diff --git a/DGE/Deseq2/mRNA/.Rhistory b/DGE/Deseq2/mRNA/.Rhistory
deleted file mode 100644
index 768671fc8456938ce255bcc0917fc8a238462f73..0000000000000000000000000000000000000000
--- a/DGE/Deseq2/mRNA/.Rhistory
+++ /dev/null
@@ -1,426 +0,0 @@
-library(GEOquery)
-library(tidyverse)
-library(magrittr)
-mRNA_GSE20347 <- getGEO("GSE20347", GSEMatrix =TRUE, AnnotGPL=TRUE,getGPL= T)
-if (length(mRNA_GSE20347) > 1) idx <- grep("GPL4508", attr(mRNA_GSE20347, "names")) else idx <- 1
-mRNA_GSE20347 <- mRNA_GSE20347[[idx]]
-GSE20347<- mRNA_GSE20347@assayData[["exprs"]]
-colx<- mRNA_GSE20347@featureData@data[["Gene symbol"]]
-GSE20347<- cbind(colx,GSE20347)
-# Convert matrix to a dataframe
-df_GSE20347 <- as.data.frame(GSE20347)
-library(DESeq2)
-# Extract only the count matrix
-count_matrix <- df_GSE20347[, 2:ncol(df_GSE20347)]
-# Sample information
-sample_names <- colnames(count_matrix)
-# Create the sample_info data frame
-conditions <- c(rep("normal adjacent esophageal tissue", 17), rep("esophageal squamous cell carcinoma", length(sample_names) - 17))
-sample_info <- data.frame(Sample = sample_names, Condition = conditions)
-#### SKIRMISH FOR DESEQ2 DATASET ###########
-count_matrix <- as.matrix(sapply(count_matrix, as.numeric))
-count_matrix <- count_matrix[complete.cases(count_matrix), ]
-count_matrix <- round(count_matrix)
-#get rownames of previous data
-row_names_GSE20347 <- rownames(GSE20347)
-# Set gene_id to rownames again
-rownames(count_matrix) <- row_names_GSE20347
-# Create the DESeqDataSet using the updated sample_info
-dds <- DESeqDataSetFromMatrix(countData = count_matrix,
-colData = sample_info,
-design = ~ Condition)
-# Perform differential gene expression analysis
-dds <- DESeq(dds)
-resultsNames(dds)
-par(mar=c(8,5,2,2))
-boxplot(log10(assays(dds)[["cooks"]]), range=0, las=2)
-res <- results(dds, name="Condition_normal.adjacent.esophageal.tissue_vs_esophageal.squamous.cell.carcinoma")
-summary(res)
-# Adjustment to overcome NA-error --> exclude rows containing NA's
-filtered_indices <- which(res$log2FoldChange < 1 & res$log2FoldChange > -1 & res$padj < 0.05 & !is.na(res$padj))
-filtered_downregulated_genes <- res[filtered_indices, ]
-gene_names <- rownames(filtered_downregulated_genes)
-gene_names
-res2 <- results(dds, name = "Condition_normal.adjacent.esophageal.tissue_vs_esophageal.squamous.cell.carcinoma")
-# Convert p-value to -log10(p-value)
-res2$log10_pvalue <- -log10(res2$pvalue)
-# Create the volcano plot
-volcano_plot <- ggplot(data = res2, aes(x = log2FoldChange, y = log10_pvalue)) +
-geom_point() +
-labs(x = "log2 Fold Change", y = "-log10 p-value") +
-theme_minimal()
-# Display the volcano plot
-print(volcano_plot)
-res2 <- results(dds, name = "Condition_normal.adjacent.esophageal.tissue_vs_esophageal.squamous.cell.carcinoma")
-# Convert p-value to -log10(p-value)
-res2$log10_pvalue <- -log10(res2$pvalue)
-# Create the volcano plot
-volcano_plot <- ggplot(data = res2, aes(x = log2FoldChange, y = log10_pvalue)) +
-geom_point() +
-labs(x = "log2 Fold Change", y = "-log10 p-value") +
-theme_minimal()
-library(GEOquery)
-library(tidyverse)
-library(magrittr)
-library(DESeq2)
-library(ggrepel)
-mRNA_GSE20347 <- getGEO("GSE20347", GSEMatrix = TRUE, AnnotGPL = TRUE, getGPL = TRUE)
-if (length(mRNA_GSE20347) > 1) idx <- grep("GPL4508", attr(mRNA_GSE20347, "names")) else idx <- 1
-mRNA_GSE20347 <- mRNA_GSE20347[[idx]]
-GSE20347 <- mRNA_GSE20347@assayData[["exprs"]]
-colx <- mRNA_GSE20347@featureData@data[["Gene symbol"]]
-GSE20347 <- cbind(colx, GSE20347)
-# Convert matrix to a dataframe
-df_GSE20347 <- as.data.frame(GSE20347)
-# Extract only the count matrix
-count_matrix <- df_GSE20347[, 2:ncol(df_GSE20347)]
-# Sample information
-sample_names <- colnames(count_matrix)
-# Create the sample_info data frame
-conditions <- c(rep("normal adjacent esophageal tissue", 17), rep("esophageal squamous cell carcinoma", length(sample_names) - 17))
-sample_info <- data.frame(Sample = sample_names, Condition = conditions)
-#### SKIRMISH FOR DESEQ2 DATASET ###########
-count_matrix <- as.matrix(sapply(count_matrix, as.numeric))
-count_matrix <- count_matrix[complete.cases(count_matrix), ]
-count_matrix <- round(count_matrix)
-# Get rownames of previous data
-row_names_GSE20347 <- rownames(GSE20347)
-# Set gene_id to rownames again
-rownames(count_matrix) <- row_names_GSE20347
-# Create the DESeqDataSet using the updated sample_info
-dds <- DESeqDataSetFromMatrix(countData = count_matrix,
-colData = sample_info,
-design = ~ Condition)
-# Perform differential gene expression analysis
-dds <- DESeq(dds)
-# Volcano plot
-res <- results(dds)
-res$logPadj <- -log10(res$padj)
-res$logFC <- res$log2FoldChange
-res$Sig <- ifelse(abs(res$log2FoldChange) > 1 & res$padj < 0.05, "Yes", "No")
-# Plot volcano plot
-ggplot(res, aes(x = logFC, y = logPadj, color = Sig)) +
-geom_point(alpha = 0.6) +
-geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") +
-geom_vline(xintercept = c(-1, 1), linetype = "dashed", color = "blue") +
-xlim(c(-max(abs(res$log2FoldChange)), max(abs(res$log2FoldChange)))) +
-labs(x = "log2 Fold Change", y = "-log10(Adjusted p-value)", title = "Volcano Plot") +
-theme_minimal() +
-theme(legend.position = "none")
-library(GEOquery)
-library(tidyverse)
-library(magrittr)
-mRNA_GSE20347 <- getGEO("GSE20347", GSEMatrix =TRUE, AnnotGPL=TRUE,getGPL= T)
-if (length(mRNA_GSE20347) > 1) idx <- grep("GPL4508", attr(mRNA_GSE20347, "names")) else idx <- 1
-mRNA_GSE20347 <- mRNA_GSE20347[[idx]]
-GSE20347<- mRNA_GSE20347@assayData[["exprs"]]
-colx<- mRNA_GSE20347@featureData@data[["Gene symbol"]]
-GSE20347<- cbind(colx,GSE20347)
-# Convert matrix to a dataframe
-df_GSE20347 <- as.data.frame(GSE20347)
-library(DESeq2)
-# Extract only the count matrix
-count_matrix <- df_GSE20347[, 2:ncol(df_GSE20347)]
-# Sample information
-sample_names <- colnames(count_matrix)
-# Create the sample_info data frame
-conditions <- c(rep("normal adjacent esophageal tissue", 17), rep("esophageal squamous cell carcinoma", length(sample_names) - 17))
-sample_info <- data.frame(Sample = sample_names, Condition = conditions)
-#### SKIRMISH FOR DESEQ2 DATASET ###########
-count_matrix <- as.matrix(sapply(count_matrix, as.numeric))
-count_matrix <- count_matrix[complete.cases(count_matrix), ]
-count_matrix <- round(count_matrix)
-#get rownames of previous data
-row_names_GSE20347 <- rownames(GSE20347)
-# Set gene_id to rownames again
-rownames(count_matrix) <- row_names_GSE20347
-# Create the DESeqDataSet using the updated sample_info
-dds <- DESeqDataSetFromMatrix(countData = count_matrix,
-colData = sample_info,
