G1", names(PeackGenes.Kowa))])
G1.buet <- unlist(PeackGenes.Buet[grep("G1", names(PeackGenes.Buet))])
G1.sasa <- unlist(PeackGenes.Sasa[grep("G1", names(PeackGenes.Sasa))])
S.kowa <- unlist(PeackGenes.Kowa[grep("S", names(PeackGenes.Kowa))])
S.buet <- unlist(PeackGenes.Buet[grep("S", names(PeackGenes.Buet))])
S.sasa <- unlist(PeackGenes.Sasa[grep("S", names(PeackGenes.Sasa))])
G2M.kowa <- unlist(PeackGenes.Kowa[grep("G2/M", names(PeackGenes.Kowa))])
G2M.buet <- unlist(PeackGenes.Buet[grep("G2M", names(PeackGenes.Buet))])
G2M.sasa <- unlist(PeackGenes.Sasa[grep("G2/M", names(PeackGenes.Sasa))])
G1.kowa.Norm <- unlist(PeackGenes.Kowa.Norm[grep("G1", names(PeackGenes.Kowa.Norm))])
G1.buet.Norm <- unlist(PeackGenes.Buet.Norm[grep("G1", names(PeackGenes.Buet.Norm))])
G1.sasa.Norm <- unlist(PeackGenes.Sasa.Norm[grep("G1", names(PeackGenes.Sasa.Norm))])
S.kowa.Norm <- unlist(PeackGenes.Kowa.Norm[grep("S", names(PeackGenes.Kowa.Norm))])
S.buet.Norm <- unlist(PeackGenes.Buet.Norm[grep("S", names(PeackGenes.Buet.Norm))])
S.sasa.Norm <- unlist(PeackGenes.Sasa.Norm[grep("S", names(PeackGenes.Sasa.Norm))])
G2M.kowa.Norm <- unlist(PeackGenes.Kowa.Norm[grep("G2/M", names(PeackGenes.Kowa.Norm))])
G2M.buet.Norm <- unlist(PeackGenes.Buet.Norm[grep("G2M", names(PeackGenes.Buet.Norm))])
G2M.sasa.Norm <- unlist(PeackGenes.Sasa.Norm[grep("G2/M", names(PeackGenes.Sasa.Norm))])
# G0.kowa <- unlist(PeackGenes.Kowa[c(1,10)])
# G1.kowa <- setdiff(unlist(PeackGenes.Kowa[2:6]),
# unlist(PeackGenes.Kowa[-c(2:6)]))
# G1.buet <- setdiff(unlist(PeackGenes.Buet[1:2]),
# unlist(PeackGenes.Buet[-c(1:2)]))
# G1.sasa <- setdiff(unlist(PeackGenes.Sasa[1:2]),
# unlist(PeackGenes.Sasa[-c(1:2)]))
#
# S.kowa <- setdiff(unlist(PeackGenes.Kowa[6:8]),
# unlist(PeackGenes.Kowa[-c(6:8)]))
# S.buet <- setdiff(unlist(PeackGenes.Buet[2:4]),
# unlist(PeackGenes.Buet[-c(2:4)]))
# S.sasa <- setdiff(unlist(PeackGenes.Sasa[2:4]),
# unlist(PeackGenes.Sasa[-c(2:4)]))
#
# G2M.kowa <- setdiff(unlist(PeackGenes.Kowa[8:10]),
# unlist(PeackGenes.Kowa[-c(8:10)]))
# G2M.buet <- setdiff(unlist(PeackGenes.Buet[4:6]),
# unlist(PeackGenes.Buet[-c(4:6)]))
# G2M.sasa <- setdiff(unlist(PeackGenes.Sasa[4:6]),
# unlist(PeackGenes.Sasa[-c(4:6)]))
# setdiff(G1.kowa, S.kowa)
# setdiff(PeackGenes.Buet[[2]], PeackGenes.Buet[[2]])
# G0.All <- G0.kowa
G1.All <- unique(c(G1.kowa, G1.buet, G1.sasa))
S.All <- c(S.kowa, S.buet, S.sasa)
G2M.All <- c(G2M.kowa, G2M.buet, G2M.sasa)
length(G1.All)
length(S.All)
length(G2M.All)
G1.Combined <- rbind(G1.All %in% G1.buet,
G1.All %in% G1.kowa,
G1.All %in% G1.sasa)
colnames(G1.Combined) <- G1.All
rownames(G1.Combined) <- c("Buet", "Kowa", "Sasa")
table(colSums(G1.Combined))
table(colSums(G1.Combined[-2,]))
table(colSums(G1.Combined[-1,]))
table(colSums(G1.Combined[-3,]))
grid.newpage()
draw.triple.venn(area1 = length(G1.buet), area2 = length(G1.sasa),
area3 = length(G1.kowa), scaled = TRUE,
n12 = length(intersect(G1.buet, G1.sasa)),
n23 = length(intersect(G1.kowa, G1.sasa)),
n13 = length(intersect(G1.buet, G1.kowa)),
n123 = length(intersect(G1.buet, intersect(G1.sasa, G1.kowa))),
category = c("Buettner et al. (G1)", "Sasagawa et al. (G1)", "Kowalczyk et al. (G1)"),
fill = c("red", "green", "blue"), cex = 3, main = "pippo")
grid.newpage()
draw.triple.venn(area1 = length(S.buet), area2 = length(S.sasa),
area3 = length(S.kowa), scaled = TRUE,
n12 = length(intersect(S.buet, S.sasa)),
n23 = length(intersect(S.kowa, S.sasa)),
n13 = length(intersect(S.buet, S.kowa)),
n123 = length(intersect(S.buet, intersect(S.sasa, S.kowa))),
category = c("Buettner et al. (S)", "Sasagawa et al. (S)", "Kowalczyk et al. (S)"),
fill = c("red", "green", "blue"), cex = 3, main = "pippo")
grid.newpage()
draw.triple.venn(area1 = length(G2M.buet), area2 = length(G2M.sasa),
area3 = length(G2M.kowa), scaled = TRUE,
n12 = length(intersect(G2M.buet, G2M.sasa)),
n23 = length(intersect(G2M.kowa, G2M.sasa)),
n13 = length(intersect(G2M.buet, G2M.kowa)),
n123 = length(intersect(G2M.buet, intersect(G2M.sasa, G2M.kowa))),
category = c("Buettner et al. (G2M)", "Sasagawa et al. (G2M)", "Kowalczyk et al. (G2M)"),
fill = c("red", "green", "blue"), cex = 3, main = "pippo")
grid.newpage()
draw.triple.venn(area1 = length(G1.buet.Norm), area2 = length(G1.sasa.Norm),
area3 = length(G1.kowa.Norm), scaled = TRUE,
n12 = length(intersect(G1.buet.Norm, G1.sasa.Norm)),
n23 = length(intersect(G1.kowa.Norm, G1.sasa.Norm)),
n13 = length(intersect(G1.buet.Norm, G1.kowa.Norm)),
n123 = length(intersect(G1.buet.Norm, intersect(G1.sasa.Norm, G1.kowa.Norm))),
category = c("Buettner et al. (Norm - G1)", "Sasagawa et al. (Norm - G1)", "Kowalczyk et al. (Norm - G1)"),
fill = c("red", "green", "blue"), cex = 3, main = "pippo")
grid.newpage()
draw.triple.venn(area1 = length(S.buet.Norm), area2 = length(S.sasa.Norm),
area3 = length(S.kowa.Norm), scaled = TRUE,
n12 = length(intersect(S.buet.Norm, S.sasa.Norm)),
n23 = length(intersect(S.kowa.Norm, S.sasa.Norm)),
n13 = length(intersect(S.buet.Norm, S.kowa.Norm)),
n123 = length(intersect(S.buet.Norm, intersect(S.sasa.Norm, S.kowa.Norm))),
category = c("Buettner et al. (S)", "Sasagawa et al. (S)", "Kowalczyk et al. (S)"),
fill = c("red", "green", "blue"), cex = 3, main = "pippo")
grid.newpage()
draw.triple.venn(area1 = length(G2M.buet.Norm), area2 = length(G2M.sasa.Norm),
area3 = length(G2M.kowa.Norm), scaled = TRUE,
n12 = length(intersect(G2M.buet.Norm, G2M.sasa.Norm)),
n23 = length(intersect(G2M.kowa.Norm, G2M.sasa.Norm)),
n13 = length(intersect(G2M.buet.Norm, G2M.kowa.Norm)),
n123 = length(intersect(G2M.buet.Norm, intersect(G2M.sasa.Norm, G2M.kowa.Norm))),
category = c("Buettner et al. (G2M)", "Sasagawa et al. (G2M)", "Kowalczyk et al. (G2M)"),
fill = c("red", "green", "blue"), cex = 3, main = "pippo")
sum(G2M.sasa %in% G2M.kowa)
G2M_Strong <- intersect(intersect(G2M.sasa, G2M.buet), G2M.kowa)
G1_Strong <- intersect(intersect(G1.sasa, G1.buet), G1.kowa)
S_Strong <- intersect(intersect(S.sasa, S.buet), S.kowa)
G2M_Strong.Norm <- intersect(intersect(G2M.sasa.Norm, G2M.buet.Norm), G2M.kowa.Norm)
G1_Strong.Norm <- intersect(intersect(G1.sasa.Norm, G1.buet.Norm), G1.kowa.Norm)
S_Strong.Norm <- intersect(intersect(S.sasa.Norm, S.buet.Norm), S.kowa.Norm)
fileConn <- file("~/Desktop/PriceCyc_Mouse.gmt")
writeLines(c(paste(c("G1", "G1 Strong", G1_Strong), collapse ='\t'),
paste(c("S", "S Strong", S_Strong), collapse ='\t'),
paste(c("G2M", "G2M Strong", G2M_Strong), collapse ='\t'),
paste(c("Core", "Core module", AllSample), collapse ='\t')),
fileConn)
close(fileConn)
fileConn <- file("~/Desktop/PriceCyc_Hum.gmt")
writeLines(c(paste(c("G1", "G1 Strong", toupper(G1_Strong)), collapse ='\t'),
paste(c("S", "S Strong", toupper(S_Strong)), collapse ='\t'),
paste(c("G2M", "G2M Strong", toupper(G2M_Strong)), collapse ='\t'),
paste(c("Core", "Core module", toupper(AllSample)), collapse ='\t')),
fileConn)
close(fileConn)
# G1.Sel <- rbind(G1.All %in% G1.kowa,
# G1.All %in% G1.buet,
# G1.All %in% G1.sasa)
# G1.All <- G1.All[which(colSums(G1.Sel) > 1)]
#
# S.Sel <- rbind(S.All %in% S.kowa,
# S.All %in% S.buet,
# S.All %in% S.sasa)
# S.All <- S.All[which(colSums(S.Sel) > 1)]
#
# G2M.Sel <- rbind(G2M.All %in% G2M.kowa,
# G2M.All %in% G2M.buet,
# G2M.All %in% G2M.sasa)
# G2M.All <- G2M.All[which(colSums(G2M.Sel) > 1)]
length(G1.All)
length(S.All)
length(G2M.All)
ToRemove <- unique(c(intersect(G1.All, S.All),
intersect(S.All, G2M.All),
intersect(G1.All, G2M.All))
)
# G1.Sel <- G1.All
G1.Sel <- G1.All
G1.Sel <- G1.Sel[!(G1.Sel %in% ToRemove)]
S.Sel <- S.All
S.Sel <- S.Sel[!(S.Sel %in% ToRemove)]
