Package Glimma. June 29, 2018

Transcription

1 Type Package Title Interactive HTML graphics Version Date Package Glimma June 29, 2018 This package generates interactive visualisations for analysis of RNA-sequencing data using output from limma, edger or DESeq2 packages in an HTML page. The interactions are built on top of the popular static representations of analysis results in order to provide additional information. biocviews DifferentialEpression, GeneEpression, Microarray, ReportWriting, RNASeq, Sequencing, Visualization Depends R (>= 3.4.0) Imports edger, grdevices, jsonlite, methods, stats, S4Vectors, utils Suggests BiocStyle, IRanges, GenomicRanges, SummarizedEperiment, DESeq2, limma, testthat, knitr, rmarkdown, pryr License GPL-3 file LICENSE URL BugReports RoygenNote NeedsCompilation no LazyData true VignetteBuilder knitr git_url git_branch RELEASE_3_7 git_last_commit 7696aca git_last_commit_date Date/Publication Author Shian Su [aut, cre], Matthew Ritchie [aut], Charity Law [aut], Stuart Lee [ctb] Maintainer Shian Su <su.s@wehi.edu.au> 1

2 2 arraydata R topics documented: arraydata as.hecol glbar glbar.default glimma gllink glmdplot glmdplot.default glmdplot.deseqdataset glmdplot.deseqresults glmdplot.dgeeact glmdplot.dgelrt glmdplot.marraylm glmdrmd glmdsplot glmdsplot.default glmdsplot.deseqdataset glmdsplot.dgelist glscatter glscatter.default gltable gltablink glxyplot is.he lymphomarnaseq makejson makejson.data.frame makejson.jschart Inde 29 arraydata Eample microarray for the study of Ezh2. Eample microarray for the study of Ezh2. Bhupinder Pal, Toula Bouras, Wei Shi, Francois Vaillant, Julie M. Sheridan, Naiyang Fu, Kelsey Breslin, Kun Jiang, Matthew E. Ritchie, Matthew Young, Geoffrey J. Lindeman, Gordon K. Smyth, Jane E. Visvader References

3 as.hecol 3 as.hecol Numeric to he colour converter Convert numbers and R colour strings into corresponding he codes for colours as.hecol() the colour value(s) to be converted to he values. he codes for colours Eamples as.hecol(c(1, 2, 3)) as.hecol(c("red", "black", "green")) glbar Glimma MD Plot Create an interactive bar plot object. glbar(,...) the data.frame containing data to plot.... additional arguments depending on input object type. A chart object containing the information to create an interactive bar plot. Shian Su

4 4 glbar.default See Also glbar.default Eamples data(mtcars) counts <- table(mtcars$gear) data <- data.frame(ngears=as.numeric(names(counts)), Count=as.numeric(counts)) plot1 <- glbar(data, "Count", "ngears", ylab="number of Gears") glimma(plot1, layout=c(1,1), launch=true) glbar.default Glimma MD Plot Default method for interactive bar plot. ## Default S3 method: glbar(, yval, names.arg = rownames(), ndigits = NULL, signif = 6, lab = NULL, ylab = yval, main = NULL, height = 400, width = 500, colval = NULL, annot = yval, flag = NULL, info = NULL,...) the data.frame containing data to plot. yval the column name for the -ais values. names.arg the column name for the label on each bar. ndigits the number of digits after the decimal to round to in the tooltip (overrides signif). signif the number of significant figures to display in the tooltip. lab the label on the -ais. ylab the label on the y-ais. main the title for the plot. height the height of the plot (in piels). width the width of the plot (in piels). colval the colours for each data point. annot the columns to display in the tooltip. flag the special flag to indicate special plot. info additional information for plotting.... additional arguments.

