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All_Embryos_Mouse.R
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All_Embryos_Mouse.R
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###---------------------------------------------------------------------------------------------------------.
###--------------------------------------- Mouse Blastomere Analysis -------------------------------------------------------
###---------------------------------------------------------------------------------------------------------.
###---------------------------------------------------------------------------------------------------------.
###--------------------------------------- parameter set-up -------------------------------------------------------
###---------------------------------------------------------------------------------------------------------.
WindowsPath <- "G:/My Drive/MS"
# GO term analysis files for mouse
GOterms <- fread("gene_association.mgi.gz")
colnames(GOterms) <- c("DatabaseDes", "MGIMarker", "MouseMarkerSymbol", "NOTdesign", "GOtermID", "MGIRefAccesssionID",
"GOEviCode", "InferredFrom",
"Ontology", "MouseMarkerName", "MouseMarkerSynonyms", "MouseMarkerType", "Taxon", "ModDate",
"AssignedBy", "AnnotationExt", "GeneProduct")
# make all letters upercase
GOterms$GeneSymbol <- toupper(GOterms$MouseMarkerSymbol)
GOannotations <- fread("go_terms.mgi.txt", header = F)
GOterms$annotation <- GOannotations$V3[match(GOterms$GOtermID, GOannotations$V2)]
# prot to gene names
prot2gene <- read.delim("uniprot_Mus_musculus_ProteinsTOGenes.tab")
convertProts <- read.delim("uniprot_Mus_musculus_ProteinsTOGenes.tab")
convertProts$LeadingGene <- sub(" .*", "", convertProts$Gene.names)
convertProts$uppercase <- toupper(convertProts$LeadingGene)
# load in the four current datasets that use within embryo normalization
x1 <- read.csv("Exp4_early2cell_ProteinsXSamples_WithinEmbryo.csv")
x2 <- read.csv("Exp2_late2cell_ProteinsXSamples_WithinEmbryo.csv")
x3 <- read.csv("Exp3_4c_ProteinsXsamples_WithinEmbryo.csv")
x4 <- read.csv("Exp6_4cMCherry_ProteinsXsamples_WithinEmbryo.csv")
x5 <- read.csv("Exp10_4c_ProteinsXsamples_WithinEmbryo.csv")
# keep names of 2 cell and 4 cell stage embryos in a dataframe
twocell <- as.data.frame(as.matrix(c(colnames(x1)[2:ncol(x1)], colnames(x2)[2:ncol(x2)])))
twocell$type <- ifelse(grepl("406|407|408|409|410|411",twocell$V1) == TRUE, "early_2c", "late_2c")
fourcell <- as.data.frame(as.matrix(c(colnames(x3)[2:ncol(x3)], colnames(x4)[2:ncol(x4)],
colnames(x5)[2:ncol(x5)])))
fourcell$type <- "_4c"
allcells <- rbind(twocell, fourcell)
# keep all proteins quantified across all datasets
df <- plyr::join_all(list(x1, x2, x3, x4, x5), by = "X", type = "full")
#write.csv(df, "MergedDF_2c_4c_proteinXblastomeres.csv")
# melt the dataframe
df.m <- reshape2::melt(df)
# create a storage df that will contain id information
dfStore <- data.frame(matrix(ncol = 2, nrow = length(unique(df.m$variable))))
colnames(dfStore) <- c("variable", "label")
dfStore$variable <- unique(df.m$variable)
dfStore$label <- sub(".*\\.", "", dfStore$variable)
dfStore$label2 <- gsub(".*sc_", "", dfStore$label)
dfStore$label3 <- paste(sub("\\..*", "", dfStore$variable),dfStore$label2)
dfStore$type <- allcells$type[match(dfStore$variable, allcells$V1)]
dfStore$cleavage <- sub("_.*", "", dfStore$label2)
dfStore$Raw.file <- sub("\\..*", "", dfStore$variable)
dfStore$embryoNum <- sub(".*_", "", dfStore$variable)
dfStore$embryoID <- paste0(dfStore$Raw.file, "_", dfStore$embryoNum)
# how many proteins per cell are quantified?
numProts <- df.m %>% dplyr::group_by(variable) %>% dplyr::summarise(numProts = sum(!is.na(value)))
numProts$bin <- floor(round(numProts$numProts, -2))
numProts$type <- dfStore$type[match(numProts$variable, dfStore$variable)]
ggplot(data = numProts, aes(x = type, y = numProts, fill = type)) + geom_boxplot() + theme_bw() +
geom_beeswarm(size = 2, alpha =0.5)+
scale_fill_brewer(palette = "OrRd") +
theme(axis.text = element_text(size = 22), axis.title =element_text(size = 22),
legend.position = "none") +
labs(x = "Stage", y = "Number of Proteins per cell") +
scale_x_discrete(labels = c("Early 2-cell", "Late 2-cell", "4-cell"))
###---------------------------------------------------------------------------------------------------------.
###---------------------------------- Cell-Cell Correlations & K-means-------------------------------------------------------
###---------------------------------------------------------------------------------------------------------.
colnames(df)[1] <- "Protein"
# missing data filter - let's observe each protein in x amount of cells
df.NA <- df[rowSums(is.na(df)) <= 0.7*ncol(df),]
# or let's use the proteins found significantly differential in 2-cell stage
sig2c_new <- read.csv("2-cell-stage-combined-sig-Prots_5percentFDR_2022_02_07.csv")
df.NA <-df %>% dplyr::filter(Protein %in% sig2c_new$Protein)
dist.mat <- cor(df.NA[,2:ncol(df.NA)], use = "pairwise.complete.obs", method = "spearman")
# change the color gradient
library(circlize)
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("yellow", "white", "purple"))
col_fun(seq(-3, 3))
Heatmap(dist.mat, col = col_fun)
#assess optimal number of clusters
library(factoextra)
factoextra::fviz_nbclust(dist.mat, kmeans, method = "silhouette")
###---------------------------------------------------------------------------------------------------------.
###----------------------------- Probability of k-means clustering assignment------------------------------------------------------
###---------------------------------------------------------------------------------------------------------.
