The Insights module of TrailmakerTM is where processed data files generated after FASTQ file processing are input for downstream analysis and visualization.
The Insights module offers advanced filtering and data cleanup, integration of multi-sample datasets, customization of data visualization and clustering, cell set annotation, differential expression and pathway analysis, and plot customization for the generation of publication-ready figures.
For paired data, the WT and immune data are automatically integrated into a single project, giving researchers a complete view of clonal structure and diversity, and gene expression signatures. Clonotypes and cell types can be visualized on an integrated UMAP, dominant clones can be identified quickly in the frequency and honeycomb plots, and conserved patterns can be detected using motif analysis.
By linking repertoire data with whole transcriptome expression, Trailmaker makes it easy to connect clonotypes to their transcriptional states, reveal clonotype-specific gene programs, and run targeted differential expression, delivering deeper, more contextualized biological insights.
Data entry to Insights module for immune repertoire analysis
Insights projects are generated automatically following a successful immune run in Trailmaker’s Pipeline module. Alternatively, in cases where the Parse pipeline was ran locally, paired whole transcriptome (WT) and immune (TCR or BCR) or immune only data can be uploaded directly to the Insights module.
Exploring an immune dataset from the dataset repository
Several paired WT and immune datasets are available to explore in the Trailmaker dataset repository: https://app.trailmaker.parsebiosciences.com/repository
- Example TCR dataset: “Evercode TCR demo - Jurkat T cell spike in”
- Example BCR dataset: “Evercode BCR demo - HyHEL sensitivity”
More information about how to use the repository is available in the article: How to explore demo datasets from Trailmaker’s dataset repository
Data Processing for immune repertoire analysis
The Data Processing page is enabled for paired WT and immune (WT+TCR or WT+BCR) projects. A thorough overview of the functionality of this page is presented in the article: Guided walkthrough: Insights Data Processing. This section provides specific information regarding Data Processing for immune data analysis in Trailmaker.
Within Data Processing steps 1-5, the WT data are filtered according to the default or user-selected filtering thresholds. After WT data filtering is complete (at the end of step 5), the immune data are filtered to match the barcodes present in the WT data. This immune filtering step is not visible on the user interface. Any failures of Data Processing at the immune filtering step will appear on the user interface as a failure in step 6 (Data integration) or step 7 (Configure embedding). In this case, reach out to support@parsebiosciences.com for help.
The functionality of Step 6: Data integration and Step 7: Configure Embedding is unaffected by the inclusion of immune data. Note that Scanpy is the default and only option for immune repertoire analysis in Trailmaker.
At the end of Data Processing, paired WT+immune projects contain separate Anndata objects for WT and immune data, which are combined into a single MuData object with matched WT+immune data for downstream analysis and visualization in Data Exploration and Plots & Tables pages.
Data Exploration for immune repertoire analysis
The Data Exploration page is enabled for paired WT and immune (WT+TCR or WT+BCR) projects, allowing exploration of genes and clonotypes in your dataset.
The overview of gene expression (WT) functionality in Data Exploration is provided in the separate article: Guided walkthrough: Insights Data Exploration. This article focuses on the exploration of immune data.
For this guided walkthrough, we’re using the “Evercode TCR demo - Jurkat T cell spike in” dataset that’s available in the repository.
The core functionality for immune data exploration is centred around the Clonotype list that is displayed as a separate tab next to the Gene list on the right side of the Data Exploration view.
One of the most common ways to begin exploring clonotypes is to identify the most abundant clonotypes in your dataset and to visualize those clonotypes on the UMAP. Abundant clonotypes can be quickly identified on the Clonotype list, which is ordered by descending frequency.
In this example, the dominant clonotype is expressed by 21764 cells within the dataset, which equates to a frequency of 0.3246.
Visualizing individual clonotypes of interest
Individual clonotypes can be selected using the eye icon in the Clonotype list in order to visualize their expression on the UMAP.
For example, one view option is to visualize a clonotype of interest, in this case the dominant clonotype, on top of the coloring by another cell set family from the Cell sets and Metadata tile. This example shows the dominant clonotype (in navy) on top of whether or not a TCR was detected:
Alternatively, the dominant clonotype (in navy) can be viewed on top of expression of a specific gene of interest (in pink), such as CD3E which is a common marker of T cells:
Together, these two plots indicate that in this project there are two major populations on the UMAP, both of which are T cells (they express CD3E and have TCRs detected). However, the dominant clonotype is only expressed by the population on the right side of the UMAP.
Another application of this feature in a mixed cell type dataset, such as PBMCs, has the potential to highlight which cell cluster (such as activated B cells) contains a specific BCR clonotype of interest.
Visualizing multiple clonotypes of interest
Multiple clonotypes can be visualized on the UMAP using the ‘Color UMAP’ function in the Clonotype list. To use the ‘Color UMAP’ function, select the clonotypes of interest using the check boxes next to each clonotype in the Clonotype list, then select ‘Color UMAP’. In the modal, the colors of individual clonotypes of interest can be customized.
In the example below, we can see that clonotype 3 (in purple) is localized to the population of cells on the right, whereas clonotypes 2 and 4 (in blue and red respectively) are localized close together within the population of cells on the left.
This might prove biologically interesting and worthy of further research.
Performing differential expression with clonotypes
Having identified that clonotypes 2 and 4 are co-localized to an adjacent area of the UMAP, we might want to investigate those cells further by comparing their gene expression profiles. To do this, we can create two separate custom clonotype cell sets for these clonotypes:
We can then use these custom clonotype cell sets to perform differential expression. In doing so, we return a list of differentially expressed genes that can be visualized in the heatmap. See this section of the user guide for more details about setting up the heatmap.
These clonotypes of interest could be subsetted into a new analysis for further exploration using the ‘Subset’ button within the Cell sets and Metadata tile that appears when the cluster of interest is selected. See this section of the user guide for more details.
Plots and Tables for immune repertoire analysis
Within the Plots and Tables module, the Clonotype Frequency and Honeycomb plots are useful for identifying differences in clonal expansion between samples or groups of interest. The Clonotype Frequency and Honeycomb plots display the same data in different ways.
In the example below, we can easily identify and visualize differences in the expanded clonotypes present in our control and test samples:
In the Honeycomb plot, we can color by sample to illustrate the representation of the abundant clonotypes across the samples in our dataset:
This feature could provide biological insight into particular clonotypes that might be present in specific samples of groups of interest.
Finally, the Motif analysis plot allows us to investigate conserved chains. In this Jurkat T cell spike-in dataset, we can easily identify the conserved TRA and TRB chain sequences of the Jurkat TCR:
As another example, this plot could be used to discover conserved chain sequences across multiple expanded clonotypes that might suggest recognition of a common antigen.
Full explanation of all features that support the analysis of immune repertoire data in Trailmaker’s Insights module are available in our user guide:
- Data Processing for immune repertoire analysis
- Data Exploration for immune repertoire analysis
- Plots & Tables for immune repertoire analysis
Key links
- Access Trailmaker
- Trailmaker user guide
- Introduction to Trailmaker video
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Free Mastering Single Cell RNA-seq Data Analysis course