Define the default tour for the subset of the Allen brain dataset.
This is only available when run on the iSEE(sce) example in ?"iSEE".
defaultTour()A data.frame where each row is a tour step. The first column specifies the UI element to be highlighted by the tour, while the second column contains the tour text.
defaultTour()
#> element
#> 1 #Welcome
#> 2 #allpanels
#> 3 #ReducedDimensionPlot1
#> 4 #ReducedDimensionPlot1_DataBoxOpen
#> 5 #ReducedDimensionPlot1_Type + .selectize-control
#> 6 #ReducedDimensionPlot1
#> 7 #ReducedDimensionPlot1_VisualBoxOpen
#> 8 #ReducedDimensionPlot1_ColorBy
#> 9 #ReducedDimensionPlot1_ColorByColumnData + .selectize-control
#> 10 #ReducedDimensionPlot1
#> 11 #ColumnDataPlot1
#> 12 #ColumnDataPlot1_DataBoxOpen
#> 13 #ColumnDataPlot1_XAxis
#> 14 #ColumnDataPlot1
#> 15 #ColumnDataPlot1_XAxisColumnData + .selectize-control
#> 16 #ColumnDataPlot1
#> 17 #ColumnDataPlot1_YAxis + .selectize-control
#> 18 #ColumnDataPlot1
#> 19 #FeatureAssayPlot1
#> 20 #ColumnDataTable1
#> 21 #RowDataTable1
#> 22 #RowDataPlot1
#> 23 #SampleAssayPlot1
#> 24 #ComplexHeatmapPlot1
#> 25 #FeatureAssayPlot1_SelectionBoxOpen
#> 26 #FeatureAssayPlot1_ColumnSelectionSource + .selectize-control
#> 27 #ReducedDimensionPlot1
#> 28 #FeatureAssayPlot1
#> 29 #FeatureAssayPlot1_ColumnSelectionRestrict
#> 30 #ReducedDimensionPlot1_INTERNAL_PanelMultiSelectInfo
#> 31 #ReducedDimensionPlot1_INTERNAL_PanelSelectLinkInfo
#> 32 #FeatureAssayPlot1
#> 33 #FeatureAssayPlot1_DataBoxOpen
#> 34 #FeatureAssayPlot1_YAxisFeatureSource + .selectize-control
#> 35 #RowDataTable1
#> 36 #FeatureAssayPlot1
#> 37 #ReducedDimensionPlot1
#> 38 #ComplexHeatmapPlot1_DataBoxOpen
#> 39 #ComplexHeatmapPlot1_Assay + .selectize-control
#> 40 #ComplexHeatmapPlot1_CustomRows
#> 41 #ComplexHeatmapPlot1_VisualBoxOpen
#> 42 #ComplexHeatmapPlot1_ColumnData + .selectize-control
#> 43 #ComplexHeatmapPlot1_RowData + .selectize-control
#> 44 #ComplexHeatmapPlot1_VisualChoices
#> 45 #ComplexHeatmapPlot1_AssayCenterRows
#> 46 #ComplexHeatmapPlot1_AssayScaleRows
#> 47 #ComplexHeatmapPlot1_DivergentColormap + .selectize-control
#> 48 #ComplexHeatmapPlot1_SelectionBoxOpen
#> 49 #ComplexHeatmapPlot1_RowSelectionSource + .selectize-control
#> 50 #ComplexHeatmapPlot1_ColumnSelectionSource + .selectize-control
#> 51 #RowDataPlot1
#> 52 #ColumnDataPlot1
#> 53 #ComplexHeatmapPlot1
#> 54 #ComplexHeatmapPlot1_ColumnSelectionRestrict
#> 55 #ComplexHeatmapPlot1_ClusterRows
#> 56 #ComplexHeatmapPlot1_ClusterRowsDistance + .selectize-control
#> 57 #ComplexHeatmapPlot1_ClusterRowsMethod + .selectize-control
#> 58 #ComplexHeatmapPlot1
#> 59 .navbar-static-top
#> 60 .navbar-static-top
#> 61 .navbar-static-top
#> 62 .navbar-static-top
#> 63 #Thanks
#> intro
#> 1 Welcome to the interactive tour for iSEE - the Interactive SummarizedExperiment Explorer.<br/><br/>You will be shown around the different components of iSEE and learn the basic usage mechanisms by doing. Highlighted elements will respond to the user's actions, while the rest of the UI will be shaded. You will be prompted at particular spots to perform some actions, which will be marked with "<strong>Action:</strong>" text. Please take care to follow these instructions, since later parts of the tour may assume that all the actions from previous steps have been performed.<br/><br/><strong>Action:</strong> now, click on the 'Next' button or use the right arrow of your keyboard to proceed into your tour.
