--- title: "DEqMS R Markdown vignettes" author: - name: Yafeng Zhu affiliation: Karolinska Institute, Stockholm, Sweden - name: Lukas Orre affiliation: Karolinska Institute, Stockholm, Sweden - name: Yan Tran affiliation: Karolinska Institute, Stockholm, Sweden - name: Georgios Mermelekas affiliation: Karolinska Institute, Stockholm, Sweden - name: Henrik Johansson affiliation: Karolinska Institute, Stockholm, Sweden - name: Alina Malyutina affiliation: University of Helsinki, Helsinki, Finland - name: Simon Anders affiliation: Heidelberg University (ZMBH), Heidelberg, Germany - name: Janne Lehtiö affiliation: Karolinska Institute, Stockholm, Sweden date: "`r Sys.Date()`" output: BiocStyle::html_document: toc_fload: true BiocStyle::pdf_document: default package: DEqMS abstract: Instructions to perform differential protein expression analysis using DEqMS vignette: > %\VignetteIndexEntry{DEqMS R Markdown vignettes} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- # Overview of DEqMS `DEqMS` builds on top of `Limma`, a widely-used R package for microarray data analysis (Smyth G. et al 2004), and improves it with proteomics data specific properties, accounting for variance dependence on the number of quantified peptides or PSMs for statistical testing of differential protein expression. Limma assumes a common prior variance for all proteinss, the function `spectraCounteBayes` in DEqMS package estimate prior variance for proteins quantified by different number of PSMs. A documentation of all R functions available in DEqMS is detailed in the PDF reference manual on the DEqMS Bioconductor page. #Load the package ```{r Loadpackage} library(DEqMS) ``` # Quick start ## Differential protein expression analysis with DEqMS using a protein table As an example, we analyzed a protemoics dataset (TMT10plex labelled) in which A431 cells (human epidermoid carcinoma cell line) were treated with three different miRNA mimics (Zhou Y. Et al Oncogene 2017). The raw MS data was searched with MS-GF+ (Kim et al Nat Communications 2016) and post processed with Percolator (Kall L. et al Nat Method 2007). A tabular text output of protein table filtered at 1% protein level FDR is used. ### Download and Read the input protein table ```{r DownloadProteinTable} options(timeout = 1000) url <- "https://ftp.ebi.ac.uk/pride-archive/2016/06/PXD004163/Yan_miR_Protein_table.flatprottable.txt" download.file(url, destfile = "./miR_Proteintable.txt",method = "auto") df.prot = read.table("miR_Proteintable.txt",stringsAsFactors = FALSE, header = TRUE, quote = "", comment.char = "",sep = "\t") ``` ### Extract quant data columns for DEqMS ```{r Extractcolumn} # filter at 1% protein FDR and extract TMT quantifications TMT_columns = seq(15,33,2) dat = df.prot[df.prot$miR.FASP_q.value<0.01,TMT_columns] rownames(dat) = df.prot[df.prot$miR.FASP_q.value<0.01,]$Protein.accession # The protein dataframe is a typical protein expression matrix structure # Samples are in columns, proteins are in rows # use unique protein IDs for rownames # to view the whole data frame, use the command View(dat) ``` If the protein table is relative abundance (ratios) or intensity values, Log2 transform the data. Systematic effects and variance components are usually assumed to be additive on log scale (Oberg AL. et al JPR 2008; Hill EG. et al JPR 2008). ```{r log2transform1} dat.log = log2(dat) #remove rows with NAs dat.log = na.omit(dat.log) ``` Use boxplot to check if the samples have medians centered. if not, do median centering. ```{r boxplot1} boxplot(dat.log,las=2,main="TMT10plex data PXD004163") # Here the data is already median centered, we skip the following step. # dat.log = equalMedianNormalization(dat.log) ``` ### Make design table. A design table is used to tell how samples are arranged in different groups/classes. ```{r,design} # if there is only one factor, such as treatment. You can define a vector