colnames(df)[1] <- "Protein"
# missing data filter - let's observe each protein in x amount of cells
df.NA <- df[rowSums(is.na(df)) <= 0.7*ncol(df),]
dist.mat <- cor(df.NA[,2:ncol(df.NA)], use = "pairwise.complete.obs", method = "spearman")
# kmeans clustering based on similarity metric
seedCHOSE <- 4
set.seed(seedCHOSE)
firTEST <- kmeans(dist.mat, centers = 2, iter.max = 25, nstart = 1)
firTEST.cluster <- as.data.frame(firTEST$cluster)
colnames(firTEST.cluster) <- "Cluster"
clusplot(dist.mat, firTEST$cluster, main = paste0("Seed = ", seedCHOSE))
# count the number of cells per embryo in this clustering
CluKTEST <- as.data.frame(firTEST$cluster)
# let's implement the loop for 100 iterations
i<-2
CluKTEST <- data.frame()
for(i in 1:200) {
seedCHOSE <- i
set.seed(seedCHOSE)
firTEST <- kmeans(dist.mat, centers = 2, iter.max = 25, nstart = 1)
firTEST.cluster <- as.data.frame(firTEST$cluster)
colnames(firTEST.cluster) <- "Cluster"
clusplot(dist.mat, firTEST$cluster, main = paste0("Seed = ", seedCHOSE))
# count the number of cells per embryo in this clustering
tempCluKTEST <- as.data.frame(firTEST$cluster)
if(i == 1) {
CluKTEST <- tempCluKTEST
colnames(CluKTEST) <- paste0("Seed_", seedCHOSE)
}
if( i != 1) {
CluKTEST <- cbind(CluKTEST, tempCluKTEST)
colnames(CluKTEST)[i] <- paste0("Seed_", seedCHOSE)
}
}
corCluK <- cor(CluKTEST)
Heatmap(corCluK)
corCluK.m <- reshape2::melt(corCluK)
ggplot(data = corCluK.m, aes(x = value)) +
geom_histogram(aes(y = ..density..), color = "black", fill = "grey50", alpha = 0.3) +
geom_density(color = "darkblue", fill = "lightblue", alpha = 0.5) +
theme_bw() +
theme(axis.text = element_text(size = 20), axis.title = element_text(size = 22)) +
labs(x = "Pearson Correlation of \nCluster Assignment Vectors")
ggplot(data = corCluK.m, aes(x = value)) +
#geom_histogram(color = "black", fill = "grey50", alpha = 0.3, binwidth = 0.05) +
geom_histogram(color = "black", fill = "red", alpha = 0.3, binwidth = 0.05) +
theme_bw() +
theme(axis.text = element_text(size = 20), axis.title = element_text(size = 22)) +
labs(x = "Pearson Correlations of \nCluster Assignment Vectors")
corCluK.m$absCor <- abs(corCluK.m$value)
ggplot(data = corCluK.m, aes(x = absCor)) +
#geom_histogram(color = "black", fill = "grey50", alpha = 0.3, binwidth = 0.05) +
geom_histogram(color = "black", fill = "red", alpha = 0.3, binwidth = 0.05) +
theme_bw() +
theme(axis.text = element_text(size = 30), axis.title = element_text(size = 30)) +
labs(x = "Absolute Pearson Correlations of \nCluster Assignment Vectors")
unique(corCluK.m$value)
# calculate the probability of cluster assignment
# cluster assignment is random, so we need to correct for this
CluKTEST.2 <- c()
i<-2
for(i in 1:ncol(CluKTEST)) {
if(i == 1) { CluKTEST.2 <- as.data.frame(CluKTEST[,i]); colnames(CluKTEST.2) <- colnames(CluKTEST)[i]}
if(i != 2) {
temp.cor <- cor(CluKTEST.2[,1], CluKTEST[,i])
if(temp.cor > 0) {
CluKTEST.2 <- cbind(CluKTEST.2,CluKTEST[,i])
colnames(CluKTEST.2)[i] <- colnames(CluKTEST)[i]
}
if(temp.cor < 0) {
temp.df <- as.data.frame(CluKTEST[,i])
temp.df$V2 <- ifelse(temp.df$`CluKTEST[, i]` ==1, 2, 1)
CluKTEST.2 <- cbind(CluKTEST.2, temp.df$V2)
colnames(CluKTEST.2)[i] <- colnames(CluKTEST)[i]
}
}
}
# now compute the mean across the rows
CluKTEST.3 <- as.data.frame(rowMeans(CluKTEST.2))
CluKTEST.3$variable <- row.names(as.data.frame(firTEST$cluster))
CluKTEST.4 <- CluKTEST.3 %>% dplyr::filter(`rowMeans(CluKTEST.2)` !=1 )%>%
dplyr::filter(`rowMeans(CluKTEST.2)` !=2 )
ggplot(data = CluKTEST.3, aes(x = `rowMeans(CluKTEST.2)`)) +
geom_histogram(color = "black", fill = "red", alpha = 0.3, binwidth = 0.05) +
theme_bw() +
theme(axis.text = element_text(size = 30), axis.title = element_text(size = 30)) +
labs(x = "Cluster Assignment", y = "No. of Blastomeres")
i<-12
for(i in 1:nrow(CluKTEST.3)) {
if(CluKTEST.3[i,1] == 1) { CluKTEST.3$prob[i] <- 1 }
if(CluKTEST.3[i,1] == 2) { CluKTEST.3$prob[i] <- 1 }
if(CluKTEST.3[i,1] != 2 |CluKTEST.3[i,1] != 2) {
temp.df <- as.data.frame(t(CluKTEST.2[i,]))
temp.count <- as.data.frame(table(temp.df))
temp.count$fraction <- temp.count$Freq/200
CluKTEST.3$prob[i] <- max(temp.count$fraction)
}
}
ggplot(data = CluKTEST.3, aes(x = prob)) +
geom_histogram(color = "black", fill = "red", alpha = 0.3, binwidth = 0.05) +
theme_bw() +
theme(axis.text = element_text(size = 30, color = "black"), axis.title = element_text(size = 30)) +
labs(x = "Probability", y = "No. of Blastomeres")
# count how many times an embryo is in each cluster
CluK$embryo <- paste(sub("\\..*", "", rownames(CluK)), sub(".*_", "", rownames(CluK)) )
CluK$cleavage <- sub("([^_]*)_([^_]*)_([^_]*)", "\\2", rownames(CluK))
for(i in 1:nrow(CluK)) {
if(CluK$cleavage[i] == "v") { CluK$cleavage[i] <- "nid"}
if(CluK$cleavage[i] == "emv") { CluK$cleavage[i] <- "em"}
CluK$Raw.file[i] <- str_split(rownames(CluK)[i], fixed("."))[[1]][1]
}
colnames(CluK)[1] <- "cluster"
CluK$clusterName <- paste0("Cluster",CluK$cluster)
countCells <- CluK %>%
dplyr::group_by(Raw.file, embryo, cleavage) %>%
dplyr::summarise(n_clu1 = sum(cluster == "1"), n_clu2 = sum(cluster == "2"))
# kmeans clustering based on similarity metric
set.seed(1)
fir <- kmeans(dist.mat, centers = 2, iter.max = 50, nstart = 25)
fir.cluster <- as.data.frame(fir$cluster)
colnames(fir.cluster) <- "Cluster"
#write.csv(fir.cluster,"2022_01_11_fircluster.csv")
clusplot(dist.mat, fir$cluster)
# count the number of cells per embryo in this clustering
CluK <- as.data.frame(fir$cluster)
# count how many times an embryo is in each cluster
CluK$embryo <- paste(sub("\\..*", "", rownames(CluK)), sub(".*_", "", rownames(CluK)) )
CluK$cleavage <- sub("([^_]*)_([^_]*)_([^_]*)", "\\2", rownames(CluK))
for(i in 1:nrow(CluK)) {
if(CluK$cleavage[i] == "v") { CluK$cleavage[i] <- "nid"}
if(CluK$cleavage[i] == "emv") { CluK$cleavage[i] <- "em"}
CluK$Raw.file[i] <- str_split(rownames(CluK)[i], fixed("."))[[1]][1]
}
colnames(CluK)[1] <- "cluster"
CluK$clusterName <- paste0("Cluster",CluK$cluster)
countCells <- CluK %>%
dplyr::group_by(Raw.file, embryo, cleavage) %>%
dplyr::summarise(n_clu1 = sum(cluster == "1"), n_clu2 = sum(cluster == "2"))
###---------------------------------------------------------------------------------------------------------.
###----------------------------- Significantly differential proteins------------------------------------------------------
###---------------------------------------------------------------------------------------------------------.
# find sig proteins at just the 2-cell stage
colnames(df)[1] <- "Protein"
df.m <- reshape2::melt(df)
df.m$cluster <- fir.cluster$Cluster[match(df.m$variable, rownames(fir.cluster))]
df.m$cluster <- paste0("Cluster",df.m$cluster)
df.m$variable_Cluster <- paste0(df.m$cluster, " ",df.m$variable)
df.m$embryoID <- dfStore$embryoID[match(df.m$variable, dfStore$variable)]
df.m$type <- dfStore$type[match(df.m$variable, dfStore$variable)]