-design = ~ Condition)
-# Perform differential gene expression analysis
-dds <- DESeq(dds)
-resultsNames(dds)
-par(mar=c(8,5,2,2))
-boxplot(log10(assays(dds)[["cooks"]]), range=0, las=2)
-res <- results(dds, name="Condition_normal.adjacent.esophageal.tissue_vs_esophageal.squamous.cell.carcinoma")
-summary(res)
-# Adjustment to overcome NA-error --> exclude rows containing NA's
-filtered_indices <- which(res$log2FoldChange < 1 & res$log2FoldChange > -1 & res$padj < 0.05 & !is.na(res$padj))
-filtered_downregulated_genes <- res[filtered_indices, ]
-gene_names <- rownames(filtered_downregulated_genes)
-gene_names
-res2 <- as.data.frame(results(dds, name = "Condition_normal.adjacent.esophageal.tissue_vs_esophageal.squamous.cell.carcinoma"))
-# Convert p-value to -log10(p-value)
-res2$log10_pvalue <- -log10(res2$pvalue)
-# Create the volcano plot
-volcano_plot <- ggplot(data = res2, aes(x = log2FoldChange, y = log10_pvalue)) +
-geom_point() +
-labs(x = "log2 Fold Change", y = "-log10 p-value") +
-theme_minimal()
-# Display the volcano plot
-print(volcano_plot)
-# Add vertical lines for fold change thresholds
-volcano_plot <- volcano_plot +
-geom_vline(xintercept = c(-1, 1), color = "red") +
-geom_hline(yintercept = -log10(0.05), color = "red")
-# Display the customized volcano plot
-print(volcano_plot)
-# Version info: R 4.2.2, Biobase 2.58.0, GEOquery 2.66.0, limma 3.54.0
-################################################################
-#   Differential expression analysis with limma
-library(GEOquery)
-library(limma)
-library(umap)
-gset <- getGEO("GSE20347", GSEMatrix =TRUE, AnnotGPL=TRUE)
-if (length(gset) > 1) idx <- grep("GPL571", attr(gset, "names")) else idx <- 1
-gset <- gset[[idx]]
-# make proper column names to match toptable
-fvarLabels(gset) <- make.names(fvarLabels(gset))
-# group membership for all samples
-gsms <- "0000000000000000011111111111111111"
-sml <- strsplit(gsms, split="")[[1]]
-# log2 transformation
-ex <- exprs(gset)
-qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
-LogC <- (qx[5] > 100) ||
-(qx[6]-qx[1] > 50 && qx[2] > 0)
-if (LogC) { ex[which(ex <= 0)] <- NaN
-exprs(gset) <- log2(ex) }
-# assign samples to groups and set up design matrix
-gs <- factor(sml)
-groups <- make.names(c("normal","cancer"))
-levels(gs) <- groups
-gset$group <- gs
-design <- model.matrix(~group + 0, gset)
-colnames(design) <- levels(gs)
-gset <- gset[complete.cases(exprs(gset)), ] # skip missing values
-fit <- lmFit(gset, design)  # fit linear model
-# set up contrasts of interest and recalculate model coefficients
-cts <- c(paste(groups[1],"-",groups[2],sep=""))
-cont.matrix <- makeContrasts(contrasts=cts, levels=design)
-fit2 <- contrasts.fit(fit, cont.matrix)
-# compute statistics and table of top significant genes
-fit2 <- eBayes(fit2, 0.01)
-tT <- topTable(fit2, adjust="fdr", sort.by="B", number=250)
-tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","GB_ACC","SPOT_ID","Gene.Symbol","Gene.symbol","Gene.title"))
-write.table(tT, file=stdout(), row.names=F, sep="\t")
-# Visualize and quality control test results.
-# Build histogram of P-values for all genes. Normal test
-# assumption is that most genes are not differentially expressed.
-tT2 <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
-hist(tT2$adj.P.Val, col = "grey", border = "white", xlab = "P-adj",
-ylab = "Number of genes", main = "P-adj value distribution")
-# summarize test results as "up", "down" or "not expressed"
-dT <- decideTests(fit2, adjust.method="fdr", p.value=0.05, lfc=1)
-# Venn diagram of results
-vennDiagram(dT, circle.col=palette())
-# create Q-Q plot for t-statistic
-t.good <- which(!is.na(fit2$F)) # filter out bad probes
-qqt(fit2$t[t.good], fit2$df.total[t.good], main="Moderated t statistic")
-# volcano plot (log P-value vs log fold change)
-colnames(fit2) # list contrast names
-ct <- 1        # choose contrast of interest
-volcanoplot(fit2, coef=ct, main=colnames(fit2)[ct], pch=20,
-highlight=length(which(dT[,ct]!=0)), names=rep('+', nrow(fit2)))
-View(tT2)
-View(tT)
-volcanoplot(fit2, coef=ct, main=colnames(fit2)[ct], pch=20,
-highlight=length(which(dT[,ct]!=0)), names=rep('+', nrow(fit2)))
-# MD plot (log fold change vs mean log expression)
-# highlight statistically significant (p-adj < 0.05) probes
-plotMD(fit2, column=ct, status=dT[,ct], legend=F, pch=20, cex=1)
-abline(h=0)
-################################################################
-# General expression data analysis
-ex <- exprs(gset)
-# box-and-whisker plot
-dev.new(width=3+ncol(gset)/6, height=5)
-ord <- order(gs)  # order samples by group
-palette(c("#1B9E77", "#7570B3", "#E7298A", "#E6AB02", "#D95F02",
-"#66A61E", "#A6761D", "#B32424", "#B324B3", "#666666"))
-library(GEOquery)
-library(limma)
-library(umap)
-gset <- getGEO("GSE43732", GSEMatrix =TRUE, AnnotGPL=FALSE)
-if (length(gset) > 1) idx <- grep("GPL16543", attr(gset, "names")) else idx <- 1
-gset <- gset[[idx]]
-# make proper column names to match toptable
-fvarLabels(gset) <- make.names(fvarLabels(gset))
-# group membership for all samples
-gsms <- paste0("11110000111100001111000011110000111100001111000011",
-"11000011110000111100001111000011110000111100001111",
-"00001111000011110000111100000111100011110000111100",
-"00111100001011110000111100001111000011110000111100",
-"00111000111100001111000011100011110000")
-sml <- strsplit(gsms, split="")[[1]]
-# log2 transformation
-ex <- exprs(gset)
-qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
-LogC <- (qx[5] > 100) ||
-(qx[6]-qx[1] > 50 && qx[2] > 0)
-if (LogC) { ex[which(ex <= 0)] <- NaN
-exprs(gset) <- log2(ex) }
-# assign samples to groups and set up design matrix
-gs <- factor(sml)
-groups <- make.names(c("Normal","Cancer"))
-levels(gs) <- groups
-gset$group <- gs
-design <- model.matrix(~group + 0, gset)
-colnames(design) <- levels(gs)
-gset <- gset[complete.cases(exprs(gset)), ] # skip missing values
-fit <- lmFit(gset, design)  # fit linear model
-# set up contrasts of interest and recalculate model coefficients
-cts <- paste(groups[1], groups[2], sep="-")
-cont.matrix <- makeContrasts(contrasts=cts, levels=design)
-fit2 <- contrasts.fit(fit, cont.matrix)
-# compute statistics and table of top significant genes
-fit2 <- eBayes(fit2, 0.01)
-tT <- topTable(fit2, adjust="fdr", sort.by="B", number=250)
-tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","SPOT_ID","miRNA_ID"))
-write.table(tT, file=stdout(), row.names=F, sep="\t")
-# Visualize and quality control test results.
-# Build histogram of P-values for all genes. Normal test
-# assumption is that most genes are not differentially expressed.