G2M.Sel <- G2M.All
G2M.Sel <- G2M.Sel[!(G2M.Sel %in% ToRemove)]
# G0.Sel <- G0.All
# G0.Sel <- G0.Sel[!(G0.Sel %in% ToRemove)]
sum(G1.buet %in% G1.Sel)
sum(S.buet %in% S.Sel)
sum(G2M.buet %in% G2M.Sel)
sum(G1.sasa %in% G1.Sel)
sum(S.sasa %in% S.Sel)
sum(G2M.sasa %in% G2M.Sel)
sum(G1.kowa %in% G1.Sel)
sum(S.kowa %in% S.Sel)
sum(G2M.kowa %in% G2M.Sel)
sum(G1.buet %in% G1.All)
sum(S.buet %in% S.All)
sum(G2M.buet %in% G2M.All)
sum(G1.sasa %in% G1.All)
sum(S.sasa %in% S.All)
sum(G2M.sasa %in% G2M.All)
sum(G1.kowa %in% G1.All)
sum(S.kowa %in% S.All)
sum(G2M.kowa %in% G2M.All)
DF <- data.frame(lapply(list(G1 = G1_Strong, S = S_Strong, G2M = G2M_Strong), length))
library(ggplot2)
library(reshape)
melt(DF)
ggplot(data = melt(DF), mapping = aes(x = variable, y = value, fill = variable)) +
geom_bar(stat = 'identity') + guides(fill='none') +
labs(title="Biomarker genes", y="Number of genes", x="Cell cycle phase")
# SelStageInfo <- list(G1 = G1.All, S = S.All, G2M = G2M.All)
# SelStageInfo <- list(G1 = G1.Sel, S = S.Sel, G2M = G2M.Sel)
# SelStageInfo <- list(G1 = G1_Strong, S = S_Strong, G2M = G2M_Strong)
SelStageInfo <- list(G1 = G1_Strong.Norm, S = S_Strong.Norm, G2M = G2M_Strong.Norm)
# SelStageInfo <- list(G1 = G1_Strong.Norm[!(G1_Strong.Norm %in% c(S_Strong.Norm, G2M_Strong.Norm))],
# S = S_Strong.Norm[!(S_Strong.Norm %in% c(G1_Strong.Norm, G2M_Strong.Norm))],
# G2M = G2M_Strong.Norm[!(G2M_Strong.Norm %in% c(G1_Strong.Norm, S_Strong.Norm))])
Staged.Sasa <- StageWithPeaks(DataStruct = Data.Sasa,
ProcStruct = Proc.Exp.Sasa,
ComputeG0 = FALSE,
FiltMax = 0,
Thr = .8,
QuantSel = .8,
StageInfo = SelStageInfo,
MinNodes = 2,
Mode = 1,
G0Level = .9,
Title = 'Sasagawa et al')
#
# TB <- table(Staged.Sasa$CellStages, Data.Sasa$Cats)
# t(TB)/colSums(TB)
#
# Data.Sasa$Analysis
#
Staged.Buet <- StageWithPeaks(DataStruct = Data.Buet,
ProcStruct = Proc.Exp.Buet,
FiltMax = .1,
Thr = .8,
QuantSel = .8,
StageInfo = SelStageInfo,
ComputeG0 = FALSE,
MinNodes = 2,
Mode = 1,
G0Level = .9,
Title = 'Buettner et al')
Staged.Kowa <- StageWithPeaks(DataStruct = Data.Kowa,
ProcStruct = Proc.Exp.Kowa,
FiltMax = .1,
Thr = .8,
QuantSel = .8,
StageInfo = SelStageInfo,
ComputeG0 = TRUE,
MinNodes = 2,
Mode = 1,
G0Level = 1.1,
Title = "Kowalczyk et al")
#
#
# library(GSEABase)
#
#
#
#
# devtools::install_url("https://github.com/satijalab/seurat/releases/download/v1.4.0/Seurat_1.4.0.16.tgz", binary = TRUE)
# library(Seurat)
#
#
#
#
# DataStruct = Data.Sasa
# ProcStruct = Proc.Exp.Sasa
# FiltMax = .1
# Thr = .75
# QuantSel = .75
# StageInfo = SelStageInfo
# ComputeG0 = TRUE
# MinNodes = 2
# Mode = 3
# G0Level = .7
#
# Staged.Kowa <- StageWithPeaks(DataStruct = Data.Kowa,
# ProcStruct = Proc.Exp.Kowa,
# FiltMax = .1,
# Thr = .75,
# QuantSel = .75,
# SinglePeack = TRUE,
# StageInfo = SelStageInfo,
# ComputeG0 = TRUE,
# MinNodes = 2,
# Mode = 3,
# G0Level = 1)
#
length(G0.Sel)
length(G1.Sel)
length(S.Sel)
length(G2M.Sel)
table(colSums(rbind(G1.All %in% G1.buet,
G1.All %in% G1.sasa,
G1.All %in% G1.kowa)))
AllMat.All <- cbind(AllMat.Sasa, AllMat.Buet)
AllMat.All <- AllMat.All[, apply(AllMat.All, 2, any)]
pheatmap::pheatmap(1*unique(AllMat.All))
pheatmap::pheatmap(1*(AllMat.All))
# pheatmap::pheatmap(cor(Proc.Exp.Buet$NodesExp, method = "spe"))
# pheatmap::pheatmap(cor(t(Proc.Exp.Buet$NodesExp), method = "spe"), show_rownames = FALSE, show_colnames = FALSE)
Max.Bue <- apply(Proc.Exp.Buet$NodesExp, 1, which.max)
Max.Sasa <- apply(Proc.Exp.Sasa$NodesExp, 1, which.max)
Shared <- intersect(names(Max.Bue), names(Max.Sasa))
plot(Max.Bue[Shared], Max.Sasa[Shared])
cor.test(Max.Bue[Shared], Max.Sasa[Shared], method = "pea")
AllGenes <- unique(c(unlist(PeackGenes.Kowa), unlist(PeackGenes.Buet), unlist(PeackGenes.Sasa)))
AllMat.Kowa <- sapply(PeackGenes.Kowa, function(x){AllGenes %in% x})
colnames(AllMat.Kowa) <- paste(colnames(AllMat.Kowa), "_Kowa", sep='')
AllMat.Buet <- sapply(PeackGenes.Buet, function(x){AllGenes %in% x})
colnames(AllMat.Buet) <- paste(colnames(AllMat.Buet), "_Buet", sep='')
AllMat.Sasa <- sapply(PeackGenes.Sasa, function(x){AllGenes %in% x})
colnames(AllMat.Sasa) <- paste(colnames(AllMat.Sasa), "_Sasa", sep='')
# AllMat.All <- cbind(AllMat.Kowa, AllMat.Buet, AllMat.Sasa)
AllMat.All <- cbind(AllMat.Sasa, AllMat.Buet)
AllMat.All <- AllMat.All[, apply(AllMat.All, 2, any)]
pheatmap::pheatmap(1*unique(AllMat.All))
pheatmap::pheatmap(1*(AllMat.All))
IntMat <- apply(AllMat.All, 2, function(x){
apply(AllMat.All, 2, function(y){ sum(x & y)/min(sum(x), sum(y)) })
})
diag(IntMat) <- NA
pheatmap::pheatmap(IntMat[rownames(IntMat) %in% c("G1_Buet", "G1+S_Buet"),
!(colnames(IntMat) %in% c("G1_Buet", "G1+S_Buet"))],
cluster_rows = TRUE, cluster_cols = TRUE)
pheatmap::pheatmap(IntMat[rownames(IntMat) %in% c("G1+S_Buet", "S_Buet", "S+G2M_Buet"),
!(colnames(IntMat) %in% c("G1+S_Buet", "S_Buet", "S+G2M_Buet"))],
cluster_rows = TRUE, cluster_cols = TRUE)
pheatmap::pheatmap(IntMat[rownames(IntMat) %in% c("G2M_Buet", "S+G2M_Buet"),
!(colnames(IntMat) %in% c("G2M_Buet", "S+G2M_Buet"))],
cluster_rows = TRUE, cluster_cols = TRUE)
AllMat.All <- cbind(AllMat.Kowa, AllMat.Buet)
AllMat.All <- AllMat.All[, apply(AllMat.All, 2, any)]
IntMat <- apply(AllMat.All, 2, function(x){
apply(AllMat.All, 2, function(y){ sum(x & y)/min(sum(x), sum(y)) })
})
diag(IntMat) <- NA
pheatmap::pheatmap(IntMat[rownames(IntMat) %in% c("G1_Buet", "G1+S_Buet"),
!(colnames(IntMat) %in% c("G1_Buet", "G1+S_Buet"))],
cluster_rows = TRUE, cluster_cols = TRUE)
pheatmap::pheatmap(IntMat[rownames(IntMat) %in% c("G1+S_Buet", "S_Buet", "S+G2M_Buet"),
!(colnames(IntMat) %in% c("G1+S_Buet", "S_Buet", "S+G2M_Buet"))],
cluster_rows = TRUE, cluster_cols = TRUE)
pheatmap::pheatmap(IntMat[rownames(IntMat) %in% c("G2M_Buet", "S+G2M_Buet"),
!(colnames(IntMat) %in% c("G2M_Buet", "S+G2M_Buet"))],
cluster_rows = TRUE, cluster_cols = TRUE)
diag(IntMat) <- 0
rownames(IntMat)[apply(IntMat, 1, which.max)]
pheatmap::pheatmap(1*(IntMat > .3),
clustering_distance_rows = "binary",
clustering_distance_cols = "binary")
FilterRepeating <- function(PKG.List) {
PKG.All <- unique(unlist(PKG.List))
GeneMat.List <- lapply(PKG.List, function(x) {
PKG.All %in% x
})
GeneMat <- NULL
for(i in 1:length(GeneMat.List)){
GeneMat <- rbind(GeneMat, GeneMat.List[[i]])
}
colnames(GeneMat) <- PKG.All
MultPh <- which(colSums(GeneMat)>1)
if(length(MultPh) == 0){
return(PKG.List)
}
print(MultPh)
MultStages <- lapply(apply(GeneMat[,MultPh], 2, which), function(x) {
names(PKG.List)[x]
})
DividedStages <- lapply(MultStages, function(x) {
strsplit(x, "+", fixed = TRUE)
})
SingleStageGenes <- lapply(DividedStages, function(x){
StagesAll <- rep(NA, length(unique(unlist(x))))
names(StagesAll) <- unique(unlist(x))
for(Stg in names(StagesAll)){
ListBel <- lapply(x, function(x){
Stg %in% x
})
StagesAll[Stg] <- all(unlist(ListBel))
}
if(any(StagesAll)){
return(which(StagesAll))
} else {
return(NA)
}
})
PKG.List <- lapply(PKG.List, function(x){
x[!(x %in% names(MultPh))]
})
SingleStageGenes <- SingleStageGenes[!unlist(lapply(SingleStageGenes, function(x){any(is.na(x))}))]
if(length(SingleStageGenes) > 0){
for(i in 1:length(SingleStageGenes)){
PKG.List[[ SingleStageGenes[[i]] ]] <- union(PKG.List[[ SingleStageGenes[[i]] ]],
names(SingleStageGenes)[i])
}
}
return(PKG.List)
}
PeackGenes.Kowa <- FilterRepeating(PeackGenes.Kowa)
PeackGenes.Buet <- FilterRepeating(PeackGenes.Buet)
PeackGenes.Sasa <- FilterRepeating(PeackGenes.Sasa)
barplot(unlist(lapply(PeackGenes.Kowa, length)), las = 2)
barplot(unlist(lapply(PeackGenes.Buet, length)), las = 2)
