Last updated: 2022-10-18

Checks: 5 2

Knit directory: cTWAS_analysis/

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.


The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

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.

The command set.seed(20211220) 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.

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

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

Using absolute paths to the files within your workflowr project makes it difficult for you and others to run your code on a different machine. Change the absolute path(s) below to the suggested relative path(s) to make your code more reproducible.

absolute relative
/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/data/ data
/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/code/ctwas_config_b38.R code/ctwas_config_b38.R

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 03d3b2c. 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:    .ipynb_checkpoints/

Untracked files:
    Untracked:  Proposal plots.R
    Untracked:  RGS14.pdf
    Untracked:  RNF186.pdf
    Untracked:  SCZ_annotation.xlsx
    Untracked:  SLC8B1.pdf
    Untracked:  UKB_analysis_allweights_scz/
    Untracked:  analysis/.ipynb_checkpoints/
    Untracked:  analysis/IBD_ME_CpG_level_0.05.Rmd
    Untracked:  analysis/LDL_Liver.Rmd
    Untracked:  cache/
    Untracked:  code/.ipynb_checkpoints/
    Untracked:  code/IBD_ME_3kb_0.05_out/
    Untracked:  code/LDL_S_out/LDL_Liver.err
    Untracked:  code/LDL_S_out/LDL_Liver.out
    Untracked:  code/LDL_out/
    Untracked:  code/run_IBD_analysis_ME_3kb_0.05.sbatch
    Untracked:  code/run_IBD_analysis_ME_3kb_0.05.sh
    Untracked:  code/run_IBD_ctwas_rss_LDR_ME_3kb_0.05.R
    Untracked:  code/run_LDL_analysis.sbatch
    Untracked:  code/run_LDL_analysis.sh
    Untracked:  code/run_LDL_ctwas_rss_LDR.R
    Untracked:  data/.ipynb_checkpoints/
    Untracked:  data/FUMA_output/
    Untracked:  data/GO_Terms/
    Untracked:  data/IBD_ME/
    Untracked:  data/LDL/
    Untracked:  data/LDL_S/
    Untracked:  data/PGC3_SCZ_wave3_public.v2.tsv
    Untracked:  data/SCZ/
    Untracked:  data/SCZ_2014_EUR/
    Untracked:  data/SCZ_2014_EUR_ME/
    Untracked:  data/SCZ_2018/
    Untracked:  data/SCZ_2018_ME/
    Untracked:  data/SCZ_2018_S/
    Untracked:  data/SCZ_2020/
    Untracked:  data/SCZ_S/
    Untracked:  data/Supplementary Table 15 - MAGMA.xlsx
    Untracked:  data/Supplementary Table 20 - Prioritised Genes.xlsx
    Untracked:  data/UKBB/
    Untracked:  data/UKBB_SNPs_Info.text
    Untracked:  data/gene_OMIM.txt
    Untracked:  data/gene_pip_0.8.txt
    Untracked:  data/gwas_sumstats/
    Untracked:  data/magma.genes.out
    Untracked:  data/mashr_Heart_Atrial_Appendage.db
    Untracked:  data/mashr_sqtl/
    Untracked:  data/notes.txt
    Untracked:  data/scz_2018.RDS
    Untracked:  data/summary_known_genes_annotations.xlsx
    Untracked:  temp_LDR/
    Untracked:  top_genes_32.txt
    Untracked:  top_genes_37.txt
    Untracked:  top_genes_43.txt
    Untracked:  top_genes_54.txt
    Untracked:  top_genes_81.txt
    Untracked:  z_snp_pos_SCZ.RData
    Untracked:  z_snp_pos_SCZ_2014_EUR.RData
    Untracked:  z_snp_pos_SCZ_2018.RData
    Untracked:  z_snp_pos_SCZ_2020.RData