#> 2 iSEE provides a Shiny interface that allows you to generate a series of panels for exploring <code>SummarizedExperiment</code> objects. Here, we use single-cell RNA sequencing data from the <a href="https://doi.org/doi:10.18129/B9.bioc.scRNAseq"><i>scRNAseq</i> package</a>, specifically a subset of gene expression profiles from cells in the mouse visual cortex <a href="https://doi.org/10.1038/nn.4216">(Tasic <i>et al.</i>, 2016)</a>. Each column of the <code>SummarizedExperiment</code> corresponds to a cell, while each row corresponds to a gene.<br/><br/>Using iSEE, you can generate a variety of different panel types to visualize the data. These are described in more detail in the following steps.
#> 3 For example, you can construct a <font color="#402ee8">Reduced dimension plot</font> to visualize a low-dimensional representation (e.g., PCA, <i>t</i>-SNE) of our dataset of interest. Here, each point represents a cell.
#> 4 For each plot panel, a variety of parameters are available to control the appearance and behaviour of the plot. These parameters are located in these collapsible boxes, such as the <font color="#402ee8">Data parameters</font> box that contains parameters related to the type of data being shown.<br /><br /><strong>Action:</strong> click on the header of this collapsible box to see the available options.
#> 5 <strong>Action:</strong> change this to <code>TSNE</code> to see the two-dimensional <i>t</i>-SNE representation.
#> 6 You can see how the panel immediately switched to the requested dimensionality reduction result.
#> 7 The <font color="#402ee8">Visual parameters</font> box contains parameters related to coloring, point appearance, and other visual aspects.<br /><br /><strong>Action:</strong> please click on the header of this box to see the available options.
#> 8 By default, the points (cells) are not colored.<br/><br/><strong>Action:</strong> select <code>Column data</code> to colour the cells by a column metadata variable.
#> 9 Currently, the points are colored by the first available column in the <code>colData</code> slot.<br/><br/><strong>Action:</strong> choose <code>passes_qc_checks_s</code> to colour cells based on whether they passed quality control (QC) checks.
#> 10 You can now see that all cells that passed QC (<code>Y</code>) are colored differently from those that did not (<code>N</code>).
#> 11 You can construct a <font color="#402ee8">Column data plot</font> involving column-level metadata, where each point represents a column (in this case, a cell) in the <code>SummarizedExperiment</code> object. Points can be displayed using violin plots stratified by an experimental factor on the x-axis, or as a scatter plot involving continuous variables on both the x- and y-axes.
#> 12 The plotting behaviour will also change depending on the type of data being plotted. This is best illustrated with <font color="#402ee8">Column data plot 1</font>.<br/><br/><strong>Action:</strong> click on the header of this collapsible box.
#> 13 Currently, there is only one violin plot.<br/><br/><strong>Action:</strong> click on <code>Column data</code>.
#> 14 You can see how this now changes to a scatter plot when plotting two continuous variables against each other.
#> 15 <strong>Action:</strong> click on (or type) <code>driver_1_s</code>.
#> 16 The plot now changes to a grouped violin plot for a categorical variable on the x-axis. Note that a horizontal violin plot can also be generated if the categorical variable is assigned to the y-axis.
#> 17 <strong>Action:</strong> click on (or type) <code>dissection_s</code>.