5 glimma 5 A chart object containing the information to create an interactive bar plot. Shian Su Eamples data(mtcars) counts <- table(mtcars$gear) data <- data.frame(ngears=as.numeric(names(counts)), Count=as.numeric(counts)) plot1 <- glbar(data, "Count", "ngears", ylab="number of Gears") glimma(plot1, layout=c(1,1), launch=true) glimma Glimma plot manager Core glimma plot manager. Generates environment for glimma plots. glimma(..., layout = c(1, 1), path = getwd(), folder = "glimma-plots", html = "inde", overwrite = TRUE, launch = TRUE)... the jschart or jslink objects for processing. layout path folder html overwrite launch the numeric vector representing the number of rows and columns in plot window. the path in which the folder will be created. the name of the fold to save html file to. the name of the html file to save plots to. the option to overwrite eisting folder if it already eists. TRUE to launch plot after call. Generates interactive plots based on filling layout row by row from left to right. Eamples data(iris) plot1 <- glscatter(iris, val="sepal.length", yval="sepal.width", colval="species") glimma(plot1, c(1,1))

6 6 glmdplot gllink Plot linkages Helper function for writing the link properties in interactive Glimma plots gllink(from, to, src = "none", dest = "none", flag = "none", both = FALSE, info = "none") from to src dest flag both info the inde of the plot from which the event is dispatched. the inde of the plot which receives the event and performs an action. the action that is performed in the "from" plot. the action that is performed in the "to" plot. indicates special links for particular chart types. creates symmetric links whereby the "dest" action in "to" also triggers the "src" action in "from". additional info for creating the link. a link object containing the plot linking information. Eamples data(iris) data <- data.frame(name=paste("flower", 1:nrow(iris), sep="-"), iris) plot1 <- glscatter(data, val="sepal.length", yval="sepal.width", colval="species") plot2 <- glscatter(data, val="species", yval="petal.length", colval="species") link1 <- gllink(1, 2, src="hover", dest="hover", both=true) glimma(plot1, plot2, link1, layout=c(1,2)) glmdplot Glimma MD Plot Draw an interactive MD plot glmdplot(,...)

7 glmdplot 7 the DE object to plot.... additional arguments affecting the plots produced. See specific methods for detailed arguments. Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average epression. The right plot shows the epression levels of a particular gene of each sample. Hovering over points on left plot will plot epression level for corresponding gene, clicking on points will fi the epression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot. Shian Su See Also glmdplot.default, glmdplot.dgelrt, glmdplot.dgeeact, glmdplot.marraylm, glmdplot.deseqdataset Eamples library(limma) library(edger) data(lymphomarnaseq) <- lymphomarnaseq sel <- rowsums(cpm($counts)>0.5)>=3 <- [sel,] genotype <- relevel($samples$group, "Smchd1-null") <- calcnormfactors(, method="tmm") des <- model.matri(~genotype) ## Apply voom with sample quality weights and fit linear model v <- voomwithqualityweights(, design=des, normalization="none", plot=false) vfit <- lmfit(v,des) ## Apply treat relative to a fold-change of 1.5 vtfit <- treat(vfit,lfc=log2(1.5)) vfit <- ebayes(vfit) results <- decidetests(vfit,p.value=0.01) glmdplot(vfit, counts=$counts, anno=$genes, groups=genotype, samples=1:7, status=results[,2], main="md plot: Wild-type vs Smchd1", display.columns=c("symbols", "GeneID", "GeneName"), folder="smchd1-lymphoma")

8 8 glmdplot.default glmdplot.default Glimma MD Plot Draw an interactive MD plot from a data.frame ## Default S3 method: glmdplot(, val, yval, counts = NULL, anno = NULL, groups = NULL, samples = NULL, status = rep(0, nrow()), transform = FALSE, main = "", lab = val, ylab = yval, side.main = "GeneID", side.lab = "Group", side.ylab = "Epression", side.log = FALSE, side.gridstep = ifelse(!transform side.log, FALSE, 0.5), jitter = 30, display.columns = side.main, cols = c("#00bfff", "#858585", "#ff3030"), sample.cols = rep("#1f77b4", ncol(counts)), path = getwd(), folder = "glimma-plots", html = "MD-Plot", launch = TRUE,...) val yval counts anno groups samples status transform main lab ylab side.main side.lab side.ylab side.log side.gridstep the data.frame object containing epression and fold change values. the column to plot on ais of left plot. the column to plot on y ais of left plot. the matri of epression values, with samples in columns. the data.frame containing gene annotations. the factor containing eperimental groups of the samples. the names of the samples. vector giving the control status of data point, of same length as the number of rows of object. If NULL, then all points are plotted in the default colour. TRUE if counts should be log-cpm transformed. the title for the left plot. the label on the ais for the left plot. the label on the y ais for the left plot. the column containing mains for right plot. label for ais on right plot. label for y ais on right plot. TRUE to plot epression on the right plot on log scale. intervals along which to place grid lines on y ais. Currently only available for linear scale. jitter the amount of jitter to apply to the samples in the epressions plot. display.columns character vector containing names of columns to display in mouseover tooltips and table.