barplot(unlist(lapply(PeackGenes.Sasa, length)), las = 2)
AllG1 <- c(PeackGenes.Kowa$`G1(late)`,
PeackGenes.Kowa$`G1(early)`,
PeackGenes.Kowa$`G1(early)+G1(late)`,
PeackGenes.Kowa$`G1(late)`,
PeackGenes.Kowa$`G1(late)+S`,
PeackGenes.Buet$G1,
PeackGenes.Buet$`G1+S`,
PeackGenes.Sasa$G1,
PeackGenes.Sasa$`G1+S`)
G1Mat <- rbind(AllG1 %in% c(PeackGenes.Kowa$`G1(late)`,
PeackGenes.Kowa$`G1(early)`,
PeackGenes.Kowa$`G1(early)+G1(late)`,
PeackGenes.Kowa$`G1(late)`,
PeackGenes.Kowa$`G1(late)+S`),
AllG1 %in% c(PeackGenes.Buet$G1,
PeackGenes.Buet$`G1+S`),
AllG1 %in% c(PeackGenes.Sasa$G1,
PeackGenes.Sasa$`G1+S`))
colnames(G1Mat) <- AllG1
table(colSums(G1Mat))
AllS <- c(PeackGenes.Kowa$S,
PeackGenes.Buet$S,
PeackGenes.Sasa$S)
SMat <- rbind(AllS %in% PeackGenes.Kowa$S,
AllS %in% PeackGenes.Buet$S,
AllS %in% PeackGenes.Sasa$S)
colnames(SMat) <- AllS
table(colSums(SMat))
AllG2M <- c(PeackGenes.Kowa$`G2/M`,
PeackGenes.Buet$G2M,
PeackGenes.Sasa$G2M)
G2MMat <- rbind(AllG2M %in% PeackGenes.Kowa$`G2/M`,
AllG2M %in% PeackGenes.Buet$G2M,
AllG2M %in% PeackGenes.Sasa$G2M)
colnames(G2MMat) <- AllG2M
table(colSums(G2MMat))
AllGenes <- unique(c(unlist(PeackGenes.Kowa), unlist(PeackGenes.Buet), unlist(PeackGenes.Sasa)))
AllMat.Kowa <- sapply(PeackGenes.Kowa, function(x){AllGenes %in% x})
colnames(AllMat.Kowa) <- paste(colnames(AllMat.Kowa), "_Kowa", sep='')
AllMat.Buet <- sapply(PeackGenes.Buet, function(x){AllGenes %in% x})
colnames(AllMat.Buet) <- paste(colnames(AllMat.Buet), "_Buet", sep='')
AllMat.Sasa <- sapply(PeackGenes.Sasa, function(x){AllGenes %in% x})
colnames(AllMat.Sasa) <- paste(colnames(AllMat.Sasa), "_Sasa", sep='')
AllMat.All <- cbind(AllMat.Kowa, AllMat.Buet, AllMat.Sasa)
AllMat.All <- AllMat.All[, apply(AllMat.All, 2, any)]
pheatmap::pheatmap(1*unique(AllMat.All))
pheatmap::pheatmap(1*(AllMat.All))
IntMat <- apply(AllMat.All, 2, function(x){
apply(AllMat.All, 2, function(y){ sum(x & y)/min(sum(x), sum(y)) })
})
diag(IntMat) <- NA
pheatmap::pheatmap(1*(IntMat>.5))
# AllFreman_Mouse
#
#
# Genes.Buett <- Data.Buet$Analysis$Genes[[length(Data.Buet$Analysis$Genes)]]
# Genes.Sasa <- Data.Sasa$Analysis$Genes[[length(Data.Sasa$Analysis$Genes)]]
# Genes.Kowa <- Data.Kowa$Analysis$Genes[[length(Data.Kowa$Analysis$Genes)]]
#
# length(Genes.Buett)
# length(Genes.Kowa)
# length(Genes.Sasa)
#
# library(VennDiagram)
#
# grid.newpage()
# draw.quad.venn(area1 = length(Genes.Buett), area2 = length(Genes.Kowa),
# area3 = length(Genes.Sasa), area4 = length(MouseGenes_GOCellCycle),
# n12 = length(intersect(Genes.Buett, Genes.Kowa)),
# n23 = length(intersect(Genes.Sasa, Genes.Kowa)),
# n13 = length(intersect(Genes.Buett, Genes.Sasa)),
# n14 = length(intersect(Genes.Buett, MouseGenes_GOCellCycle)),
# n24 = length(intersect(MouseGenes_GOCellCycle, Genes.Kowa)),
# n34 = length(intersect(Genes.Sasa, MouseGenes_GOCellCycle)),
# n123 = length(intersect(Genes.Buett, intersect(Genes.Kowa, Genes.Sasa))),
# n124 = length(intersect(Genes.Buett, intersect(Genes.Kowa, MouseGenes_GOCellCycle))),
# n134 = length(intersect(Genes.Buett, intersect(Genes.Sasa, MouseGenes_GOCellCycle))),
# n234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, MouseGenes_GOCellCycle))),
# n1234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, intersect(Genes.Buett, MouseGenes_GOCellCycle)))),
# category = c("Buettner et al.", "Kowalczyk et al.", "Sasagawa et al.", "GO"),
# fill = c("red", "green", "blue", "white"), cex = 3)
# grid.newpage()
#
#
#
#
#
# Freeman_G1S_CC4
#
#
#
#
#
#
Albluca/rpgraph documentation built on May 5, 2019, 1:35 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
library(readr)
library(Pricecycle)
library(GO.db)
library(biomaRt)
AllTerms <- GOBPOFFSPRING[["GO:0007049"]]
AllTerms <- sort(c("GO:0007049", AllTerms))
MouseGenes_GOCellCycle <- getBM(attributes = c("external_gene_name"),
filters = "go",
values = AllTerms,
mart = biomaRt::useMart("ensembl", dataset = "mmusculus_gene_ensembl"))
MouseGenes_GOCellCycle <- MouseGenes_GOCellCycle[,1]
Human_GOCellCycle <- getBM(attributes = c("external_gene_name"),
filters = "go",
values = AllTerms,
mart = biomaRt::useMart("ensembl", dataset = "hsapiens_gene_ensembl"))
Human_GOCellCycle <- Human_GOCellCycle[,1]
# Load data
Data.Sasa <- read_rds("~/Google Drive/Datasets/Sasagawa et al - Murine Stem Cells (EB5 cell line)/rPG_Last.rds")
Data.Kowa <- read_rds("~/Google Drive/Datasets/Kowalczyk et al - Murine hematopoietic stem cells/rPG_Last.rds")
Data.Buet <- read_rds("~/Google Drive/Datasets/Buettner et al. - Murie embrionic stem cells/rPG_Last.rds")
Data.Sasa.Norm <- read_rds("~/Google Drive/Datasets/Sasagawa et al - Murine Stem Cells (EB5 cell line)/rPG_Last-Filtered.rds")
Data.Kowa.Norm <- read_rds("~/Google Drive/Datasets/Kowalczyk et al - Murine hematopoietic stem cells/rPG_Last-Filtered.rds")
Data.Buet.Norm <- read_rds("~/Google Drive/Datasets/Buettner et al. - Murie embrionic stem cells/rPG_Last-Filtered.rds")
# Plot data and construct auxiliary structures
TargetStruct <- Data.Kowa$Analysis$PGStructs[[length(Data.Kowa$Analysis$PGStructs)]]
Proc.Exp.Kowa <- PlotOnStages(Structure = "Circle",
Categories = TargetStruct$Categories,
nGenes = 2,
TaxonList = TargetStruct$TaxonList[[length(TargetStruct$TaxonList)]],
PrinGraph = TargetStruct$PrinGraph,
Net = TargetStruct$Net[[length(TargetStruct$Net)]],
SelThr = .3,
ComputeOverlaps = TRUE,
ExpData = TargetStruct$FiltExp,
RotatioMatrix = TargetStruct$PCAData$rotation[,1:TargetStruct$nDims],
PCACenter = TargetStruct$PCAData$center,
PointProjections = TargetStruct$ProjPoints[[length(TargetStruct$ProjPoints)]],
OrderOnCat = TRUE,
SmoothPoints = 2,
MinCellPerNode = 2,
Title = 'Kowalczyk et al')
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Kowalczyk et al.",
ExpValues = NULL, PlotPC3 = FALSE,
PointAlpha = .5
)
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Kowalczyk et al.",
ExpValues = NULL, PlotPC3 = TRUE,
PointAlpha = .5
)
TargetStruct <- Data.Buet$Analysis$PGStructs[[length(Data.Buet$Analysis$PGStructs)]]
Proc.Exp.Buet <- PlotOnStages(Structure = "Circle",
Categories = TargetStruct$Categories,
nGenes = 2,
TaxonList = TargetStruct$TaxonList[[length(TargetStruct$TaxonList)]],
PrinGraph = TargetStruct$PrinGraph,
Net = TargetStruct$Net[[length(TargetStruct$Net)]],
SelThr = .34,
ComputeOverlaps = TRUE,
ExpData = TargetStruct$FiltExp,
RotatioMatrix = TargetStruct$PCAData$rotation[,1:TargetStruct$nDims],
PCACenter = TargetStruct$PCAData$center,
PointProjections = TargetStruct$ProjPoints[[length(TargetStruct$ProjPoints)]],
OrderOnCat = TRUE,
SmoothPoints = 2,
MinCellPerNode = 2,
Title = 'Buettner et al')
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Buettner et al.",
ExpValues = NULL, PlotPC3 = FALSE,
PointAlpha = .5
)
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Buettner et al.",
ExpValues = NULL, PlotPC3 = TRUE,
PointAlpha = .5
)
TargetStruct <- Data.Sasa$Analysis$PGStructs[[length(Data.Sasa$Analysis$PGStructs)]]
Proc.Exp.Sasa <- PlotOnStages(Structure = "Circle",
Categories = TargetStruct$Categories,
nGenes = 2,
TaxonList = TargetStruct$TaxonList[[length(TargetStruct$TaxonList)]],
PrinGraph = TargetStruct$PrinGraph,
Net = TargetStruct$Net[[length(TargetStruct$Net)]],
SelThr = .35,
ComputeOverlaps = TRUE,
ExpData = TargetStruct$FiltExp,
RotatioMatrix = TargetStruct$PCAData$rotation[,1:TargetStruct$nDims],
PCACenter = TargetStruct$PCAData$center,
PointProjections = TargetStruct$ProjPoints[[length(TargetStruct$ProjPoints)]],
OrderOnCat = TRUE,
SmoothPoints = 0,
MinCellPerNode = 1,
Title = 'Sasagawa et al')
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Sasagawa et al.",