# choose which stage you want to test proteins for?
df.m.2c <- df.m # %>% dplyr::filter(grepl("_4c", type))
ProteinsAcrossClusters <- data.frame(matrix(nrow = length(unique(df.m.2c$Protein)), ncol = 2))
colnames(ProteinsAcrossClusters) <- c("Protein", "p.value")
for(i in 1:nrow(ProteinsAcrossClusters)) {
temp.prot <- df.m.2c %>% dplyr::filter(Protein == unique(df.m.2c$Protein)[i])
temp.prot.1 <- temp.prot %>% dplyr::filter(cluster == "Cluster1")
temp.prot.2 <- temp.prot %>% dplyr::filter(cluster == "Cluster2")
if((sum(!is.na(temp.prot.1$value)) > 2) & (sum(!is.na(temp.prot.2$value)) > 2) ) {
temp.test <- kruskal.test(value ~ cluster, temp.prot)
ProteinsAcrossClusters$p.value[i] <- temp.test$p.value
}
ProteinsAcrossClusters$Protein[i] <- unique(df.m.2c$Protein)[i]
print(i)
}
ProteinsAcrossClusters.NA <- ProteinsAcrossClusters %>% dplyr::filter(!is.na(p.value))
ProteinsAcrossClusters.NA$q.value <- p.adjust(ProteinsAcrossClusters.NA$p.value, method = "fdr")
ProteinsAcrossClusters.sig <- ProteinsAcrossClusters.NA %>% dplyr::filter(q.value <= 0.05)
# save this list
df.m.2c.sig <- df.m.2c %>% dplyr::filter(Protein %in% ProteinsAcrossClusters.sig$Protein)
df.m.2c.sig.s <- reshape2::dcast(df.m.2c.sig, Protein ~ variable, value.var = "value")
#write.csv(df.m.2c.sig.s, "Late2-cell-stage-combined-sig-Prots_5percentFDR_2022_03_13.csv", row.names= F)
#write.csv(df.m.2c.sig.s, "Early-cell-stage-combined-sig-Prots_5percentFDR_2022_03_13.csv", row.names= F)
#write.csv(df.m.2c.sig.s, "2-cell-stage-combined-sig-Prots_5percentFDR_2022_02_07.csv", row.names= F)
#write.csv(df.m.2c.sig.s, "4-cell-stage-combined-sig-Prots_5percentFDR_2022_03_13.csv", row.names= F)
#write.csv(df.m.2c.sig.s, "ALL-cell-stage-combined-sig-Prots_5percentFDR_2022_03_13.csv", row.names= F)
#df.m.2c.sig.s$Gene <- convertProts$uppercase[match(df.m.2c.sig.s$Protein, convertProts$Entry)]
# compare the 4-cell stage proteins and 2-cell stage differntial proteins
sig2c_new <- read.csv("2-cell-stage-combined-sig-Prots_5percentFDR_2022_02_07.csv")
sig4c <- read.csv("4-cell-stage-combined-sig-Prots_5percentFDR_2022_03_13.csv")
sigALL <- read.csv("ALL-cell-stage-combined-sig-Prots_5percentFDR_2022_03_13.csv")
# intersect the lists: P61087
# proteins shared in 2c and 4c separate analysis
InFourTwo <- sig2c_new %>% dplyr::filter(Protein %in% sig4c$Protein)
# in 4c but not 2c
InFourNotTwo <- sig4c %>% dplyr::filter(!(Protein %in% sig2c_new$Protein))
# in 4c but not ALL
InFourNotALL <- sig4c %>% dplyr::filter(!(Protein %in% sigALL$Protein))
# in 4c but not 2c but in ALL
InFourInAllNotTwo <- sig4c %>% dplyr::filter(Protein %in% sigALL$Protein) %>%
dplyr::filter(!(Protein %in% sig2c_new$Protein))
# in 4c not in 2c or ALL
InFourNotTwoNotAll <- sig4c %>% dplyr::filter(!(Protein %in% sig2c$Protein)) %>%
dplyr::filter(!(Protein %in% sigALL$Protein))
# in 2c but not ALL
InTwoNotALL <- sig2c_new %>% dplyr::filter(!(Protein %in% sigALL$Protein))
InTwoInFourNotAll <- sig2c_new %>% dplyr::filter(!(Protein %in% sigALL$Protein)) %>%
dplyr::filter(Protein %in% sig4c$Protein)
InTwoNotFourInAll <- sig2c_new %>% dplyr::filter((Protein %in% sigALL$Protein)) %>%
dplyr::filter(!(Protein %in% sig4c$Protein))
InTwoNotFourNotAll <- sig2c_new %>% dplyr::filter(!(Protein %in% sigALL$Protein)) %>%
dplyr::filter(!(Protein %in% sig4c$Protein))
# make a venn diagram
# 81 proteins shared between 4-cell and 2-cell
# 359 in 2-cell
# 157 in 4-cell
# 455 when all stages
# 8 in 4-cell not all
INeverything <- Reduce(intersect, (list(sig2c_new$Protein, sig4c$Protein, sigALL$Protein)))
# check which proteins are in the same cluster, but increasing in magnitude across the stage
# for the 455 proteins
# set-up dataframe
medianValues1 <- df.m %>% dplyr::group_by(Protein,cluster,type) %>% dplyr::summarise(medianValue = median(value,na.rm =T))
medianValues1$cluster_type <- paste0(medianValues1$cluster, medianValues1$type)
hatter <- data.frame(matrix(nrow=nrow(sigALL), ncol = 4))
colnames(hatter) <- c("Proteins", "SameInCluster", "Magnitude", "QuantifiedIn")
hatter$Proteins <- sigALL$Protein
i<-2
for(i in 1:nrow(hatter)) {
temp.prot <- medianValues1 %>% dplyr::filter(Protein == hatter$Proteins[i])
hatter$QuantifiedIn[i] <- sum(!is.na(temp.prot$medianValue))
if(sum(!is.na(temp.prot$medianValue)) == 6) {
if((temp.prot$medianValue[temp.prot$cluster_type == "Cluster1early_2c"]) < 0 &
(temp.prot$medianValue[temp.prot$cluster_type == "Cluster1late_2c"]) < 0 &
(temp.prot$medianValue[temp.prot$cluster_type == "Cluster1_4c"]) < 0) {
hatter$SameInCluster[i] <- "yes"
}
if((temp.prot$medianValue[temp.prot$cluster_type == "Cluster1early_2c"]) > 0 &
(temp.prot$medianValue[temp.prot$cluster_type == "Cluster1late_2c"]) > 0 &
(temp.prot$medianValue[temp.prot$cluster_type == "Cluster1_4c"]) > 0) {
hatter$SameInCluster[i] <- "yes"
}
if((temp.prot$medianValue[temp.prot$cluster_type == "Cluster1early_2c"]) <
(temp.prot$medianValue[temp.prot$cluster_type == "Cluster1late_2c"]) &
(temp.prot$medianValue[temp.prot$cluster_type == "Cluster1late_2c"]) <
(temp.prot$medianValue[temp.prot$cluster_type == "Cluster1_4c"]) ) {
hatter$Magnitude[i] <- "Up"
}
if((temp.prot$medianValue[temp.prot$cluster_type == "Cluster1early_2c"]) >
(temp.prot$medianValue[temp.prot$cluster_type == "Cluster1late_2c"]) &
(temp.prot$medianValue[temp.prot$cluster_type == "Cluster1late_2c"]) >
(temp.prot$medianValue[temp.prot$cluster_type == "Cluster1_4c"]) ) {
hatter$Magnitude[i] <- "Down"
}
}
}
hatter.sameincluster1 <- hatter %>% dplyr::filter(SameInCluster == "yes")
hatter.sameincluster2 <- hatter.sameincluster %>% dplyr::filter(!is.na(Magnitude))
hatter.sameincluster3 <- hatter %>% dplyr::filter(QuantifiedIn == 6)
hatter.sameincluster4 <- hatter %>% dplyr::filter(SameInCluster == "yes") %>% dplyr::filter(!is.na(Magnitude))
write.csv( hatter.sameincluster2, "2022_04_20_hatter.csv")
# check the same for the 2-cell stage
hatter2c <- data.frame(matrix(nrow=nrow(sigALL), ncol = 4))
colnames(hatter2c) <- c("Proteins", "SameInCluster", "Magnitude", "QuantifiedIn")
hatter2c$Proteins <- sigALL$Protein
i<-2
for(i in 1:nrow(hatter2c)) {
temp.prot <- medianValues1 %>% dplyr::filter(Protein == hatter2c$Proteins[i]) %>%
dplyr::filter(type != "_4c")
hatter2c$QuantifiedIn[i] <- sum(!is.na(temp.prot$medianValue))
if(sum(!is.na(temp.prot$medianValue)) == 4) {
if((temp.prot$medianValue[temp.prot$cluster_type == "Cluster1early_2c"]) < 0 &
(temp.prot$medianValue[temp.prot$cluster_type == "Cluster1late_2c"]) < 0 ) {
hatter2c$SameInCluster[i] <- "yes"
}
if((temp.prot$medianValue[temp.prot$cluster_type == "Cluster1early_2c"]) > 0 &
(temp.prot$medianValue[temp.prot$cluster_type == "Cluster1late_2c"]) > 0) {
hatter2c$SameInCluster[i] <- "yes"
}
if((temp.prot$medianValue[temp.prot$cluster_type == "Cluster1early_2c"]) <
(temp.prot$medianValue[temp.prot$cluster_type == "Cluster1late_2c"]) ) {
hatter2c$Magnitude[i] <- "Up"
}
if((temp.prot$medianValue[temp.prot$cluster_type == "Cluster1early_2c"]) >
(temp.prot$medianValue[temp.prot$cluster_type == "Cluster1late_2c"]) ) {
hatter2c$Magnitude[i] <- "Down"
}
}
}
hatter2c.1 <- hatter2c %>% dplyr::filter(SameInCluster == "yes")
hatter2c.2 <- hatter2c.1 %>% dplyr::filter(!is.na(Magnitude))
hatter2c.3 <- hatter2c %>% dplyr::filter(QuantifiedIn == 4)
###---------------------------------------------------------------------------------------------------------.