Unstaged changes:
    Deleted:    analysis/BMI_S_results.Rmd
    Modified:   analysis/IBD_ME_CpG_level_1kb.Rmd
    Modified:   analysis/LDL_Liver_S.Rmd
    Modified:   analysis/SCZ_2018_Brain_Cortex_S.Rmd
    Modified:   analysis/SCZ_2018_all_tissues.Rmd
    Modified:   analysis/SCZ_2018_all_tissues_S.Rmd
    Modified:   analysis/SCZ_E_S_Analysis.Rmd
    Modified:   analysis/SCZ_S_test.Rmd
    Modified:   analysis/SCZ_test.Rmd
    Modified:   analysis/index.Rmd
    Deleted:    code/LDL_S_out/T2D_Liver.err
    Deleted:    code/LDL_S_out/T2D_Liver.out
    Modified:   code/SCZ_out/SCZ_Brain_Amygdala.err
    Modified:   code/SCZ_out/SCZ_Brain_Amygdala.out
    Modified:   code/SCZ_out/SCZ_Brain_Anterior_cingulate_cortex_BA24.err
    Modified:   code/SCZ_out/SCZ_Brain_Anterior_cingulate_cortex_BA24.out
    Modified:   code/SCZ_out/SCZ_Brain_Caudate_basal_ganglia.err
    Modified:   code/SCZ_out/SCZ_Brain_Caudate_basal_ganglia.out
    Modified:   code/SCZ_out/SCZ_Brain_Cerebellar_Hemisphere.err
    Modified:   code/SCZ_out/SCZ_Brain_Cerebellar_Hemisphere.out
    Modified:   code/SCZ_out/SCZ_Brain_Cerebellum.err
    Modified:   code/SCZ_out/SCZ_Brain_Cerebellum.out
    Modified:   code/SCZ_out/SCZ_Brain_Cortex.err
    Modified:   code/SCZ_out/SCZ_Brain_Cortex.out
    Modified:   code/SCZ_out/SCZ_Brain_Frontal_Cortex_BA9.err
    Modified:   code/SCZ_out/SCZ_Brain_Frontal_Cortex_BA9.out
    Modified:   code/SCZ_out/SCZ_Brain_Hippocampus.err
    Modified:   code/SCZ_out/SCZ_Brain_Hippocampus.out
    Modified:   code/SCZ_out/SCZ_Brain_Hypothalamus.err
    Modified:   code/SCZ_out/SCZ_Brain_Hypothalamus.out
    Modified:   code/SCZ_out/SCZ_Brain_Nucleus_accumbens_basal_ganglia.err
    Modified:   code/SCZ_out/SCZ_Brain_Nucleus_accumbens_basal_ganglia.out
    Modified:   code/SCZ_out/SCZ_Brain_Putamen_basal_ganglia.err
    Modified:   code/SCZ_out/SCZ_Brain_Putamen_basal_ganglia.out
    Modified:   code/SCZ_out/SCZ_Brain_Spinal_cord_cervical_c-1.err
    Modified:   code/SCZ_out/SCZ_Brain_Spinal_cord_cervical_c-1.out
    Modified:   code/SCZ_out/SCZ_Brain_Substantia_nigra.err
    Modified:   code/SCZ_out/SCZ_Brain_Substantia_nigra.out
    Deleted:    code/run_IBD_ctwas_rss_LDR_ME.R
    Modified:   code/run_LDL_analysis_S.sbatch
    Modified:   code/run_LDL_analysis_S.sh
    Modified:   code/run_LDL_ctwas_rss_LDR_S.R
    Modified:   code/run_SCZ_analysis.sbatch
    Modified:   code/run_SCZ_analysis.sh
    Modified:   code/run_SCZ_ctwas_rss_LDR.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.


There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.


