Package Glimma. April 14, 2017

Transcription

1 Type Package Title Interactive HTML graphics Version Date Author Shian Su, Matt Ritchie, Charity Law Maintainer Shian Su Package Glimma April 14, 2017 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, ReportWriting, RNASeq, Visualization Depends R (>= 3.3.0) Imports DESeq2, edger, grdevices, methods, stats, utils Suggests BiocStyle, limma License GPL-3 file LICENSE URL BugReports RoygenNote NeedsCompilation no LazyData true R topics documented: as.hecol CharToHeCol glautoinput glbar glbar.default glimma gllink glmdplot glmdplot.default glmdplot.deseqdataset glmdplot.deseqresults

2 2 as.hecol glmdplot.dgeeact glmdplot.dgelrt glmdplot.marraylm glmdsplot glmdsplot.default glmdsplot.dgelist glscatter glscatter.default gltable gltablink glxyplot hascolumns lymphomarnaseq makejson.chart makejson.data.frame NumToHeCol Inde 27 as.hecol Numeric to he colour converter Functions to convert numbers 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"))

3 CharToHeCol 3 CharToHeCol String to he colour converter Function to convert colour strings into he codes CharToHeCol() the string colour value(s) to be converted to he values. he codes for colours glautoinput Glimma Autocomplete Input Create an autocompleted input field. glautoinput(target, action, idval = "none", flag = "none") target action idval flag the inde of the plot which receives the event and performs an action. the action to be performed at target plot using input information. the column from which the autocomplete list will be populated. indicates special flags for custom features. a input object containing the input field information.

4 4 glbar.default 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 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,...)

5 glimma 5 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. 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)

6 6 gllink... 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)) 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.

7 glmdplot 7 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(,...) 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. See Also Shian Su 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

8 8 glmdplot.default <- [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") 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, samples = NULL, status = rep(0, nrow()), transform = TRUE, side.lab = "Group", side.ylab = "logcpm", side.log = FALSE, side.gridstep = ifelse(!transform side.log, FALSE, 0.5), lab = val, ylab = yval, search.by = "Symbols", jitter = 30, id.column = "GeneID", display.columns = id.column, cols = c("#0000ff", "#858585", "#B32222"), sample.cols = rep("#1f77b4", ncol(counts)), table = TRUE, path = getwd(), folder = "glimma-plots", html = "MD-Plot", launch = TRUE,...) val yval counts anno groups 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 containing all counts. the data.frame containing gene annotations. the factor containing eperimental groups of the samples.

9 glmdplot.default 9 samples status transform side.lab side.ylab side.log side.gridstep lab ylab search.by jitter id.column 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 are raw and should be cpm transformed, FALSE if counts are already transformed to epression scale. label for ais on right side plot. label for y ais on right side plot. TRUE to plot epression on the side plot on log scale. intervals along which to place grid lines on y ais. Currently only available for linear scale. the label on the ais for the left plot. the label on the y ais for the left plot. the name of the column which will be used to search for data points if table is not used. (should contain unique values) the amount of jitter to apply to the samples in the epressions plot. the column containing unique identifiers for each gene. 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 table path folder html launch vector of strings denoting colours for each sample point on the epression plot. logical variable for whether a table of the data should appear on the bottom of the HTML page. 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

10 10 glmdplot.deseqdataset glmdplot.deseqdataset Glimma MD Plot Draw an interactive MD plot from a DESeqDataSet object ## S3 method for class 'DESeqDataSet' glmdplot(, anno, groups, samples, status = rep(0, nrow()), transform = TRUE, side.lab = "Group", side.ylab = "logmean", side.log = FALSE, side.gridstep = ifelse(!transform side.log, FALSE, 0.5), search.by = "Symbols", jitter = 30, id.column = "GeneID", display.columns = NULL, cols = c("#0000ff", "#858585", "#B32222"), sample.cols = rep("#1f77b4", ncol()), table = TRUE, path = getwd(), folder = "glimma-plots", html = "MD-Plot", launch = TRUE,...) anno groups samples status transform side.lab side.ylab side.log side.gridstep search.by jitter the DESeqDataSet object. 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 are raw and should be cpm transformed, FALSE if counts are already transformed to epression scale. label for ais on right side plot. label for y ais on right side plot. TRUE to plot epression on the side plot on log scale. intervals along which to place grid lines on y ais. Currently only available for linear scale. the name of the column which will be used to search for data points. (should contain unique values) the amount of jitter to apply to the samples in the epressions plot. id.column the column containing unique identifiers for each gene. 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 table path vector of strings denoting colours for each sample point on the epression plot. logical variable for whether a table of the data should appear on the bottom of the HTML page. the path in which the folder will be created.