###----------------------------- 4-cell stage: cleavage pattern ------------------------------------------------------
###---------------------------------------------------------------------------------------------------------.
# using the classification system, need to understand what proteins and processes are differential (if any)
# 01 - calculate the FCs
mat5a <- df
rownames(mat5a) <- mat5a$Protein
mat5a <- mat5a[,-1]
mat5a.m <- reshape2::melt(as.matrix(mat5a))
# label which cluster each cell belongs to
mat5a.m$cluster <- fir.cluster$Cluster[match(mat5a.m$Var2, rownames(fir.cluster))]
mat5a.m$cluster <- paste0("Cluster",mat5a.m$cluster)
mat5a.m$variable_Cluster <- paste0(mat5a.m$cluster, " ",mat5a.m$Var2)
mat5a.m$embryoID <- dfStore$embryoID[match(mat5a.m$Var2, dfStore$variable)]
mat5a.m$type <- dfStore$type[match(mat5a.m$Var2, dfStore$variable)]
# rename the column names
colnames(mat5a.m)[1:2] <- c("X", "variable")
# obtain the FC for each protein between sister cells via dplyr
meanValues <- mat5a.m %>% dplyr::group_by(X, embryoID, type) %>%
dplyr::summarise(FC = value[cluster == "Cluster1"] - value[cluster == "Cluster2"])
# 02 - isolate just the 4 cell stage with defined cleavage pattern
meanValues$cleavage <- dfStore$cleavage[match(meanValues$embryoID, dfStore$embryoID)]
meanValues.4c <- meanValues %>% dplyr::filter(grepl("em|me|ee", cleavage))
length(unique(meanValues.4c$embryoID))
meanValues.4c$Uniprot <- sub("-.*","",meanValues.4c$X)#meanValues.4c$X#
meanValues.4c$GeneName <- convertProts$uppercase[match(meanValues.4c$Uniprot, convertProts$Entry)]
# 03 - perform protein - level kruskal wallis tests
cleavProt <- data.frame(matrix(nrow = length(unique(meanValues.4c$X)), ncol = 2))
colnames(cleavProt) <- c("Protein", "p.value")
cleavProt$Protein <- as.character(unique(meanValues.4c$X))
i<-1
for(i in 1:nrow(cleavProt)) {
#temp.prot <- meanValues.4c %>% dplyr::filter(X == cleavProt$Protein[i])
temp.prot <- meanValues.4c[meanValues.4c$X == cleavProt$Protein[i],]
temp.prot.1 <- temp.prot[temp.prot$cleavage == "ee",]
temp.prot.2 <- temp.prot[temp.prot$cleavage == "em",]
temp.prot.3 <- temp.prot[temp.prot$cleavage == "me",]
if((sum(!is.na(temp.prot.1$FC)) > 2) & (sum(!is.na(temp.prot.2$FC)) > 2) & (sum(!is.na(temp.prot.3$FC)) > 2) ) {
#temp.test <- kruskal.test(FC ~ cleavage, temp.prot)
#cleavProt$p.value[i] <- temp.test$p.value
temp.test <- aov(FC ~ cleavage, data = temp.prot)
cleavProt$p.value[i] <- summary(temp.test)[[1]][["Pr(>F)"]][1]
}
print(i)
}
# adjust the p values
cleavProt.NA <- cleavProt %>% dplyr::filter(!is.na(p.value))
cleavProt.NA$q.value <- p.adjust(cleavProt.NA$p.value, method = "fdr")
cleavProt.sig <- cleavProt.NA %>% dplyr::filter(q.value < 0.05)
ggplot(cleavProt.NA, aes(x = -log10(q.value))) + geom_histogram(color = "black", fill = "slateblue") + theme_bw() +
geom_vline(xintercept = -log10(0.05), linetype = "dashed") +
theme(axis.title = element_text(size = 22), axis.text = element_text(size = 22, color = "black"),
plot.title = element_text(size =22)) +
labs(title = "Q values of Protein-Level Analaysis")
ggplot(cleavProt.NA, aes(y = -log10(q.value))) + geom_histogram(color = "black", fill = "slateblue") + theme_bw() +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
theme(axis.title = element_blank(), axis.text = element_text(size = 22, color = "black"),
plot.title = element_blank()) +
labs(title = "Q values of Protein-Level Analaysis")
# plot the protein distributions that are signficant
meanValues.4c.sig <- meanValues.4c %>% dplyr::filter(X %in% cleavProt.sig$Protein)
ggplot(data = meanValues.4c.sig, aes(x = GeneName, y = FC, fill = cleavage)) + geom_boxplot() +
theme_bw() + theme(legend.position = "top", legend.text = element_text(size = 22),
legend.title = element_text(size = 22),
legend.key.size = unit(3, "line"),
axis.title = element_text(size = 22),
axis.text.y = element_text(size = 22),
axis.text.x = element_text(size = 20, color = "black"), plot.title = element_blank()) +
labs(x = "GeneName", y = "Alpha / Beta", title = "Cleavage Patterns, Protein-Level") +
geom_hline(yintercept = 0, linetype = "dashed") +
annotate("rect", xmin = 1.5, xmax = 2.5,
ymin = min(meanValues.4c.sig$FC, na.rm = T), ymax = max(meanValues.4c.sig$FC, na.rm = T),
alpha = 0.2, fill = "orange") +
annotate("rect", xmin = 3.5, xmax = 4.5,
ymin = min(meanValues.4c.sig$FC, na.rm = T), ymax = max(meanValues.4c.sig$FC, na.rm = T),
alpha = 0.2, fill = "orange")
# 04 - perform PSEA kruskal wallis tests
GOinDat <- GOterms %>% dplyr::filter(GeneSymbol %in% meanValues.4c$GeneName)
GOCleav <- data.frame(matrix(nrow = length(unique(GOinDat$GOtermID)), ncol = 9))
colnames(GOCleav) <- c("GOterm", "Annotation", "p.value", "Proteins", "NumProteins", "NumProteinsInvolved",
"Median_ee", "Median_em", "Median_me")
GOCleav$GOterm <- unique(GOinDat$GOtermID)
i<-5
#name the amount of proteins that are quantified, and number of proteins associated with GO term
#GOCleavFUNC<-function(GOCleav, GOinDat, GOterms, meanValues.4c){
for(i in 1:nrow(GOCleav)) {
temp.GO <- GOinDat %>% dplyr::filter(GOtermID == GOCleav$GOterm[i])
temp.GO2 <- GOterms %>% dplyr::filter(GOtermID == GOCleav$GOterm[i]) ###
temp.gene <- temp.GO$GeneSymbol
#temp.prot <- meanValues.4c %>% dplyr::filter(GeneName %in% temp.gene)###
temp.prot <- meanValues.4c[meanValues.4c$GeneName %in% temp.gene,]
#temp.prot.1 <- temp.prot %>% dplyr::filter(cleavage == "ee")
temp.prot.1 <- temp.prot[temp.prot$cleavage == "ee",]
#temp.prot.2 <- temp.prot %>% dplyr::filter(cleavage == "em")
temp.prot.2 <- temp.prot[temp.prot$cleavage == "em",]
#temp.prot.3 <- temp.prot %>% dplyr::filter(cleavage == "me")
temp.prot.3 <- temp.prot[temp.prot$cleavage == "me",]
