What is trajectory analysis?

Cells undergo changes to transition from one state to another as part of development, disease, and throughout life. These changes can be described by trajectory analysis. Progress though the biological process from the start to an end point is quantified by pseduotime. Because biological processes are rarely as simple as a direct line from one starting point and one end point, trajectory analysis builds branching trajectories where different paths can be chosen at different points along the trajectory. 

In Partek Flow, we use tools from Monocle 2 (Qui et al. 2017) to build trajectories, identify states and branch points, and calculate pseudotime values. The output of Trajectory analysis includes an interactive scatter plot visualization for viewing the trajectory and setting the root state (starting point of the trajectory) and adds a categorical cell level attribute, State. From the Trajectory analysis task report, you can run Calculate pseudotime, which adds a numeric cell level attribute, Pseudotime, which is calculated using the chosen root state. Using the state and pseudotime attributes, you can perform downstream analysis to identify genes that change over pseudotime and characterize branch points. 

Prerequisites for trajectory analysis

For Trajectory analysis to work as expected, there are a few things you do prior to running it.

1) Normalize the data

Trajectory analysis requires normalized counts as the input data. We recommend our default "CPM, Add 1, Log 2" normalization for most scRNA-Seq data.

2) Filter to cells that belong in the same trajectory

Trajectory analysis will built a single branching trajectory for all input cells. Consequently, only cells that share the biological process being studied should be included. For example, a trajectory describing progression through T cell activation should not include monocytes that do not undergo T cell activation. 

3) Filter to genes that characterize the trajectory

The trajectory should be built using a set of genes that increase or decrease as a function of progression through the biological processes being modeled. One example is using differentially expressed genes between cells collected at the beginning of the process to cells collected at the end of the process. If you have no prior knowledge about the process being studied, you can try identifying genes that are differentially expressed between clusters of cells or genes that are highly variable within the data set. Generally, you should try to filter to 1,000 to 3,000 informative genes prior to running Trajectory analysis. The list manager functionality in Partek Flow is useful for creating a list of genes to use in the filter. 

Parameters

Two parameters are exposed for trajectory analysis. First, the number of dimensions. While the trajectory is always visualized in a 2D scatter plot, the underlying structure of the trajectory may be more complex and better represented by more than 2 dimensions. Second, you can choose to scale the genes prior to building the trajectory. Scaling removes any differences in variability between genes, while not scaling allows more variable genes to have a greater weight in the trajectory. 

Task report

The Trajectory analysis task report is a 2D scatter plot. The trajectory is shown with a black line showing the trajectory, numbers at each branch point, and the cells colored by state. Like any scatter plot, you can color by genes and attributes to help identify which state is the root or origin of the trajectory. To select a root state, click any cell belonging to that state. Clicking Calculate pseudotime will run the task to calculate pseudotime values for each cell using the selected state as the root. 

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