PlateArrays.jl

Description

Create microplate layouts and place optimal controls for detecting errors.

Installing PlateArrays

Requires installation of Julia. Once Julia is installed. Install PlateArrays by navigating to package mode ]:

pkg>
add https://github.com/jensenlab/PlateArrays

The place_controls function

Place optimal controls for detecting errors in microplate experiments

Arguments

• wells: A BitMatrix indicating the shape and active wells, use trues(n,m) for a full n x m plate.
• P: The integer number of positive controls
• N: The integer number of negative controls

Keyword Arguments

• solver: The algoritm used to place the controls. There are currently two solvers available:
  1. exchange (default)
  2. MILP
• objective: The objective the solver uses to score plate array candidates.
  1. minimax -> Minimize the maximmum distance in wells from an active well to its nearest control.
  2. LHS -> Find an approximate latin hypercube sample of the available wells in the plate.
  3. hybrid (default) -> a weighted combination of both criteria

Note: MILP solvers return globally optimal solutions but their runtimes can be unpredictable; conversely, coordinate exchange algorithms are not gauranteed to be globally optimal but scale more favorably for large problems. In practice, we find that the exchange algorithm returns near optimal solutions in a fraction of the time of the MILP solver for 384 and 1536 well plate problems. See documentation for solver hyperparameters.

Example Usage

    using PlateArrays 
    plate = trues(8,12) # 96 well plate
    # place 12 positive and 12 negative controls with a minimax objective and exchange algorithm
    design=place_controls(plate,12,12;solver = exchange, objective=hybrid)
    plot(design)

The solvers can handle situations when certain wells are "blocked". Here, wells A1-E2 are unavailable. The algorithms place controls that fill the new geometry of available wells.

    plate[:,1].=false
    plate[1:5,2].=false
    design=place_controls(plate,12,12;solver = exchange, objective=hybrid)
    plot(design)

The arrayer function

Create microplate layouts for multiple experiments in three steps:

1. assign all experiments to as few plates as possible
2. partition plates that contain multiple experiments and select wells to hold each run. Use central wells first.
3. place a full complement of controls on each plate that has a given experiment

Arguments

• wells: A BitMatrix of active wells on each plate (block any inactive wells by setting them to false)
• experiments: Array a variable number of Experiment objects

Example Usage

using PlateArrays 

expt1 = Experiment(92,12,12) # an experiment that has 92 total runs. Each plate that has runs from this experiment should have 12 positive and 12 negative controls 
expt2 = Experiment(24, 6,6) 
expt3 = Experiment(72,12,12) 
plate = trues(8,12) # a standard 96 well plate 

plate_arrays = arrayer(plate,expt1,expt2,expt3) 

array_plots = plot(plate_arrays)