There are options in the Shiny runApp function to help troubleshoot these situations, and these are accessible from the runShiny function in scClustViz.
To demonstrate the convenience of sharing analysed data with scClustViz, the MouseCortex package was built with data from a recent publication exploring the development of the mouse cerebral cortex using scRNAseq Yuzwa et al. A tutorial for building similar R data packages calling scClustViz as the visualization tool can be found on the scClustViz website. The MouseCortex package contains the four data sets published in the paper, and a wrapper function for runShiny that loads each data set with the appropriate arguments.
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The embryonic day The first step in the post-clustering workflow is to assess the results of the various clustering parameterizations used. Differential gene expression is used as a metric in two ways: the number of positively differentially expressed genes between a cluster and its nearest neighbour, and the number of marker genes positively differentially expressed vs. In Figure 2a , differential expression to the nearest neighbouring cluster is represented as a series of boxplots per cluster resolution, arranged on the x-axis to indicate the number of clusters in each boxplot.
The highlighted boxplot indicates the currently selected cluster from the pulldown menu in the user interface. Both differential expression-based metrics can be visualized this way by switching the metric used, via the interface. Boxplots representing number of differentially expressed genes between neighbouring clusters for each cluster resolution. For each cluster at a specific resolution, the number of positively differentially expressed genes to its nearest neighbouring cluster is counted, and those counts are represented as a boxplot.
The boxplots are arranged along the x-axis to reflect the number of clusters found at that resolution. Highlighted in red is the cluster resolution currently selected in the interface.
This figure has been zoomed using the interactive interface to make it clear that at the selected resolution there is more than one differentially expressed gene between neighbouring clusters. The number of marker genes per cluster and average silhouette widths can be similarly viewed with the scClustViz interface.
Silhouette plot for the selected cluster resolution. A horizontal bar plot where each bar is a cell, grouped by cluster. Silhouette width represents the difference between mean distance to other cells within the cluster and mean distance to cells in the neighbouring cluster. Distance is Euclidean in reduced dimensional generally PCA space. Positive values indicate that the cell is closer to cells within its cluster. When a cluster resolution is selected, its silhouette plot is rendered to add another method of cluster assessment Figure 2b.
A silhouette plot is a horizontal bar plot where each bar is a cell, grouped by cluster. The width of each bar, referred to as silhouette width, represents the difference between mean distance to other cells within the cluster and mean distance to cells in the neighbouring cluster. Distance is Euclidean in the reduced dimensional space used in clustering this is generally PCA space, and is pulled from the input data object based on a user-defined parameter.
It is worth noting that the dimensions returned by methods such as PCA are not equally meaningful, since each explains a different proportion of the variance in the data, while Euclidean distance treats them all equally. This can be addressed by weighting the PCs by variance explained, a method implemented in newer versions of Seurat Butler et al. To prevent unexpected results caused by assuming a PC weighting option in upstream analysis, the silhouette plot in scClustViz does not reweight PCs, so users are encouraged to consider this when interpreting this plot.
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They can also save this as the default resolution for future sessions. If a cluster resolution is saved as default, a file specifying the saved resolution will be generated in the same directory as the input data or an optional output directory.
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Specifying a separate output directory is useful when the input data is part of a package, as in MouseCortex. If the same output directory is specified the next time the command is run, all saved data in that directory will be reloaded in the app. In this section, the user can explore the data set as a whole. The first panel, Figure 3a shows a two-dimensional representation of cells in gene expression space. The cells are coloured by cluster and can be labelled by cluster number or automatically annotated with a predicted cell type based on known marker genes for expected cell types passed to runShiny.
The user can select any cluster for downstream exploration by clicking on a cell from that cluster in this plot. This will highlight the cluster in other plots in the interface. Since we are interested in identifying marker genes for the precursor cell population, we may click on cluster 8 purple to select it for downstream analysis. A 2D projection of cells in gene expression space frequently a tSNE plot is coloured by cluster. Clusters can be labelled by number, or automatically annotated as seen here. An example of a metadata overlay on the tSNE plot.
The library size number of transcripts detected per cell is represented by colour scale, where darker cells have larger library sizes. Metadata can be represented as a scatter plot. The relationship between number of unique genes detected total features — y-axis and library size total counts — x-axis is shown here. The cells from the selected cluster cluster 8, cortical precursors are highlighted in red. Categorical metadata is represented as a stacked bar plot showing the number of cells contributing to each category per cluster.