with # the treatment group in the same order as samples in the protein table. cond = as.factor(c("ctrl","miR191","miR372","miR519","ctrl", "miR372","miR519","ctrl","miR191","miR372")) # The function model.matrix is used to generate the design matrix design = model.matrix(~0+cond) # 0 means no intercept for the linear model colnames(design) = gsub("cond","",colnames(design)) ``` ### Make contrasts In addition to the design, you need to define the contrast, which tells the model to compare the differences between specific groups. Start with the Limma part. ```{r,limma} # you can define one or multiple contrasts here x <- c("miR372-ctrl","miR519-ctrl","miR191-ctrl", "miR372-miR519","miR372-miR191","miR519-miR191") contrast = makeContrasts(contrasts=x,levels=design) fit1 <- lmFit(dat.log, design) fit2 <- contrasts.fit(fit1,contrasts = contrast) fit3 <- eBayes(fit2) ``` ### DEqMS analysis The above shows Limma part, now we use the function `spectraCounteBayes` in DEqMS to correct bias of variance estimate based on minimum number of psms per protein used for quantification.We use the minimum number of PSMs used for quantification within and across experiments to model the relation between variance and PSM count.(See original paper) ```{r, DEqMS1} # assign a extra variable `count` to fit3 object, telling how many PSMs are # quantifed for each protein library(matrixStats) count_columns = seq(16,34,2) psm.count.table = data.frame(count = rowMins( as.matrix(df.prot[,count_columns])), row.names = df.prot$Protein.accession) fit3$count = psm.count.table[rownames(fit3$coefficients),"count"] fit4 = spectraCounteBayes(fit3) ``` Outputs of `spectraCounteBayes`: object is augmented form of "fit" object from `eBayes` in Limma, with the additions being: `sca.t` - Spectra Count Adjusted posterior t-value `sca.p` - Spectra Count Adjusted posterior p-value `sca.dfprior` - DEqMS estimated prior degrees of freedom `sca.priorvar`- DEqMS estimated prior variance `sca.postvar` - DEqMS estimated posterior variance `model` - fitted model ### Visualize the fit curve - variance dependence on quantified PSM ```{r, plot} # n=30 limits the boxplot to show only proteins quantified by <= 30 PSMs. VarianceBoxplot(fit4,n=30,main="TMT10plex dataset PXD004163",xlab="PSM count") VarianceScatterplot(fit4,main="TMT10plex dataset PXD004163") ``` ### Extract the results as a data frame and save it ```{r, result} DEqMS.results = outputResult(fit4,coef_col = 1) #if you are not sure which coef_col refers to the specific contrast,type head(fit4$coefficients) # a quick look on the DEqMS results table head(DEqMS.results) # Save it into a tabular text file write.table(DEqMS.results,"DEqMS.results.miR372-ctrl.txt",sep = "\t", row.names = F,quote=F) ``` Explaination of the columns in `DEqMS.results`: `logFC` - log2 fold change between two groups, Here it's log2(miR372/ctrl). `AveExpr` - the mean of the log2 ratios/intensities across all samples. Since input matrix is log2 ratio values, it is the mean log2 ratios of all samples. `t` - Limma output t-statistics `P.Value`- Limma p-values `adj.P.Val` - BH method adjusted Limma p-values `B` - Limma B values `count` - PSM/peptide count values you assigned `sca.t` - DEqMS t-statistics `sca.P.Value` - DEqMS p-values `sca.adj.pval` - BH method adjusted DEqMS p-values ### Make volcanoplot We recommend to plot p-values on y-axis instead of adjusted pvalue or FDR. Read about why [here](https://support.bioconductor.org/p/98442/). ```{r volcanoplot1} library(ggrepel) # Use ggplot2 allows more flexibility in plotting DEqMS.results$log.sca.pval = -log10(DEqMS.results$sca.P.Value) ggplot(DEqMS.results, aes(x = logFC, y =log.sca.pval )) + geom_point(size=0.5 )+ theme_bw(base_size = 16) + # change theme xlab(expression("log2(miR372/ctrl)")) + # x-axis label ylab(expression(" -log10(P-value)")) + # y-axis label geom_vline(xintercept = c(-1,1), colour = "red") + # Add