#> 18 The plot now changes to a Hinton plot for two categorical variables. The size of each rectangle corresponds to the number of points in each combination of covariate levels.
#> 19 A <font color="#402ee8">Feature assay plot</font> displays the assay values for a chosen feature (here, gene expression) across all cells. Like the <font color="#402ee8">Column data plot</font>, points represent cells and can be shown in both violin or scatter plots, depending on the variable selected on the x-axis.
#> 20 A <font color="#402ee8">Column data table</font> displays the contents of the <code>colData</code> of your input object as a table.
#> 21 Similarly, a <font color="#402ee8">Row data table</font> displays the contents of the <code>rowData</code> of your input object as a table.
#> 22 You can also plot the <code>rowData</code> values against each other with a <font color="#402ee8">Row data plot</font>.
#> 23 A <font color="#402ee8">Sample assay plot</font> displays the assay values across features for a chosen sample on the y-axis (possibly against another sample on the x-axis). This is best used for bulk datasets where it is more feasible to inspect individual samples.
#> 24 You can produce a <font color="#402ee8">Complex heat map</font> to visualize a feature-by-sample matrix of assay values, for a selected subset of features.
#> 25 The <font color="#402ee8">Selection parameters</font> box contains parameters to handle sharing of multiple point selections across panels. The idea is that points selected in a "transmitting" panel can be visualized in a "receiving" panel.<br/><br/><strong>Action:</strong> click on the header of this collapsible box to see the available options.
#> 26 <strong>Action:</strong> select <font color="#402ee8">Reduced dimension plot 1</font>.<br /><br />This means that multiple point selections from <font color="#402ee8">Reduced dimension plot 1</font> will be transmitted to the current <font color="#402ee8">Feature assay plot 1</font>.
#> 27 <strong>Action:</strong> make a brush on this plot by click-and-dragging a rectangular region over some points.
#> 28 The points selected in the <font color="#402ee8">Reduced dimension plot 1</font> are shown as opaque in this plot, while all other points are semi-transparent.
#> 29 If <font color="#402ee8">Restrict</font> is set, only the selected points will be plotted in the receiving plot.
#> 30 Some information on the number of selected points is displayed in this element.
#> 31 Similarly, a summary of the relationships between panels is displayed here.
#> 32 Even though this panel is receiving a selection, it is still able to transmit a selection to other panels. In this manner, we can construct arbitrarily complex (non-circular) networks between panels to display only a subset of points that match a certain criteria. This is equivalent to multi-step gating schemes in flow cytometry, provided the selection effect is set to <font color="#402ee8">Restrict</font>.<br/><br/>Another important thing to keep in mind when selecting points from violin plots is that points will be selected only if the brushed area includes the center of the x-tick, i.e., the center of the violin plot. This simplifies point selection as users do not need to capture the width of the violin plot, the values of which have no real meaning for individual points.
#> 33 Certain parameters can also respond to single selections from other panels, which we will demonstrate with the <font color="#402ee8">Feature assay plot</font>.<br/><br/><strong>Action:</strong> open this box.
#> 34 <strong>Action:</strong> select <font color="#402ee8">Row data table 1</font>.
#> 35 <strong>Action:</strong> click on another row.
#> 36 You can see that the y-axis has now changed to reflect the new row selection in <font color="#402ee8">Row data table 1</font>. A similar approach can be used to automatically synchronize the coloring of points by assay values.
#> 37 A zooming functionality is also available by first brushing, then double-clicking on the brushed area.<br /><br /><strong>Action:</strong> click and drag to brush over an area. Now, double click inside the marked area to zoom in. To zoom out to the original plot, double-click on the plot.
#> 38 The <font color="#402ee8">Complex heat map</font> panel provides a powerful and responsive visualisation of any assay matrix in the <code>SummarizedExperiment</code> object.<br/><br/><strong>Action:</strong> open this box.
#> 39 The assay displayed can be changed here.
