QingliGuo / FFPEsig

Codes and Data for FFPEsig manuscript

FFPEsig

FFPEsig uses FFPE signature as a noise profile to correct the observed mutation counts from a given FFPE WGS sample.

1. To run FFPEsig:

• Download FFPEsig.py

• Install python 3 (3.7.6) and import the packages required in FFPEsig.py, including pandas (1.0.1), numpy (1.18.1), matplotlib (3.1.3), seaborn (0.10.1).

• Run the command line:

python FFPEsig.py [--input|-i] <Path-to-the-DataFrame> [--sample|-s] <Sample_id> [--label|-l] <Unrepaired|Repaired> [--output_dir|-o] <Path-of-output-folder>

2. Example

python FFPEsig.py --input ./Data/simulated_PCAWG_FFPE_unrepaired.csv --sample ColoRect-AdenoCA::SP21528 --label Unrepaired --output_dir FFPEsig_OUTPUT

Or

python FFPEsig.py -i ./Data/simulated_PCAWG_FFPE_unrepaired.csv -s ColoRect-AdenoCA::SP21528 -l Unrepaired -o FFPEsig_OUTPUT

Note

• Input file, [--input|-i], must be a standard CSV format dataframe which column names are the sample IDs;
• Sample ID, [--sample|-s], must be one of the sample IDs in Input file [--input|-i];
• Label option, [--label|-l], must be either of them <Unrepaired|Repaired>.
• The total running time of FFPEsig for one sample is around 1-3 mins on a local desk computer (3,1 GHz Intel Core i5).

Analysis code

Here we include analysis codes and data used in our manuscript entitled "The mutational signatures of formalin fixation on the human genome".

• FFPE signatures discovery
• Correction on simulated FFPE data
• Correction_impact on sig decomposition
• WGS CRC FFPEs
• Comparing refitted attributions of 96c and 80c sig

Citation

Guo, Q., Lakatos, E., Bakir, I.A. et al. The mutational signatures of formalin fixation on the human genome. Nat Commun 13, 4487 (2022). https://doi.org/10.1038/s41467-022-32041-5