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 informative genes

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