Weight QC

[1] 12714
[1] 10901

   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
1070  768  652  417  494  611  548  408  405  434  634  629  195  365  354  526 
  17   18   19   20   21   22 
 663  160  859  306  114  289 
[1] 0.8365

Load ctwas results

Check convergence of parameters

     gene       snp 
0.0096829 0.0001743 
  gene    snp 
45.772  9.687 
[1] 55.56
[1] 343621
[1]   10901 8696600
   gene     snp 
0.01406 0.04273 
[1] 0.05679
  gene 
0.2476 

Genes with highest PIPs

#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")

#genes with PIP>0.8 or 20 highest PIPs
head(ctwas_gene_res[order(-ctwas_gene_res$susie_pip),report_cols], max(sum(ctwas_gene_res$susie_pip>0.8), 20))
         genename region_tag susie_pip     mu2       PVE       z num_eqtl
4435        PSRC1       1_67    1.0000 1675.70 4.877e-03 -41.687        1
12008         HPR      16_38    1.0000  164.01 4.773e-04 -17.963        2
3721       INSIG2       2_69    1.0000   68.69 1.999e-04  -8.983        3
5563        ABCG8       2_27    1.0000  313.97 9.137e-04 -20.294        1
5991        FADS1      11_34    0.9999  164.33 4.782e-04  12.926        2
12687 RP4-781K5.7      1_121    0.9997  203.89 5.932e-04 -15.108        1
10657      TRIM39       6_24    0.9986   72.24 2.099e-04   8.840        3
7410        ABCA1       9_53    0.9954   70.43 2.040e-04   7.982        1
8531         TNKS       8_12    0.9911   76.67 2.211e-04  11.039        2
9390         GAS6      13_62    0.9883   71.42 2.054e-04  -8.924        1
1597         PLTP      20_28    0.9877   61.48 1.767e-04  -5.732        1
1999        PRKD2      19_33    0.9859   30.13 8.645e-05   5.072        2
7040        INHBB       2_70    0.9824   74.11 2.119e-04  -8.519        1
5544        CNIH4      1_114    0.9776   40.86 1.163e-04   6.146        2
2092          SP4       7_19    0.9775  102.48 2.915e-04  10.693        1
6093      CSNK1G3       5_75    0.9749   84.33 2.392e-04   9.116        1
8865         FUT2      19_33    0.9665  105.00 2.953e-04 -11.927        1
11790      CYP2A6      19_28    0.9616   32.05 8.969e-05   5.407        1
3247         KDSR      18_35    0.9547   24.71 6.865e-05  -4.526        1
233        NPC1L1       7_32    0.9526   87.19 2.417e-04 -10.762        1
4704        DDX56       7_32    0.9466   60.02 1.654e-04   9.642        2
6391       TTC39B       9_13    0.9362   23.31 6.349e-05  -4.334        3
6778         PKN3       9_66    0.9360   47.70 1.299e-04  -6.621        1
6220         PELO       5_31    0.9352   70.89 1.929e-04   8.288        2
1114         SRRT       7_62    0.9336   32.81 8.915e-05   5.425        2
3300     C10orf88      10_77    0.9322   37.26 1.011e-04  -6.788        2
8579       STAT5B      17_25    0.9261   30.73 8.281e-05   5.426        2
3562       ACVR1C       2_94    0.9228   25.91 6.960e-05  -4.687        2
6957         USP1       1_39    0.8945  254.14 6.616e-04  16.258        1
9062      KLHDC7A       1_13    0.8163   22.65 5.380e-05   4.124        1
8418         POP7       7_62    0.8091   40.51 9.539e-05  -5.845        1
9072      SPTY2D1      11_13    0.8084   33.57 7.898e-05  -5.557        1

Comparing z scores and PIPs

Loading required package: S4Vectors
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'
The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min

Attaching package: 'S4Vectors'
The following object is masked from 'package:base':

    expand.grid
Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: grid

sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.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] grid      stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
[1] Gviz_1.38.4          GenomicRanges_1.46.0 GenomeInfoDb_1.26.7 
[4] IRanges_2.24.1       S4Vectors_0.28.1     BiocGenerics_0.40.0 
[7] cowplot_1.1.1        ggplot2_3.3.6        workflowr_1.7.0     