9 glmdplot.deseqdataset 9 cols vector of strings denoting colours corresponding to control status -1, 0 and 1. (may be R named colours or He values) sample.cols path folder html launch vector of strings denoting colours for each sample point on the epression plot. the path in which the folder will be created. the name of the fold to save html file to. the name of the html file to save plots to. TRUE to launch plot after call.... additional arguments to be passed onto the MD plot. (main, lab, ylab can be set for the left plot) Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average epression. The right plot shows the epression levels of a particular gene of each sample. Hovering over points on left plot will plot epression level for corresponding gene, clicking on points will fi the epression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot. Shian Su glmdplot.deseqdataset Glimma MD Plot Draw an interactive MD plot from a DESeqDataSet object ## S3 method for class 'DESeqDataSet' glmdplot(, counts = NULL, anno, groups, samples = NULL, status = rep(0, nrow()), transform = FALSE, main = "", lab = "Mean Epression", ylab = "log-fold-change", side.lab = "Group", side.ylab = "logmean", side.log = FALSE, side.gridstep = ifelse(!transform side.log, FALSE, 0.5), jitter = 30, side.main = "GeneID", display.columns = NULL, cols = c("#00bfff", "#858585", "#ff3030"), sample.cols = rep("#1f77b4", ncol()), path = getwd(), folder = "glimma-plots", html = "MD-Plot", launch = TRUE,...) counts anno groups samples the DESeqDataSet object. the matri of epression values, with samples in columns. the data.frame containing gene annotations. the factor containing eperimental groups of the samples. the names of the samples.

10 10 glmdplot.deseqdataset status transform main lab ylab side.lab side.ylab side.log side.gridstep jitter vector giving the control status of data point, of same length as the number of rows of object. If NULL, then all points are plotted in the default colour. TRUE if counts should be log-cpm transformed. the title for the left plot. label for ais on left plot. label for y ais on left plot. label for ais on right plot. label for y ais on right plot. TRUE to plot epression on the right plot on log scale. intervals along which to place grid lines on y ais. Currently only available for linear scale. the amount of jitter to apply to the samples in the epressions plot. side.main the column containing mains for right plot. display.columns character vector containing names of columns to display in mouseover tooltips and table. cols vector of strings denoting colours corresponding to control status -1, 0 and 1. (may be R named colours or He values) sample.cols path folder html launch vector of strings denoting colours for each sample point on the epression plot. the path in which the folder will be created. the name of the fold to save html file to. the name of the html file to save plots to. TRUE to launch plot after call.... additional arguments to be passed onto the MD plot. (main, lab, ylab can be set for the left plot) Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average epression. The right plot shows the epression levels of a particular gene of each sample. Hovering over points on left plot will plot epression level for corresponding gene, clicking on points will fi the epression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot. Shian Su

11 glmdplot.deseqresults 11 glmdplot.deseqresults Glimma MD Plot Draw an interactive MD plot from a DESeqResults object ## S3 method for class 'DESeqResults' glmdplot(, counts = NULL, anno, groups, samples = NULL, status = rep(0, nrow()), transform = FALSE, main = "", lab = "Mean Epression", ylab = "log-fold-change", side.lab = "Group", side.ylab = "Epression", side.log = FALSE, side.gridstep = ifelse(!transform side.log, FALSE, 0.5), jitter = 30, side.main = "GeneID", display.columns = NULL, cols = c("#00bfff", "#858585", "#ff3030"), sample.cols = rep("#1f77b4", ncol(counts)), path = getwd(), folder = "glimma-plots", html = "MD-Plot", launch = TRUE,...) counts anno groups samples status transform main lab ylab side.lab side.ylab side.log side.gridstep jitter the DESeqResults object. the matri of epression values, with samples in columns. the data.frame containing gene annotations. the factor containing eperimental groups of the samples. the names of the samples. vector giving the control status of data point, of same length as the number of rows of object. If NULL, then all points are plotted in the default colour. TRUE if counts should be log-cpm transformed. the title for the left plot. label for ais on left plot. label for y ais on left plot. label for ais on right plot. label for y ais on right plot. TRUE to plot epression on the right plot on log scale. intervals along which to place grid lines on y ais. Currently only available for linear scale. the amount of jitter to apply to the samples in the epressions plot. side.main the column containing mains for right plot. display.columns character vector containing names of columns to display in mouseover tooltips and table. cols vector of strings denoting colours corresponding to control status -1, 0 and 1. (may be R named colours or He values)

