Last updated: 2022-05-19

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Knit directory: cTWAS_analysis/

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Rmd 7d08c9b sq-96 2022-05-18 update
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Rmd 2749be9 sq-96 2022-05-12 update
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library(reticulate)
use_python("/scratch/midway2/shengqian/miniconda3/envs/PythonForR/bin/python",required=T)

Weight QC

#number of imputed weights
nrow(qclist_all)
[1] 21642
#number of imputed weights by chromosome
table(qclist_all$chr)

   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
1929 1532 1343  876  885 1118 1258  748  873 1021 1288 1205  442  765  754  863 
  17   18   19   20   21   22 
1460  296 1534  750   40  662 
#number of imputed weights without missing variants
sum(qclist_all$nmiss==0)
[1] 18965
#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)
[1] 0.8763
INFO:numexpr.utils:Note: NumExpr detected 56 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
finish

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

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Check convergence of parameters

Version Author Date
2749be9 sq-96 2022-05-12
     gene       snp 
0.0063107 0.0003111 
 gene   snp 
10.20 10.34 
[1] 105318
[1]    7622 6309950
   gene     snp 
0.00466 0.19277 
[1] 0.01395 1.10685

Genes with highest PIPs

Version Author Date
2749be9 sq-96 2022-05-12
       genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
3585       LRP8       1_33    1.1028 32.85 2.908e-04 -4.820          5        5
7379    ZDHHC20       13_2    0.9020 24.49 1.796e-04 -4.784          5        6
2652     GIGYF1       7_62    0.8896 26.12 1.951e-04 -5.266          4        4
6179     SNRPA1      15_50    0.8796 20.85 1.460e-04 -4.098          4        6
811  BUB1B-PAK6      15_14    0.8551 29.84 2.051e-04  5.588          2        2
6923    TSNARE1       8_93    0.8350 27.20 1.779e-04  5.555         11       11
3570     LPCAT4      15_10    0.8112 26.23 1.599e-04  4.892          3        5
731        BDNF      11_19    0.8017 23.22 1.365e-04  4.348          3        4
613      ATP2B2        3_8    0.7968 26.02 1.436e-04  4.229          5        6
6602      THAP8      19_25    0.7784 20.83 1.195e-04  3.847          2        2
292        AKT3      1_128    0.7682 34.93 1.847e-04 -6.350          7        8
924     C2orf80      2_123    0.7584 24.25 9.972e-05  3.053         12       13
1985     DPYSL3       5_86    0.7575 23.63 1.287e-04  4.157          1        1
4279       NGEF      2_137    0.7346 30.69 1.537e-04  7.036          3        3
159      ACTR1B       2_57    0.7234 20.56 1.003e-04 -3.978          5        5
775       BRCA1      17_25    0.7052 31.00 8.244e-05 -3.837         20       22
3953   MPHOSPH9      12_75    0.6872 60.79 2.709e-04 -8.201          2        4
2244       ESAM      11_77    0.6792 35.97 1.325e-04  5.889          2        2
7239      WDR27      6_111    0.6484 16.96 3.988e-05  2.235         21       33
1856       DHPS      19_10    0.6472 25.49 1.014e-04 -4.396          1        1

Genes with highest PVE

       genename region_tag susie_pip    mu2       PVE       z num_intron
3585       LRP8       1_33    1.1028  32.85 0.0002908  -4.820          5
3953   MPHOSPH9      12_75    0.6872  60.79 0.0002709  -8.201          2
811  BUB1B-PAK6      15_14    0.8551  29.84 0.0002051   5.588          2
459        APOM       6_26    0.1818 627.31 0.0001967  11.590          3
2652     GIGYF1       7_62    0.8896  26.12 0.0001951  -5.266          4
292        AKT3      1_128    0.7682  34.93 0.0001847  -6.350          7
7379    ZDHHC20       13_2    0.9020  24.49 0.0001796  -4.784          5
6923    TSNARE1       8_93    0.8350  27.20 0.0001779   5.555         11
3570     LPCAT4      15_10    0.8112  26.23 0.0001599   4.892          3
3622       LSM2       6_26    0.1619 635.43 0.0001581 -11.599          1
6484      TAOK2      16_24    0.6049  46.28 0.0001540   7.024          5
4279       NGEF      2_137    0.7346  30.69 0.0001537   7.036          3
7159       VARS       6_26    0.1563 629.91 0.0001462 -11.620          1
6179     SNRPA1      15_50    0.8796  20.85 0.0001460  -4.098          4
4405      NT5C2      10_66    0.5910  46.04 0.0001450  -8.511         11
613      ATP2B2        3_8    0.7968  26.02 0.0001436   4.229          5
731        BDNF      11_19    0.8017  23.22 0.0001365   4.348          3
2244       ESAM      11_77    0.6792  35.97 0.0001325   5.889          2
1985     DPYSL3       5_86    0.7575  23.63 0.0001287   4.157          1
6602      THAP8      19_25    0.7784  20.83 0.0001195   3.847          2
     num_sqtl
3585        5
3953        4
811         2
459         3
2652        4
292         8
7379        6
6923       11
3570        5
3622        1
6484        6
4279        3
7159        1
6179        6
4405       15
613         6
731         4
2244        2
1985        1
6602        2

