Package varistran. July 25, 2016

Transcription

1 Package varistran July 25, 2016 Title Variance Stabilizing Transformations appropriate for RNA-Seq expression data Transform RNA-Seq count data so that variance due to biological noise plus its count-based nature is stable across genes with different average expression levels. This takes the form of a log transformation which is moderated for low counts. Transforming data in this way makes many statistical and machine learning techniques applicable. A Shiny report function showing diagnostic plots based on the transformed data is also provided. Author Paul Harrison Maintainer Paul Harrison <paul.harrison@monash.edu> URL Version 0.1 License LGPL-2.1 Depends grid Imports MASS, ggplot2, ggdendro, shiny, seriation Suggests edger, limma RoxygenNote R topics documented: composable_shiny_app ensure_reactive ordering_grob plot_biplot plot_heatmap plot_stability shiny_mds_plot

2 2 composable_shiny_app shiny_plot shiny_report varistran_grob vst vst_advice Index 12 composable_shiny_app Composable Shiny App. Create a Shiny app that can be used immediately, or incorporated into a larger Shiny app. composable_shiny_app(ui, server, title = NULL) ui server UI part of the Shiny app. Server part of the Shiny app. This should take a single parameter, env. Details Unlike a normal Shiny app, the server function is passed an environment, "env". This contains $input, $output and $server elements conventionally passed to a Shiny server function. However env can also be used to store reactive expressions that other components of a larger app might need to access. A shiny.appobj. This object has two additional properties, $component_ui and $component_server. A larger composable Shiny app would incorporate $component_ui into its own ui, and make sure to call $component_server(env) from from its own server function. Note that Shiny now has its own system for creating Shiny "modules". Varistran components will be converted to also support this style over time. shiny_plot already supports this. Paul Harrison

3 ensure_reactive 3 ensure_reactive Make a value reactive if it is not already reactive. This is used to allow parameters to Shiny functions to be optionally reactive. ensure_reactive(item) ordering_grob orderinggrob Display the dendrogram of an ordering, as a grid grob. ordering_grob(ordering, transpose = FALSE, mirror = FALSE, hint_size = unit(5, "lines")) See Also make_ordering plot_biplot Biplot of expression data Produce a ggplot object containing a biplot of expression data. plot_biplot(x, sample_labels = NULL, feature_labels = NULL, n_features = 20, balance = 0.25, text_size = 0.025)

4 4 plot_biplot x sample_labels Matrix of expression levels, with features (eg genes) as rows and samples as columns. For example, you could use the output of varistran::vst here. Sample labels. feature_labels Feature labels. n_features balance text_size Number of extreme features to label. Relative scaling of features and samples. plot_biplot attempts to stop labels from overlapping. Adjust this so that text just doesn t overlap. Set to zero to allow labels to completely overlap. Details Biplot based on the Singular Decomposition of the matrix x. The dimensions corresponding to the two largest singular values are shown. Genes are shown in blue and samples in red. The dot product of the gene and sample vectors approximates the difference from the average expression level of that gene in that sample. Sample points (red) are scaled to have the same variance in the two dimensions. Therefore the gene points (blue) may have greater variance along dimension 1 if dimension 1 explains more of the variance than dimension 2. A ggplot object. This must be print()-ed to actually plot. Paul Harrison Examples # Assuming counts is a matrix of read counts. y <- varistran::vst(counts) print( varistran::plot_biplot(y) )

5 plot_heatmap 5 plot_heatmap Plot a heatmap. Produces a heatmap as a grid grob. plot_heatmap(y, cluster_samples = FALSE, cluster_features = TRUE, sample_labels = NULL, feature_labels = NULL) y A matrix of expression levels, such as a transformed counts matrix. cluster_samples Should samples (columns) be clustered? cluster_features Should features (rows) be clustered? sample_labels Names for each sample. If not given and y has column names, these will be used instead. feature_labels Names for each feature. If not given and y has row names, these will be used instead. Details Clustering is performed using the "seriation" package, and is approximately a Travelling Salesman Problem ordering. If there are many features (more than a couple of thousand) clustering may be slow. A grid grob. print()-ing this value will cause it to be displayed. Additionally $info$row_order will contain row ordering and $info$col_order will contain column ordering. Paul Harrison.

