In WuyangFF95/SynSigRun: Run Mutational Signature Analysis Software Packages Using Mutational Spectra generated by SynSigGen

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SynSigRun

Run mutational signature analysis software packages Packages and benchmarking the performance of these packages.

Purpose

A package to 1. wrap R-based signature analysis packages in functions handy for non-expert users, by wrapping default argument values and all necessary steps in the function bodies. 2. reproduce benchmarking analysis of signature analysis packages in papers by Rozen Lab.

Typically, a benchmarking analysis to evaluation accuracy of signature extraction and/or exposure inference involves the 3 steps below:

• Generation of synthetic tumor spectra based on signatures and synthetic tumor exposures using wrapper functions in SynSigGen. Usually,
• signatures are real signatures downloaded from COSMIC,

• synthetic tumor exposures are drawn from a distribution which mimics the distribution of a real tumor type.

• Run of computational approaches (can be an R/Python/Julia/C++ package) on generated data sets. It involves two steps:

• Number of signatures (K) to be extracted is estimated
  • heuristically - starting from an user-provided or default number, without providing the range of possible K s.
  • semi-automatically - selecting from a range of possible K s.
  • manually - requiring users to choose the best K based on the diagnostic plot or table generated by the software.
• Extract a specific number of signatures, AND/OR infer the exposures of extracted signatures

For computational approaches based on R and can do signature extraction which heuristically or semi-automatically selects K AND/OR exposure inference (attribution), we wrote wrapper functions in R/ folder of this package for non-expert users to run these approaches with a simple function call.

• Evaluation of accuracy on signature extraction AND/OR exposure inference. Many of the evaluation functions are in package SynSigEval.

Installation

Install the development version of SynSigRun from GitHub with the R command line:

install.packages("devtools")
devtools::install_github("WuyangFF95/SynSigRun", ref = "1.0.0-branch")

Usage

Benchmarking analysis in Alexandrov et al. (2020)

Nature paper "The repertoire of mutational signatures in human cancer" (link) involves benchmarking analysis compared to SigProfiler (the ancestor of SigProfilerExtractor) and SignatureAnalyzer.

It used some functions and top-level codes in this package. Some of the codes are in data-raw/Alexandrov_2020.

Re-produce benchmarking analysis in Wu et al. (2022)

Scientific Reports paper "Accuracy of mutational signature software on correlated signatures" involves benchmarking signature extraction accuracy of 18 methods on 20 synthetic datasets with correlated exposures to SBS1 and SBS5 signature.

In order to reproduce this benchmarking, users can go to data-raw/Wu_2022/1_scripts.for.SBS1SBS5 to generate the main figure and the full data of this analysis. The sub-folders hold scripts for:

• 1_data_generation - Calls SynSigGen generation script to generate 20 SBS1-SBS5 datasets at data-raw/ or other repositories.

• 2_running_approaches - running computational approaches directly or using SynSigRun wrapper functions. The results are generated as a 5-level folder structure:

Level 1: Datasets (e.g. S.0.1.Rsq.0.1);
Level 2: De-novo extraction without specifying K = 2 (ExtrAttr), or extraction with number of ground-truth signature K = 2 provided to computational approaches (ExtrAttrExact);
Level 3: Results of computational approaches (e.g. hdp.results);
Level 4: Results of runs with seeds (e.g. seed.1, run.1).

• 3_evaluation - evaluating performance of signature extraction by calling evaluation functions in SynSigEval.

Re-produce benchmarking analysis in Liu et al. (2022)

The paper for new computational approach mSigHdp, "mSigHdp: hierarchical Dirichlet processes in mutational signature extraction", Liu et al. (2022) (Manuscript in revision) includes a benchmarking study on real-tumor-based synthetic spectra with SBS or indel mutations.

The benchmarking code of this study calls the wrapper function in SynSigRun to run computational approaches signeR and SignatureAnalyzer.

Reference manual

https://github.com/WuyangFF95/SynSigRun/blob/master/data-raw/SynSigRun_1.0.0.pdf

WuyangFF95/SynSigRun documentation built on Oct. 7, 2022, 1:16 p.m.