if(sum(!is.na(temp.prot.1$FC)) > 2 & sum(!is.na(temp.prot.2$FC)) > 2 & sum(!is.na(temp.prot.3$FC)) > 2 ){
#temp.test <- kruskal.test(FC ~ cleavage, data = temp.prot)
#GOCleav$p.value[i] <- temp.test$p.value
temp.test <- aov(FC ~ cleavage, data = temp.prot)
GOCleav$p.value[i] <- summary(temp.test)[[1]][["Pr(>F)"]][1]
GOCleav$Median_ee[i] <- median(temp.prot.1$FC, na.rm = T)
GOCleav$Median_em[i] <- median(temp.prot.2$FC, na.rm = T)
GOCleav$Median_me[i] <- median(temp.prot.3$FC, na.rm = T)
}
GOCleav$Annotation[i] <- unique(temp.GO$annotation)
GOCleav$Proteins[i] <- paste(unique(temp.prot$GeneName), collapse = " ")
GOCleav$NumProteins[i] <- length(unique(temp.prot$GeneName))
GOCleav$NumProteinsInvolved[i] <- length(unique(temp.GO2$GeneSymbol))
print(i)
}
#return(GOCleav)
#}
GOCleav$fraction <- GOCleav$NumProteins / GOCleav$NumProteinsInvolved
GOCleav.NA <- GOCleav %>% dplyr::filter(!is.na(p.value)) %>% dplyr::filter(fraction > 0.5)
GOCleav.NA$q.value <- p.adjust(GOCleav.NA$p.value, method = "fdr")
GOCleav.NA.sig <- GOCleav.NA %>% dplyr::filter(q.value <0.05)
ggplot(GOCleav.NA, aes(x = -log10(q.value))) + geom_histogram(color = "black", fill = "slateblue") + theme_bw() +
geom_vline(xintercept = -log10(0.05), linetype = "dashed") +
theme(axis.title = element_text(size = 22), axis.text = element_text(size = 22),
plot.title = element_text(size =22)) +
labs(title = "Q values of GO-Level Analaysis")
ggplot(GOCleav.NA, aes(y = -log10(q.value))) + geom_histogram(color = "black", fill = "slateblue") + theme_bw() +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
theme(axis.title = element_blank(), axis.text = element_text(size = 22, color = "black"),
plot.title = element_blank()) +
labs(title = "Q values of Analaysis")
GOCleav.NA.sig.m <- reshape2::melt(GOCleav.NA.sig[,c(2,7:9)])
ggplot(data = GOCleav.NA.sig.m, aes(fill = Annotation, y = value, x = variable)) + geom_bar(stat = "identity") +
theme(legend.position = "bottom", axis.title = element_text(size = 22), axis.text = element_text(size = 22),
plot.title = element_text(size =22))
# collect the protein FCs for each significant GO term and plot distributions
protdistr <- c()
i<-1
for(i in 1:length(unique(GOCleav.NA.sig$GOterm))) {
temp.GO <- GOinDat %>% dplyr::filter(GOtermID == GOCleav.NA.sig$GOterm[i])
temp.gene <- temp.GO$GeneSymbol
temp.prot <- meanValues.4c[meanValues.4c$GeneName %in% temp.gene,]
temp.prot$GOAnnot <- unique(temp.GO$annotation)
if(i == 1) { protdistr <- temp.prot }
if(i != 1) { protdistr <- rbind(protdistr, temp.prot)}
}
ggplot(data = protdistr, aes(y = GOAnnot, x = FC, fill = cleavage)) + geom_boxplot() +
theme_bw() +
theme(legend.position = "top", legend.text = element_text(size = 22),
legend.title = element_text(size =22),
legend.key.size = unit(3, "line"),
axis.title = element_blank(), axis.text.y = element_text(size = 22),
axis.text.x = element_text(size = 22)) +
labs(y = "Annotation", x = "Alpha / Beta") +
coord_cartesian(xlim = c(-1, 1)) + geom_vline(xintercept = 0, linetype = "dashed")+
annotate("rect", ymin = c(1.5, 3.5,5.5,7.5, 9.5, 11.5, 13.5), ymax = c(2.5,4.5,6.5,8.5,10.5, 12.5, 14.5),
xmin = min(meanValues.4c.sig$FC, na.rm = T), xmax = max(meanValues.4c.sig$FC, na.rm = T),
alpha = 0.2, fill = "orange")
###---------------------------------------------------------------------------------------------------------.
###----------------------------- PSEA between clusters ------------------------------------------------------
###---------------------------------------------------------------------------------------------------------.
# find GO terms that are differential between the clusters
# then plot boxplots of specific GO terms between each stage (early 2c, mid/late 2c, 4c)
# code below is taken from Secion 04a of this code
mat4 <- df
rownames(mat4) <- mat4$Protein
mat4 <- mat4[,-1]
mat4.m <- reshape2::melt(as.matrix(mat4))
# label which cluster each cell belongs to
mat4.m$cluster <- fir.cluster$Cluster[match(mat4.m$Var2, rownames(fir.cluster))]
mat4.m$cluster <- paste0("Cluster",mat4.m$cluster)
# GSEA
mat4.m$Uniprot <- sub("-.*","",mat4.m$Var1)
mat4.m$GeneName <- convertProts$uppercase[match(mat4.m$Uniprot, convertProts$Entry)]
mat4.m$stage <- dfStore$type[match(mat4.m$Var2, dfStore$variable)]
mat4.m <- mat4.m #%>% dplyr::filter(stage == "_4c")
GOinDat <- GOterms %>% dplyr::filter(GeneSymbol %in% mat4.m$GeneName)
GOAcrossClusters <- data.frame(matrix(nrow = length(unique(GOinDat$GOtermID)), ncol = 8))
colnames(GOAcrossClusters) <- c("GOterm", "Annotation", "p.value", "Proteins", "NumProteins", "NumProteinsInvolved",
"median_Cluster1", "median_Cluster2")
GOAcrossClusters$GOterm <- unique(GOinDat$GOtermID)
i<-449
#name the amount of proteins that are quantified, and number of proteins associated with GO term
for(i in 1:nrow(GOAcrossClusters)) {
temp.GO <- GOinDat %>% dplyr::filter(GOtermID == GOAcrossClusters$GOterm[i])
temp.GO2 <- GOterms %>% dplyr::filter(GOtermID == GOAcrossClusters$GOterm[i])
temp.prot <- mat4.m %>% dplyr::filter(GeneName %in% temp.GO$GeneSymbol)
temp1 <- temp.prot %>% dplyr::filter( cluster == "Cluster1")
temp2 <- temp.prot %>% dplyr::filter( cluster == "Cluster2")
if(sum(!is.na(temp1$value)) >3 & sum(!is.na(temp2$value)) >3 ){
#& length(unique(temp.prot$GeneName)) >= 4 & length(unique(temp.GO2$GeneSymbol)) <= 200) { #
temp.test <- kruskal.test(value ~ cluster, data = temp.prot)
GOAcrossClusters$p.value[i] <- temp.test$p.value
GOAcrossClusters$median_Cluster1[i] <- median(temp1$value, na.rm = T)
GOAcrossClusters$median_Cluster2[i] <- median(temp2$value, na.rm = T)
temp1med <- temp1 %>% dplyr::group_by(Var2) %>% dplyr::summarise(medLevel = median(value, na.rm = T))
temp1med.t <- as.data.frame(as.matrix(t(temp1med[,-1])))