6 6 shiny_mds_plot plot_stability Stability plot. Produce a ggplot object containing a plot of residual standard deviation against mean count. plot_stability(y, x = NULL, design = NULL, bins = 20) y x design bins Transformed counts matrix. Optional, original counts matrix. Matrix specifying a linear model with which to calculate residuals. Number points in the graph. Details Genes are partitioned evenly into "bins" bins by average expression level. Mean residual standard deviation is plotted against mean count. A ggplot object. This must be print()-ed to actually plot. Paul Harrison shiny_mds_plot Shiny wrapper for limma s MDS plot Shiny wrapper for limma s MDS plot shiny_mds_plot(x, sample_labels = NULL, prefix = "")

7 shiny_plot 7 shiny_plot Shiny plot. Convert a function that produces a plot into a Shiny app. shiny_plot(callback, width = 500, height = 500, dlname = "plot", prefix = "",...) shiny_plot_ui(id, width = 500, height = 500,...) shiny_plot_server(input, output, session, callback, dlname = "plot") callback Function to produce the plot. In shiny_plot, this takes one argument, "env" (see composable_shiny_app). In shiny_plot_server, it takes no arguements.... Extra arguments to shiny_plot and shiny_plot_ui are passed on to plotoutput, for example brushing options. Details shiny_plot is the varistran-style composable app object. shiny_plot_ui and shiny_plot_server are appropriate for the normal Shiny module system. shiny_plot() returns a composable shiny.appobj. Functions shiny_plot_ui: UI part of Shiny module. shiny_plot_server: server part of Shiny module, to be invoked from a server function with callmodule. Paul Harrison

8 8 shiny_report shiny_report Shiny report. Produce an interactive Shiny report showing diagnostic plots of transformed counts. shiny_report(y = NULL, counts = NULL, sample_labels = NULL, feature_labels = NULL, prefix = "") y counts sample_labels A matrix of exprssion levels, such as a transformed counts matrix. Original counts. Optional. Sample names. feature_labels Optional. Feature names. prefix Optional, to facilitate use as a component of a larger Shiny app. Inputs and outputs are given this prefix. A shiny.appobj. Either y or counts or both must be given. Used interactively, the shiny report runs immediately. Otherwise it can be launched by print()-ing it. A call to this function can also be the last line in an app.r file in a Shiny app directory. Paul Harrison Examples y <- varistran::vst(counts) varistran::shiny_report(y, counts)

9 varistran_grob 9 varistran_grob Make a grob better. Give a grob a margin, and size hints for layout, and make it print()-able. varistran_grob(grob, width = unit(1, "null"), height = unit(1, "null"), pad = 0) vst Variance Stabilizing Transformation Perform a Variance Stabilizing Transformation (VST) of a matrix of count data. vst(x, method = "anscombe.nb", lib.size = NULL, cpm = FALSE, dispersion = NULL, design = NULL) x method lib.size cpm dispersion design A matrix of counts. Rows are genes (or other features), and columns are samples. VST to use, see details. Optional, estimated if not given. Should the output be in log2 Counts Per Million, rather than simply log2. Optional, estimated if not given. Dispersion parameter of the negative binomial distribution of the data. Optional. If dispersion isn t given, a design matrix to use when estimating dispersion.

10 10 vst_advice Details Several methods are available. "anscombe.nb" is recommended. Methods: "anscombe.nb": Default, asinh(sqrt((x+3/8)/(1/dispersion-3/4))). Anscombe s VST for the negative binomial distribution. "anscombe.nb.simple": log(x+0.5/dispersion), a simplified VST also given by Anscombe. "anscombe.poisson": sqrt(x+3/8). Anscombe s VST for the Poisson distribution. Only appropriate if you know there is no biological noise. "naive.nb": asinh(sqrt(x/dispersion)). Resultant variance is slightly inflated at low counts. "naive.poisson": sqrt(x). Resultant variance is slightly inflated at low counts. Dispersion: edger s estimate of the common dispersion of the count matrix would be a reasonable choice of dispersion. However Poisson noise in RNA-Seq data may be over-dispersed, in which case a slightly smaller dispersion may work better. I recommend not providing a dispersion and letting varistran pick an appropriate value. If "dispersion" is not given, it is chosen so as to minimize sd(residual s.d.)/mean(residual s.d.). Residuals are calculated from the linear model specified by the parameter "design". If "design" also isn t given, a linear model containing only an intercept term is used. This may lead to an over-estimate of the dispersion, so do give a design if possible. A transformed matrix. Paul Harrison References Anscombe, F.J. (1948) "The transformation of Poisson, binomial, and negative-binomial data", Biometrika 35 (3-4): vst_advice Advise how VST will transform data Advise how VST will transform data vst_advice(what = "anscombe.nb", dispersion = NULL, cpm = FALSE, lib.size = NULL)

11 vst_advice 11 what Either the output of a call to vst() or the name of a VST method (see vst() help). A data frame giving an indication of how an average sample will be transformed. The column "twofold_step" shows the step from the previous to current row. With a log2 transformation this would be uniformly 1, but with a VST and small counts the step is less than 1. This therefore provides advice on how compacted the VST is near zero-count, as compared to a log2 transformation. Note that the results given are for an average sample. Where library sizes differ wildly, the VST may perform poorly.

12 Index composable_shiny_app, 2 ensure_reactive, 3 make_ordering, 3 ordering_grob, 3 plot_biplot, 3 plot_heatmap, 5 plot_stability, 6 shiny_mds_plot, 6 shiny_plot, 7 shiny_plot_server (shiny_plot), 7 shiny_plot_ui (shiny_plot), 7 shiny_report, 8 varistran_grob, 9 vst, 9 vst_advice, 10 12