12 12 glmdplot.dgeeact sample.cols path folder html launch vector of strings denoting colours for each sample point on the epression plot. the path in which the folder will be created. the name of the fold to save html file to. the name of the html file to save plots to. TRUE to launch plot after call.... additional arguments to be passed onto the MD plot. (main, lab, ylab can be set for the left plot) Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average epression. The right plot shows the epression levels of a particular gene of each sample. Hovering over points on left plot will plot epression level for corresponding gene, clicking on points will fi the epression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot. Shian Su glmdplot.dgeeact Glimma MD Plot Draw an interactive MD plot from a DGELRT objet ## S3 method for class 'DGEEact' glmdplot(, counts = NULL, anno = NULL, groups = NULL, samples = NULL, status = rep(0, nrow()), transform = FALSE, main = "", lab = "Average log CPM", ylab = "log-fold-change", side.lab = "Group", side.ylab = "Epression", side.log = FALSE, side.gridstep = ifelse(!transform side.log, FALSE, 0.5), p.adj.method = "BH", jitter = 30, side.main = "GeneID", display.columns = NULL, cols = c("#00bfff", "#858585", "#ff3030"), sample.cols = rep("#1f77b4", ncol(counts)), path = getwd(), folder = "glimma-plots", html = "MD-Plot", launch = TRUE,...) counts anno groups samples status the DGEEact object. the matri of epression values, with samples in columns. the data.frame containing gene annotations. the factor containing eperimental groups of the samples. the names of the samples. vector giving the control status of data point, of same length as the number of rows of object. If NULL, then all points are plotted in the default colour.

13 glmdplot.dgeeact 13 transform main lab ylab side.lab side.ylab side.log side.gridstep p.adj.method jitter TRUE if counts should be log-cpm transformed. the title for the left plot. label for ais on left plot. label for y ais on left plot. label for ais on right plot. label for y ais on right plot. TRUE to plot epression on the right plot on log scale. intervals along which to place grid lines on y ais. Currently only available for linear scale. character vector indicating multiple testing correction method. See p.adjust for available methods. (defaults to "BH") the amount of jitter to apply to the samples in the epressions plot. side.main the column containing mains for right plot. display.columns character vector containing names of columns to display in mouseover tooltips and table. cols vector of strings denoting colours corresponding to control status -1, 0 and 1. (may be R named colours or He values) sample.cols path folder html launch vector of strings denoting colours for each sample point on the epression plot. the path in which the folder will be created. the name of the fold to save html file to. the name of the html file to save plots to. TRUE to launch plot after call.... additional arguments to be passed onto the MD plot. (main, lab, ylab can be set for the left plot) Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average epression. The right plot shows the epression levels of a particular gene of each sample. Hovering over points on left plot will plot epression level for corresponding gene, clicking on points will fi the epression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot. Shian Su

14 14 glmdplot.dgelrt glmdplot.dgelrt Glimma MD Plot Draw an interactive MD plot from a DGELRT object ## S3 method for class 'DGELRT' glmdplot(, counts = NULL, anno = NULL, groups = NULL, samples = NULL, status = rep(0, nrow()), transform = FALSE, main = "", lab = "Average log CPM", ylab = "log-fold-change", side.lab = "Group", side.ylab = "Epression", side.log = FALSE, side.gridstep = ifelse(!transform side.log, FALSE, 0.5), p.adj.method = "BH", jitter = 30, side.main = "GeneID", display.columns = NULL, cols = c("#00bfff", "#858585", "#ff3030"), sample.cols = rep("#1f77b4", ncol(counts)), path = getwd(), folder = "glimma-plots", html = "MD-Plot", launch = TRUE,...) counts anno groups samples status transform main lab ylab side.lab side.ylab side.log side.gridstep p.adj.method jitter the DGELRT object. the matri of epression values, with samples in columns. the data.frame containing gene annotations. the factor containing eperimental groups of the samples. the names of the samples. vector giving the control status of data point, of same length as the number of rows of object. If NULL, then all points are plotted in the default colour. TRUE if counts should be log-cpm transformed. the title for the left plot. label for ais on left plot. label for y ais on left plot. label for ais on right plot. label for y ais on right plot. TRUE to plot epression on the right plot on log scale. intervals along which to place grid lines on y ais. Currently only available for linear scale. character vector indicating multiple testing correction method. See p.adjust for available methods. (defaults to "BH") the amount of jitter to apply to the samples in the epressions plot. side.main the column containing mains for right plot. display.columns character vector containing names of columns to display in mouseover tooltips and table.