ExpValues = NULL, PlotPC3 = FALSE,
PointAlpha = .5
)
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Sasagawa et al.",
ExpValues = NULL, PlotPC3 = TRUE,
PointAlpha = .5
)
TargetStruct <- Data.Kowa.Norm$Analysis$PGStructs[[length(Data.Kowa.Norm$Analysis$PGStructs)]]
Proc.Exp.Kowa.Norm <- PlotOnStages(Structure = "Circle",
Categories = TargetStruct$Categories,
nGenes = 2,
TaxonList = TargetStruct$TaxonList[[length(TargetStruct$TaxonList)]],
PrinGraph = TargetStruct$PrinGraph,
Net = TargetStruct$Net[[length(TargetStruct$Net)]],
SelThr = .292,
ComputeOverlaps = TRUE,
ExpData = TargetStruct$FiltExp,
RotatioMatrix = TargetStruct$PCAData$rotation[,1:TargetStruct$nDims],
PCACenter = TargetStruct$PCAData$center,
PointProjections = TargetStruct$ProjPoints[[length(TargetStruct$ProjPoints)]],
OrderOnCat = TRUE,
SmoothPoints = 1,
MinCellPerNode = 2,
Title = 'Kowalczyk et al')
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Kowalczyk et al. (Norm)",
ExpValues = NULL, PlotPC3 = FALSE,
PointAlpha = .5
)
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Kowalczyk et al. (Norm)",
ExpValues = NULL, PlotPC3 = TRUE,
PointAlpha = .5
)
TargetStruct <- Data.Buet.Norm$Analysis$PGStructs[[length(Data.Buet.Norm$Analysis$PGStructs)]]
Proc.Exp.Buet.Norm <- PlotOnStages(Structure = "Circle",
Categories = TargetStruct$Categories,
nGenes = 2,
TaxonList = TargetStruct$TaxonList[[length(TargetStruct$TaxonList)]],
PrinGraph = TargetStruct$PrinGraph,
Net = TargetStruct$Net[[length(TargetStruct$Net)]],
SelThr = .34,
ComputeOverlaps = TRUE,
ExpData = TargetStruct$FiltExp,
RotatioMatrix = TargetStruct$PCAData$rotation[,1:TargetStruct$nDims],
PCACenter = TargetStruct$PCAData$center,
PointProjections = TargetStruct$ProjPoints[[length(TargetStruct$ProjPoints)]],
OrderOnCat = TRUE,
SmoothPoints = 2,
MinCellPerNode = 2,
Title = 'Buettner et al')
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Buettner et al. (Norm)",
ExpValues = NULL, PlotPC3 = FALSE,
PointAlpha = .5
)
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Buettner et al. (Norm)",
ExpValues = NULL, PlotPC3 = TRUE,
PointAlpha = .5
)
TargetStruct <- Data.Sasa.Norm$Analysis$PGStructs[[length(Data.Sasa.Norm$Analysis$PGStructs)]]
Proc.Exp.Sasa.Norm <- PlotOnStages(Structure = "Circle",
Categories = TargetStruct$Categories,
nGenes = 2,
TaxonList = TargetStruct$TaxonList[[length(TargetStruct$TaxonList)]],
PrinGraph = TargetStruct$PrinGraph,
Net = TargetStruct$Net[[length(TargetStruct$Net)]],
SelThr = .35,
ComputeOverlaps = TRUE,
ExpData = TargetStruct$FiltExp,
RotatioMatrix = TargetStruct$PCAData$rotation[,1:TargetStruct$nDims],
PCACenter = TargetStruct$PCAData$center,
PointProjections = TargetStruct$ProjPoints[[length(TargetStruct$ProjPoints)]],
OrderOnCat = TRUE,
SmoothPoints = 0,
MinCellPerNode = 1,
Title = 'Sasagawa et al')
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Sasagawa et al. (Norm)",
ExpValues = NULL, PlotPC3 = FALSE,
PointAlpha = .5
)
ProjectOnCircle(
Points = TargetStruct$Data,
Edges = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Edges,
Nodes = TargetStruct$IntGrahs[[length(TargetStruct$IntGrahs)]]$Nodes,
Categories = TargetStruct$Categories,
Title = "Sasagawa et al. (Norm)",
ExpValues = NULL, PlotPC3 = TRUE,
PointAlpha = .5
)
# Create structure to use thge dashboard
InputList <- list(
list(Name = "Buettner et al", Expression = Data.Buet$ExpMat,
Categories = Data.Buet$Cats,
OrderedData = Proc.Exp.Buet,
PGStruct = Data.Buet$Analysis$PGStructs[[length(Data.Buet$Analysis$PGStructs)]],
TaxonList = Data.Buet$Analysis$PGStructs[[length(Data.Buet$Analysis$PGStructs)]]$TaxonList
),
list(Name = "Buettner et al (Norm)", Expression = Data.Buet.Norm$ExpMat,
Categories = Data.Buet.Norm$Cats,
OrderedData = Proc.Exp.Buet.Norm,
PGStruct = Data.Buet.Norm$Analysis$PGStructs[[length(Data.Buet.Norm$Analysis$PGStructs)]],
TaxonList = Data.Buet.Norm$Analysis$PGStructs[[length(Data.Buet.Norm$Analysis$PGStructs)]]$TaxonList
),
list(Name = "Kowalczyk et al", Expression = Data.Kowa$ExpMat,
Categories = Data.Kowa$Cats,
OrderedData = Proc.Exp.Kowa,
PGStruct = Data.Kowa$Analysis$PGStructs[[length(Data.Kowa$Analysis$PGStructs)]],
TaxonList = Data.Kowa$Analysis$PGStructs[[length(Data.Kowa$Analysis$PGStructs)]]$TaxonList
),
list(Name = "Kowalczyk et al (Norm)", Expression = Data.Kowa.Norm$ExpMat,
Categories = Data.Kowa.Norm$Cats,
OrderedData = Proc.Exp.Kowa.Norm,
PGStruct = Data.Kowa.Norm$Analysis$PGStructs[[length(Data.Kowa.Norm$Analysis$PGStructs)]],
TaxonList = Data.Kowa.Norm$Analysis$PGStructs[[length(Data.Kowa.Norm$Analysis$PGStructs)]]$TaxonList
),
list(Name = "Sasagawa et al", Expression = Data.Sasa$ExpMat,
Categories = Data.Sasa$Cats,
OrderedData = Proc.Exp.Sasa,
PGStruct = Data.Sasa$Analysis$PGStructs[[length(Data.Sasa$Analysis$PGStructs)]],
TaxonList = Data.Sasa$Analysis$PGStructs[[length(Data.Sasa$Analysis$PGStructs)]]$TaxonList
),
list(Name = "Sasagawa et al (Norm)", Expression = Data.Sasa.Norm$ExpMat,
Categories = Data.Sasa.Norm$Cats,
OrderedData = Proc.Exp.Sasa.Norm,
PGStruct = Data.Sasa.Norm$Analysis$PGStructs[[length(Data.Sasa.Norm$Analysis$PGStructs)]],
TaxonList = Data.Sasa.Norm$Analysis$PGStructs[[length(Data.Sasa.Norm$Analysis$PGStructs)]]$TaxonList
)
)
SpanVect <- c(.1, .1, .05, .05, .3, .3)
for(i in 1:length(InputList)){
p <- PlotOnPseudotime(WorkStruct = InputList[[i]]$OrderedData,
Expression = InputList[[i]]$Expression,
Name = InputList[[i]]$Name,
gName = "Cdk1", SpanVal = SpanVect[i], CatOrder = NULL)
print(p)
}
for(i in 1:length(InputList)){
p <- PlotOnPseudotime(WorkStruct = InputList[[i]]$OrderedData,
Expression = InputList[[i]]$Expression,
Name = InputList[[i]]$Name,
gName = "Cdk2", SpanVal = SpanVect[i], CatOrder = NULL)
print(p)
}
for(i in 1:length(InputList)){
p <- PlotOnPseudotime(WorkStruct = InputList[[i]]$OrderedData,
Expression = InputList[[i]]$Expression,
Name = InputList[[i]]$Name,
gName = "Cdk4", SpanVal = SpanVect[i], CatOrder = NULL)
print(p)
}
# Launch dashboard
# CompareAcrossData(InputList, NULL)
# AllFreman_Mouse
#
#
Genes.Buett <- Data.Buet$Analysis$Genes[[length(Data.Buet$Analysis$Genes)]]
Genes.Sasa <- Data.Sasa$Analysis$Genes[[length(Data.Sasa$Analysis$Genes)]]
Genes.Kowa <- Data.Kowa$Analysis$Genes[[length(Data.Kowa$Analysis$Genes)]]
Genes.Buett.Norm <- Data.Buet.Norm$Analysis$Genes[[length(Data.Buet.Norm$Analysis$Genes)]]
Genes.Sasa.Norm <- Data.Sasa.Norm$Analysis$Genes[[length(Data.Sasa.Norm$Analysis$Genes)]]
Genes.Kowa.Norm <- Data.Kowa.Norm$Analysis$Genes[[length(Data.Kowa.Norm$Analysis$Genes)]]
length(Genes.Buett)
length(Genes.Kowa)
length(Genes.Sasa)
length(Genes.Buett.Norm)
length(Genes.Kowa.Norm)
length(Genes.Sasa.Norm)
FreemanData <- read_delim("~/Google Drive/Datasets/Gene List/Freeman.gmt",
"\t", escape_double = FALSE, col_names = FALSE,
trim_ws = TRUE)
Freeman_CC1 <- unlist(FreemanData[1,-c(1,2)], use.names = FALSE)
Freeman_CC2 <- unlist(FreemanData[2,-c(1,2)], use.names = FALSE)
Freeman_G1S_CC4 <- unlist(FreemanData[3,-c(1,2)], use.names = FALSE)
Freeman_G2M_CC6 <- unlist(FreemanData[4,-c(1,2)], use.names = FALSE)
Freeman_CC9 <- unlist(FreemanData[5,-c(1,2)], use.names = FALSE)
Freeman_G1S_CC4A <- unlist(FreemanData[6,-c(1,2)], use.names = FALSE)
Freeman_S_CC4B <- unlist(FreemanData[7,-c(1,2)], use.names = FALSE)
Freeman_G2_CC6A <- unlist(FreemanData[8,-c(1,2)], use.names = FALSE)
Freeman_M_CC6B <- unlist(FreemanData[9,-c(1,2)], use.names = FALSE)
Freeman_CC1_Mouse <- ConvertNames("human", "mouse", Freeman_CC1)
Freeman_CC2_Mouse <- ConvertNames("human", "mouse", Freeman_CC2)
