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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. 

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

Parameters

Dimensionality of the reduced space

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

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You can use the control panel on the left to color, size, and shape by genes and attributes to help identify which state is the root of the trajectory (Figure 2).

You can also split by any categorical attribute (Figure 3)

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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). 

To use the selected state as the root state for pseudotime calculation, select the state and then click the Calculate pseudotime button (Figure 5). 

This will run a task on the analysis pipeline, Calculate pseudotime, and output a new Pseudotime result data node (Figure 6).

The Calcuate pseudotime task report is the same as the Trajectory analysis task report, but is colored by the newly calculated cell-level attribute, Pseudotime, by default (Figure 7). 

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[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.

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