-tT2 <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
-hist(tT2$adj.P.Val, col = "grey", border = "white", xlab = "P-adj",
-ylab = "Number of genes", main = "P-adj value distribution")
-# summarize test results as "up", "down" or "not expressed"
-dT <- decideTests(fit2, adjust.method="fdr", p.value=0.05, lfc=1)
-# Venn diagram of results
-vennDiagram(dT, circle.col=palette())
-# create Q-Q plot for t-statistic
-t.good <- which(!is.na(fit2$F)) # filter out bad probes
-qqt(fit2$t[t.good], fit2$df.total[t.good], main="Moderated t statistic")
-# volcano plot (log P-value vs log fold change)
-colnames(fit2) # list contrast names
-ct <- 1        # choose contrast of interest
-volcanoplot(fit2, coef=ct, main=colnames(fit2)[ct], pch=20,
-highlight=length(which(dT[,ct]!=0)), names=rep('+', nrow(fit2)))
-# MD plot (log fold change vs mean log expression)
-# highlight statistically significant (p-adj < 0.05) probes
-plotMD(fit2, column=ct, status=dT[,ct], legend=F, pch=20, cex=1)
-abline(h=0)
-################################################################
-# General expression data analysis
-ex <- exprs(gset)
-# box-and-whisker plot
-dev.new(width=3+ncol(gset)/6, height=5)
-ord <- order(gs)  # order samples by group
-palette(c("#1B9E77", "#7570B3", "#E7298A", "#E6AB02", "#D95F02",
-"#66A61E", "#A6761D", "#B32424", "#B324B3", "#666666"))
-par(mar=c(7,4,2,1))
-title <- paste ("GSE43732", "/", annotation(gset), sep ="")
-boxplot(ex[,ord], boxwex=0.6, notch=T, main=title, outline=FALSE, las=2, col=gs[ord])
-legend("topleft", groups, fill=palette(), bty="n")
-dev.off()
-# expression value distribution
-par(mar=c(4,4,2,1))
-title <- paste ("GSE43732", "/", annotation(gset), " value distribution", sep ="")
-plotDensities(ex, group=gs, main=title, legend ="topright")
-# UMAP plot (dimensionality reduction)
-ex <- na.omit(ex) # eliminate rows with NAs
-ex <- ex[!duplicated(ex), ]  # remove duplicates
-ump <- umap(t(ex), n_neighbors = 15, random_state = 123)
-par(mar=c(3,3,2,6), xpd=TRUE)
-plot(ump$layout, main="UMAP plot, nbrs=15", xlab="", ylab="", col=gs, pch=20, cex=1.5)
-legend("topright", inset=c(-0.15,0), legend=levels(gs), pch=20,
-col=1:nlevels(gs), title="Group", pt.cex=1.5)
-library("maptools")  # point labels without overlaps
-pointLabel(ump$layout, labels = rownames(ump$layout), method="SANN", cex=0.6)
-# mean-variance trend, helps to see if precision weights are needed
-plotSA(fit2, main="Mean variance trend, GSE43732")
-# Version info: R 4.2.2, Biobase 2.58.0, GEOquery 2.66.0, limma 3.54.0
-################################################################
-#   Differential expression analysis with limma
-library(GEOquery)
-library(limma)
-library(umap)
-gset <- getGEO("GSE43732", GSEMatrix =TRUE, AnnotGPL=FALSE)
-if (length(gset) > 1) idx <- grep("GPL16543", attr(gset, "names")) else idx <- 1
-gset <- gset[[idx]]
-# make proper column names to match toptable
-fvarLabels(gset) <- make.names(fvarLabels(gset))
-# group membership for all samples
-gsms <- paste0("00001111000011110000111100001111000011110000111100",
-"00111100001111000011110000111100001111000011110000",
-"11110000111100001111000011111000011100001111000011",
-"11000011110100001111000011110000111100001111000011",
-"11000111000011110000111100011100001111")
-sml <- strsplit(gsms, split="")[[1]]
-# log2 transformation
-ex <- exprs(gset)
-qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
-LogC <- (qx[5] > 100) ||
-(qx[6]-qx[1] > 50 && qx[2] > 0)
-if (LogC) { ex[which(ex <= 0)] <- NaN
-exprs(gset) <- log2(ex) }
-# assign samples to groups and set up design matrix
-gs <- factor(sml)
-groups <- make.names(c("cancer","normal"))
-levels(gs) <- groups
-gset$group <- gs
-design <- model.matrix(~group + 0, gset)
-colnames(design) <- levels(gs)
-gset <- gset[complete.cases(exprs(gset)), ] # skip missing values
-fit <- lmFit(gset, design)  # fit linear model
-# set up contrasts of interest and recalculate model coefficients
-cts <- paste(groups[1], groups[2], sep="-")
-cont.matrix <- makeContrasts(contrasts=cts, levels=design)
-fit2 <- contrasts.fit(fit, cont.matrix)
-# compute statistics and table of top significant genes
-fit2 <- eBayes(fit2, 0.01)
-tT <- topTable(fit2, adjust="fdr", sort.by="B", number=250)
-tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","SPOT_ID","miRNA_ID"))
-write.table(tT, file=stdout(), row.names=F, sep="\t")
-# Visualize and quality control test results.
-# Build histogram of P-values for all genes. Normal test
-# assumption is that most genes are not differentially expressed.
-tT2 <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
-hist(tT2$adj.P.Val, col = "grey", border = "white", xlab = "P-adj",
-ylab = "Number of genes", main = "P-adj value distribution")
-# summarize test results as "up", "down" or "not expressed"
-dT <- decideTests(fit2, adjust.method="fdr", p.value=0.05, lfc=0)
-# Venn diagram of results
-vennDiagram(dT, circle.col=palette())
-# create Q-Q plot for t-statistic
-t.good <- which(!is.na(fit2$F)) # filter out bad probes
-qqt(fit2$t[t.good], fit2$df.total[t.good], main="Moderated t statistic")
-# volcano plot (log P-value vs log fold change)
-colnames(fit2) # list contrast names
-ct <- 1        # choose contrast of interest
-volcanoplot(fit2, coef=ct, main=colnames(fit2)[ct], pch=20,
-highlight=length(which(dT[,ct]!=0)), names=rep('+', nrow(fit2)))
-# MD plot (log fold change vs mean log expression)
-# highlight statistically significant (p-adj < 0.05) probes
-plotMD(fit2, column=ct, status=dT[,ct], legend=F, pch=20, cex=1)
-abline(h=0)
-################################################################
-# General expression data analysis
-ex <- exprs(gset)
-# box-and-whisker plot
-dev.new(width=3+ncol(gset)/6, height=5)
-ord <- order(gs)  # order samples by group
-palette(c("#1B9E77", "#7570B3", "#E7298A", "#E6AB02", "#D95F02",
-"#66A61E", "#A6761D", "#B32424", "#B324B3", "#666666"))
-par(mar=c(7,4,2,1))
-title <- paste ("GSE43732", "/", annotation(gset), sep ="")
-boxplot(ex[,ord], boxwex=0.6, notch=T, main=title, outline=FALSE, las=2, col=gs[ord])
-legend("topleft", groups, fill=palette(), bty="n")
-dev.off()
-# expression value distribution
-par(mar=c(4,4,2,1))
-title <- paste ("GSE43732", "/", annotation(gset), " value distribution", sep ="")
-plotDensities(ex, group=gs, main=title, legend ="topright")
-# UMAP plot (dimensionality reduction)
-ex <- na.omit(ex) # eliminate rows with NAs
-ex <- ex[!duplicated(ex), ]  # remove duplicates
-ump <- umap(t(ex), n_neighbors = 15, random_state = 123)
-par(mar=c(3,3,2,6), xpd=TRUE)
-plot(ump$layout, main="UMAP plot, nbrs=15", xlab="", ylab="", col=gs, pch=20, cex=1.5)
-legend("topright", inset=c(-0.15,0), legend=levels(gs), pch=20,
