Last updated: 2020-05-25

Checks: 7 0

Knit directory: analysis_pipelines/

This reproducible R Markdown analysis was created with workflowr (version 1.6.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

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.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20200524)

The command set.seed(20200524) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 3405a02

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

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:    .Rproj.user/

Untracked files:
    Untracked:  analysis/make_ldsc_binary_annot.R
    Untracked:  analysis/make_ldsc_continuous_annot.R

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 R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd 3405a02 kevinlkx 2020-05-25 wflow_publish(“analysis/twas_fusion_pipeline.Rmd”)
html 81ec4c3 kevinlkx 2020-05-25 Build site.
Rmd 94a32c0 kevinlkx 2020-05-25 wflow_publish(“analysis/twas_fusion_pipeline.Rmd”)

Install the FUSION software

FUSION software is implemented in R. Installation is easy: simply download and unpack the FUSION software package from github: https://github.com/gusevlab/fusion_twas

wget https://github.com/gusevlab/fusion_twas/archive/master.zip
unzip master.zip
cd fusion_twas-master

Then, install required R libraries.

install.packages(c('optparse','RColorBrewer'))
install.packages('plink2R-master/plink2R/',repos=NULL)
install.packages(c('glmnet','methods'))

You might need to install other libraries or packages to compute your own weights.

Please see the detail instructions: http://gusevlab.org/projects/fusion/

Run TWAS analysis

FUSION website (http://gusevlab.org/projects/fusion/) provides detail instructions and examples to run TWAS analysis, compute your own weights, and joint/conditional analysis, etc.

The website includes pretrained weights for RNA-seq data from GTEx and TCGA. It is easy to run TWAS using their pretrained weights.

Compute your own weights

If you want to compute your own weights, please follow their instructions in the section “Computing your own functional weights”. You will need to compute weights one gene at a time.

  1. Prepare the input genotype and expression (or other molecular trait) data for each gene
  2. Run FUSION.compute_weights.R function for each gene, one gene at a time
  3. After all genes have been evaluated, make a WGTLIST file which lists paths to each of the *.RDat files that were generated and call Rscript utils/FUSION.profile_wgt.R <WGTLIST> to output a per-gene profile as well as an overall summary of the data and model performance.

Reference

  • Gusev et al. “Integrative approaches for large-scale transcriptome-wide association studies” 2016 Nature Genetics

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.3        rprojroot_1.3-2   digest_0.6.23     later_1.0.0      
 [5] R6_2.4.1          backports_1.1.5   git2r_0.26.1.9000 magrittr_1.5     
 [9] evaluate_0.14     stringi_1.4.5     rlang_0.4.4       fs_1.3.1         
[13] promises_1.1.0    whisker_0.4       rmarkdown_2.1     tools_3.5.1      
[17] stringr_1.4.0     glue_1.3.1        httpuv_1.5.2      xfun_0.12        
[21] yaml_2.2.0        compiler_3.5.1    htmltools_0.4.0   knitr_1.28