Freeman_G1S_CC4_Mouse <- ConvertNames("human", "mouse", Freeman_G1S_CC4)
Freeman_G2M_CC6_Mouse <- ConvertNames("human", "mouse", Freeman_G2M_CC6)
Freeman_CC9_Mouse <- ConvertNames("human", "mouse", Freeman_CC9)
Freeman_G1S_CC4A_Mouse <- ConvertNames("human", "mouse", Freeman_G1S_CC4A)
Freeman_S_CC4B_Mouse <- ConvertNames("human", "mouse", Freeman_S_CC4B)
Freeman_G2_CC6A_Mouse <- ConvertNames("human", "mouse", Freeman_G2_CC6A)
Freeman_M_CC6B_Mouse <- ConvertNames("human", "mouse", Freeman_M_CC6B)
AllFreman_Mouse <- c(Freeman_CC1_Mouse, Freeman_CC2_Mouse, Freeman_CC9_Mouse,
Freeman_G1S_CC4_Mouse, Freeman_G1S_CC4A_Mouse, Freeman_G2_CC6A_Mouse,
Freeman_G2M_CC6_Mouse, Freeman_M_CC6B_Mouse, Freeman_S_CC4B_Mouse)
AllFreman_Mouse <- unique(AllFreman_Mouse)
AllFreman_Mouse_CCP <- c(Freeman_G1S_CC4_Mouse, Freeman_G1S_CC4A_Mouse, Freeman_G2_CC6A_Mouse,
Freeman_G2M_CC6_Mouse, Freeman_M_CC6B_Mouse, Freeman_S_CC4B_Mouse)
AllFreman_Mouse_CCP <- unique(AllFreman_Mouse_CCP)
AllWit_Mouse <- ConvertNames("human", "mouse", unlist(StageAssociation_Whit_Ext[-c(1,2)], use.names = FALSE))
WitAndFree <- intersect(AllFreman_Mouse, AllWit_Mouse)
library(VennDiagram)
#
# grid.newpage()
# draw.pairwise.venn(area1 = length(Genes.Buett), area2 = length(AllFreman_Mouse),
# cross.area = length(intersect(Genes.Buett, AllFreman_Mouse)),
# category = c("Buettner et al.", "Freeman"),
# fill = c("blue", "green"), cex = 3)
#
# grid.newpage()
# draw.pairwise.venn(area1 = length(Genes.Buett), area2 = length(AllWit_Mouse),
# cross.area = length(intersect(Genes.Buett, AllWit_Mouse)),
# category = c("Buettner et al.", "Withfield"),
# fill = c("blue", "green"), cex = 3)
#
#
# grid.newpage()
# draw.pairwise.venn(area1 = length(Genes.Buett), area2 = length(WitAndFree),
# cross.area = length(intersect(Genes.Buett, WitAndFree)),
# category = c("Buettner et al.", "Withfield"),
# fill = c("blue", "green"), cex = 3)
#
#
#
#
#
# grid.newpage()
# draw.quad.venn(area1 = length(Genes.Buett), area2 = length(Genes.Kowa),
# area3 = length(Genes.Sasa), area4 = length(AllFreman_Mouse),
# n12 = length(intersect(Genes.Buett, Genes.Kowa)),
# n23 = length(intersect(Genes.Sasa, Genes.Kowa)),
# n13 = length(intersect(Genes.Buett, Genes.Sasa)),
# n14 = length(intersect(Genes.Buett, AllFreman_Mouse)),
# n24 = length(intersect(AllFreman_Mouse, Genes.Kowa)),
# n34 = length(intersect(Genes.Sasa, AllFreman_Mouse)),
# n123 = length(intersect(Genes.Buett, intersect(Genes.Kowa, Genes.Sasa))),
# n124 = length(intersect(Genes.Buett, intersect(Genes.Kowa, AllFreman_Mouse))),
# n134 = length(intersect(Genes.Buett, intersect(Genes.Sasa, AllFreman_Mouse))),
# n234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, AllFreman_Mouse))),
# n1234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, intersect(Genes.Buett, AllFreman_Mouse)))),
# category = c("Buettner et al.", "Kowalczyk et al.", "Sasagawa et al.", "Freeman"),
# fill = c("red", "green", "blue", "white"), cex = 3)
# grid.newpage()
#
#
#
#
#
#
# grid.newpage()
# draw.quad.venn(area1 = length(Genes.Buett), area2 = length(Genes.Kowa),
# area3 = length(Genes.Sasa), area4 = length(AllWit_Mouse),
# n12 = length(intersect(Genes.Buett, Genes.Kowa)),
# n23 = length(intersect(Genes.Sasa, Genes.Kowa)),
# n13 = length(intersect(Genes.Buett, Genes.Sasa)),
# n14 = length(intersect(Genes.Buett, AllWit_Mouse)),
# n24 = length(intersect(AllWit_Mouse, Genes.Kowa)),
# n34 = length(intersect(Genes.Sasa, AllWit_Mouse)),
# n123 = length(intersect(Genes.Buett, intersect(Genes.Kowa, Genes.Sasa))),
# n124 = length(intersect(Genes.Buett, intersect(Genes.Kowa, AllWit_Mouse))),
# n134 = length(intersect(Genes.Buett, intersect(Genes.Sasa, AllWit_Mouse))),
# n234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, AllWit_Mouse))),
# n1234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, intersect(Genes.Buett, AllWit_Mouse)))),
# category = c("Buettner et al.", "Kowalczyk et al.", "Sasagawa et al.", "Freeman"),
# fill = c("red", "green", "blue", "white"), cex = 3)
# grid.newpage()
#
#
#
#
#
#
#
#
#
#
#
# grid.newpage()
# draw.quad.venn(area1 = length(Genes.Buett), area2 = length(Genes.Kowa),
# area3 = length(Genes.Sasa), area4 = length(MouseGenes_GOCellCycle),
# n12 = length(intersect(Genes.Buett, Genes.Kowa)),
# n23 = length(intersect(Genes.Sasa, Genes.Kowa)),
# n13 = length(intersect(Genes.Buett, Genes.Sasa)),
# n14 = length(intersect(Genes.Buett, MouseGenes_GOCellCycle)),
# n24 = length(intersect(MouseGenes_GOCellCycle, Genes.Kowa)),
# n34 = length(intersect(Genes.Sasa, MouseGenes_GOCellCycle)),
# n123 = length(intersect(Genes.Buett, intersect(Genes.Kowa, Genes.Sasa))),
# n124 = length(intersect(Genes.Buett, intersect(Genes.Kowa, MouseGenes_GOCellCycle))),
# n134 = length(intersect(Genes.Buett, intersect(Genes.Sasa, MouseGenes_GOCellCycle))),
# n234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, MouseGenes_GOCellCycle))),
# n1234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, intersect(Genes.Buett, MouseGenes_GOCellCycle)))),
# category = c("Buettner et al.", "Kowalczyk et al.", "Sasagawa et al.", "GO"),
# fill = c("red", "green", "blue", "white"), cex = 3)
# grid.newpage()
grid.newpage()
draw.triple.venn(area1 = length(Genes.Buett), area2 = length(Genes.Kowa),
area3 = length(Genes.Sasa),
n12 = length(intersect(Genes.Buett, Genes.Kowa)),
n23 = length(intersect(Genes.Sasa, Genes.Kowa)),
n13 = length(intersect(Genes.Buett, Genes.Sasa)),
n123 = length(intersect(Genes.Buett, intersect(Genes.Kowa, Genes.Sasa))),
category = c("Buettner et al.", "Kowalczyk et al.", "Sasagawa et al."),
fill = c("red", "green", "blue"), cex = 3)
grid.newpage()
draw.triple.venn(area1 = length(Genes.Buett.Norm), area2 = length(Genes.Kowa.Norm),
area3 = length(Genes.Sasa.Norm),
n12 = length(intersect(Genes.Buett.Norm, Genes.Kowa.Norm)),
n23 = length(intersect(Genes.Sasa.Norm, Genes.Kowa.Norm)),
n13 = length(intersect(Genes.Buett.Norm, Genes.Sasa.Norm)),
n123 = length(intersect(Genes.Buett.Norm, intersect(Genes.Kowa.Norm, Genes.Sasa.Norm))),
category = c("Buettner et al. (Norm)", "Kowalczyk et al. (Norm)", "Sasagawa et al. (Norm)"),
fill = c("red", "green", "blue"), cex = 3)
grid.newpage()
draw.pairwise.venn(area1 = length(Genes.Buett),
area2 = length(Genes.Buett.Norm),
cross.area = length(intersect(Genes.Buett, Genes.Buett.Norm)),
category = c("Buettner et al.", "Buettner et al. (Norm)"),
fill = c("red", "blue"), cex = 2)
grid.newpage()
draw.pairwise.venn(area1 = length(Genes.Kowa),
area2 = length(Genes.Kowa.Norm),
cross.area = length(intersect(Genes.Kowa, Genes.Kowa.Norm)),
category = c("Kowalczyk et al.", "Kowalczyk et al. (Norm)"),
fill = c("red", "blue"), cex = 2)
grid.newpage()
draw.pairwise.venn(area1 = length(Genes.Sasa),
area2 = length(Genes.Sasa.Norm),
cross.area = length(intersect(Genes.Sasa, Genes.Sasa.Norm)),
category = c("Sasagawa et al.", "Sasagawa et al. (Norm)"),
fill = c("red", "blue"), cex = 2)
AllSample <- intersect(Genes.Buett, intersect(Genes.Sasa, Genes.Kowa))
AllSample.Norm <- intersect(Genes.Buett.Norm, intersect(Genes.Sasa.Norm, Genes.Kowa.Norm))
grid.newpage()
draw.pairwise.venn(area1 = length(AllSample),
area2 = length(AllSample.Norm),
cross.area = length(intersect(AllSample, AllSample.Norm)),
category = c("Intersection", "Intersection (Norm)"),
fill = c("red", "blue"), cex = 2)
grid.newpage()
draw.triple.venn(area1 = length(AllSample), area2 = length(AllWit_Mouse),
area3 = length(AllFreman_Mouse),
n12 = length(intersect(AllSample, AllWit_Mouse)),
n23 = length(intersect(AllFreman_Mouse, AllWit_Mouse)),
