Last updated: 2020-07-08

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Knit directory: mmbr-rss-dsc/

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Rmd 0805f5b zouyuxin 2020-07-08 wflow_publish(“analysis/mmbr_missing_0616.Rmd”)
html 7908346 zouyuxin 2020-07-08 Build site.
Rmd 3784fc9 zouyuxin 2020-07-08 wflow_publish(“analysis/mmbr_missing_0616.Rmd”) 
library(dplyr)
library(kableExtra)

This is result with missing values. There are 300 datasets, each with 300 SNPs. There are 6 conditions and we generate signals using artificial priors (details). We fit with oracle prior and residual variance. We estimate prior weights using ‘simple’ method (comparing with 0).

Overall: mvSuSiE with missing data has lower power than full data as we expected. RSS version has very high FDR, I need to further investigate what’s happening.

Conditional specific PIP

PIP calibration

Without missing

With missing

Power

Left: without missing. Right: with missing

Global PIP

PIP calibration

Without missing

With missing

Power

Left: without missing. Right: with missing


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] kableExtra_1.1.0 dplyr_0.8.0.1   

loaded via a namespace (and not attached):
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 [7] tools_3.5.1       digest_0.6.25     viridisLite_0.3.0
[10] evaluate_0.12     tibble_2.1.3      lifecycle_0.1.0  
[13] pkgconfig_2.0.3   rlang_0.4.4       rstudioapi_0.10  
[16] yaml_2.2.0        stringr_1.4.0     httr_1.3.1       
[19] knitr_1.20        xml2_1.2.0        fs_1.3.1         
[22] vctrs_0.2.3       hms_0.5.3         webshot_0.5.1    
[25] rprojroot_1.3-2   tidyselect_0.2.5  glue_1.3.1       
[28] R6_2.4.1          rmarkdown_1.10    purrr_0.3.2      
[31] readr_1.3.1       magrittr_1.5      whisker_0.3-2    
[34] backports_1.1.5   scales_1.1.0      promises_1.0.1   
[37] htmltools_0.3.6   assertthat_0.2.1  rvest_0.3.2      
[40] colorspace_1.4-1  httpuv_1.4.5      stringi_1.4.3    
[43] munsell_0.5.0     crayon_1.3.4