--- title: "Evaluation and statistics of expression data using NormalyzerDE" author: "Jakob Willforss, Aakash Chawade and Fredrik Levander" date: "`r format(Sys.Date(), '%m/%d/%Y')`" abstract: > Technical biases reduces the ability to see the desired biological changes when performing omics experiments. There are numerous normalization techniques available to counter the biases, but to find the optimal normalization is often a non-trivial task. Furthermore there are limited tools available to counter biases such as retention-time based biases caused by fluctuating electrospray intensities. NormalyzerDE helps this process by performing a wide range of normalization techniques including a general and openly available approach to countering retention-time based biases. More importantly it calculates and visualizes a number of performance measures to guide the users selection of normalization technique. Furthermore, NormalyzerDE provides means to easily perform differential expression statistics using either the empirical Bayes Limma approach or ANOVA. Evaluation visualizations are available for both normalization performance measures and as P-value histograms for the subsequent differential expression analysis comparisons. NormalyzerDE package version: `r packageVersion("NormalyzerDE")` output: rmarkdown::html_document: highlight: pygments toc: true fig_width: 5 vignette: > %\VignetteIndexEntry{Differential expression and countering technical biases using NormalyzerDE} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\usepackage[utf8]{inputenc} --- ```{r setup, echo=FALSE, results="hide"} # knitr::opts_chunk$set(tidy=FALSE, cache=TRUE, dev="png", message=FALSE, # error=FALSE, warning=TRUE) ``` # Installation Installation is preferably performed using BiocManager (requires R version >= 3.5): ``` install.packages("BiocManager") BiocManager::install("NormalyzerDE") ``` # Default use ## Citing Willforss, J., Chawade, A., Levander, F. Submitted article. ## Input format NormalyzerDE expects a raw data file. Columns can contain annotation information or sample data. Each column should start with a header. ``` pepseq s1 s2 s3 s4 ATAAGG 20.0 21.2 19.4 18.5 AWAG 23.3 24.1 23.5 17.3 ACATGM 22.1 22.3 22.5 23.2 ``` This data should be provided with a design matrix where all data samples should be represented. One column (default header "sample") should match the columns containing samples in the raw data. Another column (default header "group") should contain condition levels which could be used for group-based evaluations. ``` sample group s1 condA s2 condA s3 condB s4 condB ``` Alternatively the data can be provided as an instance of a `SummarizedExperiment` S4 class. ## Running NormalyzerDE evaluation The evaluation step can be performed with one command, `normalyzer`. This command expects a path to the data file, a name for the run-job, a path to a design matrix and finally a path to an output directory. Alternatively the `designPath` and `dataPath` arguments can be replaced with the `experimentObj` argument where the first assay should contain the data matrix of interest, the `colData` attribute the design matrix and the `rowData` attribute the annotation columns. ```{r} library(NormalyzerDE) outDir <- tempdir() designFp <- system.file(package="NormalyzerDE", "extdata", "tiny_design.tsv") dataFp <- system.file(package="NormalyzerDE", "extdata", "tiny_data.tsv") normalyzer(jobName="vignette_run", designPath=designFp, dataPath=dataFp, outputDir=outDir) ``` ## Running NormalyzerDE statistical comparisons When you after performing the evaluation and having evaluated the report have decided for which normalization approach seems to work best you can continue to the statistical step. Here, expected parameters are the path to the target normalization matrix, the sample design matrix as in the previous step, a job name, the path to an output directory and a list of the pairwise comparisons for which you want to calculate contrasts. They are provided as a character vector with conditions to compare separated by a dash ("-"). Similarly as for the normalization step the `designPath` and `dataPath` arguments can be replaced with an instance of `SummarizedExperiment` sent to the `experimentObj` argument. ```{r} normMatrixPath <- paste(outDir, "vignette_run/CycLoess-normalized.txt", sep="/") normalyzerDE("vignette_run", comparisons=c("4-5"), designPath=designFp, dataPath=normMatrixPath, outputDir=outDir, condCol="group") ``` ## Running NormalyzerDE using a SummarizedExperiment object as input A benefit of using a SummarizedExperiment object as input is that it allows executing NormalyzerDE using variables as input instead of loading from file. The conversion of `designMatrix$sample` is required if using `read.table` as it otherwise is interpreted as a factor. For more intuitive behaviour you can use `read_tsv` from the `readr` package instead of `read.table`. ```{r} dataMatrix <- read.table(dataFp, sep="\t", header = TRUE) designMatrix <- read.table(designFp, sep="\t", header = TRUE) designMatrix$sample <- as.character(designMatrix$sample) dataOnly <- dataMatrix[, designMatrix$sample] annotOnly <- dataMatrix[, !