15 glmdplot.marraylm 15 cols vector of strings denoting colours corresponding to control status -1, 0 and 1. (may be R named colours or He values) sample.cols path folder html launch vector of strings denoting colours for each sample point on the epression plot. the path in which the folder will be created. the name of the fold to save html file to. the name of the html file to save plots to. TRUE to launch plot after call.... additional arguments to be passed onto the MD plot. (main, lab, ylab can be set for the left plot) Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average epression. The right plot shows the epression levels of a particular gene of each sample. Hovering over points on left plot will plot epression level for corresponding gene, clicking on points will fi the epression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot. Shian Su glmdplot.marraylm Glimma MD Plot Draw an interactive MD plot from a MArrayLM object ## S3 method for class 'MArrayLM' glmdplot(, counts = NULL, anno = NULL, groups = NULL, samples = NULL, status = rep(0, nrow()), transform = FALSE, main = "", lab = "Average log CPM", ylab = "log-fold-change", side.main = "GeneID", side.lab = "Group", side.ylab = "Epression", side.log = FALSE, side.gridstep = ifelse(!transform side.log, FALSE, 0.5), coef = ncol($coefficients), p.adj.method = "BH", jitter = 30, display.columns = NULL, cols = c("#00bfff", "#858585", "#ff3030"), sample.cols = rep("#1f77b4", ncol(counts)), path = getwd(), folder = "glimma-plots", html = "MD-Plot", launch = TRUE,...) counts anno groups samples the MArrayLM object. the matri of epression values, with samples in columns. the data.frame containing gene annotations. the factor containing eperimental groups of the samples. the names of the samples.

16 16 glmdplot.marraylm status transform main lab ylab side.main side.lab side.ylab side.log side.gridstep coef p.adj.method vector giving the control status of data point, of same length as the number of rows of object. If NULL, then all points are plotted in the default colour. TRUE if counts should be log-cpm transformed. the title for the left plot. label for ais on left plot. label for y ais on left plot. the column containing mains for right plot. label for ais on right plot. label for y ais on right plot. TRUE to plot epression on the right plot on log scale. intervals along which to place grid lines on y ais. Currently only available for linear scale. integer or character inde vector indicating which column of object to plot. character vector indicating multiple testing correction method. See p.adjust for available methods. (defaults to "BH") jitter the amount of jitter to apply to the samples in the epressions plot. display.columns character vector containing names of columns to display in mouseover tooltips and table. cols vector of strings denoting colours corresponding to control status -1, 0 and 1. (may be R named colours or He values) sample.cols path folder html launch vector of strings denoting colours for each sample point on the epression plot. the path in which the folder will be created. the name of the fold to save html file to. the name of the html file to save plots to. TRUE to launch plot after call.... additional arguments to be passed onto the MD plot. (main, lab, ylab can be set for the left plot) Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average epression. The right plot shows the epression levels of a particular gene of each sample. Hovering over points on left plot will plot epression level for corresponding gene, clicking on points will fi the epression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot. Shian Su Eamples library(limma) library(edger)

