Last updated: 2022-04-08

Checks: 2 0

Knit directory: diff_driver_analysis-main/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version b1ad9bd. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Unstaged changes:
    Modified:   analysis/simulation_power_binary_conti.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index.Rmd) and HTML (docs/index.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd b1ad9bd Jie Zhou 2022-04-07 generate the final html file
html b1ad9bd Jie Zhou 2022-04-07 generate the final html file
Rmd 2867792 Jie Zhou 2022-04-07 update index file
Rmd 9cf37d0 Jie Zhou 2022-04-07 continuous phenotype with new baseline model
html 9cf37d0 Jie Zhou 2022-04-07 continuous phenotype with new baseline model

Statistical methods to study differential selection problems in cancer

Introduction

Introduction

Model

Model

Simulations

Power analysis based on simulations- only mutation count

Power analysis based on simulations- add one functional feature

Power analysis based on simulations- add one functional feature-V2

Power analysis based on simulations- add one continuois phenotype with update baseline model

R package

We have developed an R package for our method. Currently it implements the approximated model for binary phenotype. We are extending it to quantitative phenotype.