# Trajectory inference across multiple conditions with condiments

## TL;DR

The paper introduces condiments, a method to analyze and compare cell differentiation trajectories across different biological conditions using single-cell RNA sequencing data.

## Contribution

The novel contribution is a framework for trajectory inference and interpretation across multiple conditions, enabling comparison at trajectory, population, and gene levels.

## Key findings

- Condiments integrates datasets from multiple conditions into a unified trajectory for analysis.
- The method detects large-scale changes in cell progression and fate selection between conditions.
- It identifies genes with differential behavior along a differentiation path between conditions.

## Abstract

In single-cell RNA sequencing (scRNA-Seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolution. In such dynamic biological systems, cellular states form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-Seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present condiments, a method for the inference and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the cell’s conditions along the trajectory’s path, we can detect large-scale changes, indicative of differential progression or fate selection. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

scRNA-Seq has enabled the study of dynamic systems such as response to a drug at the individual cell and gene levels. Here the authors introduce a framework to interpret differences at the trajectory, cell populations, and individual gene levels.