n13 = length(intersect(AllSample, AllFreman_Mouse)),
n123 = length(intersect(AllSample, intersect(AllWit_Mouse, AllFreman_Mouse))),
category = c("Core module", "Withfield", "Giotti"),
fill = c("red", "green", "blue"), cex = 3)
grid.newpage()
draw.triple.venn(area1 = length(AllSample.Norm), area2 = length(AllWit_Mouse),
area3 = length(AllFreman_Mouse),
n12 = length(intersect(AllSample.Norm, AllWit_Mouse)),
n23 = length(intersect(AllFreman_Mouse, AllWit_Mouse)),
n13 = length(intersect(AllSample.Norm, AllFreman_Mouse)),
n123 = length(intersect(AllSample.Norm, intersect(AllWit_Mouse, AllFreman_Mouse))),
category = c("Core module", "Withfield", "Giotti"),
fill = c("red", "green", "blue"), cex = 3)
grid.newpage()
length(intersect(MouseGenes_GOCellCycle, AllFreman_Mouse)) / length(MouseGenes_GOCellCycle)
length(intersect(AllSample, AllFreman_Mouse)) / length(MouseGenes_GOCellCycle)
length(intersect(MouseGenes_GOCellCycle, AllWit_Mouse)) / length(MouseGenes_GOCellCycle)
length(intersect(AllSample, AllWit_Mouse)) / length(MouseGenes_GOCellCycle)
length(intersect(AllSample, MouseGenes_GOCellCycle))
New <- setdiff(AllSample, MouseGenes_GOCellCycle)
toupper(New) %in% Human_GOCellCycle
BaseBuett <- Genes.Buett
BaseKowa <- Genes.Kowa
BaseSasa <- Genes.Sasa
Buet.Sasa <- intersect(Genes.Buett, Genes.Sasa)
Buet.Kowa <- intersect(Genes.Buett, Genes.Kowa)
Sasa.Kowa <- intersect(Genes.Sasa, Genes.Kowa)
View(matrix(setdiff(unique(c(Buet.Sasa, Buet.Kowa, Sasa.Kowa)), AllSample), ncol = 1))
All.Buett <- length(AllSample)/length(BaseBuett)
Two.Buett <- length(setdiff(c(Buet.Sasa, Buet.Kowa), AllSample))/length(BaseBuett)
One.Buett <- length(setdiff(BaseBuett, c(BaseKowa, BaseSasa)))/length(BaseBuett)
All.Sasa <- length(AllSample)/length(BaseSasa)
Two.Sasa <- length(setdiff(c(Buet.Sasa, Sasa.Kowa), AllSample))/length(BaseSasa)
One.Sasa <- length(setdiff(BaseSasa, c(BaseBuett, BaseKowa)))/length(BaseSasa)
All.Kowa <- length(AllSample)/length(BaseKowa)
Two.Kowa <- length(setdiff(c(Sasa.Kowa, Buet.Kowa), AllSample))/length(BaseKowa)
One.Kowa <- length(setdiff(BaseKowa, c(BaseBuett, BaseSasa)))/length(BaseKowa)
barplot(c(One.Buett, Two.Buett, All.Buett), main="Buettner")
barplot(c(One.Kowa, Two.Kowa, All.Kowa), main="Kowa")
barplot(c(One.Sasa, Two.Sasa, All.Sasa), main="Sasa")
length(intersect(BaseSasa, MouseGenes_GOCellCycle))/length(BaseSasa)
length(intersect(BaseKowa, MouseGenes_GOCellCycle))/length(BaseKowa)
length(intersect(BaseBuett, MouseGenes_GOCellCycle))/length(BaseBuett)
# CatOrder = c("G0", "G0+G1", "G1", "G1+S", "S", "S+G2M", "G2M", "G2M+G0", "G1M+G1")
# DataStruct = Data.Buet
# ProcStruct = Proc.Exp.Buet
# MinNodes = 2
# FiltMax = .2
# Thr = .9
# QuantSel = .75
# SinglePeack = FALSE
View(matrix(sort(unique(AllSample)), ncol=8))
PeackGenes.Sasa <- GetGenesWithPeaks(DataStruct = Data.Sasa,
ProcStruct = Proc.Exp.Sasa,
FiltMax = .1, Thr = .8,
QuantSel = .5, SinglePeack = FALSE,
AllGenes = TRUE)
PeackGenes.Kowa <- GetGenesWithPeaks(DataStruct = Data.Kowa,
ProcStruct = Proc.Exp.Kowa,
FiltMax = .1, Thr = .8,
QuantSel = .5, SinglePeack = FALSE,
AllGenes = TRUE)
PeackGenes.Buet <- GetGenesWithPeaks(DataStruct = Data.Buet,
ProcStruct = Proc.Exp.Buet,
FiltMax = .1, Thr = .8,
QuantSel = .5, SinglePeack = FALSE,
AllGenes = TRUE)
PeackGenes.Sasa.Norm <- GetGenesWithPeaks(DataStruct = Data.Sasa.Norm,
ProcStruct = Proc.Exp.Sasa.Norm,
FiltMax = .1, Thr = .8,
QuantSel = .5, SinglePeack = FALSE,
AllGenes = TRUE)
PeackGenes.Kowa.Norm <- GetGenesWithPeaks(DataStruct = Data.Kowa.Norm,
ProcStruct = Proc.Exp.Kowa.Norm,
FiltMax = .1, Thr = .8,
QuantSel = .5, SinglePeack = FALSE,
AllGenes = TRUE)
PeackGenes.Buet.Norm <- GetGenesWithPeaks(DataStruct = Data.Buet.Norm,
ProcStruct = Proc.Exp.Buet.Norm,
FiltMax = .1, Thr = .8,
QuantSel = .5, SinglePeack = FALSE,
AllGenes = TRUE)
# DataStruct = Data.Buet
# ProcStruct = Proc.Exp.Buet
# FiltMax = .1
# Thr = .9
# QuantSel = .5
# SinglePeack = FALSE
# MinNodes = 2
names(PeackGenes.Kowa)
names(PeackGenes.Buet)
names(PeackGenes.Sasa)
G1.kowa <- unlist(PeackGenes.Kowa[grep("G1", names(PeackGenes.Kowa))])
G1.buet <- unlist(PeackGenes.Buet[grep("G1", names(PeackGenes.Buet))])
G1.sasa <- unlist(PeackGenes.Sasa[grep("G1", names(PeackGenes.Sasa))])
S.kowa <- unlist(PeackGenes.Kowa[grep("S", names(PeackGenes.Kowa))])
S.buet <- unlist(PeackGenes.Buet[grep("S", names(PeackGenes.Buet))])
S.sasa <- unlist(PeackGenes.Sasa[grep("S", names(PeackGenes.Sasa))])
G2M.kowa <- unlist(PeackGenes.Kowa[grep("G2/M", names(PeackGenes.Kowa))])
G2M.buet <- unlist(PeackGenes.Buet[grep("G2M", names(PeackGenes.Buet))])
G2M.sasa <- unlist(PeackGenes.Sasa[grep("G2/M", names(PeackGenes.Sasa))])
G1.kowa.Norm <- unlist(PeackGenes.Kowa.Norm[grep("G1", names(PeackGenes.Kowa.Norm))])
G1.buet.Norm <- unlist(PeackGenes.Buet.Norm[grep("G1", names(PeackGenes.Buet.Norm))])
G1.sasa.Norm <- unlist(PeackGenes.Sasa.Norm[grep("G1", names(PeackGenes.Sasa.Norm))])
S.kowa.Norm <- unlist(PeackGenes.Kowa.Norm[grep("S", names(PeackGenes.Kowa.Norm))])
S.buet.Norm <- unlist(PeackGenes.Buet.Norm[grep("S", names(PeackGenes.Buet.Norm))])
S.sasa.Norm <- unlist(PeackGenes.Sasa.Norm[grep("S", names(PeackGenes.Sasa.Norm))])
G2M.kowa.Norm <- unlist(PeackGenes.Kowa.Norm[grep("G2/M", names(PeackGenes.Kowa.Norm))])
G2M.buet.Norm <- unlist(PeackGenes.Buet.Norm[grep("G2M", names(PeackGenes.Buet.Norm))])
G2M.sasa.Norm <- unlist(PeackGenes.Sasa.Norm[grep("G2/M", names(PeackGenes.Sasa.Norm))])
# G0.kowa <- unlist(PeackGenes.Kowa[c(1,10)])
# G1.kowa <- setdiff(unlist(PeackGenes.Kowa[2:6]),
# unlist(PeackGenes.Kowa[-c(2:6)]))
# G1.buet <- setdiff(unlist(PeackGenes.Buet[1:2]),
# unlist(PeackGenes.Buet[-c(1:2)]))
# G1.sasa <- setdiff(unlist(PeackGenes.Sasa[1:2]),
# unlist(PeackGenes.Sasa[-c(1:2)]))
#
# S.kowa <- setdiff(unlist(PeackGenes.Kowa[6:8]),
# unlist(PeackGenes.Kowa[-c(6:8)]))
# S.buet <- setdiff(unlist(PeackGenes.Buet[2:4]),
# unlist(PeackGenes.Buet[-c(2:4)]))
# S.sasa <- setdiff(unlist(PeackGenes.Sasa[2:4]),
# unlist(PeackGenes.Sasa[-c(2:4)]))
#
# G2M.kowa <- setdiff(unlist(PeackGenes.Kowa[8:10]),
# unlist(PeackGenes.Kowa[-c(8:10)]))
# G2M.buet <- setdiff(unlist(PeackGenes.Buet[4:6]),
# unlist(PeackGenes.Buet[-c(4:6)]))
# G2M.sasa <- setdiff(unlist(PeackGenes.Sasa[4:6]),
# unlist(PeackGenes.Sasa[-c(4:6)]))
# setdiff(G1.kowa, S.kowa)
# setdiff(PeackGenes.Buet[[2]], PeackGenes.Buet[[2]])
# G0.All <- G0.kowa
G1.All <- unique(c(G1.kowa, G1.buet, G1.sasa))
S.All <- c(S.kowa, S.buet, S.sasa)
G2M.All <- c(G2M.kowa, G2M.buet, G2M.sasa)
length(G1.All)
length(S.All)
length(G2M.All)
G1.Combined <- rbind(G1.All %in% G1.buet,
G1.All %in% G1.kowa,
G1.All %in% G1.sasa)
colnames(G1.Combined) <- G1.All
rownames(G1.Combined) <- c("Buet", "Kowa", "Sasa")
table(colSums(G1.Combined))
table(colSums(G1.Combined[-2,]))
table(colSums(G1.Combined[-1,]))
table(colSums(G1.Combined[-3,]))
grid.newpage()
draw.triple.venn(area1 = length(G1.buet), area2 = length(G1.sasa),
area3 = length(G1.kowa), scaled = TRUE,
n12 = length(intersect(G1.buet, G1.sasa)),
n23 = length(intersect(G1.kowa, G1.sasa)),
n13 = length(intersect(G1.buet, G1.kowa)),
n123 = length(intersect(G1.buet, intersect(G1.sasa, G1.kowa))),
category = c("Buettner et al. (G1)", "Sasagawa et al. (G1)", "Kowalczyk et al. (G1)"),
fill = c("red", "green", "blue"), cex = 3, main = "pippo")
grid.newpage()
draw.triple.venn(area1 = length(S.buet), area2 = length(S.sasa),
area3 = length(S.kowa), scaled = TRUE,
n12 = length(intersect(S.buet, S.sasa)),
n23 = length(intersect(S.kowa, S.sasa)),
n13 = length(intersect(S.buet, S.kowa)),
n123 = length(intersect(S.buet, intersect(S.sasa, S.kowa))),