17 glmdrmd 17 data(lymphomarnaseq) <- lymphomarnaseq sel <- rowsums(cpm($counts)>0.5)>=3 <- [sel,] genotype <- relevel($samples$group, "Smchd1-null") <- calcnormfactors(, method="tmm") des <- model.matri(~genotype) ## Apply voom with sample quality weights and fit linear model v <- voomwithqualityweights(, design=des, normalization="none", plot=false) vfit <- lmfit(v,des) ## Apply treat relative to a fold-change of 1.5 vtfit <- treat(vfit,lfc=log2(1.5)) vfit <- ebayes(vfit) results <- decidetests(vfit,p.value=0.01) glmdplot(vfit, counts=$counts, anno=$genes, groups=genotype, samples=1:7, status=results[,2], main="md plot: Wild-type vs Smchd1", display.columns=c("symbols", "GeneID", "GeneName"), folder="smchd1-lymphoma") glmdrmd glmdplot Rmarkdown link and instructions When run inside of a tet-block of Rmarkdown document using r... this produces a link and instructions about the usage of the interactive plots. glmdrmd(html = "MD-Plot") html name of the HTML page containing plots from glmdplot. None See Also glmdplot Eamples glmdrmd()

18 18 glmdsplot glmdsplot Glimma MDS Plot Glimma MDS Plot Draw an interactive MD plot from a DGEList object with distances calculated from most variable genes. glmdsplot(,...) the matri containing the gene epressions.... additional arguments. Draws a two-panel interactive MDS plot in an html page. The left panel contains the plot between two MDS dimensions, with annotations displayed on hover. The right panel contains a bar plot of the eigenvalues of each dimension, clicking on any of the bars will plot the corresponding dimension against the net dimension. Shian Su, Gordon Smyth See Also glmdsplot.default, glmdsplot.dgelist Eamples data(lymphomarnaseq) genotype <- relevel(lymphomarnaseq$samples$group, "Smchd1-null") glmdsplot(lymphomarnaseq, labels=1:7, groups=genotype)

19 glmdsplot.default 19 glmdsplot.default Glimma MDS Plot Glimma MDS Plot Draw an interactive MD plot from a DGEList object with distances calculated from most variable genes. ## Default S3 method: glmdsplot(, top = 500, labels = seq_cols(), groups = rep(1, ncol()), gene.selection = c("pairwise", "common"), main = "MDS Plot", path = getwd(), folder = "glimma-plots", html = "MDS-Plot", launch = TRUE,...) top labels groups the matri containing the gene epressions. the number of top most variable genes to use. the labels for each sample. the eperimental group to which samples belong. gene.selection "pairwise" if most variable genes are to be chosen for each pair of samples or "common" to select the same genes for all comparisons. main path folder html launch the title of the plot. the path in which the folder will be created. the name of the fold to save html file to. the name of the html file to save plots to. TRUE to launch plot after call.... additional arguments. Draws a two-panel interactive MDS plot in an html page. The left panel contains the plot between two MDS dimensions, with annotations displayed on hover. The right panel contains a bar plot of the eigenvalues of each dimension, clicking on any of the bars will plot the corresponding dimension against the net dimension. Shian Su, Gordon Smyth

20 20 glmdsplot.deseqdataset glmdsplot.deseqdataset Glimma MDS Plot Glimma MDS Plot Draw an interactive MD plot from a DGEList object with distances calculated from most variable genes. ## S3 method for class 'DESeqDataSet' glmdsplot(, top = 500, labels = NULL, groups = NULL, gene.selection = c("pairwise", "common"), prior.count = 0.25, main = "MDS Plot", path = getwd(), folder = "glimma-plots", html = "MDS-Plot", launch = TRUE,...) top labels groups the DESeqDataSet containing the gene epressions. the number of top most variable genes to use. the labels for each sample. the eperimental group to which samples belong. gene.selection "pairwise" if most variable genes are to be chosen for each pair of samples or "common" to select the same genes for all comparisons. prior.count main path folder html launch average count to be added to each observation to avoid taking log of zero. Used only if log=true. the title of the plot. the path in which the folder will be created. the name of the fold to save html file to. the name of the html file to save plots to. TRUE to launch plot after call.... additional arguments. Draws a two-panel interactive MDS plot in an html page. The left panel contains the plot between two MDS dimensions, with annotations displayed on hover. The right panel contains a bar plot of the eigenvalues of each dimension, clicking on any of the bars will plot the corresponding dimension against the net dimension. Shian Su, Gordon Smyth

