genBart package Vignette" In genBart: Generate 'BART' File

knitr::opts_chunkset(collapse = TRUE, comment = "#>") library(genBart) library(limma)  BART (Biostatistical Analysis Reporting Tool) is a user friendly, point and click, R shiny application. With the genBart package, biostatisticians/bioinformaticians analyze high throughput biological data obtained from RNA-Seq, Microarray, Flow Cytometry, and metabolomic experiments and upload the result into BART. The tool allows the R programmer who conducted the analysis and their colleagues to efficiently examine the results. BART provides users the ability to easily view, modify and download the tables and figures generated by the app. BART offers one reporting source for all analyses in a project workflow. • Sample information (e.g. technical, biological, clinical, and demographics) summary statistics • Sample quality control metrics • Unsupervised analysis (heatmap and cluster analysis) • Differential expression (sortable genelists, venn diagrams) • Gene set analysis • Correlation analysis within and between biological data types In order to effectively use BART, we introduce the genBart package. genBart is a series of functions that transforms R objects generated from statistical and modeling packages such as limma, DESeq2, and edgeR into files that are uploaded into BART. In the following sections, we illustrate how to use the functions available in genBart by walking through the analysis of a longitudinal microarray study tracking gene expression changes in 38 healthy cynomolgus macaques infected with M. tuberculosis (Skinner et al.). Briefly, this study consisted of data measurements taken at baseline (before infection) and at 11 additional timepoints, up to 6 months post infection. Each macaque was identified as developing symptomatic (active) TB or asymptomatic (latent) infection. For the purposes of illustration and time, a subset of the microarray data was chosen for analysis. We randomly selected 4000 probes and 10 macaques and include 3 time points (days 0, 20, and 42). In addition to microarray data, flow cytometry data consisting of 13 variables was collected on 19 of the 38 macaques. Flow cytometry data analyses results are incorporated to demonstrate the flexibility of the BART app. \vspace{12pt} \begin{center} \large{\textbf{Summary of genBart package}} \end{center} | Function | Purpose | |:--------------------|:--------------------------------------------------------------------------------------| | metaData() | Aligns design and expression files. Defines elements of design for further analyses. | | normalizeData() | Normalizes data. | | clusterData() | Clusters normalized expression data. | | genModScores() | Generates sample level module scores for plotting. | | genModelResults() | Processes and formats differential expression modeling results. | | runQgen() | Runs gene set analysis using Q-Gen (generalized QuSAGE) algorithm. | | crossCorr() | Runs pairwise cross platform correlations (e.g. metabolites vs gene expression). | | genFile() | Generates and saves BART ready R data file. | | updateFile() | Updates/adds analyses to existing BART file. | | runBart() | Runs BART shiny app. | Table: Functions in the genBart package ordered to demonstrate a typical analysis workflow. The genBart package contains 11 functions, each of which is listed in the table above. Greater detail on each of these functions, their parameters, and how they are integrated into a typical workflow is available via the help commands in R. \vspace{12pt} \begin{center} \large{\textbf{Design Information}} \end{center} Before running any analysis, the first step to generate a BART ready file is defining the design and sample information elements. Keep in mind that not every element is necessary for the rest of the genBart functions to run, so in practice, much of this section is left to user discretion and the particular project at hand. library(genBart) library(limma)  Below are the first six lines of the design file from the TB microarray study described in the introduction. head(tb.design)  The metaData function serves two general purposes. The first is to ensure that the design and expression data align by running through a series of checks (i.e. equal number of samples and same sample names). If any of the checks fail, the user is notified with a warning or message and a hint at where the issue is. The columnname argument is the key to running these checks since it specifies the column name in the design that contains the column names of the expression. If columnname is not specified or misspecified, then metaData has no point of reference to run the checks. The second purpose is to store a list of user defined design and sample information parameters that can be used for downstream analyses and in the BART app itself. In our example, since the study design is longitudinal, we can specify long = TRUE and declare the column in the design that contains the baseline value (baseline.var = “timepoint”, baseline.val = 0). The complete function call would be: meta <- metaData(y = tb.expr, design = tb.design, data.type = "microarray", columnname = "columnname", long = TRUE, sample.id = "sample_id", subject.id = "monkey_id", time.var = "timepoint", baseline.var = "timepoint", baseline.val = 0)  We proceed similarly for