Spectacle and cellcuratoR were developed by an interdisciplinary team within the Institute for Vision Research at the
University of Iowa. The Institute of Vision Research is deeply dedicated to treating and curing
visual diseases. More information can be found at our website
ivr.uiowa.edu
Powered by cellcuratoR
cellcuratoR is an R package for sharing interactive single-cell expression data from Seurat. Any single-cell RNA
sequencing dataset processed with Seurat (v3) can be converted into objects interpretable by cellcuratoR. Code and
documentation are available at
github/drewvoigt10/cellcuratoR
as well as animated instructions for navigating the user interface.
The Spectacle interface is organized into three columns. (A) The left most column comprises the sidebar
which contains five menu items: “Dashboard”, “About”, “Citation and Dataset Info”, “How to guide”, and “cellcuratoR”.
When the “Dashboard” menu item is selected, the interactive single-cell app can be accessed. The dataset of interest
can be selected within the dataset dropdown (*). The three horizontal bars (arrow) can be selected to hide/show
the sidebar for more screen real estate. Within the Dashboard menu item, each reactive content space has multiple tabs that can be selected
to display different visualizations. Below each tab, there is a space for reactive helper functions that provide
user inputs to customize the overlying visualizations.The “About” menu item contains information about the Iowa Institute for
Vision Research, which developed Spectacle and cellcuratoR. The “Citation and Dataset Info” menu item contains information about how to cite
Spectacle and the datasets contained in this platform. The “How to guide” menu item contains screen shots and tutorial videos for
using Spectacle. The “cellcuratoR” menu item provides information about the R package (cellcuratoR) that powers Spectacle, along
with a link to the source code on GitHub.
In the dashboard menu item (arrow), once a dataset is selected, an interactive plot is displayed in the Dimensionality Reduction Tab.
Cells in this plot can be colored according to their cluster classification (A), or by library composition of the
cells (B) by selecting the color scheme from the drop-down helper, Dimensionality reduction plot color.
A. Expression patterns for a gene of interest can be overlaid on the dimensionality reduction plot when the
“Heatmap” tab (arrow) is selected. Once the heatmap tab is selected, the “gene for heatmap” helper input appears
below the plot, where the user can input (as text) the gene name to query. By default, only 100 genes are avaialble
in this reactive text input box at a time. Simply start typing the name of the gene, and a string-matching search
will return gene names with matches to the text input. To the right of the heatmap, a custom
legend mapping color to expression value appears. The scale of expression in this legend is “TP.10K,” or
transcripts per 10,000, as all datasets used in Spectacle were normalized with a scale.factor of 10000 using the
NormalizeData() function.
B. Alternatively, expression can be visualized on a per-cluster level with violin plots. On the right side of
the violin plots, a dendrogram depicts the relationships between each cluster. By default, the export_seurat_object
function creates a dendrogram by identifying the 100 most differentially expressed genes in each cluster, finding
the average expression of these genes across all clusters, and performing hierarchical clustering on the resulting
expression matrix with a Canberra distance metric and complete linkage. On the right side of the plot, violins are
drawn depicting expression in each cluster. If less than 25% of the cells in a cluster do not express the gene of
interest above background, a vertical line is drawn representing the range of detected expression levels, but the
violin distribution is not displayed. Multiple genes can be simultaneously visualized within the violin plot by
adding gene names to the helper input “Violin genes.”
Clusters of interest can be re-clustered in a lower dimensional feature space within the “Recluster” tab (arrow).
Once selected, the user will be prompted to choose one or more clusters of interest to recluster in the input helper
selection (A). In this example, cone clusters 3 and 4 were selected with the checkbox input. The user can then
initiate the reclustering by pushing the “start reclustering (slow)” action button. A progress bar will be displayed
on the lower right corner of the window indicating each step of the reclustering. When complete, a plot of the
reclustered cells appears. By default, these cells are clustered according to the original cluster color, as depicted
in the dimensionality reduction tab on the left side of the screen. However, cells can also be colored by libraryID
or by expression of a gene of interest by changing the input to “How should the cells be colored?” at the bottom of
the helper box.
