CNV simulation and inference of formation rate and selection coefficient

This is the repository for the paper:

Avecilla, Grace, Julie N Chuong, Fangfei Li, Gavin Sherlock, David Gresham, and Yoav Ram. Neural Networks Enable Efficient and Accurate Simulation-Based Inference of Evolutionary Parameters from Adaptation Dynamics. PLOS Biology 20, no. 5 (May 27, 2022): e3001633. doi:10.1371/journal.pbio.3001633.

Data

Data to generate figures can be found at OSF.

Inference:

• Models:
  • Wright-Fisher and Chemostat: cnv_simulation.py
  • Determining the effective population size in the chemostat: Pop_sampling_variance_sims.ipynb
  • Time it takes to run a simulation using each model: Simulation_time.ipynb
• Observations:
  • Single synthetic observations: generate_pseudo_obs.py
  • Sets of multiple synthetic observations: Generate_synthetic_obs_multi.ipynb
  • Interpolation of barcoded population data (so that it has the same timepoints as gln01-gln09): Interpolating_bc.ipynb
• Scripts used for inference:
  • Generating presimulated data used for rejection ABC and NPE: generate_presimulated_data.py
  • Rejection ABC: infer_rejectionABC.py
  • SMC-ABC (using pyABC, adaptive Euclidean distance): infer_pyABC.py
  • NPE, single observations (using sbi): infer_sbi.py
  • NPE, sets of multiple observations (using sbi): infer_sbi_mult.py
  • NPE, on empirical data from Lauer et al 2018 (using sbi): infer_sbi_Lauer.py

Barcode DFE:
Note, population bc04 is _bc0_1 in the paper.

• Extract barcodes from fastqs and cluster them (using bartender): get_bc.sh
• Combines barcode counts from different timepoints: combine_bc.sh
• Barcode DFE inference overview as well as checks for mean fitness convergence, etc., and supplementary figure 13: 2021-09-16_analysis_Grace.ipynb
• Barcode DFE inference detailed: fitmut_v2_a_20210916.py

Fitness assays:

• Fitting models and extracting selection coefficients from competitions between CNV containing clones and the ancestral strain: fitness_assays.R

Figures:

• Figure 1A: Fig1A.R
• Supplementary Figure 1: Interpolating_bc.ipynb
• Figure 1D inset: Fig1Dinset.ipynb
• Figures 3-7, and associated supplemental material (html associated with each Rmd): Figure3andSup.Rmd, Figure4andSup.Rmd, Figure5andSup.Rmd, Figure6andSup.Rmd, Figure7andSup.Rmd