Welcome to Escort!

Escort is a framework that evaluates various data processing decisions in terms of their effect on trajectory inference with single-cell RNA-seq data. Escort guides users through a trajectory analysis by providing evaluations of embeddings, which represent combinations of analysis choices including feature selection, dimension reduction, normalization, and/or trajectory inference-specific hyperparameters.

In Step 1, Escort will assess the evidence of a trajectory signal in the dataset. Sometimes data are not suitable for trajectory analysis, for example, when cells come from biologically distinct clusters or have insufficient heterogeneity. In these cases, Escort will alert the user and offers guidance to further investigate the appropriateness of trajectory analysis.

In Step 2, Escort will compare various embeddings, specifically in terms of how well they preserve cellular relationships and the distribution of cells in the embedding.

In Step 3, Escort evaluates how well a specific trajectory inference method interacts with a given embedding. This allows for evaluaton of additional graph structures used by specific methods and consideration of method-specific parameters. Finally, Escort provides an overall score for each option, as well as, a classification to help researchers select more optinal analysis choices for inferring a trajectory from their data.

For any questions or issues, please submit a comment to our GitHub issues page .

Additional explanations are provided in our vignette for shinyEscort .

Step 1: Detecting existence of a trajectory signal

Before fitting a trajectory, Escort determines whether a given dataset is appropriate for trajectory analysis. There are two common scenarios that may render trajectory analysis inappropriate or require more careful consideration:
- Datasets that contain distinct/disjoint cell types
- Datasets in which the cells are too homogeneous/similar

Disjoint cell types evaluation: If this module fails, Escort will perform cluster-specific differential expression testing and display the top cluster-specific genes. Users should further examine this list to determine whether fitting a trajectory that connects these particular cell types is biologically reasonable. If so, then users should re-examine whether sufficient intermediate cells exist between these two groups, or whether batch effects have been exist and have been addressed.

Homogenous cells evaluation: If this module fails, Escort will report the top highly variable genes in the dataset along with their enrichments. Users should further examine this list to re-examine whether an underlying trajectory is appropriate. If so, then this output should be used to identify what other biological processes may be overriding or diluting the biological signal of interest (e.g. cell cycle) or excessive signal from ribosomal or mitochondrial genes.

Upload scRNA-seq datasets:

Please upload either .csv files or .rds files for both raw data and normalized data. More information on how to prepare these files from a Seurat object or SingleCellExperiment is provided xxHERExx.

Generate embeddings:

Embeddings can be generated invidivually here (in combination with the preferred trajectory method used in Step 3), or users can generate their own embeddings following the workflow in the Generating data objects for Escort R/Shiny Vignette .

Fit preliminary trajectory:

Download .rds object

Step 2: Evaluating the trajectory characteristics of embeddings

Next, Escort identifies preferred embeddings for performing trajectory inference. We will evaluate three characteristics of each embedding:
- The retention of inter-cellular relationships.
- The preservation of similarity relationships.
- Distribution of cells in the embedding space.

Inter-cellular relationships


Preservation of similarity relationships


Cell spread


Load all embeddings: (multiple allowed)

Step 3: Quantifying trajectory fitting performance

Embeddings are also evaluated in the context of a trajectory inference method. A preliminary trajectory in inferred and Escort assesses the proportion of cells having an ambiguous projection to the trajectory. For example, trajectories in a U-shape tend to be inaccurate because some cells will have map to opposing pseudotimes with equal probability.

Percentage of ambiguous cells


Escort suggestions for embedding selection

Below is a table with each embedding's rating according to their overall performance. Embeddings with a score larger than zero are Recommended for trajectoy inference.
Download the table