(colnames(dataMatrix) %in% designMatrix$sample)] sumExpObj <- SummarizedExperiment::SummarizedExperiment( as.matrix(dataOnly), colData=designMatrix, rowData=annotOnly ) normalyzer(jobName="sumExpRun", experimentObj = sumExpObj, outputDir=outDir) ``` # Retention time normalization Retention time based normalization can be performed with an arbitrary normalization matrix. ## Basic usage There are two points of access for the higher order normalization. Either by calling `getRTNormalizedMatrix` which applies the target normalization approach stepwise over the matrix based on retention times, or by calling `getSmoothedRTNormalizedMatrix` which generates multiple layered matrices and combines them. To use them you need your raw data loaded into a matrix, a list containing retention times and a normalization matrix able to take a raw matrix and return a normalized in similar format. ```{r} fullDf <- read.csv(dataFp, sep="\t") designDf <- read.csv(designFp, sep="\t") head(fullDf, 1) head(designDf, 1) ``` At this point we have loaded the full data into dataframes. Next, we use the sample names present in the design matrix to extract sample columns from the raw data. Be careful that the sample names is a character vector. If it is a factor it will extract wrong columns. Make sure that sample names extracted from design matrix are in right format. We expect it to be in 'character' format. ```{r} sampleNames <- as.character(designDf$sample) typeof(sampleNames) ``` Now we are ready to extract the data matrix from the full matrix. We also need to get the retention time column from the full matrix. ```{r} dataMat <- as.matrix(fullDf[, sampleNames]) retentionTimes <- fullDf$Average.RT head(dataMat, 1) ``` If everything is fine the data matrix should be `double`, and have the same number of rows as the number of retention time values we have. ```{r} typeof(dataMat) print("Rows and columns of data") dim(dataMat) print("Number of retention times") length(retentionTimes) ``` The normalization function is expected to take a raw intensity matrix and return log transformed values. We borrow the wrapper function for Loess normalization from NormalyzerDE. It can be replaced with any custom function as long as the wrapper has the same input/output format. ```{r} performCyclicLoessNormalization <- function(rawMatrix) { log2Matrix <- log2(rawMatrix) normMatrix <- limma::normalizeCyclicLoess(log2Matrix, method="fast") colnames(normMatrix) <- colnames(rawMatrix) normMatrix } ``` We are ready to perform the normalization. ```{r} rtNormMat <- getRTNormalizedMatrix(dataMat, retentionTimes, performCyclicLoessNormalization, stepSizeMinutes=1, windowMinCount=100) ``` Let's double check the results. We expect a matrix in the same format and shape as before. Furthermore, we expect similar but not the exact same values as if we'd applied the normalization globally. ```{r} globalNormMat <- performCyclicLoessNormalization(dataMat) dim(rtNormMat) dim(globalNormMat) head(rtNormMat, 1) head(globalNormMat, 1) ``` ## Performing layered normalization We have everything set up to perform the layered normalization. The result here is expected to be overall similar to the normal retention time approach. ```{r} layeredRtNormMat <- getSmoothedRTNormalizedMatrix( dataMat, retentionTimes, performCyclicLoessNormalization, stepSizeMinutes=1, windowMinCount=100, windowShifts=3, mergeMethod="mean") dim(layeredRtNormMat) head(layeredRtNormMat, 1) ``` # Stepwise processing (normalization part) NormalyzerDE consists of a set of steps. The workflow can be run as a whole, or step by step. ## Step 1: Loading data This step performs input validation of the data, and generates an object of the class NormalyzerDataset. ```{r} jobName <- "vignette_run" experimentObj <- setupRawDataObject(dataFp, designFp, "default", TRUE, "sample", "group") normObj <- getVerifiedNormalyzerObject(jobName, experimentObj) ``` The function `setupRawDataObject` returns a `SummarizedExperiment` object. This object can be prepared directly and should in that case contain the raw data as the default assay, the design matrix as `colData` and annotation rows as `rowData`. ## Step 2: Generate normalizations Here, normalizations are performed. This generates a NormalyzerResults object containing both a reference to its original dataset object, but also generated normalization