category = c("Buettner et al. (S)", "Sasagawa et al. (S)", "Kowalczyk et al. (S)"),
fill = c("red", "green", "blue"), cex = 3, main = "pippo")
grid.newpage()
draw.triple.venn(area1 = length(G2M.buet), area2 = length(G2M.sasa),
area3 = length(G2M.kowa), scaled = TRUE,
n12 = length(intersect(G2M.buet, G2M.sasa)),
n23 = length(intersect(G2M.kowa, G2M.sasa)),
n13 = length(intersect(G2M.buet, G2M.kowa)),
n123 = length(intersect(G2M.buet, intersect(G2M.sasa, G2M.kowa))),
category = c("Buettner et al. (G2M)", "Sasagawa et al. (G2M)", "Kowalczyk et al. (G2M)"),
fill = c("red", "green", "blue"), cex = 3, main = "pippo")
grid.newpage()
draw.triple.venn(area1 = length(G1.buet.Norm), area2 = length(G1.sasa.Norm),
area3 = length(G1.kowa.Norm), scaled = TRUE,
n12 = length(intersect(G1.buet.Norm, G1.sasa.Norm)),
n23 = length(intersect(G1.kowa.Norm, G1.sasa.Norm)),
n13 = length(intersect(G1.buet.Norm, G1.kowa.Norm)),
n123 = length(intersect(G1.buet.Norm, intersect(G1.sasa.Norm, G1.kowa.Norm))),
category = c("Buettner et al. (Norm - G1)", "Sasagawa et al. (Norm - G1)", "Kowalczyk et al. (Norm - G1)"),
fill = c("red", "green", "blue"), cex = 3, main = "pippo")
grid.newpage()
draw.triple.venn(area1 = length(S.buet.Norm), area2 = length(S.sasa.Norm),
area3 = length(S.kowa.Norm), scaled = TRUE,
n12 = length(intersect(S.buet.Norm, S.sasa.Norm)),
n23 = length(intersect(S.kowa.Norm, S.sasa.Norm)),
n13 = length(intersect(S.buet.Norm, S.kowa.Norm)),
n123 = length(intersect(S.buet.Norm, intersect(S.sasa.Norm, S.kowa.Norm))),
category = c("Buettner et al. (S)", "Sasagawa et al. (S)", "Kowalczyk et al. (S)"),
fill = c("red", "green", "blue"), cex = 3, main = "pippo")
grid.newpage()
draw.triple.venn(area1 = length(G2M.buet.Norm), area2 = length(G2M.sasa.Norm),
area3 = length(G2M.kowa.Norm), scaled = TRUE,
n12 = length(intersect(G2M.buet.Norm, G2M.sasa.Norm)),
n23 = length(intersect(G2M.kowa.Norm, G2M.sasa.Norm)),
n13 = length(intersect(G2M.buet.Norm, G2M.kowa.Norm)),
n123 = length(intersect(G2M.buet.Norm, intersect(G2M.sasa.Norm, G2M.kowa.Norm))),
category = c("Buettner et al. (G2M)", "Sasagawa et al. (G2M)", "Kowalczyk et al. (G2M)"),
fill = c("red", "green", "blue"), cex = 3, main = "pippo")
sum(G2M.sasa %in% G2M.kowa)
G2M_Strong <- intersect(intersect(G2M.sasa, G2M.buet), G2M.kowa)
G1_Strong <- intersect(intersect(G1.sasa, G1.buet), G1.kowa)
S_Strong <- intersect(intersect(S.sasa, S.buet), S.kowa)
G2M_Strong.Norm <- intersect(intersect(G2M.sasa.Norm, G2M.buet.Norm), G2M.kowa.Norm)
G1_Strong.Norm <- intersect(intersect(G1.sasa.Norm, G1.buet.Norm), G1.kowa.Norm)
S_Strong.Norm <- intersect(intersect(S.sasa.Norm, S.buet.Norm), S.kowa.Norm)
fileConn <- file("~/Desktop/PriceCyc_Mouse.gmt")
writeLines(c(paste(c("G1", "G1 Strong", G1_Strong), collapse ='\t'),
paste(c("S", "S Strong", S_Strong), collapse ='\t'),
paste(c("G2M", "G2M Strong", G2M_Strong), collapse ='\t'),
paste(c("Core", "Core module", AllSample), collapse ='\t')),
fileConn)
close(fileConn)
fileConn <- file("~/Desktop/PriceCyc_Hum.gmt")
writeLines(c(paste(c("G1", "G1 Strong", toupper(G1_Strong)), collapse ='\t'),
paste(c("S", "S Strong", toupper(S_Strong)), collapse ='\t'),
paste(c("G2M", "G2M Strong", toupper(G2M_Strong)), collapse ='\t'),
paste(c("Core", "Core module", toupper(AllSample)), collapse ='\t')),
fileConn)
close(fileConn)
# G1.Sel <- rbind(G1.All %in% G1.kowa,
# G1.All %in% G1.buet,
# G1.All %in% G1.sasa)
# G1.All <- G1.All[which(colSums(G1.Sel) > 1)]
#
# S.Sel <- rbind(S.All %in% S.kowa,
# S.All %in% S.buet,
# S.All %in% S.sasa)
# S.All <- S.All[which(colSums(S.Sel) > 1)]
#
# G2M.Sel <- rbind(G2M.All %in% G2M.kowa,
# G2M.All %in% G2M.buet,
# G2M.All %in% G2M.sasa)
# G2M.All <- G2M.All[which(colSums(G2M.Sel) > 1)]
length(G1.All)
length(S.All)
length(G2M.All)
ToRemove <- unique(c(intersect(G1.All, S.All),
intersect(S.All, G2M.All),
intersect(G1.All, G2M.All))
)
# G1.Sel <- G1.All
G1.Sel <- G1.All
G1.Sel <- G1.Sel[!(G1.Sel %in% ToRemove)]
S.Sel <- S.All
S.Sel <- S.Sel[!(S.Sel %in% ToRemove)]
G2M.Sel <- G2M.All
G2M.Sel <- G2M.Sel[!(G2M.Sel %in% ToRemove)]
# G0.Sel <- G0.All
# G0.Sel <- G0.Sel[!(G0.Sel %in% ToRemove)]
sum(G1.buet %in% G1.Sel)
sum(S.buet %in% S.Sel)
sum(G2M.buet %in% G2M.Sel)
sum(G1.sasa %in% G1.Sel)
sum(S.sasa %in% S.Sel)
sum(G2M.sasa %in% G2M.Sel)
sum(G1.kowa %in% G1.Sel)
sum(S.kowa %in% S.Sel)
sum(G2M.kowa %in% G2M.Sel)
sum(G1.buet %in% G1.All)
sum(S.buet %in% S.All)
sum(G2M.buet %in% G2M.All)
sum(G1.sasa %in% G1.All)
sum(S.sasa %in% S.All)
sum(G2M.sasa %in% G2M.All)
sum(G1.kowa %in% G1.All)
sum(S.kowa %in% S.All)
sum(G2M.kowa %in% G2M.All)
DF <- data.frame(lapply(list(G1 = G1_Strong, S = S_Strong, G2M = G2M_Strong), length))
library(ggplot2)
library(reshape)
melt(DF)
ggplot(data = melt(DF), mapping = aes(x = variable, y = value, fill = variable)) +
geom_bar(stat = 'identity') + guides(fill='none') +
labs(title="Biomarker genes", y="Number of genes", x="Cell cycle phase")
# SelStageInfo <- list(G1 = G1.All, S = S.All, G2M = G2M.All)
# SelStageInfo <- list(G1 = G1.Sel, S = S.Sel, G2M = G2M.Sel)
# SelStageInfo <- list(G1 = G1_Strong, S = S_Strong, G2M = G2M_Strong)
SelStageInfo <- list(G1 = G1_Strong.Norm, S = S_Strong.Norm, G2M = G2M_Strong.Norm)
# SelStageInfo <- list(G1 = G1_Strong.Norm[!(G1_Strong.Norm %in% c(S_Strong.Norm, G2M_Strong.Norm))],
# S = S_Strong.Norm[!(S_Strong.Norm %in% c(G1_Strong.Norm, G2M_Strong.Norm))],
# G2M = G2M_Strong.Norm[!(G2M_Strong.Norm %in% c(G1_Strong.Norm, S_Strong.Norm))])
Staged.Sasa <- StageWithPeaks(DataStruct = Data.Sasa,
ProcStruct = Proc.Exp.Sasa,
ComputeG0 = FALSE,
FiltMax = 0,
Thr = .8,
QuantSel = .8,
StageInfo = SelStageInfo,
MinNodes = 2,
Mode = 1,
G0Level = .9,
Title = 'Sasagawa et al')
#
# TB <- table(Staged.Sasa$CellStages, Data.Sasa$Cats)
# t(TB)/colSums(TB)
#
# Data.Sasa$Analysis
#
Staged.Buet <- StageWithPeaks(DataStruct = Data.Buet,
ProcStruct = Proc.Exp.Buet,
FiltMax = .1,
Thr = .8,
QuantSel = .8,
StageInfo = SelStageInfo,
ComputeG0 = FALSE,
MinNodes = 2,
Mode = 1,
G0Level = .9,
Title = 'Buettner et al')
Staged.Kowa <- StageWithPeaks(DataStruct = Data.Kowa,
ProcStruct = Proc.Exp.Kowa,
FiltMax = .1,
Thr = .8,
QuantSel = .8,
StageInfo = SelStageInfo,
ComputeG0 = TRUE,
MinNodes = 2,
Mode = 1,
G0Level = 1.1,
Title = "Kowalczyk et al")
#
#
# library(GSEABase)
#
#
#
#
# devtools::install_url("https://github.com/satijalab/seurat/releases/download/v1.4.0/Seurat_1.4.0.16.tgz", binary = TRUE)
# library(Seurat)
#
#
#
#
# DataStruct = Data.Sasa
# ProcStruct = Proc.Exp.Sasa
# FiltMax = .1
# Thr = .75
# QuantSel = .75
# StageInfo = SelStageInfo
# ComputeG0 = TRUE
# MinNodes = 2
# Mode = 3
# G0Level = .7
#
# Staged.Kowa <- StageWithPeaks(DataStruct = Data.Kowa,
# ProcStruct = Proc.Exp.Kowa,
# FiltMax = .1,
# Thr = .75,
# QuantSel = .75,
# SinglePeack = TRUE,
# StageInfo = SelStageInfo,
# ComputeG0 = TRUE,
# MinNodes = 2,
# Mode = 3,
# G0Level = 1)
#
length(G0.Sel)
length(G1.Sel)
length(S.Sel)
length(G2M.Sel)
table(colSums(rbind(G1.All %in% G1.buet,
G1.All %in% G1.sasa,
G1.All %in% G1.kowa)))
AllMat.All <- cbind(AllMat.Sasa, AllMat.Buet)
AllMat.All <- AllMat.All[, apply(AllMat.All, 2, any)]
pheatmap::pheatmap(1*unique(AllMat.All))
pheatmap::pheatmap(1*(AllMat.All))
# pheatmap::pheatmap(cor(Proc.Exp.Buet$NodesExp, method = "spe"))
# pheatmap::pheatmap(cor(t(Proc.Exp.Buet$NodesExp), method = "spe"), show_rownames = FALSE, show_colnames = FALSE)
Max.Bue <- apply(Proc.Exp.Buet$NodesExp, 1, which.max)
Max.Sasa <- apply(Proc.Exp.Sasa$NodesExp, 1, which.max)
Shared <- intersect(names(Max.Bue), names(Max.Sasa))