-col=1:nlevels(gs), title="Group", pt.cex=1.5)
-library("maptools")  # point labels without overlaps
-pointLabel(ump$layout, labels = rownames(ump$layout), method="SANN", cex=0.6)
-# mean-variance trend, helps to see if precision weights are needed
-plotSA(fit2, main="Mean variance trend, GSE43732")
diff --git a/DGE/Deseq2/miRNA/GSE43732_miRNA.R b/DGE/Deseq2/miRNA/GSE43732_miRNA.R
deleted file mode 100644
index 4c9831a5b6a506bf1b5fd985bcb904d6e5fb87a0..0000000000000000000000000000000000000000
--- a/DGE/Deseq2/miRNA/GSE43732_miRNA.R
+++ /dev/null
@@ -1,115 +0,0 @@
-library(GEOquery)
-library(tidyverse)
-library(magrittr)
-library(multiMiR)
-library(writexl)
-
-miRNA_GSE43732 <- getGEO("GSE43732", GSEMatrix =TRUE, AnnotGPL=TRUE,getGPL= T)
-if (length(miRNA_GSE43732) > 1) idx <- grep("GPL4508", attr(miRNA_GSE43732, "names")) else idx <- 1
-miRNA_GSE43732 <- miRNA_GSE43732[[idx]]
-GSE43732 <- miRNA_GSE43732@assayData[["exprs"]]
-colx<- miRNA_GSE43732@featureData@data[["Name"]]
-GSE43732 <- cbind(colx,GSE43732)
-
-
-# Convert matrix to a dataframe
-df_GSE43732 <- as.data.frame(GSE43732)
-
-library(DESeq2)
-
-# Extract only the count matrix
-count_matrix <- df_GSE43732[, 1:ncol(df_GSE43732)]
-
-# Sample information
-sample_names <- colnames(count_matrix)
-conditions = c("cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",  
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",  
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "normal", 
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal", 
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal")
-
-sample_info <- data.frame(Sample = sample_names, condition = conditions)
-
-#### SKIRMISH FOR DESEQ2 DATASET ###########
-#make NA's to 0
-count_matrix <- as.matrix(sapply(count_matrix, as.numeric))
-count_matrix[is.na(count_matrix)] <- 0
-count_matrix <- round(count_matrix)
-
-#get rownames of previous data
-row_names_GSE43732 <- rownames(GSE43732)
-
-# Set gene_id to rownames again
-rownames(count_matrix) <- row_names_GSE43732
-
-#negative values to abs() --> simulate counts
-count_matrix <- abs(count_matrix)
-
-
-# Create the DESeqDataSet using the updated sample_info
-dds <- DESeqDataSetFromMatrix(countData = count_matrix,
-                              colData = sample_info,
-                              design = ~ condition)
-
-# Perform differential gene expression analysis
-dds <- DESeq(dds)
-
-resultsNames(dds)
-
-par(mar=c(8,5,2,2))
-boxplot(log10(assays(dds)[["cooks"]]), range=0, las=2)
-
-res <- results(dds, name="condition_normal_vs_cancer")
-summary(res)
-
-# Adjustment to overcome NA-error --> exclude rows containing NA's
-filtered_indices <- which(res$log2FoldChange > 1 & res$padj < 0.05 & !is.na(res$padj))
-filtered_downregulated_genes <- res[filtered_indices, ]
-gene_names <- rownames(filtered_downregulated_genes)
-gene_names
-
-multimir_results <- get_multimir(org     = 'hsa',
-                                 mirna   = gene_names,
-                                 table   = 'validated',
-                                 summary = TRUE)
-
-targets <- multimir_results@data
-excel_file <- "C:/Users/Emre/Desktop/Bioinformatik/Bioinformatik 2.Semester Master/Data Science in the Life Sciences/Project/multimir_results.xlsx"
-write_xlsx(targets, path = excel_file )
-
-rld <- rlog(dds, blind=TRUE) 
-PCA1 <- plotPCA(rld, intgroup = "condition")
-PCA1
-
-vst <- assay(vst(dds))
-p <- pca(vst, removeVar = 0.1)
-screeplot(p, axisLabSize = 18, titleLabSize = 22)
-
-biplot(p, showLoadings = TRUE,
-       labSize = 5, pointSize = 5, sizeLoadingsNames = 5)
diff --git a/DGE/Deseq2/miRNA/GSE55857 miRNA errors.R b/DGE/Deseq2/miRNA/GSE55857 miRNA errors.R
deleted file mode 100644
index 08e8e85c7f09a00eb473cfa87c796e41889a2b79..0000000000000000000000000000000000000000
--- a/DGE/Deseq2/miRNA/GSE55857 miRNA errors.R	
+++ /dev/null
@@ -1,78 +0,0 @@
-library(GEOquery)
-library(tidyverse)
-library(magrittr)
-library(DESeq2)
-
-miRNA_GSE55856 <- getGEO("GSE55856", GSEMatrix =TRUE, AnnotGPL=TRUE,getGPL= T)
-GSE55856 <- miRNA_GSE55856[["GSE55856_series_matrix.txt.gz"]]@assayData[["exprs"]]
-GSE55856 <- cbind(colx,GSE55856)
-
-
-# Convert matrix to a dataframe
-df_GSE55856 <- as.data.frame(GSE55856)
-
-# Extract only the count matrix
-count_matrix <- df_GSE55856[, 1:ncol(df_GSE55856)]
-
-# Sample information
-sample_names <- colnames(count_matrix)
-conditions = c("normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", 
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal",
-               "normal", "tumor", "normal", "tumor", "normal", "tumor", "normal")
-
-sample_info <- data.frame(Sample = sample_names, condition = conditions)
-
-#### SKIRMISH FOR DESEQ2 DATASET ###########
-#make NA's to 0
-count_matrix <- as.matrix(sapply(count_matrix, as.numeric))
-count_matrix[is.na(count_matrix)] <- 0
-count_matrix <- round(count_matrix)
-
-#get rownames of previous data
-row_names_GSE55856 <- rownames(GSE55856)
-
-# Set gene_id to rownames again
-rownames(count_matrix) <- row_names_GSE55856
-
-#negative values to abs() --> simulate counts
-#count_matrix <- abs(count_matrix)
-
-
-# Create the DESeqDataSet using the updated sample_info
-dds <- DESeqDataSetFromMatrix(countData = count_matrix,
-                              colData = sample_info,
-                              design = ~ condition)
-
-# Perform differential gene expression analysis
-dds <- DESeq(dds)
-
-resultsNames(dds)
-res <- results(dds, name="condition_tumor_vs_normal")
-summary(res)
-
-# Adjustment to overcome NA-error --> exclude rows containing NA's
-filtered_indices <- which(res$log2FoldChange > 1 & res$padj < 0.05 & !is.na(res$padj))
-filtered_downregulated_genes <- res[filtered_indices, ]
-gene_names <- rownames(filtered_downregulated_genes)
-gene_names
-
-rld <- rlog(dds, blind=TRUE) 
-PCA1 <- plotPCA(rld, intgroup = "condition")
-PCA1
diff --git a/DGE/Deseq2/miRNA/GSE6188 miRNA.R b/DGE/Deseq2/miRNA/GSE6188 miRNA.R
deleted file mode 100644
index 634b4f55d1f205a568e82fd6460efe07f84be81e..0000000000000000000000000000000000000000
--- a/DGE/Deseq2/miRNA/GSE6188 miRNA.R	
+++ /dev/null
@@ -1,110 +0,0 @@
-library(GEOquery)
-library(tidyverse)
-library(dplyr)
-library(PCAtools)
-library(DESeq2)
-
-miRNA_GSE6188 <- getGEO("GSE6188", GSEMatrix =TRUE, AnnotGPL=TRUE,getGPL= T)
-if (length(miRNA_GSE6188) > 1) idx <- grep("GPL4508", attr(miRNA_GSE6188, "names")) else idx <- 1
-miRNA_GSE6188 <- miRNA_GSE6188[[idx]]
-GSE6188 <- miRNA_GSE6188@assayData[["exprs"]]
-colx<- miRNA_GSE6188@featureData@data[["Name"]]
-GSE6188 <- cbind(colx,GSE6188)
-
-# Convert matrix to a dataframe
-df_GSE6188 <- as.data.frame(GSE6188)