21 glmdsplot.dgelist 21 glmdsplot.dgelist Glimma MDS Plot Glimma MDS Plot Draw an interactive MD plot from a DGEList object with distances calculated from most variable genes. ## S3 method for class 'DGEList' glmdsplot(, top = 500, labels = NULL, groups = rep(1, ncol()), gene.selection = c("pairwise", "common"), prior.count = 0.25, main = "MDS Plot", path = getwd(), folder = "glimma-plots", html = "MDS-Plot", launch = TRUE,...) top labels groups the DGEList containing the gene epressions. the number of top most variable genes to use. the labels for each sample. the eperimental group to which samples belong. gene.selection "pairwise" if most variable genes are to be chosen for each pair of samples or "common" to select the same genes for all comparisons. prior.count main path folder html launch average count to be added to each observation to avoid taking log of zero. Used only if log=true. the title of the plot. the path in which the folder will be created. the name of the fold to save html file to. the name of the html file to save plots to. TRUE to launch plot after call.... additional arguments. Draws a two-panel interactive MDS plot in an html page. The left panel contains the plot between two MDS dimensions, with annotations displayed on hover. The right panel contains a bar plot of the eigenvalues of each dimension, clicking on any of the bars will plot the corresponding dimension against the net dimension. Shian Su, Gordon Smyth

22 22 glscatter.default glscatter Glimma Scatter Plot Create an interactive scatter plot object glscatter(,...) the data.frame containing data to plot.... additional arguments depending on input object type. A chart object containing the information to create an interactive scatter plot. Shian Su Eamples data(iris) plot1 <- glscatter(iris, val="sepal.length", yval="sepal.width", colval="species") glimma(plot1, c(1,1)) glscatter.default Glimma Scatter Plot Default method for creating an interactive scatter plot ## Default S3 method: glscatter(, val = "", yval = "y", idval = NULL, point.size = 2,.jitter = 0, y.jitter = 0, ndigits = NULL, signif = 6, log = "", grid = FALSE, ygrid = FALSE, step = FALSE, ystep = FALSE, lab = val, ylab = yval, main = NULL, height = 400, width = 500, colval = NULL, annot = c(val, yval), annot.lab = NULL, flag = NULL, info = NULL, hide = FALSE, disable = NULL,...)

23 glscatter.default 23 val yval idval point.size.jitter y.jitter ndigits signif log grid ygrid step ystep lab ylab main height width colval annot annot.lab flag info hide disable the data.frame containing data to plot. the column name for the -ais values. the column name for the y-ais values. the column name for unique identifiers. the size of the data points. the amount of jittering to add to values along the ais. the amount of jittering to add to values along the y ais. the number of digits after the decimal to round to in the tooltip (overrides signif). the number of significant figures to display in the tooltip. a character string which contains "" if the ais is to be logarithmic, "y" if the y ais is to be logarithmic and "y" or "y" if both aes are to be logarithmic. TRUE if grid lines should be placed along ais. TRUE if grid lines should be placed y ais. the interval at which to set grid lines along the ais. the interval at which to set grid lines along the y ais. the label on the -ais. the label on the y-ais. the title for the plot. the height of the plot (in piels). the width of the plot (in piels). the colours for each data point. the columns to display in the tooltip. alternative labels for the values displayed in the tooltip. the special flag to indicate special plot. additional information for plotting. TRUE to hide the plot when page starts. the events to disable, options are "click", "hover", "zoom".... additional arguments. A chart object containing the information to create an interactive scatter plot. Shian Su Eamples data(iris) plot1 <- glscatter(iris, val="sepal.length", yval="sepal.width", colval="species") glimma(plot1, c(1,1))

24 24 gltablink gltable Glimma Table Create a table using the data from a chart. gltable(target, columns) target columns the inde of the plot from which data is drawn. the columns of data to plot. a input object containing the input field information. gltablink Plot linkages Helper function for writing the link properties in interactive Glimma plots gltablink(from, to, action = "none", info = "none") from to action info the inde of the source table. the inde of the plot which receives the event and performs an action. the action that is performed in the plot. additional info for creating the link. a link object containing the plot linking information.