11 glmdplot.deseqresults 11 folder html launch 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.deseqresults Glimma MD Plot Draw an interactive MD plot from a DESeqResults object ## S3 method for class 'DESeqResults' glmdplot(, counts, anno, groups, samples, status = rep(0, nrow()), transform = TRUE, side.lab = "Group", side.ylab = "logcpm", side.log = FALSE, side.gridstep = ifelse(!transform side.log, FALSE, 0.5), search.by = "Symbols", jitter = 30, id.column = "GeneID", display.columns = NULL, cols = c("#0000ff", "#858585", "#B32222"), sample.cols = rep("#1f77b4", ncol(counts)), table = TRUE, path = getwd(), folder = "glimma-plots", html = "MD-Plot", launch = TRUE,...) counts anno groups samples status transform the DESeqResults object. the matri containing all counts. 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 are raw and should be cpm transformed, FALSE if counts are already transformed to epression scale.

12 12 glmdplot.dgeeact side.lab side.ylab side.log side.gridstep search.by jitter id.column label for ais on right side plot. label for y ais on right side plot. TRUE to plot epression on the side plot on log scale. intervals along which to place grid lines on y ais. Currently only available for linear scale. the name of the column which will be used to search for data points. (should contain unique values) the amount of jitter to apply to the samples in the epressions plot. the column containing unique identifiers for each gene. 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 table path folder html launch vector of strings denoting colours for each sample point on the epression plot. logical variable for whether a table of the data should appear on the bottom of the HTML page. 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

13 glmdplot.dgeeact 13 ## S3 method for class 'DGEEact' glmdplot(, counts = NULL, anno = NULL, groups = rep(0, ncol()), samples = NULL, status = rep(0, nrow()), transform = TRUE, side.lab = "Group", side.ylab = "logcpm", side.log = FALSE, side.gridstep = ifelse(!transform side.log, FALSE, 0.5), p.adj.method = "BH", search.by = "Symbols", jitter = 30, id.column = "GeneID", display.columns = NULL, cols = c("#0000ff", "#858585", "#B32222"), sample.cols = rep("#1f77b4", ncol(counts)), table = TRUE, path = getwd(), folder = "glimma-plots", html = "MD-Plot", launch = TRUE,...) counts anno groups samples status transform side.lab side.ylab side.log side.gridstep p.adj.method search.by jitter the DGEEact object. the matri containing all counts. 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 are raw and should be cpm transformed, FALSE if counts are already transformed to epression scale. label for ais on right side plot. label for y ais on right side plot. TRUE to plot epression on the side 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. (defaults to "BH") the name of the column which will be used to search for data points. (should contain unique values) the amount of jitter to apply to the samples in the epressions plot. id.column the column containing unique identifiers for each gene. 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 table path folder html launch vector of strings denoting colours for each sample point on the epression plot. logical variable for whether a table of the data should appear on the bottom of the HTML page. 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)

14 14 glmdplot.dgelrt 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.dgelrt Glimma MD Plot Draw an interactive MD plot from a DGELRT object ## S3 method for class 'DGELRT' glmdplot(, counts = NULL, anno = NULL, groups = rep(0, ncol()), samples = NULL, status = rep(0, nrow()), transform = TRUE, side.lab = "Group", side.ylab = "logcpm", side.log = FALSE, side.gridstep = ifelse(!transform side.log, FALSE, 0.5), p.adj.method = "BH", search.by = "Symbols", jitter = 30, id.column = "GeneID", display.columns = NULL, cols = c("#0000ff", "#858585", "#B32222"), sample.cols = rep("#1f77b4", ncol(counts)), table = TRUE, path = getwd(), folder = "glimma-plots", html = "MD-Plot", launch = TRUE,...) counts anno groups samples status transform side.lab side.ylab side.log side.gridstep the DGELRT object. the matri containing all counts. 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 are raw and should be cpm transformed, FALSE if counts are already transformed to epression scale. label for ais on right side plot. label for y ais on right side plot. TRUE to plot epression on the side plot on log scale. intervals along which to place grid lines on y ais. Currently only available for linear scale.

15 glmdplot.marraylm 15 p.adj.method character vector indicating multiple testing correction method. (defaults to "BH") search.by the name of the column which will be used to search for data points. (should contain unique values) jitter the amount of jitter to apply to the samples in the epressions plot. id.column the column containing unique identifiers for each gene. 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 vector of strings denoting colours for each sample point on the epression plot. table logical variable for whether a table of the data should appear on the bottom of the HTML page. path the path in which the folder will be created. folder the name of the fold to save html file to. html the name of the html file to save plots to. launch 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 = rep(0, ncol()), samples = NULL, status = rep(0, nrow()), transform = TRUE, side.lab = "Group", side.ylab = "logcpm", side.log = FALSE, side.gridstep = ifelse(!transform side.log, FALSE, 0.5), coef = ncol($coefficients), p.adj.method = "BH", search.by = "Symbols", jitter = 30, id.column = "GeneID", display.columns = NULL, cols = c("#0000ff", "#858585", "#B32222"), sample.cols = rep("#1f77b4", ncol(counts)), table = TRUE, path = getwd(), folder = "glimma-plots", html = "MD-Plot", launch = TRUE,...)