-df_GSE6188[is.na(df_GSE6188)] <- 0
-
-
-# Group the data frame by 'colx' and calculate the mean for other columns
-df_mean <- df_GSE6188 %>%
-  group_by(colx) %>%
-  summarise(everything(), mean, na.rm = TRUE)
-
-
-# Extract only the count matrix
-count_matrix <- df_GSE43732[, 1:ncol(df_GSE43732)]
-
-# Sample information
-sample_names <- colnames(count_matrix)
-conditions = c("cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",  
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",  
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "normal", 
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal", 
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "normal", "normal", "normal",
-               "cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal")
-
-sample_info <- data.frame(Sample = sample_names, condition = conditions)
-
-#### SKIRMISH FOR DESEQ2 DATASET ###########
-#make NA's to 0
-count_matrix <- as.matrix(sapply(count_matrix, as.numeric))
-count_matrix[is.na(count_matrix)] <- 0
-count_matrix <- round(count_matrix)
-
-#get rownames of previous data
-row_names_GSE43732 <- rownames(GSE43732)
-
-# Set gene_id to rownames again
-rownames(count_matrix) <- row_names_GSE43732
-
-#negative values to abs() --> simulate counts
-count_matrix <- abs(count_matrix)
-
-
-# Create the DESeqDataSet using the updated sample_info
-dds <- DESeqDataSetFromMatrix(countData = count_matrix,
-                              colData = sample_info,
-                              design = ~ condition)
-
-# Perform differential gene expression analysis
-dds <- DESeq(dds)
-resultsNames(dds)
-
-par(mar=c(8,5,2,2))
-boxplot(log10(assays(dds)[["cooks"]]), range=0, las=2)
-
-res <- results(dds, name="condition_normal_vs_cancer")
-summary(res)
-
-# Adjustment to overcome NA-error --> exclude rows containing NA's
-filtered_indices <- which(res$log2FoldChange > 1 & res$padj < 0.05 & !is.na(res$padj))
-filtered_downregulated_genes <- res[filtered_indices, ]
-gene_names <- rownames(filtered_downregulated_genes)
-gene_names
-
-rld <- rlog(dds, blind=TRUE) 
-PCA1 <- plotPCA(rld, intgroup = "condition")
-PCA1
-
-vst <- assay(vst(dds))
-p <- pca(vst, removeVar = 0.1)
-screeplot(p, axisLabSize = 18, titleLabSize = 22)
-
-biplot(p, showLoadings = TRUE,
-       labSize = 5, pointSize = 5, sizeLoadingsNames = 5)
\ No newline at end of file
diff --git a/DGE/Limma/miRNA/.Rhistory b/DGE/Limma/miRNA/.Rhistory
deleted file mode 100644
index e03d32136de402b075be7dd28946957cb28d8937..0000000000000000000000000000000000000000
--- a/DGE/Limma/miRNA/.Rhistory
+++ /dev/null
@@ -1,512 +0,0 @@
-fit2 <- eBayes(fit2, 0.01)
-tT <- topTable(fit2, adjust="fdr", sort.by="B", number=250)
-tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","SPOT_ID","miRNA_ID"))
-write.table(tT, file=stdout(), row.names=F, sep="\t")
-# Visualize and quality control test results.
-# Build histogram of P-values for all genes. Normal test
-# assumption is that most genes are not differentially expressed.
-tT2 <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
-hist(tT2$adj.P.Val, col = "grey", border = "white", xlab = "P-adj",
-ylab = "Number of genes", main = "P-adj value distribution")
-# summarize test results as "up", "down" or "not expressed"
-dT <- decideTests(fit2, adjust.method="fdr", p.value=0.05, lfc=1)
-# Venn diagram of results
-vennDiagram(dT, circle.col=palette())
-# create Q-Q plot for t-statistic
-t.good <- which(!is.na(fit2$F)) # filter out bad probes
-qqt(fit2$t[t.good], fit2$df.total[t.good], main="Moderated t statistic")
-# volcano plot (log P-value vs log fold change)
-colnames(fit2) # list contrast names
-ct <- 1        # choose contrast of interest
-volcanoplot(fit2, coef=ct, main=colnames(fit2)[ct], pch=20,
-highlight=length(which(dT[,ct]!=0)), names=rep('+', nrow(fit2)))
-# MD plot (log fold change vs mean log expression)
-# highlight statistically significant (p-adj < 0.05) probes
-plotMD(fit2, column=ct, status=dT[,ct], legend=F, pch=20, cex=1)
-abline(h=0)
-################################################################
-# General expression data analysis
-ex <- exprs(gset)
-# box-and-whisker plot
-dev.new(width=3+ncol(gset)/6, height=5)
-ord <- order(gs)  # order samples by group
-palette(c("#1B9E77", "#7570B3", "#E7298A", "#E6AB02", "#D95F02",
-"#66A61E", "#A6761D", "#B32424", "#B324B3", "#666666"))
-par(mar=c(7,4,2,1))
-title <- paste ("GSE43732", "/", annotation(gset), sep ="")
-boxplot(ex[,ord], boxwex=0.6, notch=T, main=title, outline=FALSE, las=2, col=gs[ord])
-legend("topleft", groups, fill=palette(), bty="n")
-dev.off()
-# expression value distribution
-par(mar=c(4,4,2,1))
-title <- paste ("GSE43732", "/", annotation(gset), " value distribution", sep ="")
-plotDensities(ex, group=gs, main=title, legend ="topright")
-# UMAP plot (dimensionality reduction)
-ex <- na.omit(ex) # eliminate rows with NAs
-ex <- ex[!duplicated(ex), ]  # remove duplicates
-ump <- umap(t(ex), n_neighbors = 15, random_state = 123)
-par(mar=c(3,3,2,6), xpd=TRUE)
-plot(ump$layout, main="UMAP plot, nbrs=15", xlab="", ylab="", col=gs, pch=20, cex=1.5)
-legend("topright", inset=c(-0.15,0), legend=levels(gs), pch=20,
-col=1:nlevels(gs), title="Group", pt.cex=1.5)
-library("maptools")  # point labels without overlaps
-pointLabel(ump$layout, labels = rownames(ump$layout), method="SANN", cex=0.6)
-# mean-variance trend, helps to see if precision weights are needed
-plotSA(fit2, main="Mean variance trend, GSE43732")
-# volcano plot (log P-value vs log fold change)
-colnames(fit2) # list contrast names
-ct <- 1        # choose contrast of interest
-volcanoplot(fit2, coef=ct, main=colnames(fit2)[ct], pch=20,
-highlight=length(which(dT[,ct]!=0)), names=rep('+', nrow(fit2)))
-ggplot(fit2, aes(x = logFC, y = -log10p)) +
-geom_point(color = "grey", alpha = 0.8) +
-geom_point(data = subset(volcano_df, logFC > logFC_threshold & -log10p > p_threshold),
-color = "red", alpha = 0.8) +
-xlim(c(-max(abs(fold_changes)), max(abs(fold_changes)))) +
-ylim(c(0, max(-log10(p_values)))) +
-labs(x = "Log2 Fold Change", y = "-log10 p-value", title = "Volcano Plot") +
-theme_minimal()
-# Example code
-library(ggplot)
-# Example code
-library(ggplot2)
-ggplot(fit2, aes(x = logFC, y = -log10p)) +
-geom_point(color = "grey", alpha = 0.8) +
-geom_point(data = subset(volcano_df, logFC > logFC_threshold & -log10p > p_threshold),
-color = "red", alpha = 0.8) +
-xlim(c(-max(abs(fold_changes)), max(abs(fold_changes)))) +
-ylim(c(0, max(-log10(p_values)))) +
-labs(x = "Log2 Fold Change", y = "-log10 p-value", title = "Volcano Plot") +
-theme_minimal()
-library(GEOquery)
-library(tidyverse)
-library(magrittr)
-library(multiMiR)
-library(writexl)