colnames(temp1med.t) <- temp1med$Var2
temp2med <- temp2 %>% dplyr::group_by(Var2) %>% dplyr::summarise(medLevel = median(value, na.rm = T))
temp2med.t <- as.data.frame(as.matrix(t(temp2med[,-1])))
colnames(temp2med.t) <- temp2med$Var2
tempmed <- cbind(temp1med.t, temp2med.t)
GOAcrossClusters[i,9:(8+ncol(tempmed))] <- tempmed
}
GOAcrossClusters$GOterm[i] <- unique(temp.GO$GOtermID)
GOAcrossClusters$Annotation[i] <- unique(temp.GO$annotation)
GOAcrossClusters$Proteins[i] <- paste(unique(temp.prot$GeneName), collapse = " ")
GOAcrossClusters$NumProteins[i] <- length(unique(temp.prot$GeneName))
GOAcrossClusters$NumProteinsInvolved[i] <- length(unique(temp.GO2$GeneSymbol))
print(i)
}
#write.csv(GOAcrossClusters, "2021_11_11_GOAcrossCluster_allstages.csv", row.names = FALSE)
#write.csv(GOAcrossClusters, "2021_11_11_GOAcrossCluster_early2c.csv", row.names = FALSE)
#write.csv(GOAcrossClusters, "2021_11_11_GOAcrossCluster_late2c.csv", row.names = FALSE)
#write.csv(GOAcrossClusters, "2021_11_11_GOAcrossCluster_4c.csv", row.names = FALSE)
#................ PLOTTING OPTIONS ARE BELOW ....................................................#
# read in file
#GOAcrossClusters2<- read.csv("GOAcrossClusters_2021_09_29_B_3orMoreObsperGroup_ExtraProtFils.csv")
#GOAcrossClusters <- GOAcrossClusters[,-1]
GOAcrossClusters <- read.csv("2021_11_11_GOAcrossCluster_allstages.csv")
q.value.filter <- 0.05
GOAcrossClusters.NA <- GOAcrossClusters
GOAcrossClusters.NA <- GOAcrossClusters.NA %>% dplyr::filter(!is.na(p.value))
GOAcrossClusters.NA$q.value <- p.adjust(GOAcrossClusters.NA$p.value, method = "fdr")
GOAcrossClusters.NA$fraction <- GOAcrossClusters.NA$NumProteins / GOAcrossClusters.NA$NumProteinsInvolved
GOAcrossClusters.NA$Clus1_vs_Clus2 <- GOAcrossClusters.NA$median_Cluster1 - GOAcrossClusters.NA$median_Cluster2
GOAcrossClusters.NA$group <- ifelse(GOAcrossClusters.NA$median_Cluster1 > GOAcrossClusters.NA$median_Cluster2,
"Greater in Clus1", "Greater in Clus2")
#GOAcrossClusters.NA <- GOAcrossClusters
#GOAcrossClusters.NA <- GOAcrossClusters.NA %>% dplyr::filter(!is.na(p.value))
# save the GOAcrossClusters
#write.csv(GOAcrossClusters, "GOAcrossClusters_2021_09_28_A_3orMoreObsperGroup.csv") # if(sum(!is.na(temp1$value)) >3 & sum(!is.na(temp2$value)) >3 )
#write.csv(GOAcrossClusters, "GOAcrossClusters_2021_09_29_B_3orMoreObsperGroup_ExtraProtFils.csv")
# take specific GO term themes and try to see patterns that emerge
GOAcrossClusters.sig <- GOAcrossClusters.NA %>% dplyr::filter(q.value <= 0.05) %>% dplyr::filter(fraction >= 0.3)
# filter simply on q-value
GOAcrossClusters.sig2 <- GOAcrossClusters.NA %>% dplyr::filter(q.value <= 0.05)
GOAcrossClusters.sig2.prot <- GOAcrossClusters.sig2 %>% dplyr::filter(NumProteins > 2)
# look at effect sizes
GOAcrossClusters.sig3 <- GOAcrossClusters.sig[order(GOAcrossClusters.sig$Clus1_vs_Clus2),]
GOAcrossClusters.sig3 <- GOAcrossClusters.NA %>% dplyr::filter(NumProteins > 5)
GOAcrossClusters.sig3$q.value <- p.adjust(GOAcrossClusters.sig3$p.value, method = "fdr")
GOAcrossClusters.sig4 <- GOAcrossClusters.sig3 %>% dplyr::filter(abs(Clus1_vs_Clus2) > 0.2)
bindDF <- GOAcrossClusters.sig2
for(i in 1:nrow(bindDF)) {
if(grepl("proteas|ubi|apop|peptidase|autopha|neddyl",bindDF$Annotation[i]) ==TRUE ) {
bindDF$type[i] <- "degradation" }
else if(grepl("transport|vesicle|channel|SNARE|myosin|dyne",bindDF$Annotation[i]) ==TRUE) {
bindDF$type[i] <- "transport" }
else if(grepl("translation|ribosom|endoplasmic reticul",bindDF$Annotation[i]) ==TRUE ) {
bindDF$type[i] <- "translation" }
else if(grepl("splic|snRN",bindDF$Annotation[i]) ==TRUE ) {
bindDF$type[i] <- "mRNA splicing" }
else if(grepl("chromatin|chromosom",bindDF$Annotation[i]) ==TRUE ) {
bindDF$type[i] <- "chromatin, chromosome" }
else if(grepl("kinase|phosph|calcium|calmodulin|migrat|secret|receptor",bindDF$Annotation[i]) ==TRUE ) {
bindDF$type[i] <- "signaling" }
else if(grepl("mitocho|metabo|electron transport|ATP|catab|glutam|oxid|hydrogenase",bindDF$Annotation[i]) ==TRUE) {
bindDF$type[i] <- "mitochondrial|metabolism" }
else if(grepl("cell cycl|kinetochore|spindle|mitos|meios",bindDF$Annotation[i]) ==TRUE) {
bindDF$type[i] <- "cell cycle" }
else{ bindDF$type[i] <- "No theme"}
print(i)
}
# plot density plots of themes
bindDF.plot <- bindDF %>% dplyr::filter(type != "No theme") %>%
dplyr::filter(type == "mitochondrial|metabolism" | type == "transport" | type == "translation" | type == "degradation" | type == "mRNA splicing")
ggplot(data = bindDF.plot, aes(x = Clus1_vs_Clus2, fill = type)) + geom_density(alpha = 0.5) +
theme_bw() + theme(axis.title = element_text(size = 14), plot.title = element_text(size = 18),
legend.position = "none",#legend.position = c(0.85, 0.1),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 16, face = "bold", color = "white"),
strip.background = element_rect(fill = "black")) +
scale_fill_discrete(name = "Biological Process Theme") +
labs(title = "Median Relative Abundance of GO terms per Cluster", x = "Cluster 1 - Cluster 2") +
facet_wrap(~type) + geom_vline(xintercept = 0)
# let's select for a theme and break it down
# for example, degradation- break it down with apoptosis, proteasome, ubiquitin ligases
bindDF.plot <- bindDF %>% dplyr::filter(type == "degradation") %>%
dplyr::mutate(granularType = case_when(#grepl("apop", Annotation) == TRUE ~ "Apoptosis",
grepl("proteasom", Annotation) == TRUE &
grepl("ubi", Annotation) == FALSE~ "Proteasome",
grepl("ubiqui|neddyl", Annotation) == TRUE~ "Ubiquitin",
grepl("autop", Annotation) == TRUE ~ "Autophagy")) %>%
dplyr::filter(granularType != "NA")
# Version 1 plot
ggplot(data = bindDF.plot, aes(x = Clus1_vs_Clus2, fill = granularType)) + geom_density(alpha = 0.5) +