16 16 glmdplot.marraylm counts anno groups samples status transform side.lab side.ylab side.log side.gridstep coef p.adj.method search.by jitter the MArrayLM object. the matri containing all counts. 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 are raw and should be cpm transformed, FALSE if counts are already transformed to epression scale. label for ais on right side plot. label for y ais on right side plot. TRUE to plot epression on the side 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. (defaults to "BH") the name of the column which will be used to search for data points. (should contain unique values) the amount of jitter to apply to the samples in the epressions plot. id.column the column containing unique identifiers for each gene. 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 table path folder html launch vector of strings denoting colours for each sample point on the epression plot. logical variable for whether a table of the data should appear on the bottom of the HTML page. 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.

17 glmdsplot 17 Shian Su 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") glmdsplot Glimma MDS Plot Draw an interactive MDS plot gene epression matri with distances calculated from most variable genes. glmdsplot(,...) the data.frame containing data to plot.... additional arguments affecting the plots produced. See specific methods for detailed arguments.

18 18 glmdsplot.default 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. See Also Shian Su, Gordon Smyth glmdsplot.default, glmdsplot.dgelist Eamples data(lymphomarnaseq) genotype <- relevel(lymphomarnaseq$samples$group, "Smchd1-null") glmdsplot(lymphomarnaseq, labels=1:7, groups=genotype) glmdsplot.default Glimma MDS Plot Draw an interactive MDS plot from a gene epression matri with distances calculated from most variable genes. ## Default S3 method: glmdsplot(, top = 500, labels = 1:ncol(), groups = rep(1, ncol()), gene.selection = "pairwise", 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 the title of the plot. the path in which the folder will be created. the name of the fold to save html file to.

19 glmdsplot.dgelist 19 html the name of the html file to save plots to. launch 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 glmdsplot.dgelist 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 = 1:ncol(), groups = rep(1, ncol()), gene.selection = "pairwise", 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. 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.

20 20 glscatter 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 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))

21 glscatter.default 21 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,...) the data.frame containing data to plot. val the column name for the -ais values. yval the column name for the y-ais values. idval the column name for unique identifiers. point.size the size of the data points..jitter the amount of jittering to add to values along the ais. y.jitter the amount of jittering to add to values along the y ais. 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. log 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. grid TRUE if grid lines should be placed along ais. ygrid TRUE if grid lines should be placed y ais. step the interval at which to set grid lines along the ais. ystep the interval at which to set grid lines along the y ais. 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. annot.lab alternative labels for the values displayed in the tooltip. flag the special flag to indicate special plot. info additional information for plotting. hide TRUE to hide the plot when page starts. disable the events to disable, options are "click", "hover", "zoom".... additional arguments.

22 22 gltablink 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)) 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")

23 glxyplot 23 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. 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, id.column = "GeneID", lab = "", ylab = "y", side.lab = "Group", side.ylab = "logcpm", sample.cols = rep("#1f77b4", length(groups)), cols = c("#0000ff", "#858585", "#B32222"), 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 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 colou anno the data.frame containing gene annotations. display.columns character vector containing names of columns to display in mouseover tooltips and table. id.column lab ylab side.lab side.ylab sample.cols the column containing unique identifiers for each gene. the label on the ais for the left plot. the label on the y ais for the left 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.

24 24 hascolumns 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) 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", id.column="plantid") hascolumns Column checker Check if data.frame controls all the listed columns hascolumns(df, columns) df columns the data frame to check. the columns that should eist in the data frame. stops program with an error if column cannot be found in df

25 lymphomarnaseq 25 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.chart JSON converter for chart objects Function to make json object from a chart, ignoring the json property ## S3 method for class 'chart' makejson(chart) chart the chart object to be converted into JSON a stringified JSON object containing the chart data.

26 26 NumToHeCol 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) df the data.frame to be converted into JSON convert.logical whether to convert logicals into strings "TRUE" and "FALSE" 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. NumToHeCol Numeric to he colour converter Functions to convert numbers into corresponding he codes for colours NumToHeCol() the colour value(s) to be converted to he values. he codes for colours

27 Inde Topic RNAseq lymphomarnaseq, 25 as.hecol, 2 CharToHeCol, 3 glautoinput, 3 glbar, 4 glbar.default, 4, 4 glimma, 5 gllink, 6 glmdplot, 7 glmdplot.default, 7, 8 glmdplot.deseqdataset, 7, 10 glmdplot.deseqresults, 11 glmdplot.dgeeact, 7, 12 glmdplot.dgelrt, 7, 14 glmdplot.marraylm, 7, 15 glmdsplot, 17 glmdsplot.default, 18, 18 glmdsplot.dgelist, 18, 19 glscatter, 20 glscatter.default, 21 gltable, 22 gltablink, 22 glxyplot, 23 hascolumns, 24 lymphomarnaseq, 25 makejson.chart, 25 makejson.data.frame, 26 NumToHeCol, 26 27