25 glxyplot 25 glxyplot Glimma XY Plot Draw an interactive XY plot with multiple panels glxyplot(, y, counts = NULL, groups = NULL, samples = NULL, status = rep(0, nrow(data)), anno = NULL, display.columns = NULL, lab = "", ylab = "y", side.main = "GeneID", side.lab = "Group", side.ylab = "Epression", sample.cols = rep("#1f77b4", length(groups)), cols = c("#00bfff", "#858585", "#ff3030"), jitter = 30, path = getwd(), folder = "glimma-plots", html = "XY-Plot", launch = TRUE,...) y counts groups samples status a numeric vector of values to plot on the -ais of the summary plot. a numeric vector of values to plot on the y-ais of the summary plot. the matri containing all counts, the column order should correspond to the order of the and y vectors. the factor containing eperimental groups of the samples. the names of the samples. vector giving the control status of data point, of same length as the number of rows of object. If NULL, then all points are plotted in the default colour anno the data.frame containing gene annotations. display.columns character vector containing names of columns to display in mouseover tooltips and table. lab ylab side.main side.lab side.ylab sample.cols the label on the ais for the left plot. the label on the y ais for the left plot. the column containing mains for right plot. the label on the ais for the right plot. the label on the y ais for the right plot. vector of strings denoting colours for each sample point on the epression plot. cols vector of strings denoting colours corresponding to control status -1, 0 and 1. (may be R named colours or He values) jitter path folder html launch the amount of jitter to apply to the samples in the epressions plot. the path in which the folder will be created. the name of the fold to save html file to. the name of the html file to save plots to. TRUE to launch plot after call.... additional arguments to be passed onto the MD plot. (main, etc. can be set for the left plot)

26 26 is.he Draws a two-panel interactive XY scatter plot in an html page. The left plot shows the and y values specified. The right plot shows the epression levels of a particular gene in each sample. Hovering over points on left plot will plot epression level for the corresponding gene, clicking on points will fi the epression plot to that gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot. This function generates a display that is similar in style to glmdplot, ecept that it provides more fleibility in what the user can provide. Charity Law and Shian Su Eamples data(iris) glxyplot(iris$sepal.width, iris$sepal.length, lab="sepal.width", ylab="sepal.length", side.main="plantid") is.he Hecode colours Check if string(s) are valid he colour representation is.he() the colour value(s) to check. Logical vector indicating if strings(s) are valid he representations

27 lymphomarnaseq 27 lymphomarnaseq Mouse based RNAseq data for study of smchd1 gene. Mouse based RNAseq data for study of smchd1 gene. Ruijie Liu, Kelan Chen, Natasha Jansz, Marnie E. Blewitt, Matthew E. Ritchie References makejson JSON converter for R objects Function to generate json strings from makejson(,...) the object to be converted into JSON... additional arguments a stringified JSON object.

28 28 makejson.jschart makejson.data.frame JSON converter for data frames Function to create a JSON from a data.frame ## S3 method for class 'data.frame' makejson(df, convert.logical = TRUE, dataframe = c("rows", "columns")) df the data.frame to be converted into JSON convert.logical whether to convert logicals into strings "TRUE" and "FALSE" dataframe how to encode data.frame objects: must be one of rows, columns a stringified JSON, the data.frame is encoded as a vector of objects, with each column being one object with keys corresponding to column names. makejson.jschart JSON converter for chart objects Function to make json object from a chart, ignoring the json property ## S3 method for class 'jschart' makejson(chart) chart the chart object to be converted into JSON a stringified JSON object containing the chart data.

29 Inde Topic RNAseq lymphomarnaseq, 27 Topic microarray arraydata, 2 arraydata, 2 as.hecol, 3 glbar, 3 glbar.default, 4, 4 glimma, 5 gllink, 6 glmdplot, 6, 17 glmdplot.default, 7, 8 glmdplot.deseqdataset, 7, 9 glmdplot.deseqresults, 11 glmdplot.dgeeact, 7, 12 glmdplot.dgelrt, 7, 14 glmdplot.marraylm, 7, 15 glmdrmd, 17 glmdsplot, 18 glmdsplot.default, 18, 19 glmdsplot.deseqdataset, 20 glmdsplot.dgelist, 18, 21 glscatter, 22 glscatter.default, 22 gltable, 24 gltablink, 24 glxyplot, 25 is.he, 26 lymphomarnaseq, 27 makejson, 27 makejson.data.frame, 28 makejson.jschart, 28 p.adjust, 13, 14, 16 29