loaded via a namespace (and not attached):
  [1] colorspace_2.0-3            rjson_0.2.20               
  [3] ellipsis_0.3.2              rprojroot_2.0.3            
  [5] htmlTable_2.2.1             biovizBase_1.42.0          
  [7] XVector_0.34.0              base64enc_0.1-3            
  [9] fs_1.5.2                    dichromat_2.0-0.1          
 [11] rstudioapi_0.13             farver_2.1.0               
 [13] bit64_4.0.5                 AnnotationDbi_1.56.1       
 [15] fansi_1.0.3                 xml2_1.3.2                 
 [17] splines_4.1.0               cachem_1.0.6               
 [19] knitr_1.33                  Formula_1.2-4              
 [21] jsonlite_1.8.0              Rsamtools_2.10.0           
 [23] cluster_2.1.2               dbplyr_2.1.1               
 [25] png_0.1-7                   compiler_4.1.0             
 [27] httr_1.4.3                  backports_1.2.1            
 [29] lazyeval_0.2.2              assertthat_0.2.1           
 [31] Matrix_1.3-3                fastmap_1.1.0              
 [33] cli_3.3.0                   later_1.2.0                
 [35] htmltools_0.5.3             prettyunits_1.1.1          
 [37] tools_4.1.0                 gtable_0.3.0               
 [39] glue_1.6.2                  GenomeInfoDbData_1.2.7     
 [41] dplyr_1.0.9                 rappdirs_0.3.3             
 [43] Rcpp_1.0.9                  Biobase_2.54.0             
 [45] jquerylib_0.1.4             vctrs_0.4.1                
 [47] Biostrings_2.62.0           rtracklayer_1.54.0         
 [49] xfun_0.24                   stringr_1.4.0              
 [51] ps_1.7.0                    lifecycle_1.0.1            
 [53] ensembldb_2.18.4            restfulr_0.0.13            
 [55] XML_3.99-0.6                getPass_0.2-2              
 [57] zlibbioc_1.40.0             scales_1.2.0               
 [59] BSgenome_1.62.0             VariantAnnotation_1.40.0   
 [61] ProtGenerics_1.26.0         hms_1.1.1                  
 [63] promises_1.2.0.1            MatrixGenerics_1.6.0       
 [65] parallel_4.1.0              SummarizedExperiment_1.24.0
 [67] AnnotationFilter_1.18.0     RColorBrewer_1.1-3         
 [69] yaml_2.2.1                  curl_4.3.2                 
 [71] gridExtra_2.3               memoise_2.0.1              
 [73] sass_0.4.0                  rpart_4.1-15               
 [75] biomaRt_2.50.0              latticeExtra_0.6-29        
 [77] stringi_1.7.6               RSQLite_2.2.14             
 [79] highr_0.9                   BiocIO_1.4.0               
 [81] checkmate_2.0.0             GenomicFeatures_1.46.1     
 [83] filelock_1.0.2              BiocParallel_1.28.0        
 [85] rlang_1.0.4                 pkgconfig_2.0.3            
 [87] matrixStats_0.62.0          bitops_1.0-7               
 [89] evaluate_0.15               lattice_0.20-44            
 [91] purrr_0.3.4                 htmlwidgets_1.5.3          
 [93] GenomicAlignments_1.30.0    labeling_0.4.2             
 [95] bit_4.0.4                   processx_3.5.3             
 [97] tidyselect_1.1.2            magrittr_2.0.3             
 [99] R6_2.5.1                    generics_0.1.2             
[101] Hmisc_4.5-0                 DelayedArray_0.20.0        
[103] DBI_1.1.2                   foreign_0.8-81             
[105] pillar_1.7.0                whisker_0.4                
[107] withr_2.5.0                 nnet_7.3-16                
[109] survival_3.2-11             KEGGREST_1.34.0            
[111] RCurl_1.98-1.6              tibble_3.1.7               
[113] crayon_1.5.1                utf8_1.2.2                 
[115] BiocFileCache_2.2.0         rmarkdown_2.9              
[117] jpeg_0.1-8.1                progress_1.2.2             
[119] data.table_1.14.2           blob_1.2.3                 
[121] callr_3.7.0                 git2r_0.28.0               
[123] digest_0.6.29               httpuv_1.6.1               
[125] munsell_0.5.0               bslib_0.4.0