Downsampling is the process of selecting a limited number of data points/events from a sample or gated population. A new Downsample Gate is created containing the selected subset of events, which can be used like any other gated subset or population node within FlowJo.

Downsampling is often a necessary prerequisite to algorithmic calculations like t-Distributed Stochastic Neighbor Embedding (tSNE), as reducing the number of events fed into an algorithm by creating and selecting a Downsample gate will increase calculation speed and provide an output in a reasonable period of time.

The Downsample gate function is located within the Platform drop down menu.

Selecting Downsample Gate… –> Create New… will bring up a new Create Downsample Gate dialog window.

Downsample_Gate         Create_Downsample_Gate3


Within the Create Downsample Gate window:

  1. Name the new downsample gate/subset.
  2. Select the number of events you wish to end up with in the downsample gate. (Options include Percent of subset or Total events.)
  3. Choose the type of downsampling algorithm you wish to perform.
    • Deterministic sampling selects the same set of events even when recalculated. Deterministic options include selecting just the first events, or the last events in a sample, selecting half first and half last events, or weighting the selection towards a marker of interest using very strong density-dependent selection.
    • Random sampling selects a different subset of events from each recalculation. Random options include selecting events across the entire sample, or using mild, medium or very strong density-dependent selection to weight.
  4. If a density-dependent selection option is chosen, the Parameter(s) on which the population density is computed must also be selected.
    • Density-dependent options favor selection of rare events based on the distributions of the selected parameter(s). In such a case, the Downsample population will not reflect the original sample, but will be biased towards over-representation of rare subsets in the chosen parameter(s).
  5. Click the Create button in the lower right corner to run the selected algorithm and create the new Downsample gate.

A new population node is created based on the specified options. The Downsample Gate can be group-applied within a gating hierarchy, and the population outputs used for subsequent algorithms, concatenated, exported, or analyzed like any other gated population within FlowJo.

Menubar_and_tSNE_v9_Tutorial1   Menubar_and_tSNE_v9_Tutorial1 3



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t-distributed Stochastic Neighbor Embedding (tSNE)


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