theme_bw() + theme(axis.title = element_text(size = 24),
plot.title = element_text(size = 24, face = "bold"),
axis.text = element_text(size = 18),
#legend.position = "none",
legend.position = c(0.27, 0.77),
legend.background = element_rect(fill = "transparent", color = "transparent"),
legend.text = element_text(size = 18),
legend.title = element_text(size = 24),
strip.text = element_text(size = 16, face = "bold", color = "white"),
strip.background = element_rect(fill = "black")) +
coord_cartesian(xlim = c(-0.3, 0.3)) +
#facet_wrap(~granularType) + geom_vline(xintercept = 0) +
scale_fill_discrete(name = "Protein Degradation \nGO term umbrellas") +
#labs(title = "Protein Degradation GO term umbrellas") +
xlab(bquote("Median"~log[2]~"FC (Alpha - Beta)"))
# Version 2 plot
ggplot(data = bindDF.plot, aes(x = Clus1_vs_Clus2, fill = granularType)) + geom_density(alpha = 0.5) +
theme_bw() + theme(axis.title = element_text(size = 24),
plot.title = element_text(size = 24, face = "bold"),
axis.text = element_text(size = 18),
legend.position = c(0.22, 0.85),
#legend.background = element_rect(fill = "transparent", color = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.text = element_text(size = 26, family = "Courier"),
legend.title = element_blank()) +
#coord_cartesian(xlim = c(-0.3, 0.3)) +
xlab(bquote("Median"~log[2]~"FC (Alpha - Beta)"))
test <- bindDF.plot %>% dplyr::filter(granularType == "Autophagy")
# now let's look at protein transport
# transport|vesicle|channel|SNARE|myosin|dyne
bindDF.plot <- bindDF %>% dplyr::filter(type == "transport") %>%
dplyr::mutate(granularType = case_when(#grepl("voltage", Annotation) == TRUE &
grepl("voltage|proton|potassium|calcium|sodium", Annotation) == TRUE &
#!(grepl("negative", Annotation) == TRUE)~ "Ion",
!(grepl("negative", Annotation) == TRUE)~ "Channel & Signaling",
grepl("vesicle|Golgi|SNARE", Annotation) == TRUE ~ "Vesicle",
grepl("dyne|actin", Annotation) == TRUE~ "Molecular Motors"
#grepl("ATP", Annotation) == TRUE ~ "ATP"
)) %>%
dplyr::filter(granularType != "NA")
#test <- bindDF.plot %>% dplyr::filter(granularType == "Voltage Channels")
ggplot(data = bindDF.plot, aes(x = Clus1_vs_Clus2, fill = granularType)) + geom_density(alpha = 0.5) +
theme_bw() + theme(axis.title = element_text(size = 24), plot.title = element_text(size = 24, face = "bold"),
#legend.position = "none",
legend.position = c(0.7, 0.75),
axis.text = element_text(size = 18),
legend.text = element_text(size = 18),
legend.title = element_text(size = 24),
strip.text = element_text(size = 16, face = "bold", color = "white"),
strip.background = element_rect(fill = "indianred")) +
coord_cartesian(xlim = c(-0.4, 0.4)) +
#labs(title = "Protein Transport GO term umbrellas") +
scale_fill_brewer(name = "Protein Transport \nGO term umbrellas", palette = "Set1") +
#facet_wrap(~granularType) +
#geom_vline(xintercept = 0) +
xlab(bquote("Median"~log[2]~"FC (Alpha - Beta)"))
# Version 2 plot
ggplot(data = bindDF.plot, aes(x = Clus1_vs_Clus2, fill = granularType)) + geom_density(alpha = 0.5) +
theme_bw() + theme(axis.title = element_text(size = 24), plot.title = element_text(size = 24, face = "bold"),
legend.position = c(0.73, 0.85),
#legend.position = c(0.7, 0.75),
axis.text = element_text(size = 18),
legend.text = element_text(size = 26),
legend.background = element_rect(fill = "transparent"),
legend.title = element_blank()) +
coord_cartesian(xlim = c(-0.4, 0.4)) +
scale_fill_brewer( palette = "Set1") +
xlab(bquote("Median"~log[2]~"FC (Alpha - Beta)"))
test <- bindDF.plot %>% dplyr::filter(granularType == "Ion") %>% dplyr::filter(group == "Greater in Clus2")
test2 <- bindDF.plot %>% dplyr::filter(granularType == "Ion") %>% dplyr::filter(group == "Greater in Clus1")
# now let's look at translation
# translation|ribosom|endoplasmic reticul
bindDF.plot <- bindDF %>% dplyr::filter(type == "translation") %>%
dplyr::mutate(granularType = case_when(grepl("initiation", Annotation) == TRUE ~ "Initiation Factors",
grepl("ribosom", Annotation) == TRUE ~ "Ribosome",
grepl("endoplasmic reticul", Annotation) == TRUE~ "Endoplasmic Reticulum"
)) %>%
dplyr::filter(granularType != "NA")
ggplot(data = bindDF.plot, aes(x = Clus1_vs_Clus2, fill = granularType)) + geom_density(alpha = 0.5) +
theme_bw() + theme(axis.title = element_text(size = 20),
#legend.position = "none",
legend.position = c(0.23, 0.8),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 16, face = "bold", color = "white"),
strip.background = element_rect(fill = "black"),
axis.text = element_text(size = 18),
plot.title = element_text(size = 25)) +
#coord_cartesian(xlim = c(-0.2, 0.2)) +
labs(title = "Protein Translation Biological Processes", x = "Alpha / Beta") +
#facet_wrap(~granularType) +
geom_vline(xintercept = 0) +
scale_fill_discrete(name = "Themes")
# Version 2 plot
ggplot(data = bindDF.plot, aes(x = Clus1_vs_Clus2, fill = granularType)) + geom_density(alpha = 0.6) +
theme_bw() + theme(axis.title = element_blank(),
legend.position = c(0.25, 0.8),
axis.text = element_text(size = 18, color = "black"),
legend.text = element_text(size = 24),
legend.background = element_rect(fill = "transparent"),
legend.title = element_blank()) +
#coord_cartesian(xlim = c(-0.4, 0.4)) +
scale_fill_brewer(palette = "Spectral",
labels = c("Endo. Reticulum", "Initation Factors", "Ribosome")) +
xlab(bquote("Median"~log[2]~"FC (Alpha - Beta)"))
###---------------------------------------------------------------------------------------------------------.
###--------------------------------------- Sister Euclidean Distance -------------------------------------------------------
###---------------------------------------------------------------------------------------------------------.