-miRNA_GSE43732 <- getGEO("GSE43732", GSEMatrix =TRUE, AnnotGPL=TRUE,getGPL= T)
-if (length(miRNA_GSE43732) > 1) idx <- grep("GPL4508", attr(miRNA_GSE43732, "names")) else idx <- 1
-miRNA_GSE43732 <- miRNA_GSE43732[[idx]]
-GSE43732 <- miRNA_GSE43732@assayData[["exprs"]]
-colx<- miRNA_GSE43732@featureData@data[["Name"]]
-GSE43732 <- cbind(colx,GSE43732)
-# Convert matrix to a dataframe
-df_GSE43732 <- as.data.frame(GSE43732)
-library(DESeq2)
-# Extract only the count matrix
-count_matrix <- df_GSE43732[, 1:ncol(df_GSE43732)]
-# Sample information
-sample_names <- colnames(count_matrix)
-conditions = c("cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "normal", "normal", "normal",
-"cancer", "cancer", "cancer", "cancer", "normal", "normal", "normal", "normal")
-sample_info <- data.frame(Sample = sample_names, condition = conditions)
-#### SKIRMISH FOR DESEQ2 DATASET ###########
-#make NA's to 0
-count_matrix <- as.matrix(sapply(count_matrix, as.numeric))
-count_matrix[is.na(count_matrix)] <- 0
-count_matrix <- round(count_matrix)
-#get rownames of previous data
-row_names_GSE43732 <- rownames(GSE43732)
-# Set gene_id to rownames again
-rownames(count_matrix) <- row_names_GSE43732
-#negative values to abs() --> simulate counts
-count_matrix <- abs(count_matrix)
-# Create the DESeqDataSet using the updated sample_info
-dds <- DESeqDataSetFromMatrix(countData = count_matrix,
-colData = sample_info,
-design = ~ condition)
-# Perform differential gene expression analysis
-dds <- DESeq(dds)
-resultsNames(dds)
-par(mar=c(8,5,2,2))
-boxplot(log10(assays(dds)[["cooks"]]), range=0, las=2)
-res <- results(dds, name="condition_normal_vs_cancer")
-summary(res)
-# Adjustment to overcome NA-error --> exclude rows containing NA's
-filtered_indices <- which(res$log2FoldChange > 1 & res$padj < 0.05 & !is.na(res$padj))
-filtered_downregulated_genes <- res[filtered_indices, ]
-gene_names <- rownames(filtered_downregulated_genes)
-gene_names
-results(dds)
-results <- results(dds)
-# Calculate the log2 fold change and -log10 adjusted p-values
-results$log2FoldChange <- with(results, ifelse(baseMean > 0, log2FoldChange, -log2FoldChange))
-results$negLog10padj <- -log10(results$padj)
-# Plot the volcano plot
-plot(results$log2FoldChange, results$negLog10padj,
-xlim = c(-max(abs(results$log2FoldChange)), max(abs(results$log2FoldChange)) + 1),
-ylim = c(0, max(results$negLog10padj) + 1),
-xlab = "Log2 Fold Change",
-ylab = "-log10(Adjusted p-value)",
-main = "Volcano Plot")
-# Calculate the log2 fold change and -log10 adjusted p-values
-results$log2FoldChange <- with(results, ifelse(baseMean > 0, log2FoldChange, -log2FoldChange))
-results$negLog10padj <- -log10(results$padj)
-# Determine the range of log2 fold change values
-logFC_range <- range(results$log2FoldChange, na.rm = TRUE)
-# Plot the volcano plot
-plot(results$log2FoldChange, results$negLog10padj,
-xlim = c(logFC_range[1] - 1, logFC_range[2] + 1),
-ylim = c(0, max(results$negLog10padj) + 1),
-xlab = "Log2 Fold Change",
-ylab = "-log10(Adjusted p-value)",
-main = "Volcano Plot")
-# Calculate the log2 fold change and -log10 adjusted p-values
-results$log2FoldChange <- with(results, ifelse(baseMean > 0, log2FoldChange, -log2FoldChange))
-results$negLog10padj <- -log10(results$padj)
-# Determine the range of -log10 adjusted p-values
-logP_range <- range(results$negLog10padj, na.rm = TRUE)
-# Plot the volcano plot
-plot(results$log2FoldChange, results$negLog10padj,
-xlim = c(min(results$log2FoldChange), max(results$log2FoldChange)),
-ylim = c(logP_range[1] - 1, logP_range[2] + 1),
-xlab = "Log2 Fold Change",
-ylab = "-log10(Adjusted p-value)",
-main = "Volcano Plot")
-# Add points for significantly differentially expressed genes
-significant_genes <- subset(results, padj < 0.05)
-points(significant_genes$log2FoldChange, significant_genes$negLog10padj,
-col = "red", pch = 16)
-# Calculate the log2 fold change and -log10 adjusted p-values
-results$log2FoldChange <- with(results, ifelse(baseMean > 0, log2FoldChange, -log2FoldChange))
-results$negLog10padj <- -log10(results$padj)
-ggplot(res_tableOE_tb, aes(x = log2FoldChange, y = -log10(pvalue))) +
-geom_point(aes(colour = threshold_OE)) +
-geom_text_repel(aes(label = genelabels)) +
-xlab("log2 fold change") +
-ylab("-log10 p-value") +
-theme(legend.position = c(0.8, 0.2),
-plot.title = element_text(size = rel(1.5), hjust = 0.5),
-axis.title = element_text(size = rel(1.25)))
-ggplot(results, aes(x = log2FoldChange, y = -log10(pvalue))) +
-geom_point(aes(colour = threshold_OE)) +
-geom_text_repel(aes(label = genelabels)) +
-xlab("log2 fold change") +
-ylab("-log10 p-value") +
-theme(legend.position = c(0.8, 0.2),
-plot.title = element_text(size = rel(1.5), hjust = 0.5),
-axis.title = element_text(size = rel(1.25)))
-View(results)
-library(GEOquery)
-library(limma)
-library(umap)
-gset <- getGEO("GSE43732", GSEMatrix =TRUE, AnnotGPL=FALSE)
-if (length(gset) > 1) idx <- grep("GPL16543", attr(gset, "names")) else idx <- 1
-gset <- gset[[idx]]
-# make proper column names to match toptable
-fvarLabels(gset) <- make.names(fvarLabels(gset))
-# group membership for all samples
-gsms <- paste0("11110000111100001111000011110000111100001111000011",
-"11000011110000111100001111000011110000111100001111",
-"00001111000011110000111100000111100011110000111100",
-"00111100001011110000111100001111000011110000111100",
-"00111000111100001111000011100011110000")
-sml <- strsplit(gsms, split="")[[1]]
-# log2 transformation
-ex <- exprs(gset)
-qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
-LogC <- (qx[5] > 100) ||
-(qx[6]-qx[1] > 50 && qx[2] > 0)
-if (LogC) { ex[which(ex <= 0)] <- NaN
-exprs(gset) <- log2(ex) }
-# assign samples to groups and set up design matrix
-gs <- factor(sml)
-groups <- make.names(c("Normal","Cancer"))
-levels(gs) <- groups
-gset$group <- gs
-design <- model.matrix(~group + 0, gset)
-colnames(design) <- levels(gs)
-gset <- gset[complete.cases(exprs(gset)), ] # skip missing values
-fit <- lmFit(gset, design)  # fit linear model
-# set up contrasts of interest and recalculate model coefficients
-cts <- paste(groups[1], groups[2], sep="-")
-cont.matrix <- makeContrasts(contrasts=cts, levels=design)
-fit2 <- contrasts.fit(fit, cont.matrix)
-# compute statistics and table of top significant genes
-fit2 <- eBayes(fit2, 0.01)
-tT <- topTable(fit2, adjust="fdr", sort.by="B", number=250)
-tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","SPOT_ID","miRNA_ID"))
-write.table(tT, file=stdout(), row.names=F, sep="\t")
-# Visualize and quality control test results.
-# Build histogram of P-values for all genes. Normal test
-# assumption is that most genes are not differentially expressed.