matrices. ```{r} normResults <- normMethods(normObj) ``` ## Step 3: Generate performance measures Performance measures are calculated for normalizations. These are stored in an object NormalizationEvaluationResults. A NormalyzerResults object similar to the one sent in is returned, but with this field added. ```{r} normResultsWithEval <- analyzeNormalizations(normResults) ``` ## Step 4: Output matrices to file Generated normalization matrices are written to the provided folder. ```{r} jobDir <- setupJobDir("vignette_run", tempdir()) writeNormalizedDatasets(normResultsWithEval, jobDir) ``` ## Step 5: Generate evaluation plots Performance measures are used to generate evaluation figures which is written in an evaluation report. ```{r} generatePlots(normResultsWithEval, jobDir) ``` After this evaluation of normalizations and progression to statistics follows as described previously in this report. # Stepwise processing (differential expression analysis part) ## Step 1: Setup folders and data matrices For continued processing you select the matrix containing the normalized data from the best performing normalization. The design matrix is the same as for the normalization step. ```{r} bestNormMatPath <- paste(jobDir, "RT-Loess-normalized.txt", sep="/") experimentObj <- setupRawContrastObject(bestNormMatPath, designFp, "sample") nst <- NormalyzerStatistics(experimentObj, logTrans=FALSE) ``` Similarly as to for the normalization evaluation step the `experimentObj` above can be prepared directly as a `SummarizedExperiment` object. ## Step 2: Calculate statistics Now we are ready to perform the contrasts. Contrasts are provided as a vector in the format `c("condA-condB", "condB-condC")`, where `condX` is the group levels. ```{r} comparisons <- c("4-5") nst <- calculateContrasts(nst, comparisons, condCol="group", leastRepCount=2) ``` ## Step 3: Generate final matrix and output Finally we generate a table containing the statistics results for each feature and write it to file together with an evaluation report containing P-value histograms for each comparison. ```{r} annotDf <- generateAnnotatedMatrix(nst) utils::write.table(annotDf, file=paste(jobDir, "stat_table.tsv", sep="/")) generateStatsReport(nst, "Vignette stats", jobDir) ``` # Code organization NormalyzerDE consists of a number of scripts and classes. They are focused around two separate workflows. One is for normalizing and evaluating the normalizations. The second is for performing differential expression analysis. Classes are contained in scripts with the same name. Code organization: ![NormalyzerDE schematics](180813_normalyzerde_schematics.png) The standard workflow for the normalization is the following: * The `normalyzer` function in the `NormalyzerDE.R` script is called, starting the process. * If applicable (that is, input is in Proteois or MaxQuant format), the dataset is preprocessed into the standard format using code in `preparsers.R`. * The input is verified to capture standard errors early on using code in `inputVerification.R`. This results in an instance of the `NormalyzerDataset` class. * The data is normalized using several normalization methods present in `normMethods.R`. This yields an instance of `NormalyzerResults` which links to the original `NormalyzerDataset` instance and also contains all the resulting normalized datasets. * If specified (and if a column with retention time values is present) retention-time segmented approaches are performed by applying normalizations from `normMethods.R` over retention time using functions present in `higherOrderNormMethods.R`. * The results are analyzed using functions present in `analyzeResults.R`. This yields an instance of `NormalyzerEvaluationResults` containing the evaluation results. This instance is attached to the `NormalyzerResults` object. * The final results are sent to `outputUtils.R` where the normalizations are written to an output directory, and to `generatePlots.R` which contains visualizations for the performance measures. It also uses code in `printMeta.R` and `printPlots.R` to output the results in a desired format. When a normalized matrix is selected the analysis proceeds to the statistical analysis. * The `normalyzerde` function in the `NormalyzerDE.R` script is called starting the differential expression analysis pipeline. * An instance of `NormalyzerStatistics` is prepared containing the input data. * Code in the `calculateStatistics.R` script is used to calculate the statistical contrasts. The results are attached to the `NormalyzerStatistics` object. * The resulting statistics are used to generate a report and an annotated output matrix where key statistical measures are attached to the original matrix. # Used packages ```{r} sessionInfo() ```