the flow data: meta.flow <- metaData(y = tb.flow, design = tb.flow.des, data.type = "flow", columnname = "columnname", long = TRUE, sample.id = "sample_id", subject.id = "monkey_id", time.var = "timepoint", baseline.var = "timepoint", baseline.val = 0)  Please refer to the R help for metaData for more detail on function parameters. \vspace{12pt} \begin{center} \large{\textbf{Expression Normalization and Clustering}} \end{center} Since BART's primary function is to serve as a reporting tool for analyzed data, we normalize and cluster the data beforehand so that users can efficiently sort through various heat maps without having to wait for the computationally intensive task of clustering thousands of genes. In genBart, normalization and hierarchical clustering are completed by two simple functions. First, the object generated by metaData containing the expression and design information is input into normalizeData. This function normalizes the data in various ways, depending on the study design specified in metaData. In our TB microarray example with repeated measurements, normalizeData returns three normalized datasets: all samples normalized to the mean or median (specified by norm.method argument), baseline samples normalized to the mean or median, and all samples normalized to baseline. For baseline normalization, instead of subtracting by the mean or median of the baseline samples, each sample's own baseline is subtracted. The list returned by normalizeData is then input into clusterData, which performs hierarchical clustering on each of the normalized datasets and returns a list of dendrograms. For more detail on the various normalization and hierarchical clustering methods, please refer to the R help for normalizeData and clusterData. norm.data <- normalizeData(meta = meta, norm.method = "mean") cluster.data <- clusterData(norm.data = norm.data, dist.method = "euclidean", agg.method = "complete")  \vspace{12pt} \begin{center} \large{\textbf{Generate Gene Set Figures}} \end{center} It is often of interest to examine the behavior of genes that have been grouped together based on similar biological function or other metric. The genModScores function calculates the percentage of genes within a gene set that are up or down regulated with respect to baseline and/or a set of controls. These up or down percentages, referred to as module scores, are calculated for every gene set and sample in the study and are plotted within BART as a heatmap. As an example, we form 10 gene sets defined by clusters from hierarchical clustering and compute their sample level module scores. # Form clusters tb.expr.scale <- data.frame(t(scale(t(tb.expr)))) # center and scale probes hc <- fastcluster::hclust(dist(tb.expr.scale)) cls <- cutree(hc, 10) clusters <- list() for(i in 1:10){ clusters[[i]] <- names(cls)[which(cls == i)] names(clusters)[i] <- paste0("C",i) } # Generate module scores mod.scores <- genModScores(meta = meta, gene.sets = clusters, sd.lim = 2)  Refer to the R help for genModScores for more detail on function parameters and how the module scores are calculated. \vspace{12pt} \begin{center} \large{\textbf{Differential Analysis}} \end{center} Differential expression analysis can contain numerous comparisons, making it time consuming to sort through the results. BART provides a solution through an interface in which users can efficiently sort through differential results across all comparisons. The genModelResults function acts as a bridge between BART and common differential analysis pipelines found in R packages limma, DESeq2, and edgeR. The function takes result objects returned by any one of these pipelines and creates a data frame of results properly formatted for BART. There are a few things to note regarding genModelResults arguments: 1) method must be correctly specified as one of “limma”, “deseq2”, or “edgeR” in order for the function to work properly 2) object argument generated by DESeq2 or edgeR must be wrapped in a list (not necessary if using limma) 3) lm.Fit argument only necessary when method = “limma” and data.type = “microarray” or “rnaseq”. For more details about linear modeling in limma, DESeq2, and edgeR, please refer to the following user guides: 1) limma: Linear Models for Microarray and RNA-Seq Data 2) Analyzing RNA-seq data with DESeq2 3) edgeR: differential expression analysis of digital gene expression data. \begin{center} \large{\textbf{Sample Differential Expression Analysis with limma}} \end{center} Now we demonstrate a quick walkthrough of differential expression analysis of microarray and flow cytometry data using limma. Microarray Create a grouping factor by combining clinical status and time point. tb.designGroup <- paste(tb.design$clinical_status, tb.design$timepoint, sep = "")
grp <- factor(tb.design$Group)  Create a design matrix that includes separate coefficients for each level within the grouping factor so that the desired differences can be extracted as contrasts. design2 <- model.matrix(~0+grp) colnames(design2) <- levels(grp)  Since there are repeated measurements on each monkey, we treat monkey_id as a random effect and estimate the correlation between measurements on the same subject. In limma, this is done using duplicateCorrelation. dupcor <- duplicateCorrelation(tb.expr, design2, block = tb.design$monkey_id)