#> 40 This checkbox switches the selection of features between two modes:<ul><li>A manual selection using a text editor to paste and edit a custom list of features.</li><li>The same dynamic selection mechanism used for other row-based panels.</li></ul><strong>Action:</strong> untick the box to enable dynamic selection (i.e. disable the custom list of features).
#> 41 The <font color="#402ee8">Complex heat map</font> panel also includes a number of visual effects to augment the plot with additional integrated information.<br/><br/><strong>Action:</strong> open this box.
#> 42 Column-level metadata can be selected and displayed above the heatmap.<br/><br/><strong>Action:</strong> Select a few metadata fields. Note how items can be reordered by drag-and-drop.
#> 43 The same can be done to display row-level metadata on the left of the heatmap.<br/><br/><strong>Action:</strong> Select a few metadata fields.
#> 44 Data transformation can also be applied to rows of the matrix, to facilitate intepretation without the need to store additional assay matrices.<br/><br/><strong>Action:</strong> tick the 'Transform' box. You may also untick the 'Annotations' box to free up space in the Visual parameters box.
#> 45 Row values can be centered using this checkbox.<br/><br/><strong>Action:</strong> tick the box.
#> 46 Once centered, row values can additional be scaled using this checkbox.<br/><br/><strong>Action:</strong> tick this box.
#> 47 Similarly, if rows are centered, a colormap suitable for centered values can be selected in this menu.<br/><br/><strong>Action:</strong> choose any colormap.
#> 48 The two-dimensional nature of the <font color="#402ee8">Complex heat map</font> panel makes it the only builtin panel capable of receiving selections on both dimensions simultaneously.<br/><br/><strong>Action:</strong> open this box.
#> 49 The source of incoming row selections (i.e., features) can be selected here.<br/><br/><strong>Action:</strong> Select the panel <font color="#402ee8">Row data plot 1</font>
#> 50 Similarly, the source of incoming column selections (i.e., samples) can be selected here.<br/><br/><strong>Action:</strong> Select the panel <font color="#402ee8">Column data plot 1</font>
#> 51 <strong>Action:</strong> Select data points representing features in this <font color="#402ee8">Row data plot</font>.
#> 52 <strong>Action:</strong> Select samples representing samples in this <font color="#402ee8">Column data plot</font>.
#> 53 You can see the result of our interactions so far, including the selections applied to both rows and columns of this plot.
#> 54 By default, all samples are shown in the heatmap and column selections are marked by a separate colored annotation bar. Alternatively, the heatmap can be restricted to show only the selected samples.<br/><br/><strong>Action:</strong> enable the 'Restrict' option.
#> 55 Having seleted features of interest, it is often helpful to cluster them by expression profile.<br/><br/><strong>Action:</strong> tick this box.
#> 56 A number of clustering distances are available to choose from.
#> 57 So are clustering methods.
#> 58 At this point, we have constructed a rich heatmap that combines assay data with column and row metadata for a selection of features and samples, with features clustered by expression profile.
#> 59 A number of useful features are available from the dashboard header.<br /><br /><strong>Action:</strong> click on the panels icon to open the Panel Organization menu.<br /><br />Clicking on the <i>Organize panels</i> button opens a modal window, which contains a multi-selectize input widget to add, remove, resize, and reorder panels in the main interface. You can see how the boxes below this control mirror the order of the panels in the main body of the app.
#> 60 <strong>Action:</strong> click on the download icon to open the Export dropdown.<br /><br />In this dropdown menu, you can access a graph representation of the existing links between panels, the code required to reproduce all of the plots, and the code required to reproduce the current app state.
#> 61 <strong>Action:</strong> click on the question mark icon to open the Documentation dropdown.<br/><br/> This contains the button to start the <code>introjs</code>-based tour, which you are currently taking, as well as a link to the vignette for the iSEE package, in case you want to consult it while running the app.
#> 62 <strong>Action:</strong> click on the info icon.<br/><br/>This reports the session information from which you are running iSEE (which you should report in case of issues or bugs), the appropriate citation if you use iSEE in your work, as well as information on how to contact the iSEE development team.
#> 63 Thank you for taking the tour of iSEE!