Scripts for simulating and plotting an approach for population-scale SARS-CoV2 testing using LAMP-Seq, accompanying preprint. Most model details are in Supplementary Note 1 and Supplementary Note 2 (PDFs available in repo).
An easy-to-use interactive graphing calculator for plotting/calculating error rates only accounting for barcode loss in scenario 2 is available here: https://www.desmos.com/calculator/okym5pyunh. You can vary any parameter.
Files:
- Supplementary Note 1.pdf is a PDF of Supplementary Note 1, describing the single-indexed (FIP barcode) models.
- Supplementary Note 2.pdf is a PDF of Supplementary Note 2, describing the dual-indexed (FIP and BIP barcode) models.
- For numerical simulation:
- SARSCoV2barcoding.ipynb contains original numerical simulations for skewing errors across samples due to viral titer load, including plots that produced the numbers used in Supplementary Note 1.
- SARSCoV2barcoding_v2.ipynb is the most up-to-date script for numerical simulations of single-indexed models, featuring parallelized computation, and the ability to model barcode loss, sample skewing, or both.
- SARSCoV2barcoding_v3.ipynb is the most up-to-date script for numerical simulations of dual-indexed models, featuring parallelized computation, and the ability to model barcode loss, sample skewing, and template switching.
- For plotting figures:
- SARS_CoV2_Scalable_Testing_Simulations_v3.m contains code for making all of the figures in the old preprint and in Supplementary Note 1.
- SARS_CoV2_Scalable_Testing_Simulations_v3.mlx is the live notebook version of the previous file, with a table of contents and integrated inline plots.
- SARS_CoV2_Scalable_Testing_Simulations_v4.m contains code for making all of the barcoding model figures in the updated preprint (Figure 3, panels B, C, F, and G).
- LampSeqSensitivityv2.m contains code for fitting sensitivity data using a Poisson-distributed model, and for plotting the resulting figure (Figure 1, panel H).
- LampSeqSensitivity.m is deprecated and contains old code for fitting sensitivity data to a constant molecule model of RNA detection.