plot(Max.Bue[Shared], Max.Sasa[Shared])
cor.test(Max.Bue[Shared], Max.Sasa[Shared], method = "pea")
AllGenes <- unique(c(unlist(PeackGenes.Kowa), unlist(PeackGenes.Buet), unlist(PeackGenes.Sasa)))
AllMat.Kowa <- sapply(PeackGenes.Kowa, function(x){AllGenes %in% x})
colnames(AllMat.Kowa) <- paste(colnames(AllMat.Kowa), "_Kowa", sep='')
AllMat.Buet <- sapply(PeackGenes.Buet, function(x){AllGenes %in% x})
colnames(AllMat.Buet) <- paste(colnames(AllMat.Buet), "_Buet", sep='')
AllMat.Sasa <- sapply(PeackGenes.Sasa, function(x){AllGenes %in% x})
colnames(AllMat.Sasa) <- paste(colnames(AllMat.Sasa), "_Sasa", sep='')
# AllMat.All <- cbind(AllMat.Kowa, AllMat.Buet, AllMat.Sasa)
AllMat.All <- cbind(AllMat.Sasa, AllMat.Buet)
AllMat.All <- AllMat.All[, apply(AllMat.All, 2, any)]
pheatmap::pheatmap(1*unique(AllMat.All))
pheatmap::pheatmap(1*(AllMat.All))
IntMat <- apply(AllMat.All, 2, function(x){
apply(AllMat.All, 2, function(y){ sum(x & y)/min(sum(x), sum(y)) })
})
diag(IntMat) <- NA
pheatmap::pheatmap(IntMat[rownames(IntMat) %in% c("G1_Buet", "G1+S_Buet"),
!(colnames(IntMat) %in% c("G1_Buet", "G1+S_Buet"))],
cluster_rows = TRUE, cluster_cols = TRUE)
pheatmap::pheatmap(IntMat[rownames(IntMat) %in% c("G1+S_Buet", "S_Buet", "S+G2M_Buet"),
!(colnames(IntMat) %in% c("G1+S_Buet", "S_Buet", "S+G2M_Buet"))],
cluster_rows = TRUE, cluster_cols = TRUE)
pheatmap::pheatmap(IntMat[rownames(IntMat) %in% c("G2M_Buet", "S+G2M_Buet"),
!(colnames(IntMat) %in% c("G2M_Buet", "S+G2M_Buet"))],
cluster_rows = TRUE, cluster_cols = TRUE)
AllMat.All <- cbind(AllMat.Kowa, AllMat.Buet)
AllMat.All <- AllMat.All[, apply(AllMat.All, 2, any)]
IntMat <- apply(AllMat.All, 2, function(x){
apply(AllMat.All, 2, function(y){ sum(x & y)/min(sum(x), sum(y)) })
})
diag(IntMat) <- NA
pheatmap::pheatmap(IntMat[rownames(IntMat) %in% c("G1_Buet", "G1+S_Buet"),
!(colnames(IntMat) %in% c("G1_Buet", "G1+S_Buet"))],
cluster_rows = TRUE, cluster_cols = TRUE)
pheatmap::pheatmap(IntMat[rownames(IntMat) %in% c("G1+S_Buet", "S_Buet", "S+G2M_Buet"),
!(colnames(IntMat) %in% c("G1+S_Buet", "S_Buet", "S+G2M_Buet"))],
cluster_rows = TRUE, cluster_cols = TRUE)
pheatmap::pheatmap(IntMat[rownames(IntMat) %in% c("G2M_Buet", "S+G2M_Buet"),
!(colnames(IntMat) %in% c("G2M_Buet", "S+G2M_Buet"))],
cluster_rows = TRUE, cluster_cols = TRUE)
diag(IntMat) <- 0
rownames(IntMat)[apply(IntMat, 1, which.max)]
pheatmap::pheatmap(1*(IntMat > .3),
clustering_distance_rows = "binary",
clustering_distance_cols = "binary")
FilterRepeating <- function(PKG.List) {
PKG.All <- unique(unlist(PKG.List))
GeneMat.List <- lapply(PKG.List, function(x) {
PKG.All %in% x
})
GeneMat <- NULL
for(i in 1:length(GeneMat.List)){
GeneMat <- rbind(GeneMat, GeneMat.List[[i]])
}
colnames(GeneMat) <- PKG.All
MultPh <- which(colSums(GeneMat)>1)
if(length(MultPh) == 0){
return(PKG.List)
}
print(MultPh)
MultStages <- lapply(apply(GeneMat[,MultPh], 2, which), function(x) {
names(PKG.List)[x]
})
DividedStages <- lapply(MultStages, function(x) {
strsplit(x, "+", fixed = TRUE)
})
SingleStageGenes <- lapply(DividedStages, function(x){
StagesAll <- rep(NA, length(unique(unlist(x))))
names(StagesAll) <- unique(unlist(x))
for(Stg in names(StagesAll)){
ListBel <- lapply(x, function(x){
Stg %in% x
})
StagesAll[Stg] <- all(unlist(ListBel))
}
if(any(StagesAll)){
return(which(StagesAll))
} else {
return(NA)
}
})
PKG.List <- lapply(PKG.List, function(x){
x[!(x %in% names(MultPh))]
})
SingleStageGenes <- SingleStageGenes[!unlist(lapply(SingleStageGenes, function(x){any(is.na(x))}))]
if(length(SingleStageGenes) > 0){
for(i in 1:length(SingleStageGenes)){
PKG.List[[ SingleStageGenes[[i]] ]] <- union(PKG.List[[ SingleStageGenes[[i]] ]],
names(SingleStageGenes)[i])
}
}
return(PKG.List)
}
PeackGenes.Kowa <- FilterRepeating(PeackGenes.Kowa)
PeackGenes.Buet <- FilterRepeating(PeackGenes.Buet)
PeackGenes.Sasa <- FilterRepeating(PeackGenes.Sasa)
barplot(unlist(lapply(PeackGenes.Kowa, length)), las = 2)
barplot(unlist(lapply(PeackGenes.Buet, length)), las = 2)
barplot(unlist(lapply(PeackGenes.Sasa, length)), las = 2)
AllG1 <- c(PeackGenes.Kowa$`G1(late)`,
PeackGenes.Kowa$`G1(early)`,
PeackGenes.Kowa$`G1(early)+G1(late)`,
PeackGenes.Kowa$`G1(late)`,
PeackGenes.Kowa$`G1(late)+S`,
PeackGenes.Buet$G1,
PeackGenes.Buet$`G1+S`,
PeackGenes.Sasa$G1,
PeackGenes.Sasa$`G1+S`)
G1Mat <- rbind(AllG1 %in% c(PeackGenes.Kowa$`G1(late)`,
PeackGenes.Kowa$`G1(early)`,
PeackGenes.Kowa$`G1(early)+G1(late)`,
PeackGenes.Kowa$`G1(late)`,
PeackGenes.Kowa$`G1(late)+S`),
AllG1 %in% c(PeackGenes.Buet$G1,
PeackGenes.Buet$`G1+S`),
AllG1 %in% c(PeackGenes.Sasa$G1,
PeackGenes.Sasa$`G1+S`))
colnames(G1Mat) <- AllG1
table(colSums(G1Mat))
AllS <- c(PeackGenes.Kowa$S,
PeackGenes.Buet$S,
PeackGenes.Sasa$S)
SMat <- rbind(AllS %in% PeackGenes.Kowa$S,
AllS %in% PeackGenes.Buet$S,
AllS %in% PeackGenes.Sasa$S)
colnames(SMat) <- AllS
table(colSums(SMat))
AllG2M <- c(PeackGenes.Kowa$`G2/M`,
PeackGenes.Buet$G2M,
PeackGenes.Sasa$G2M)
G2MMat <- rbind(AllG2M %in% PeackGenes.Kowa$`G2/M`,
AllG2M %in% PeackGenes.Buet$G2M,
AllG2M %in% PeackGenes.Sasa$G2M)
colnames(G2MMat) <- AllG2M
table(colSums(G2MMat))
AllGenes <- unique(c(unlist(PeackGenes.Kowa), unlist(PeackGenes.Buet), unlist(PeackGenes.Sasa)))
AllMat.Kowa <- sapply(PeackGenes.Kowa, function(x){AllGenes %in% x})
colnames(AllMat.Kowa) <- paste(colnames(AllMat.Kowa), "_Kowa", sep='')
AllMat.Buet <- sapply(PeackGenes.Buet, function(x){AllGenes %in% x})
colnames(AllMat.Buet) <- paste(colnames(AllMat.Buet), "_Buet", sep='')
AllMat.Sasa <- sapply(PeackGenes.Sasa, function(x){AllGenes %in% x})
colnames(AllMat.Sasa) <- paste(colnames(AllMat.Sasa), "_Sasa", sep='')
AllMat.All <- cbind(AllMat.Kowa, AllMat.Buet, AllMat.Sasa)
AllMat.All <- AllMat.All[, apply(AllMat.All, 2, any)]
pheatmap::pheatmap(1*unique(AllMat.All))
pheatmap::pheatmap(1*(AllMat.All))
IntMat <- apply(AllMat.All, 2, function(x){
apply(AllMat.All, 2, function(y){ sum(x & y)/min(sum(x), sum(y)) })
})
diag(IntMat) <- NA
pheatmap::pheatmap(1*(IntMat>.5))
# AllFreman_Mouse
#
#
# Genes.Buett <- Data.Buet$Analysis$Genes[[length(Data.Buet$Analysis$Genes)]]
# Genes.Sasa <- Data.Sasa$Analysis$Genes[[length(Data.Sasa$Analysis$Genes)]]
# Genes.Kowa <- Data.Kowa$Analysis$Genes[[length(Data.Kowa$Analysis$Genes)]]
#
# length(Genes.Buett)
# length(Genes.Kowa)
# length(Genes.Sasa)
#
# library(VennDiagram)
#
# grid.newpage()
# draw.quad.venn(area1 = length(Genes.Buett), area2 = length(Genes.Kowa),
# area3 = length(Genes.Sasa), area4 = length(MouseGenes_GOCellCycle),
# n12 = length(intersect(Genes.Buett, Genes.Kowa)),
# n23 = length(intersect(Genes.Sasa, Genes.Kowa)),
# n13 = length(intersect(Genes.Buett, Genes.Sasa)),
# n14 = length(intersect(Genes.Buett, MouseGenes_GOCellCycle)),
# n24 = length(intersect(MouseGenes_GOCellCycle, Genes.Kowa)),
# n34 = length(intersect(Genes.Sasa, MouseGenes_GOCellCycle)),
# n123 = length(intersect(Genes.Buett, intersect(Genes.Kowa, Genes.Sasa))),
# n124 = length(intersect(Genes.Buett, intersect(Genes.Kowa, MouseGenes_GOCellCycle))),
# n134 = length(intersect(Genes.Buett, intersect(Genes.Sasa, MouseGenes_GOCellCycle))),
# n234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, MouseGenes_GOCellCycle))),
# n1234 = length(intersect(Genes.Sasa, intersect(Genes.Kowa, intersect(Genes.Buett, MouseGenes_GOCellCycle)))),
# category = c("Buettner et al.", "Kowalczyk et al.", "Sasagawa et al.", "GO"),
# fill = c("red", "green", "blue", "white"), cex = 3)
# grid.newpage()
#
#
#
#
#
# Freeman_G1S_CC4
#
#
#
#
#
#
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