Comparing z scores and PIPs

Version Author Date
2749be9 sq-96 2022-05-12

Version Author Date
2749be9 sq-96 2022-05-12
[1] 0.01719
     genename region_tag susie_pip    mu2       PVE       z num_intron num_sqtl
7159     VARS       6_26   0.15633 629.91 1.462e-04 -11.620          1        1
3622     LSM2       6_26   0.16187 635.43 1.581e-04 -11.599          1        1
459      APOM       6_26   0.18177 627.31 1.967e-04  11.590          3        3
682      BAG6       6_26   0.11475 627.31 7.843e-05 -11.590          6        6
1732  CYP21A2       6_26   0.01511 659.18 1.430e-06 -11.340          1        1
7160    VARS2       6_25   0.05583 101.38 3.000e-06 -11.137          1        1
941  C6orf136       6_24   0.06069  79.63 2.785e-06 -11.031          2        2
2501    FLOT1       6_24   0.14500  78.29 1.560e-05  10.981          6        7
808    BTN3A2       6_20   0.08647  90.16 2.598e-06 -10.659          3        3
2949    HLA-B       6_25   0.05761  76.72 9.724e-07  10.150         11       21
805    BTN2A1       6_20   0.08574  82.29 3.455e-06  10.110          5        6
1153   CCHCR1       6_25   0.05360  62.58 9.944e-07  -9.358         10       14
1799     DDR1       6_25   0.01101  67.83 7.808e-08   9.016          1        1
2950  HLA-DMA       6_27   0.05258  65.16 9.905e-07   8.596          4        7
4405    NT5C2      10_66   0.59099  46.04 1.450e-04  -8.511         11       15
3664   MAD1L1        7_3   0.31932  63.77 4.772e-05  -8.215          3        3
3953 MPHOSPH9      12_75   0.68723  60.79 2.709e-04  -8.201          2        4
554     AS3MT      10_66   0.21828  44.51 1.947e-05   8.051          6        7
4030     MSH5       6_26   0.00000 236.73 0.000e+00  -7.892          3        3
841  C12orf65      12_75   0.04490  54.18 9.788e-07  -7.754          2        2

GO enrichment analysis for genes with PIP>0.5

#number of genes for gene set enrichment
length(genes)
[1] 35
Uploading data to Enrichr... Done.
  Querying GO_Biological_Process_2021... Done.
  Querying GO_Cellular_Component_2021... Done.
  Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"

Version Author Date
2749be9 sq-96 2022-05-12
[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"

Version Author Date
2749be9 sq-96 2022-05-12
[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"

Version Author Date
2749be9 sq-96 2022-05-12
[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)

DisGeNET enrichment analysis for genes with PIP>0.5

                                                  Description     FDR Ratio
48                                                    Measles 0.02256  1/18
69                                              Schizophrenia 0.02256  7/18
85                              Electroencephalogram abnormal 0.02256  1/18
178                                 Sporadic Breast Carcinoma 0.02256  1/18
181                              Primary peritoneal carcinoma 0.02256  1/18
190     BREAST-OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.02256  1/18
191             BREAST CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.02256  1/18
192            OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.02256  1/18
193 HEMOLYTIC UREMIC SYNDROME, ATYPICAL, SUSCEPTIBILITY TO, 2 0.02256  1/18
198                                   SENIOR-LOKEN SYNDROME 7 0.02256  1/18
     BgRatio
48    1/9703
69  883/9703
85    1/9703
178   1/9703
181   1/9703
190   1/9703
191   1/9703
192   1/9703
193   1/9703
198   1/9703