Fit model and extract contrasts of interest.

fit <- lmFit(tb.expr, design2, block = tb.design$monkey_id, correlation = dupcor$consensus.correlation)
contrasts <- makeContrasts(A_20vsPre = Active20-Active0, A_42vsPre = Active42-Active0,
L_20vsPre = Latent20-Latent0, L_42vsPre = Latent42-Latent0,
levels=design2)

fit2 <- contrasts.fit(fit, contrasts)
fit2 <- eBayes(fit2, trend = FALSE)


Flow Cytometry

Analysis of flow data in limma proceeds similarly.

tb.flow.des$Group <- paste(tb.flow.des$clinical_status, tb.flow.des$timepoint, sep = "") grp <- factor(tb.flow.des$Group)

design2 <- model.matrix(~0+grp)
colnames(design2) <- levels(grp)
tb.flow.l2 <- log2(tb.flow + 1) # log2 transform
dupcor <- duplicateCorrelation(tb.flow.l2, design2, block = tb.flow.des$monkey_id) fit.flow <- lmFit(tb.flow.l2, design2, block = tb.flow.des$monkey_id,
correlation = dupcorconsensus.correlation) # Additional contrasts since all timepoints available. contrasts <- makeContrasts(A_20vsPre = Active20-Active0, A_30vsPre = Active30-Active0, A_42vsPre = Active42-Active0, A_56vsPre = Active56-Active0, L_20vsPre = Latent20-Latent0, L_30vsPre = Latent30-Latent0, L_42vsPre = Latent42-Latent0, L_46vsPre = Latent56-Latent0, levels=design2) fit2.flow <- contrasts.fit(fit.flow, contrasts) fit2.flow <- eBayes(fit2.flow, trend = FALSE)  \vspace{12pt} \begin{center} \large{\textbf{Generate Model Results File}} \end{center} The next step in the process is generating a model results file that is formatted specifically for BART. This can be done using genModelResults, which takes as input the expression data used for modeling, as well as model result output from limma, DESeq2, or edgeR. In our example, we ran linear models using limma for both the microarray and flow data. data(gene.symbols) # gene symbols for Illumina probes mod.results <- genModelResults(y = tb.expr, data.type = "microarray", object = fit2, lm.Fit = fit, method = "limma", var.symbols = gene.symbols) mod.results.flow <- genModelResults(y = tb.flow, data.type = "flow", object = fit2.flow, lm.Fit = fit.flow, method = "limma")  \vspace{12pt} \begin{center} \large{\textbf{Gene Set Analysis: runQgen}} \end{center} Next we run gene set analysis by incorporating functions available in the qusage package, which tests whether the average log2 fold change of the genes within a gene set are different than 0. runQgen simplifies the process by requiring two input parameters, the model results object produced by genModelResults and a list of gene sets. qus <- runQgen(model.results = mod.results, gene.sets = modules)  \vspace{12pt} \begin{center} \large{\textbf{Correlations}} \end{center} In addition to providing tools for standard analysis, BART also offers a tool that allows users to visualize and sort through potentially hundreds of thousands pairwise correlations across multiple platforms (e.g. gene expression vs metabolites) or versus clinical outcomes. crossCorr takes two numeric data frames and outputs a long format file of all pairwise correlations. The function has an option to run correlations by a grouping factor and various additional parameters for labeling in BART. We walk through an example in which we wish to correlate gene expression with flow variables. Since this is a longitudinal setting, we correlate by time. # Format expression data to align with flow data gene.data <- data.frame(t(tb.expr)) rownames(gene.data) <- paste0(tb.designmonkey_id, "_", tb.design$timepoint) flow.data <- data.frame(t(tb.flow)) flow.data <- flow.data[match(rownames(gene.data), rownames(flow.data), nomatch = 0), ] gene.data <- gene.data[match(rownames(flow.data), rownames(gene.data), nomatch = 0), ] # Create time variable time <- tb.flow.des$timepoint[match(rownames(flow.data),tb.flow.des\$columnname,nomatch = 0)]

# Run correlations formatted for BART
corrs <- crossCorr(x = gene.data, y = flow.data, by = time, by.name = "days",
description = "Genes vs Flow", x.var = "Genes",
y.var = "Flow", method = "spearman")


\vspace{12pt} \begin{center} \large{\textbf{Generate BART file}} \end{center}

The last step in the process is to generate the final file that can be uploaded into BART. This is done through the function genFile, which takes as its arguments the objects generated from the previous functions. Not every object is required to generate a BART file. For example, if gene set analysis and correlations are not run, metaData, clusterData, genModScores, and genModelResults objects can still be run through genFile. BART will only show the results that are input.

genFile(meta = list(meta, meta.flow), module.scores = mod.scores,
dendrograms = cluster.data, model.results = list(mod.results, mod.results.flow),
project.name = "BART example")


In our example, we did run gene set analysis and correlations. Adding these results into BART is made simple through the updateFile function. The user must simply provide the path to the BART file that needs to be updated and add the new additions in the same way that genFile takes them.

path <- paste0(getwd(), "/", "BART example")
updateFile(load.path = path, qusage.results = qus, corr.results = list(corrs))


\vspace{12pt} \begin{center} \large{\textbf{Run BART App}} \end{center}

Now that a BART file has been created, the application can be run with a call to runBart. Please note that the default parameters for figures and tables (e.g. height, width, labeling, significance thresholds, etc.) are not optimized for any given project. As a result, it is likely that initial visualizations are far from expected or desired. However, default parameters can be easily adjusted via various widgets provided in the tool.

runBart()


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genBart documentation built on May 2, 2019, 9:13 a.m.