-tT2 <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
-hist(tT2$adj.P.Val, col = "grey", border = "white", xlab = "P-adj",
-ylab = "Number of genes", main = "P-adj value distribution")
-# summarize test results as "up", "down" or "not expressed"
-dT <- decideTests(fit2, adjust.method="fdr", p.value=0.05, lfc=1)
-# Venn diagram of results
-vennDiagram(dT, circle.col=palette())
-# create Q-Q plot for t-statistic
-t.good <- which(!is.na(fit2$F)) # filter out bad probes
-qqt(fit2$t[t.good], fit2$df.total[t.good], main="Moderated t statistic")
-# volcano plot (log P-value vs log fold change)
-colnames(fit2) # list contrast names
-ct <- 1        # choose contrast of interest
-volcanoplot(fit2, coef=ct, main=colnames(fit2)[ct], pch=20,
-highlight=length(which(dT[,ct]!=0)), names=rep('+', nrow(fit2)))
-# MD plot (log fold change vs mean log expression)
-# highlight statistically significant (p-adj < 0.05) probes
-plotMD(fit2, column=ct, status=dT[,ct], legend=F, pch=20, cex=1)
-abline(h=0)
-library(GEOquery)
-library(limma)
-library(umap)
-gset <- getGEO("GSE43732", GSEMatrix =TRUE, AnnotGPL=FALSE)
-if (length(gset) > 1) idx <- grep("GPL16543", attr(gset, "names")) else idx <- 1
-gset <- gset[[idx]]
-# make proper column names to match toptable
-fvarLabels(gset) <- make.names(fvarLabels(gset))
-# group membership for all samples
-gsms <- paste0("11110000111100001111000011110000111100001111000011",
-"11000011110000111100001111000011110000111100001111",
-"00001111000011110000111100000111100011110000111100",
-"00111100001011110000111100001111000011110000111100",
-"00111000111100001111000011100011110000")
-sml <- strsplit(gsms, split="")[[1]]
-# log2 transformation
-ex <- exprs(gset)
-qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
-LogC <- (qx[5] > 100) ||
-(qx[6]-qx[1] > 50 && qx[2] > 0)
-if (LogC) { ex[which(ex <= 0)] <- NaN
-exprs(gset) <- log2(ex) }
-# assign samples to groups and set up design matrix
-gs <- factor(sml)
-groups <- make.names(c("Normal","Cancer"))
-levels(gs) <- groups
-gset$group <- gs
-design <- model.matrix(~group + 0, gset)
-colnames(design) <- levels(gs)
-gset <- gset[complete.cases(exprs(gset)), ] # skip missing values
-fit <- lmFit(gset, design)  # fit linear model
-# set up contrasts of interest and recalculate model coefficients
-cts <- paste(groups[1], groups[2], sep="-")
-cont.matrix <- makeContrasts(contrasts=cts, levels=design)
-fit2 <- contrasts.fit(fit, cont.matrix)
-# compute statistics and table of top significant genes
-fit2 <- eBayes(fit2, 0.01)
-tT <- topTable(fit2, adjust="fdr", sort.by="B", number=250)
-tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","SPOT_ID","miRNA_ID"))
-write.table(tT, file=stdout(), row.names=F, sep="\t")
-# Visualize and quality control test results.
-# Build histogram of P-values for all genes. Normal test
-# assumption is that most genes are not differentially expressed.
-tT2 <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
-hist(tT2$adj.P.Val, col = "grey", border = "white", xlab = "P-adj",
-ylab = "Number of genes", main = "P-adj value distribution")
-# summarize test results as "up", "down" or "not expressed"
-dT <- decideTests(fit2, adjust.method="fdr", p.value=0.05, lfc=1)
-# Venn diagram of results
-vennDiagram(dT, circle.col=palette())
-# create Q-Q plot for t-statistic
-t.good <- which(!is.na(fit2$F)) # filter out bad probes
-qqt(fit2$t[t.good], fit2$df.total[t.good], main="Moderated t statistic")
-library(GEOquery)
-library(limma)
-library(umap)
-gset <- getGEO("GSE43732", GSEMatrix =TRUE, AnnotGPL=FALSE)
-if (length(gset) > 1) idx <- grep("GPL16543", attr(gset, "names")) else idx <- 1
-gset <- gset[[idx]]
-# make proper column names to match toptable
-fvarLabels(gset) <- make.names(fvarLabels(gset))
-# group membership for all samples
-gsms <- paste0("11110000111100001111000011110000111100001111000011",
-"11000011110000111100001111000011110000111100001111",
-"00001111000011110000111100000111100011110000111100",
-"00111100001011110000111100001111000011110000111100",
-"00111000111100001111000011100011110000")
-sml <- strsplit(gsms, split="")[[1]]
-# log2 transformation
-ex <- exprs(gset)
-qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
-LogC <- (qx[5] > 100) ||
-(qx[6]-qx[1] > 50 && qx[2] > 0)
-if (LogC) { ex[which(ex <= 0)] <- NaN
-exprs(gset) <- log2(ex) }
-# assign samples to groups and set up design matrix
-gs <- factor(sml)
-groups <- make.names(c("Normal","Cancer"))
-levels(gs) <- groups
-gset$group <- gs
-design <- model.matrix(~group + 0, gset)
-colnames(design) <- levels(gs)
-gset <- gset[complete.cases(exprs(gset)), ] # skip missing values
-fit <- lmFit(gset, design)  # fit linear model
-# set up contrasts of interest and recalculate model coefficients
-cts <- paste(groups[1], groups[2], sep="-")
-cont.matrix <- makeContrasts(contrasts=cts, levels=design)
-fit2 <- contrasts.fit(fit, cont.matrix)
-# compute statistics and table of top significant genes
-fit2 <- eBayes(fit2, 0.01)
-tT <- topTable(fit2, adjust="fdr", sort.by="B", number=250)
-tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","SPOT_ID","miRNA_ID"))
-write.table(tT, file=stdout(), row.names=F, sep="\t")
-# Visualize and quality control test results.
-# Build histogram of P-values for all genes. Normal test
-# assumption is that most genes are not differentially expressed.
-tT2 <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
-hist(tT2$adj.P.Val, col = "grey", border = "white", xlab = "P-adj",
-ylab = "Number of genes", main = "P-adj value distribution")
-# summarize test results as "up", "down" or "not expressed"
-dT <- decideTests(fit2, adjust.method="fdr", p.value=0.05, lfc=1)
-# Venn diagram of results
-vennDiagram(dT, circle.col=palette())
-# create Q-Q plot for t-statistic
-t.good <- which(!is.na(fit2$F)) # filter out bad probes
-qqt(fit2$t[t.good], fit2$df.total[t.good], main="Moderated t statistic")
-# volcano plot (log P-value vs log fold change)
-colnames(fit2) # list contrast names
-ct <- 1        # choose contrast of interest
-volcanoplot(fit2, coef=ct, main=colnames(fit2)[ct], pch=20,
-highlight=length(which(dT[,ct]!=0)), names=rep('+', nrow(fit2)))
-library(GEOquery)
-library(limma)
-library(umap)
-gset <- getGEO("GSE43732", GSEMatrix =TRUE, AnnotGPL=FALSE)
-if (length(gset) > 1) idx <- grep("GPL16543", attr(gset, "names")) else idx <- 1
-gset <- gset[[idx]]
-# make proper column names to match toptable
-fvarLabels(gset) <- make.names(fvarLabels(gset))
-# group membership for all samples
-gsms <- paste0("11110000111100001111000011110000111100001111000011",
-"11000011110000111100001111000011110000111100001111",
-"00001111000011110000111100000111100011110000111100",
-"00111100001011110000111100001111000011110000111100",
-"00111000111100001111000011100011110000")
-sml <- strsplit(gsms, split="")[[1]]
-# log2 transformation
-ex <- exprs(gset)
-qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
-LogC <- (qx[5] > 100) ||
-(qx[6]-qx[1] > 50 && qx[2] > 0)
-if (LogC) { ex[which(ex <= 0)] <- NaN
-exprs(gset) <- log2(ex) }
-# assign samples to groups and set up design matrix
-gs <- factor(sml)
-groups <- make.names(c("Normal","Cancer"))
-levels(gs) <- groups
-gset$group <- gs
-design <- model.matrix(~group + 0, gset)
-colnames(design) <- levels(gs)
-gset <- gset[complete.cases(exprs(gset)), ] # skip missing values
-fit <- lmFit(gset, design)  # fit linear model
-# set up contrasts of interest and recalculate model coefficients
-cts <- paste(groups[1], groups[2], sep="-")
-cont.matrix <- makeContrasts(contrasts=cts, levels=design)
-fit2 <- contrasts.fit(fit, cont.matrix)
-# compute statistics and table of top significant genes
-fit2 <- eBayes(fit2, 0.01)
-tT <- topTable(fit2, adjust="fdr", sort.by="B", number=250)
-tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","SPOT_ID","miRNA_ID"))
-write.table(tT, file=stdout(), row.names=F, sep="\t")
-# Visualize and quality control test results.