fold change cutoffs geom_hline(yintercept = 3, colour = "red") + # Add significance cutoffs geom_vline(xintercept = 0, colour = "black") + # Add 0 lines scale_colour_gradient(low = "black", high = "black", guide = FALSE)+ geom_text_repel(data=subset(DEqMS.results, abs(logFC)>1&log.sca.pval > 3), aes( logFC, log.sca.pval ,label=gene)) # add gene label ``` you can also use `volcanoplot` function from Limma. However, it uses `p.value` from Limma. If you want to plot `sca.pvalue` from DEqMS, you need to modify the `fit4` object. ```{r volcanoplot2} fit4$p.value = fit4$sca.p # volcanoplot highlight top 20 proteins ranked by p-value here volcanoplot(fit4,coef=1, style = "p-value", highlight = 20, names=rownames(fit4$coefficients)) ``` ## DEqMS analysis using MaxQuant outputs (label-free data) Here we analyze a published label-free benchmark dataset in which either 10 or 30 µg of E. coli protein extract was spiked into human protein extracts (50 µg) in triplicates (Cox J et al MCP 2014). The data was searched by MaxQuant software and the output file "proteinGroups.txt" was used here. ```{r DownloadLabelfreeData} url2 <- "https://ftp.ebi.ac.uk/pride-archive/2014/09/PXD000279/proteomebenchmark.zip" download.file(url2, destfile = "./PXD000279.zip",method = "auto") unzip("PXD000279.zip") ``` ### Read protein table as input and filter it ```{r LFQprotein} df.prot = read.table("proteinGroups.txt",header=T,sep="\t",stringsAsFactors = F, comment.char = "",quote ="") # remove decoy matches and matches to contaminant df.prot = df.prot[!df.prot$Reverse=="+",] df.prot = df.prot[!df.prot$Contaminant=="+",] # Extract columns of LFQ intensites df.LFQ = df.prot[,89:94] df.LFQ[df.LFQ==0] <- NA rownames(df.LFQ) = df.prot$Majority.protein.IDs df.LFQ$na_count_H = apply(df.LFQ,1,function(x) sum(is.na(x[1:3]))) df.LFQ$na_count_L = apply(df.LFQ,1,function(x) sum(is.na(x[4:6]))) # Filter protein table. DEqMS require minimum two values for each group. df.LFQ.filter = df.LFQ[df.LFQ$na_count_H<2 & df.LFQ$na_count_L<2,1:6] ``` ### Make a data frame of unique peptide count per protein ```{r pepCountTable} library(matrixStats) # we use minimum peptide count among six samples # count unique+razor peptides used for quantification pep.count.table = data.frame(count = rowMins(as.matrix(df.prot[,19:24])), row.names = df.prot$Majority.protein.IDs) # Minimum peptide count of some proteins can be 0 # add pseudocount 1 to all proteins pep.count.table$count = pep.count.table$count+1 ``` ### DEqMS analysis on LFQ data ```{r labelfreeDEqMS} protein.matrix = log2(as.matrix(df.LFQ.filter)) class = as.factor(c("H","H","H","L","L","L")) design = model.matrix(~0+class) # fitting without intercept fit1 = lmFit(protein.matrix,design = design) cont <- makeContrasts(classH-classL, levels = design) fit2 = contrasts.fit(fit1,contrasts = cont) fit3 <- eBayes(fit2) fit3$count = pep.count.table[rownames(fit3$coefficients),"count"] #check the values in the vector fit3$count #if min(fit3$count) return NA or 0, you should troubleshoot the error first min(fit3$count) fit4 = spectraCounteBayes(fit3) ``` ### Visualize the fit curve ```{r LFQboxplot} VarianceBoxplot(fit4, n=20, main = "Label-free dataset PXD000279", xlab="peptide count + 1") ``` ### Extract outputs from DEqMS ```{r LFQresult} DEqMS.results = outputResult(fit4,coef_col = 1) # Add Gene names to the data frame rownames(df.prot) = df.prot$Majority.protein.IDs DEqMS.results$Gene.name = df.prot[DEqMS.results$gene,]$Gene.names head(DEqMS.results) write.table(DEqMS.results,"H-L.DEqMS.results.txt",sep = "\t", row.names = F,quote=F) ``` ## DEqMS analysis using a PSM table (isobaric labelled data) If you want to try different methods to estimate protein abundance,you can start with a PSM table and use provided functions in DEqMS to summarize PSM quant data into protein quant data. Four different functions are included: `medianSweeping`,`medianSummary`,`medpolishSummary`,`farmsSummary`. Check PDF reference manual for detailed description. ### Read PSM table input ```{r retrieveExampleData, message=FALSE} ### retrieve example PSM dataset from ExperimentHub library(ExperimentHub) eh = ExperimentHub() query(eh, "DEqMS") dat.psm = eh[["EH1663"]] ``` ```{r log2transform2} dat.psm.log = dat.psm dat.psm.log[,3:12] = log2(dat.psm[,3:12]) head(dat.psm.log) ``` ### Summarization and Normalization Here, median sweeping is used to summarize PSMs intensities to protein log2 ratios. In this procedure, we substract the spectrum log2 intensity from the median log2 intensities of all samples. The relative abundance estimate for each protein is calculated as the median over all PSMs belonging to this protein.(Herbrich et al JPR 2012 and D'Angelo et al JPR 2016). Assume the log2 intensity of PSM `i` in sample `j` is $y_{i,j}$, its relative log2 intensity of PSM `i` in sample `j` is $y'_{i,j}$: $$y'_{i,j} = y_{i,j} - median_{j'\in ctrl}\ y_{i,j'} $$ Relative abundance of protein `k` in sample `j` $Y_{k,j}$ is calculated as: $$Y_{k,j} = median_{i\in protein\ k}\ y'_{i,j} $$ Correction for differences in amounts of material loaded in the channels is then done by subtracting the channel median from the relative abundance (log2 ratio), centering all channels to have median log2 value of zero. ```{r boxplot2} dat.gene.nm = medianSweeping(dat.psm.log,group_col = 2) boxplot(dat.gene.nm,las=2,ylab="log2 ratio",main="TMT10plex dataset PXD004163") ``` ### DEqMS analysis ```{r DEqMS2} gene.matrix = as.matrix(dat.gene.nm) # make design table cond = as.factor(c("ctrl","miR191","miR372","miR519","ctrl", "miR372","miR519","ctrl","miR191","miR372")) design = model.matrix(~0+cond) colnames(design) = gsub("cond","",colnames(design)) #limma part analysis fit1 <- lmFit(gene.matrix,design) x <- c("miR372-ctrl","miR519-ctrl","miR191-ctrl") contrast = makeContrasts(contrasts=x,levels=design) fit2 <- eBayes(contrasts.fit(fit1,contrasts = contrast)) #DEqMS part analysis psm.count.table = as.data.frame(table(dat.psm$gene)) rownames(psm.count.table) = psm.count.table$Var1 fit2$count = psm.count.table[rownames(fit2$coefficients),2] fit3 = spectraCounteBayes(fit2) # extract DEqMS results DEqMS.results = outputResult(fit3,coef_col = 1) head(DEqMS.results) write.table(DEqMS.results,"DEqMS.results.miR372-ctrl.fromPSMtable.txt", sep = "\t",row.names = F,quote=F) ``` Generate variance ~ PMS count boxplot, check if the DEqMS correctly find the relation between prior variance and PSM count ```{r PriorVarianceTrend} VarianceBoxplot(fit3,n=20, xlab="PSM count",main="TMT10plex dataset PXD004163") ``` ### PSM/Peptide profile plot Only possible if you read a PSM or peptide table as input. `peptideProfilePlot` function will plot log2 intensity of each PSM/peptide of the protein in the input table. ```{r PSMintensity} peptideProfilePlot(dat=dat.psm.log,col=2,gene="TGFBR2") # col=2 is tell in which column of dat.psm.log to look for the gene ``` # Comparing DEqMS to other methods The following steps are not required for get the results from DEqMS. it is used to help users to understand the method better and the differences to other methods. Here we use the TMT labelled data PXD004163 as an example. ## Compare the variance estimate in DEqMS and Limma ### Prior variance comparison between DEqMS and Limma ```{r PriorVar} VarianceScatterplot(fit3, xlab="log2(PSM count)") limma.prior = fit3$s2.prior abline(h = log(limma.prior),col="green",lwd=3 ) legend("topright",legend=c("DEqMS prior variance","Limma prior variance"), col=c("red","green"),lwd=3) ``` ### Residual plot for DEqMS and Limma ```{r Residualplot} op <- par(mfrow=c(1,2), mar=c(4,4,4,1), oma=c(0.5,0.5,0.5,0)) Residualplot(fit3, xlab="log2(PSM count)",main="DEqMS") x = fit3$count y = log(limma.prior) - log(fit3$sigma^2) plot(log2(x),y,ylim=c(-6,2),ylab="Variance(estimated-observed)", pch=20, cex=0.5, xlab = "log2(PSMcount)",main="Limma") ``` ### Posterior variance comparison between DEqMS and Limma The plot here shows posterior variance of proteins "shrink" toward the fitted value to different extent depending on PSM number. ```{r PostVar, echo=TRUE, fig.height=5, fig.width=10} library(LSD) op <- par(mfrow=c(1,2), mar=c(4,4,4,1), oma=c(0.5,0.5,0.5,0)) x = fit3$count y = fit3$s2.post heatscatter(log2(x),log(y),pch=20, xlab = "log2(PSMcount)", ylab="log(Variance)", main="Posterior Variance in Limma") y = fit3$sca.postvar heatscatter(log2(x),log(y),pch=20, xlab = "log2(PSMcount)", ylab="log(Variance)", main="Posterior Variance in DEqMS") ``` ## Compare p-values from DEqMS to ordinary t-test, ANOVA and Limma We first apply t.test to detect significant protein changes between ctrl samples and miR372 treated samples, both have three replicates. ### T-test analysis ```{r t-test} pval.372 = apply(dat.gene.nm, 1, function(x) t.test(as.numeric(x[c(1,5,8)]), as.numeric(x[c(3,6,10)]))$p.value) logFC.372 = rowMeans(dat.gene.nm[,c(3,6,10)])-rowMeans(dat.gene.nm[,c(1,5,8)]) ``` Generate a data.frame of t.test results, add PSM count values and order the table by p-value. ```{r,echo=TRUE} ttest.results = data.frame(gene=rownames(dat.gene.nm), logFC=logFC.372,P.Value = pval.372, adj.pval = p.adjust(pval.372,method = "BH")) ttest.results$PSMcount = psm.count.table[ttest.results$gene,"count"] ttest.results = ttest.results[with(ttest.results, order(P.Value)), ] head(ttest.results) ``` ### Anova analysis Anova analysis is equivalent to linear model analysis. The difference to Limma analysis is that estimated variance is not moderated using empirical bayesian approach as it is done in Limma. ```{r Anova} ord.t = fit1$coefficients[, 1]/fit1$sigma/fit1$stdev.unscaled[, 1] ord.p = 2*pt(abs(ord.t), fit1$df.residual, lower.tail = FALSE) ord.q = p.adjust(ord.p,method = "BH") anova.results = data.frame(gene=names(fit1$sigma), logFC=fit1$coefficients[,1], t=ord.t, P.Value=ord.p, adj.P.Val = ord.q) anova.results$PSMcount = psm.count.table[anova.results$gene,"count"] anova.results = anova.results[with(anova.results,order(P.Value)),] head(anova.results) ``` ### Limma Extract limma results using `topTable` function, `coef = 1` allows you to extract the specific contrast (miR372-ctrl), option `n= Inf` output all rows. ```{r,echo=TRUE} limma.results = topTable(fit2,coef = 1,n= Inf) limma.results$gene = rownames(limma.results) #Add PSM count values in the data frame limma.results$PSMcount = psm.count.table[limma.results$gene,"count"] head(limma.results) ``` ### Visualize the distribution of p-values by different analysis plotting all proteins ranked by p-values. ```{r pvalueall} plot(sort(-log10(limma.results$P.Value),decreasing = TRUE), type="l",lty=2,lwd=2, ylab="-log10(p-value)",ylim = c(0,10), xlab="Proteins ranked by p-values", col="purple") lines(sort(-log10(DEqMS.results$sca.P.Value),decreasing = TRUE), lty=1,lwd=2,col="red") lines(sort(-log10(anova.results$P.Value),decreasing = TRUE), lty=2,lwd=2,col="blue") lines(sort(-log10(ttest.results$P.Value),decreasing = TRUE), lty=2,lwd=2,col="orange") legend("topright",legend = c("Limma","DEqMS","Anova","t.test"), col = c("purple","red","blue","orange"),lty=c(2,1,2,2),lwd=2) ``` plotting top 500 proteins ranked by p-values. ```{r pvalue500} plot(sort(-log10(limma.results$P.Value),decreasing = TRUE)[1:500], type="l",lty=2,lwd=2, ylab="-log10(p-value)", ylim = c(2,10), xlab="Proteins ranked by p-values", col="purple") lines(sort(-log10(DEqMS.results$sca.P.Value),decreasing = TRUE)[1:500], lty=1,lwd=2,col="red") lines(sort(-log10(anova.results$P.Value),decreasing = TRUE)[1:500], lty=2,lwd=2,col="blue") lines(sort(-log10(ttest.results$P.Value),decreasing = TRUE)[1:500], lty=2,lwd=2,col="orange") legend("topright",legend = c("Limma","DEqMS","Anova","t.test"), col = c("purple","red","blue","orange"),lty=c(2,1,2,2),lwd=2) ```