WebGestalt enrichment analysis for genes with PIP>0.5

Warning: replacing previous import 'lifecycle::last_warnings' by
'rlang::last_warnings' when loading 'hms'
Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...
Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet, minNum =
minNum, : No significant gene set is identified based on FDR 0.05!
NULL

PIP Manhattan Plot

Version Author Date
2749be9 sq-96 2022-05-12

Sensitivity, specificity and precision for silver standard genes

#number of genes in known annotations
print(length(known_annotations))
[1] 130
#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))
[1] 51
#significance threshold for TWAS
print(sig_thresh)
[1] 4.507
#number of ctwas genes
length(ctwas_genes)
[1] 8
#number of TWAS genes
length(twas_genes)
[1] 131
#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols]
     genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
731      BDNF      11_19    0.8017 23.22 0.0001365  4.348          3        4
6179   SNRPA1      15_50    0.8796 20.85 0.0001460 -4.098          4        6
#sensitivity / recall
print(sensitivity)
   ctwas     TWAS 
0.007692 0.100000 
#specificity
print(specificity)
 ctwas   TWAS 
0.9991 0.9844 
#precision / PPV
print(precision)
  ctwas    TWAS 
0.12500 0.09924 

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] readxl_1.4.0      forcats_0.5.1     stringr_1.4.0     purrr_0.3.4      
 [5] readr_1.4.0       tidyr_1.1.3       tidyverse_1.3.1   tibble_3.1.7     
 [9] WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0       cowplot_1.1.1    
[13] ggplot2_3.3.5     dplyr_1.0.7       reticulate_1.25   workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] fs_1.5.0          lubridate_1.7.10  doParallel_1.0.16 httr_1.4.2       
 [5] rprojroot_2.0.2   tools_4.1.0       backports_1.2.1   doRNG_1.8.2      
 [9] bslib_0.2.5.1     utf8_1.2.1        R6_2.5.0          vipor_0.4.5      
[13] DBI_1.1.1         colorspace_2.0-2  withr_2.4.2       ggrastr_1.0.1    
[17] tidyselect_1.1.1  processx_3.5.2    curl_4.3.2        compiler_4.1.0   
[21] git2r_0.28.0      rvest_1.0.0       cli_3.0.0         Cairo_1.5-15     
[25] xml2_1.3.2        labeling_0.4.2    sass_0.4.0        scales_1.1.1     
[29] callr_3.7.0       systemfonts_1.0.4 apcluster_1.4.9   digest_0.6.27    
[33] rmarkdown_2.9     svglite_2.0.0     pkgconfig_2.0.3   htmltools_0.5.1.1
[37] dbplyr_2.1.1      highr_0.9         rlang_1.0.2       rstudioapi_0.13  
[41] jquerylib_0.1.4   farver_2.1.0      generics_0.1.0    jsonlite_1.7.2   
[45] magrittr_2.0.1    Matrix_1.3-3      ggbeeswarm_0.6.0  Rcpp_1.0.7       
[49] munsell_0.5.0     fansi_0.5.0       lifecycle_1.0.0   stringi_1.6.2    
[53] whisker_0.4       yaml_2.2.1        plyr_1.8.6        grid_4.1.0       
[57] ggrepel_0.9.1     parallel_4.1.0    promises_1.2.0.1  crayon_1.4.1     
[61] lattice_0.20-44   haven_2.4.1       hms_1.1.0         knitr_1.33       
[65] ps_1.6.0          pillar_1.7.0      igraph_1.2.6      rjson_0.2.20     
[69] rngtools_1.5      reshape2_1.4.4    codetools_0.2-18  reprex_2.0.0     
[73] glue_1.4.2        evaluate_0.14     getPass_0.2-2     modelr_0.1.8     
[77] data.table_1.14.0 png_0.1-7         vctrs_0.3.8       httpuv_1.6.1     
[81] foreach_1.5.1     cellranger_1.1.0  gtable_0.3.0      assertthat_0.2.1 
[85] xfun_0.24         broom_0.7.8       later_1.2.0       iterators_1.0.13 
[89] beeswarm_0.4.0    ellipsis_0.3.2    here_1.0.1