-# Build histogram of P-values for all genes. Normal test
-# assumption is that most genes are not differentially expressed.
-tT2 <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
-hist(tT2$adj.P.Val, col = "grey", border = "white", xlab = "P-adj",
-ylab = "Number of genes", main = "P-adj value distribution")
-# summarize test results as "up", "down" or "not expressed"
-dT <- decideTests(fit2, adjust.method="fdr", p.value=0.05, lfc=1)
-# Venn diagram of results
-vennDiagram(dT, circle.col=palette())
-# create Q-Q plot for t-statistic
-t.good <- which(!is.na(fit2$F)) # filter out bad probes
-qqt(fit2$t[t.good], fit2$df.total[t.good], main="Moderated t statistic")
-# volcano plot (log P-value vs log fold change)
-colnames(fit2) # list contrast names
-ct <- 1        # choose contrast of interest
-volcanoplot(fit2, coef=ct, main=colnames(fit2)[ct], pch=20,
-highlight=length(which(dT[,ct]!=0)), names=rep('+', nrow(fit2)))
-# MD plot (log fold change vs mean log expression)
-# highlight statistically significant (p-adj < 0.05) probes
-plotMD(fit2, column=ct, status=dT[,ct], legend=F, pch=20, cex=1)
-abline(h=0)
-################################################################
-# General expression data analysis
-ex <- exprs(gset)
-# box-and-whisker plot
-dev.new(width=3+ncol(gset)/6, height=5)
-ord <- order(gs)  # order samples by group
-palette(c("#1B9E77", "#7570B3", "#E7298A", "#E6AB02", "#D95F02",
-"#66A61E", "#A6761D", "#B32424", "#B324B3", "#666666"))
-par(mar=c(7,4,2,1))
-title <- paste ("GSE43732", "/", annotation(gset), sep ="")
-boxplot(ex[,ord], boxwex=0.6, notch=T, main=title, outline=FALSE, las=2, col=gs[ord])
-legend("topleft", groups, fill=palette(), bty="n")
-dev.off()
-# expression value distribution
-par(mar=c(4,4,2,1))
-title <- paste ("GSE43732", "/", annotation(gset), " value distribution", sep ="")
-plotDensities(ex, group=gs, main=title, legend ="topright")
-# UMAP plot (dimensionality reduction)
-ex <- na.omit(ex) # eliminate rows with NAs
-ex <- ex[!duplicated(ex), ]  # remove duplicates
-ump <- umap(t(ex), n_neighbors = 15, random_state = 123)
-par(mar=c(3,3,2,6), xpd=TRUE)
-plot(ump$layout, main="UMAP plot, nbrs=15", xlab="", ylab="", col=gs, pch=20, cex=1.5)
-legend("topright", inset=c(-0.15,0), legend=levels(gs), pch=20,
-col=1:nlevels(gs), title="Group", pt.cex=1.5)
-library("maptools")  # point labels without overlaps
-pointLabel(ump$layout, labels = rownames(ump$layout), method="SANN", cex=0.6)
-# mean-variance trend, helps to see if precision weights are needed
-plotSA(fit2, main="Mean variance trend, GSE43732")
-mirnames <- read.csv("C:/Users/Emre/Desktop/miRNAs.csv")
-mirnames <- read.csv("C:/Users/Emre/Desktop/miRNAs.xlsx")
-View(mirnames)
-mirnames <- read.csv("C:/Users/Emre/Desktop/miRNAs.xlsx", header = TRUE)
-library(multiMiR)
-library(readxl)
-mirnames <- read_excel("C:/Users/Emre/Desktop/miRNAs.xlsx", header = TRUE)
-mirnames <- read_excel("C:/Users/Emre/Desktop/miRNAs.xlsx")
-View(mirnames)
-library(multiMiR)
-multimir_results <- get_multimir(org     = 'hsa',
-mirna   = mirnames,
-table   = 'validated',
-summary = TRUE)
-head(multimir_results@data)
-targets <- multimir_results@data
-excel_file <- "C:/Users/Emre/Desktop/multimir_results_cyto.xlsx"
-write_xlsx(targets, path = excel_file )
-library("xlsx")
-library(xlsx)
-install.packages(xlsx)
-install.packages("xlsx")
-library(xlsx)
-write_xlsx(targets, path = excel_file )
-targets <- multimir_results@data
-excel_file <- ("C:/Users/Emre/Desktop/multimir_results_cyto.xlsx")
-library(xlsx)
-write_xlsx(targets, path = excel_file )
-write_xlsx(targets, path = excel_file )
-library(writexl)
-write_xlsx(targets, path = excel_file )
diff --git a/DGE/Limma/miRNA/GSE6188.R b/DGE/Limma/miRNA/GSE6188.R
index 0fa5a582fdd4f4f4bf6138ea522a67cbebbebc13..c65f2a01863eac19b5ddf209efe57e5a3e53f4f0 100644
--- a/DGE/Limma/miRNA/GSE6188.R
+++ b/DGE/Limma/miRNA/GSE6188.R
@@ -1,11 +1,8 @@
-# Version info: R 4.2.2, Biobase 2.58.0, GEOquery 2.66.0, limma 3.54.0
-################################################################
-#   Differential expression analysis with limma
 library(GEOquery)
 library(limma)
 library(umap)
 
-# load series and platform data from GEO
+# load data from GEO
 
 gset <- getGEO("GSE6188", GSEMatrix =TRUE, AnnotGPL=FALSE)
 if (length(gset) > 1) idx <- grep("GPL4508", attr(gset, "names")) else idx <- 1
@@ -31,7 +28,7 @@ LogC <- (qx[5] > 100) ||
 if (LogC) { ex[which(ex <= 0)] <- NaN
 exprs(gset) <- log2(ex) }
 
-# assign samples to groups and set up design matrix
+# assign samples and set up design matrix
 gs <- factor(sml)
 groups <- make.names(c("Normal","Cancer"))
 levels(gs) <- groups
@@ -43,7 +40,7 @@ gset <- gset[complete.cases(exprs(gset)), ] # skip missing values
 
 fit <- lmFit(gset, design)  # fit linear model
 
-# set up contrasts of interest and recalculate model coefficients
+# set up contrasts
 cts <- c(paste(groups[1],"-",groups[2],sep=""))
 cont.matrix <- makeContrasts(contrasts=cts, levels=design)
 fit2 <- contrasts.fit(fit, cont.matrix)
@@ -57,7 +54,6 @@ write.table(tT, file=stdout(), row.names=F, sep="\t")
 
 # Visualize and quality control test results.
 # Build histogram of P-values for all genes. Normal test
-# assumption is that most genes are not differentially expressed.
 tT2 <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
 hist(tT2$adj.P.Val, col = "grey", border = "white", xlab = "P-adj",
      ylab = "Number of genes", main = "P-adj value distribution")
diff --git a/MultiMIR/multimir_results.xlsx b/MultiMIR/multimir_results.xlsx
deleted file mode 100644
index 4efaed048d3a9e42642d670fc34f188419f4bafb..0000000000000000000000000000000000000000
Binary files a/MultiMIR/multimir_results.xlsx and /dev/null differ
diff --git a/README.md b/README.md
index 6fda776694696b504b4ee7720daf97d868787409..817028162ee79140141d262d2620e576e925204b 100644
--- a/README.md
+++ b/README.md
@@ -2,91 +2,6 @@
 
 
 
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://git.imp.fu-berlin.de/aakan96/datascience-ss2023.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://git.imp.fu-berlin.de/aakan96/datascience-ss2023/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+## Introduction
+All results from the project "Integrative Analysis of miRNA and mRNA Expression Profiles in Squamous Cell Carcinoma" are stored in this GitLab repository  
+with the corresponding code snippets
\ No newline at end of file