The distribution of various cellular metadata can be visualized in Figure 3b. Metadata is selected from a pulldown menu and is represented as colours on the cells in the 2D projection. In this manner the user can inspect the impact of technical artefacts such as gene detection rate, library size, or cell cycle stage on the clustering results. Numeric metadata can also be assessed as a scatter plot, where the axes can be defined by selecting from pulldown menus.
Figure 3c shows the relationship between number of genes detected and library size per cell for both the data set as a whole and the selected cluster. The cells from cluster 8, a cortical precursor cluster, were selected in the previous plot and are thus highlighted in red here. The cluster 8 cells are similar to other cells in the data, thus do not seem to be biased by the measures visualized in this plot. If this was not the case, we may want to consider investigating confounding variables in the normalization process. For example, many authors have noted that gene detection rate is often strongly correlated with the first few principal components, and can unduly influence clustering results Finak et al.
There are a few ways to handle this, from simply excluding those principal components, or explicitly normalizing for those factors when scaling the data as implemented in Seurat , to including the offending technical variables as covariates in more complex dimensionality reduction i. MAST models. While those specialized analyses are outside the scope of this tool, it is important to be able to visualize these technical factors in the analysed data to assess the efficacy of the chosen correction method.
Categorical metadata is represented as a stacked bar plot in Figure 3d , as either absolute counts or relative proportions. Here we see that by E This fits expectations from known developmental biology, since neurogenesis is nearly complete by this stage, and the stem cell population that persists into adulthood is thought to enter quiescence around E For demonstration purposes, we will continue to focus on cluster 8, which is predicted to form the adult neurogenic stem cell population in the cerebral cortex. We will aim to identify marker genes for these cells.
Once the user is satisfied that their cluster solution is appropriate and unaffected by technical factors, the next step in data interpretation is to determine the cellular identity of each cluster by its gene expression profile. The differential expression tests done prior to running the visualization assist with this by highlighting the most informative genes in the data set.
A more conservative form of this is the identification of marker genes — those genes that are significantly positively differentially expressed in all pairwise tests between a cluster and all other clusters. This highlights genes expected to be found at a significantly higher expression in this cluster than anywhere else in the data.
Finally, there is the testing between each cluster and its nearest neighbour to highlight local differences in expression. Each of these sets of differentially expressed genes can be presented as a dot plot comparing clusters, as seen in Figure 4. A dot plot is a modified heatmap where each dot encodes both detection rate by dot diameter and average gene expression in detected cells by dot colour for a gene in a cluster.
Here up to the top ten marker genes per cluster are shown, but both the type of differential expression test used to generate the gene set and the number of differentially expressed genes contributed per cluster can be adjusted using the interactive interface. This may be of value if the user is using this platform to share the data online, or with those who would prefer not to use R for further analysis.
In this dot plot, we can see the top 10 marker genes for our putatively quiescent cortical precursor cell population cluster 8 include known marker genes for cortical radial precursors Fabp7 , Slc1a3 , Ptprz1 , and Vim , a known marker for adult neural stem cells Dbi , as well as novel marker genes for this population Mfge8 , Ttyh1 , Pea15a , and Ednrb Yuzwa et al. The dot plot format also shows us that while Ckb and Gpmgb are significantly positively differentially expressed in cluster 8 relative to all other clusters, they are still detected in high proportions in all clusters, and thus would not be optimal marker genes.
A dot plot showing the relative expression of a subset of marker genes x-axis across all clusters y-axis. A dot plot is a modified heatmap where each dot encodes both detection rate and average gene expression in detected cells for a gene in a cluster. Darker colour indicates higher average gene expression from the cells in which the gene was detected, and larger dot diameter indicates that the gene was detected in greater proportion of cells from the cluster.
Cluster colours are indicated for reference on the left side of the plot. Cluster numbers are also indicated on the left side, along with the number of differentially expressed genes in each cluster. The genes included can be changed to reflect those differentially expressed per cluster when compared to the rest of the data set as a whole i.
This figure shows marker genes per cluster. The number of differentially expressed genes contributed per cluster can also be adjusted, here set to To more closely inspect the gene expression of an individual cluster, scClustViz presents gene expression data per cluster as a scatter plot with the proportion of cells from that cluster in which a gene is detected more than zero transcript counts on the x-axis, and mean normalized transcript count from cells in which the gene was detected on the y-axis, as seen in Figure 5a.
This visualization method helps separate the contribution of zeros from the mean gene expression value, since like the dot plot it separates magnitude of gene expression from gene detection rate. In this figure, the cortical precursor cluster 8 is shown, but the user can select the cluster shown from a pulldown menu in this panel as well. There are three ways to highlight various genes in this plot.
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