Single Cell Data Analysis Made Easy with Trailmaker

Flexible Data Entry and Exit Points

Trailmaker provides end-to-end analysis of Evercode data with flexibility built in to support count matrices from various single cell technologies. Advanced analysis is enabled through Seurat object download for code-based methods.

FASTQ File Processing

Trailmaker’s Pipeline module processes Parse Biosciences Evercode™ FASTQ files, generating reports and count matrices for viewing or downloading.

Data is automatically sent to the Insights module for downstream processing and visualization.

In-Depth Filtering and QC

You are guided step-by-step through the necessary filtering and integration processes.

Automatically determined default settings deliver ready-to-explore data analysis, with user friendly controls for adjusting filtering thresholds, parameters and methods.

Automatic Cell Type Annotation

Trailmaker’s automatic annotation feature helps identify the cell types present in your dataset. The cell type prediction methods available are: ScType, Decoupler, and CellTypist.

Gene Expression

• Search for genes of interest and view expression on a UMAP or t-SNE plot in a single click
• Violin plots make it easy to visualize gene expression across clusters, samples or groups
• View gene sets of interest in dot plots or heatmaps

Custom Cell Set Generation

If Leiden or Louvain clusters don’t quite fit your use case, you can generate custom cell sets using the lasso or gene expression features. It’s also easy to combine clusters or to subset specific clusters into a new analysis.

Differential Expression Analysis

Differential expression analysis calculates the differences in gene expression profiles between clusters or cell populations of interest.

Differential expression results can be filtered, plotted in a Volcano plot, sent for pathway analysis and downloaded.

Immune Repertoire Analysis

The fully integrated immune repertoire and whole transcriptome data analysis workflow in Trailmaker enables researchers to visualize clonotypes and cell types on an integrated UMAP, quickly identify dominant clones, and detect conserved patterns.

By linking repertoire data with gene 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.

Fully Customizable Publication-Ready Plots

Pre-loaded plots are available to view, customize and download.

Customization options include:

• Specify genes, cell sets and samples/groups to be plotted
• Adjust plot size, axes labels, ranges, sizes, and font
• Specify the title and legend values and location
• Define the colour scheme
• Export the data (CSV file) and plot (SVG or PNG image file)

Trajectory Analysis

Calculate trajectories and plot pseudotime in a customizable trajectory plot. Methods supported include Monocle3 in Seurat and Partition-based graph abstraction (PAGA) in Scanpy.

Data Sharing

Share individual projects with colleagues or collaborators to work together on data analysis.

Datasets Repository

Grab a dataset from the Trailmaker datasets repository to start exploring Trailmaker’s data analysis and visualization capabilities today!