contributing

We welcome contributions to the package via pull requests. One such example could be adding a new variation of the base model to the model module. All models within BarBay.jl that can be fit with the mcmc or the vi modules are standardized to take as the first four arguments the following:

• R̲̲: Array-like object that contains the raw barcode counts. These can be either a matrix, a tensor, or a list of arrays. The main feature is that each "face" of the array-like object represents a matrix where each column contains the time series data for a single barcode. Here are some examples of the types used in different models:
  • R̲̲::Matrix{Int64}: T × B matrix–split into a vector of vectors for computational efficiency–where T is the number of time points in the data set and B is the number of barcodes. Each column represents the barcode count trajectory for a single lineage.
  • R̲̲::Array{Int64, 3}:: T × B × R where T is the number of time points in the data set, B is the number of barcodes, and R is the number of experimental replicates. For each slice in the R axis, each column represents the barcode count trajectory for a single lineage.
  • R̲̲::Vector{Matrix{Int64}}:: Length R vector wth T × B matrices where T is the number of time points in the data set, B is the number of barcodes, and R is the number of experimental replicates. For each matrix in the vector, each column represents the barcode count trajectory for a single lineage.
• n̲ₜ: Array-like object with the total number of barcode counts for each time point. As with R̲̲, the structure of n̲ₜ must be adapted for the needs of your data structure. Here are some examples of the types used in different models:
  • n̲ₜ::Vector{Int64}: Vector with the total number of barcode counts for each time point. NOTE: This vector must be equivalent to computing vec(sum(R̲̲, dims=2)).
  • n̲ₜ::Vector{Vector{Int64}}: Vector of vectors with the total number of barcode counts for each time point on each replicate. NOTE: This vector must be equivalent to computing vec.(sum.(R̲̲, dims=2)).
• n_neutral::Int: Number of neutral lineages in dataset.
• n_bc::Int: Number of mutant lineages in dataset.

The rest of the arguments fed to the model function must be optional keyword arguments. This means that they should be listed after the semi-colon that separates the first four inputs from the rest and they should have a default value. As an example, take a look at the definition of the input arguments for one of the base models:

Turing.@model function fitness_normal(
    R̲̲::Matrix{Int64},
    n̲ₜ::Vector{Int64},
    n_neutral::Int,
    n_bc::Int;
    s_pop_prior::VecOrMat{Float64}=[0.0, 2.0],
    logσ_pop_prior::VecOrMat{Float64}=[0.0, 1.0],
    s_bc_prior::VecOrMat{Float64}=[0.0, 2.0],
    logσ_bc_prior::VecOrMat{Float64}=[0.0, 1.0],
    logλ_prior::VecOrMat{Float64}=[3.0, 3.0]
)

Furthermore, the four inputs to the model are automatically generated within the mcmc and the vi modules via the data_to_arrays function from the utils module. Take a look at the source code for this function to familiarize yourself with it. In case this is inconvenient, please open an issue in the GitHub repository and we will be happy to make changes to adapt the package to your needs!

If your defined model follows these standards, you should be able to fit it with the tools provided within this package. Here are some resources to get familiar with the model structure within Turing.jl:

• Getting Started with Turing.jl
• Bayesian Statistics using Julia and Turing

Furthermore, we recommend looking at the source code within this package GitHub repository. Every function and model is highly annotated and can serve as a guide to get you going!