Reclustering parameters can be tuned after clicking the advanced settings checkbox. The user can adjust the
default dimensionality reduction strategy for visualization, which includes principal component analysis (pca), t-distributed
stochastic neighbor embedding (tSNE), or uniform manifold approximation and projection (UMAP). Likewise, the user can adjust parameters
important in normalization, such as the number of variable features and how these features are selected. Finally, the user can
adjust the granularity of the reclustered object, which can be visualized by coloring cells 'by new cluster' under the 'How should
the cells be colored?' dropdown menu. Conversely, if the user selects to color reclustered cells by gene (bottom),
an additional selection appears where the gene of interest can be input. Of note, the dimesnionality reduction method
and granularity of the reclustered object can be modified without re-executing the reclustering process. However, adjusting the
number of variable features or how they are selected requires re-clicking 'start reclustering (slow)' for the changes to take effect.
Differential expression can be conducted between clusters of cells. First, the user should select the two differential
expression tabs at the top of the dashboard window (arrows). This will result in display of the differential expression helper
input appearing (A). The user may adjust the logFC.threshold, which removes genes with mean expression differences
between the two groups that are less than the specified threshold. Additionally the user may adjust the minimum
percent threshold, which removes genes that are not expressed in at least the selected proportion of cells in either of
the comparison groups. Thresholds with larger values result in faster differential expression testing with less sensitivity,
while thresholds with smaller values result in slower differential expression testing with more sensitivity.
The lower limit of both thresholds is set to 0.1 to minimize load on the hosted server. Next, the user selects the dataset for differential
expression. If a reclustering procedure has been performed (see previous tabs), then the reclustered object is avaialble for differential
expression. However in most cases, the default object containing all cells is most appropriate for differential expression testing. Next, the user
should select what groups will be compared with the differential expression (horizontal arrows). If the user selects the “predefined
clusters” radio button, an additional differential expression helper will be displayed on the right side of the screen
where the user can select one or more clusters of cells to be compared. In this example, cone cluster 3 is being compared
to cone cluster 4. When complete, the user then may click the “Start Differential Expression Analysis” action button,
prompting differential expression to start. A progress bar will appear in the lower right corner of the window that displays
the progress of the differential expression testing. When complete, the differential expression results are displayed both
visually and in table format. In (C), the average log fold-change (y-axis) and delta.percent (x-axis) of the differentially
expressed genes are displayed, where each point depicts the differential expression results of a single gene. The
delta.percent variable depicts the percentage of cells in group 1 that express a gene above background minus the percent
of cells in group 2 that express the gene above background. For example, transferrin (TF) is expressed in 80.9% of cones
in Cluster 4 and 6.4% of cones in Cluster 3. Therefore, the delta.percent of TF is 0.064 – 0.809 = -0.745. This allows for
communication of the absolute expression level on the y-axis (logFC) and the proportion of cells expressing each gene on
the x-axis (delta.percent). This plot is fully interactive, and the user can hover the mouse over each gene to visualize
more information regarding the differential expression analysis. Of note, positive logFC and delta.percent values represent
increased expression in clusters selected in Group 1. In (D), cells on the dimensionality reduction plot are colored
according to their selected group, with cells in group 1 colored red and cells in group 2 colored blue. In (E), differential
expression results are displayed in table format. Genes of interest can be reactively searched in the search bar, and the
table can be reordered to display genes enriched in each comparison group by arranging genes based on each column. In addition,
results from the differential expression can be exported into .csv or .xlsx files.
In contrast, the user may also manually identify groups for differential expression interactively. If the user selects the
“manually select populations (draw lasso)” radio button (A), an additional, interactive dimensionality reduction plot appears
on the right side of the screen. The lasso tool is automatically selected (arrow, top), and the user can draw a lasso around
the cell population for Group 1 on the left plot (B) and for Group 2 on the right plot (C). The user then may select the start
differential expression analysis action button, and differential expression results will be performed as displayed
in the previous tab.
The user may also perform differential expression between other binary identities corresponding to different biologically
meaningful features that are identities in the meta.data. For
example, in this experiment, foveal and peripheral libraries were independently prepared. By selecting (A) “Perform differential
expression between region,” differential expression is performed between foveal and peripheral libraries for all cells selected
in the group (in this case, glial cell populations) (B). This feature is also compatible with the lasso selection tool.
Dataset Citation:
if you use expression data from a dataset hosted on spectacle, please consider citing that dataset (citation information in the above Datasets tab.)