The Plots and Tables page of TrailmakerTM provides a wide range of pre-loaded data visualization options to quickly and easily obtain insights from your data. It also allows users to customize the plots and export them in a variety of formats, including high resolution images for publication. The Plots and Tables overview page shows the plot types available:
A detailed explanation of each individual plot and table option within this module is provided in the user guide. The goal of this article is to provide a guided walkthrough on how you might use some of these plots to gain biological insights into your dataset. This walkthrough uses the “Human PBMCs - Evercode v3” dataset that’s available in Trailmaker’s datasets repository.
The Frequency plot is particularly useful for visualizing the cell types present in each sample or metadata group present in the dataset. The example below shows the count of cells in the annotated clusters that were generated using the scType automatic annotation for human immune cells. The frequency plot helps us to identify any sample or group that could be considered an outlier within the dataset in terms of total number of cells, or the number of cells in a specific cell type. In the example below, the population of Naive CD4+ T cells looks to be over-represented in sample “Donor_2” compared to the other samples. This observation could be worthy of further investigation or it might simply be the result of an expected level of variation between donors.
The frequency plot can be adjusted to show proportion (percentage) rather than count, and metadata groups (in this case F and M to represent Female and Male, respectively) rather than individual samples. The resulting plot, shown below, suggests that the proportion of Naive CD4+ T cells is over-represented in the F group compared to the M group. Conversely, Classical Monocytes appear to be present in higher proportion in the M group compared to F. These apparent differences might be biologically relevant to the researcher and could warrant further investigation.
The Categorical embedding plot can be used as an alternative way to visualize any interesting findings from the Frequency plot. For example, the Categorical embedding can plot a UMAP showing the annotated clusters for our groups of interest, in this case showing groups F and M in separate plots:
In doing so, we can again highlight the potential differences in Naive CD4+ T cells and Classical Monocytes between the F and M groups.
As a next step, it’s typical to want to perform differential expression between metadata groups of interest, across all cell populations present in the dataset. The fastest way to do this is using the Batch differential expression table. Using this feature, it is possible to calculate the differentially expressed genes for a F versus M comparison, in all cell types that were annotated using the scType automatic annotation for human immune cells.
This results in a series of CSV files that are downloaded to your local computer, each of which contains a full list of differentially expressed genes for a given cell type.
In order to visualize these differences in gene expression profiles, we recommend the Volcano plot. In this example, we’ve compared Classical Monocytes between F and M. The genes in blue are upregulated in the first group (in this case F) while the genes in red are upregulated in the second group (in this case M):
These so-called genes of interest that are most differentially expressed between the Classical Monocyte populations in our 2 metadata groups (F and M) can then be visualized in different ways, as per the examples below.
The Violin plot (top) and Continuous embedding (bottom) are useful for visualizing individual genes across cell types, samples or groups. The examples below show the top differentially expressed gene from the F versus M comparison in Classical Monocytes:
The Dot plot (top) and heatmap (bottom) provide a visually attractive option for plotting multiple genes. In the example plots below, the top differentially expressed genes in Classical Monocytes when comparing F to M are plotted. These genes were copied from the differentially expressed gene results list (in Data Exploration) and then pasted into the relevant plot type in the Plots and Tables module.
Trajectory analysis is available for mapping pseudotime where cell types with different differentiation states are present within the dataset. See the article How to reproduce a published trajectory analysis plot with Trailmaker for further guidance on this plot.
Data tables, such as lists of differentially expressed genes, or proportion of cells, are available to download within several individual plots and via the Normalized expression matrix feature.
Key links
- Access Trailmaker
- Trailmaker user guide
- Introduction to Trailmaker video
- Free Mastering Single Cell RNA-seq Data Analysis course
Other articles in the Trailmaker guided walkthrough series
- Guided walkthrough: Pipeline module set-up
- Guided walkthrough: Pipeline Outputs
- Guided walkthrough: Insights module set-up
- Guided walkthrough: Insights Data Processing
- Guided walkthrough: Insights Data Exploration