Please note that Monocle 2 has officially been deprecated and has been left in place for Partek Flow users with historical data. For any new analysis, please use Monocle 3.

What is trajectory analysis?

Cells undergo changes to transition from one state to another as part of development, disease, and throughout life. Because these changes can be gradual, trajectory analysis attempts to describe progress through a biological process as a position along a path. Because biological processes are often complex, trajectory analysis builds branching trajectories where different paths can be chosen at different points along the trajectory. The progress of a cell along a trajectory from the starting point or root, can be quantified as a numeric value, pseudotime. 

In Partek Flow, we use tools from Monocle 2 [1] 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 a second task, Calculate pseudotime, which adds a numeric cell-level attribute, Pseudotime, 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

Note that trajectory analysis will only work on data with <600,000,000 observations (number of cells × number of features). If your data set exceeds this limit, the Trajectory analysis task will not appear in the toolbox. Prior to performing trajectory analysis, you should: 

...

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 performing trajectory analysis. The list manager functionality in Partek Flow is useful for creating a list of genes to use in the filter. To learn more, please see our documentation on List management. 

Parameters

Dimensionality of the reduced space

...

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 building the trajectory. 

Task report

The Trajectory analysis task report is a 2D scatter plot (Figure 1).

...

Calculating pseudotime

To calculate pseudotime, you must choose a root state. The tip of the root state branch will have a value of 0 for pseudotime. Click any cell belonging to that state to select the state. The selected state will be bold while unselected cells are dimmed (Figure 4). 

...

References

[1] Xiaojie Qiu, Qi Mao, Ying Tang, Li Wang, Raghav Chawla, Hannah Pliner, and Cole Trapnell. Reversed graph embedding resolves complex single-cell developmental trajectories. Nature methods, 2017.

...