# take the euclidean distance between each sister cell within each embryo
sigALL <- read.csv("ALL-cell-stage-combined-sig-Prots_5percentFDR_2022_03_13.csv")
df.sister <- df
df.sister[df.sister == 0] <- NA
# use proteins quantified in every cell: #na.omit(df.sister)
r <- na.omit(df.sister)#df.sister %>% dplyr::filter(Protein %in% sigALL$Protein)
df.m <- reshape2::melt(r)
df.m$cluster <- fir.cluster$Cluster[match(df.m$variable, rownames(fir.cluster))]
df.m$cluster <- paste0("Cluster",df.m$cluster)
df.m$variable_Cluster <- paste0(df.m$cluster, " ",df.m$variable)
df.m$embryoID <- dfStore$embryoID[match(df.m$variable, dfStore$variable)]
df.m$type <- dfStore$type[match(df.m$variable, dfStore$variable)]
countembryos <- df.m %>% dplyr::group_by(type) %>% dplyr::summarise(n_embryo = length(unique(embryoID)))
DistanceEmbryo <- as.data.frame(matrix(ncol = 4, nrow = ((21*6)+((15+21))))) #based on embryo comparisons
colnames(DistanceEmbryo) <- c("Embryo", "type", "Comparison", "Distance")
ProteinEmbryo <- as.data.frame(matrix(ncol = 4, nrow = ((21*6)+((15+21)))))
colnames(ProteinEmbryo) <- c("Embryo", "type", "Comparison", "NumProteins")
i<-1
rowCounter <- 1
for(i in 1:length(unique(df.m$embryoID))) {
temp.embryo <- df.m %>% dplyr::filter(embryoID == unique(df.m$embryoID)[i])
temp.embryo.d <- reshape2::dcast(temp.embryo, variable ~ Protein, value.var = "value")
temp.dis <- dist(temp.embryo.d[,2:ncol(temp.embryo.d)], method = "euclidean")
# count the number of shared observations between protein (i.e how many single cells have both proteins quantified)
obser <- pairwiseCount(t(temp.embryo.d[,2:ncol(temp.embryo.d)]))
colnames(obser) <- temp.embryo.d$variable
rownames(obser) <- temp.embryo.d$variable
# obtain the upper triangle of shared observation matrix
obser[lower.tri(obser, diag = T)] <- 188
obser.m <- reshape2::melt(obser)
obser.m <- obser.m %>% dplyr::filter(value!=188)
obser.m$Var1Var2 <- paste(obser.m$Var1, ":", obser.m$Var2)
if(grepl("_2c", unique(temp.embryo$type))) {
#temp.embryo.d <- reshape2::dcast(temp.embryo, variable ~ Protein, value.var = "value")
#temp.dis <- dist(temp.embryo.d[,2:ncol(temp.embryo.d)], method = "euclidean")
DistanceEmbryo$Distance[rowCounter] <- temp.dis
DistanceEmbryo$Embryo[rowCounter] <- unique(temp.embryo$embryoID)
DistanceEmbryo$type[rowCounter] <- unique(temp.embryo$type)
DistanceEmbryo$Comparison[rowCounter] <- paste(unique(temp.embryo$variable)[1], ":", unique(temp.embryo$variable)[2])
ProteinEmbryo$Comparison[rowCounter] <- paste(unique(temp.embryo.d$variable)[1], ":", unique(temp.embryo$variable)[2])
ProteinEmbryo$NumProteins[rowCounter] <- obser.m$value[obser.m$Var1Var2 == ProteinEmbryo$Comparison[rowCounter]]
#ProteinEmbryo$NumProteins[rowCounter] <- rowSums(!is.na(temp.embryo.d[,2:ncol(temp.embryo.d)]))[1]
rowCounter <- rowCounter + 1
}
else if(unique(temp.embryo$type) == "_4c") {
for(j in 1:6) {
DistanceEmbryo$Distance[rowCounter] <- temp.dis[j]
DistanceEmbryo$Embryo[rowCounter] <- unique(temp.embryo$embryoID)
DistanceEmbryo$type[rowCounter] <- unique(temp.embryo$type)
if(j == 1) {
DistanceEmbryo$Comparison[rowCounter] <- paste(unique(temp.embryo.d$variable)[1], ":", unique(temp.embryo$variable)[2])
ProteinEmbryo$Comparison[rowCounter] <- paste(unique(temp.embryo.d$variable)[1], ":", unique(temp.embryo$variable)[2])
ProteinEmbryo$NumProteins[rowCounter] <- obser.m$value[obser.m$Var1Var2 == ProteinEmbryo$Comparison[rowCounter]]
rowCounter <- rowCounter + 1
}
if(j == 2) {
DistanceEmbryo$Comparison[rowCounter] <- paste(unique(temp.embryo.d$variable)[1], ":", unique(temp.embryo$variable)[3])
ProteinEmbryo$Comparison[rowCounter] <- paste(unique(temp.embryo.d$variable)[1], ":", unique(temp.embryo$variable)[3])
ProteinEmbryo$NumProteins[rowCounter] <- obser.m$value[obser.m$Var1Var2 == ProteinEmbryo$Comparison[rowCounter]]
rowCounter <- rowCounter + 1
}
if(j == 3) {
DistanceEmbryo$Comparison[rowCounter] <- paste(unique(temp.embryo.d$variable)[1], ":", unique(temp.embryo$variable)[4])
ProteinEmbryo$Comparison[rowCounter] <- paste(unique(temp.embryo.d$variable)[1], ":", unique(temp.embryo$variable)[4])
ProteinEmbryo$NumProteins[rowCounter] <- obser.m$value[obser.m$Var1Var2 == ProteinEmbryo$Comparison[rowCounter]]
rowCounter <- rowCounter + 1
}
if(j == 4) {
DistanceEmbryo$Comparison[rowCounter] <- paste(unique(temp.embryo.d$variable)[2], ":", unique(temp.embryo$variable)[3])
ProteinEmbryo$Comparison[rowCounter] <- paste(unique(temp.embryo.d$variable)[2], ":", unique(temp.embryo$variable)[3])
ProteinEmbryo$NumProteins[rowCounter] <- obser.m$value[obser.m$Var1Var2 == ProteinEmbryo$Comparison[rowCounter]]
rowCounter <- rowCounter + 1
}
if(j == 5) {
DistanceEmbryo$Comparison[rowCounter] <- paste(unique(temp.embryo.d$variable)[2], ":", unique(temp.embryo$variable)[4])
ProteinEmbryo$Comparison[rowCounter] <- paste(unique(temp.embryo.d$variable)[2], ":", unique(temp.embryo$variable)[4])
ProteinEmbryo$NumProteins[rowCounter] <- obser.m$value[obser.m$Var1Var2 == ProteinEmbryo$Comparison[rowCounter]]
rowCounter <- rowCounter + 1
}
if(j == 6) {
DistanceEmbryo$Comparison[rowCounter] <- paste(unique(temp.embryo.d$variable)[3], ":", unique(temp.embryo$variable)[4])
ProteinEmbryo$Comparison[rowCounter] <- paste(unique(temp.embryo.d$variable)[3], ":", unique(temp.embryo$variable)[4])
ProteinEmbryo$NumProteins[rowCounter] <- obser.m$value[obser.m$Var1Var2 == ProteinEmbryo$Comparison[rowCounter]]
rowCounter <- rowCounter + 1
}
}
}
}
DistanceEmbryo$type <- factor(DistanceEmbryo$type, levels = c("early_2c", "late_2c", "_4c"))
ggplot(data = DistanceEmbryo, aes(y = Distance, x =type, fill = type)) + geom_boxplot()+ theme_bw() +
scale_fill_brewer(palette = "OrRd") +
#coord_cartesian(ylim = c(3,8)) +
theme(axis.text.y = element_text(size = 25, color = "black"), axis.title =element_blank(),
axis.text.x = element_text(size =25, angle = 45, hjust = 1, color = "black"),
legend.position = "none") +
labs(x = "Stage", y = "Sister Blastomere Distances") +
scale_x_discrete(labels = c("Early 2-cell", "Late 2-cell", "4-cell"))
DistanceEmbryo$NumProteins <- ProteinEmbryo$NumProteins
DistanceEmbryo$NormDist <- DistanceEmbryo$Distance / DistanceEmbryo$NumProteins
ggplot(data = DistanceEmbryo, aes(y = log10(NormDist), x =type, fill = type)) + geom_boxplot()+ theme_bw() +
geom_beeswarm(size = 2, alpha =0.5)+
scale_fill_brewer(palette = "OrRd") +
#coord_cartesian(ylim = c(3,8)) +
theme(axis.text = element_text(size = 22), axis.title =element_text(size = 22),
legend.position = "none") +
labs(x = "Stage", y = "Sister Blastomere Distances") +
scale_x_discrete(labels = c("Early 2-cell", "Late 2-cell", "4-cell"))
DistanceEmbryo.1 <- DistanceEmbryo %>% dplyr::filter(type == "early_2c")
DistanceEmbryo.2 <- DistanceEmbryo %>% dplyr::filter(type == "late_2c")
t.test(log10(DistanceEmbryo.1$NormDist), log10(DistanceEmbryo.2$NormDist))
t <- aov(log10(DistanceEmbryo$NormDist) ~ type, data = DistanceEmbryo)
summary(t)[[1]][["Pr(>F)"]][1]
###---------------------------------------------------------------------------------------------------------.