SCZ 2018 - Brain_Hypothalamus

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Last updated: 2022-05-19

Checks: 5 2

Knit directory: cTWAS_analysis/

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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] 19902

#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 
1843 1406 1208  781  846 1042 1147  677  814  921 1170 1073  396  705  657  781 
  17   18   19   20   21   22 
1382  282 1439  658   36  638 

#number of imputed weights without missing variants
sum(qclist_all$nmiss==0)

[1] 17601

#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)

[1] 0.8844

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

Past versions of unnamed-chunk-12-1.png

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

     gene       snp 
0.0083920 0.0003085 

 gene   snp 
11.94 10.24 

[1] 105318

[1]    7334 6309950

    gene      snp 
0.006979 0.189243 

[1] 0.01838 1.05816

Genes with highest PIPs

Past versions of unnamed-chunk-14-1.png

Version	Author	Date
bcaadf3	sq-96	2022-05-19
be614ed	sq-96	2022-05-19
2749be9	sq-96	2022-05-12

      genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
4991    R3HDM2      12_36    1.1229 43.52 0.0004828 -6.634          4        4
2390     FEZF1       7_74    1.0238 24.62 0.0002438 -4.812          3        3
3351 LINC00320       21_6    0.9653 29.24 0.0002429 -5.336          5        5
3449      LRP8       1_33    0.9575 23.82 0.0002046  4.654          3        4
286       AKT3      1_128    0.9508 34.79 0.0002780 -6.291          8        8
2566    GIGYF2      2_137    0.9417 56.62 0.0004418  8.128          3        3
712       BDNF      11_19    0.9141 23.31 0.0001849 -4.348          2        2
4239     NRXN2      11_36    0.9073 24.81 0.0001918  4.723          3        3
7140      ZIC4       3_91    0.9031 23.46 0.0001755 -4.221          3        4
6335     THAP8      19_25    0.8792 19.47 0.0001426  3.847          2        2
4951     PTPRF       1_27    0.8792 37.18 0.0002644  6.680          4        4
1545     CRTAP       3_24    0.8764 19.92 0.0001445  3.929          2        2
148     ACTR1B       2_57    0.8316 19.24 0.0001263 -3.978          4        4
2565    GIGYF1       7_63    0.8088 28.55 0.0001764 -5.266          3        3
3873    MRPS33       7_87    0.7742 23.44 0.0001275 -4.304          4        5
6663   TSNARE1       8_93    0.7719 28.75 0.0001581  5.782          7       10
1922    DPYSL3       5_86    0.7459 22.30 0.0001178  4.157          1        1
2181      ETF1       5_82    0.7438 33.82 0.0001776  6.112          1        1
5639     SF3B1      2_117    0.7398 45.62 0.0002320  7.053          2        2
6848    UQCRC2      16_19    0.7381 22.09 0.0001143  4.716          2        2

Genes with highest PVE

      genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
4991    R3HDM2      12_36    1.1229 43.52 0.0004828 -6.634          4        4
2566    GIGYF2      2_137    0.9417 56.62 0.0004418  8.128          3        3
286       AKT3      1_128    0.9508 34.79 0.0002780 -6.291          8        8
4951     PTPRF       1_27    0.8792 37.18 0.0002644  6.680          4        4
2390     FEZF1       7_74    1.0238 24.62 0.0002438 -4.812          3        3
3351 LINC00320       21_6    0.9653 29.24 0.0002429 -5.336          5        5
5639     SF3B1      2_117    0.7398 45.62 0.0002320  7.053          2        2
442     APOPT1      14_54    0.6940 46.11 0.0002066  7.429          4        7
3449      LRP8       1_33    0.9575 23.82 0.0002046  4.654          3        4
4239     NRXN2      11_36    0.9073 24.81 0.0001918  4.723          3        3
712       BDNF      11_19    0.9141 23.31 0.0001849 -4.348          2        2
2181      ETF1       5_82    0.7438 33.82 0.0001776  6.112          1        1
2565    GIGYF1       7_63    0.8088 28.55 0.0001764 -5.266          3        3
7140      ZIC4       3_91    0.9031 23.46 0.0001755 -4.221          3        4
6663   TSNARE1       8_93    0.7719 28.75 0.0001581  5.782          7       10
6221     TAOK2      16_24    0.6069 47.40 0.0001572 -7.024          5        5
1545     CRTAP       3_24    0.8764 19.92 0.0001445  3.929          2        2
6335     THAP8      19_25    0.8792 19.47 0.0001426  3.847          2        2
3873    MRPS33       7_87    0.7742 23.44 0.0001275 -4.304          4        5
148     ACTR1B       2_57    0.8316 19.24 0.0001263 -3.978          4        4

Comparing z scores and PIPs

Past versions of unnamed-chunk-16-1.png

Version	Author	Date
be614ed	sq-96	2022-05-19
2749be9	sq-96	2022-05-12

Past versions of unnamed-chunk-16-2.png

Version	Author	Date
bcaadf3	sq-96	2022-05-19
be614ed	sq-96	2022-05-19
2749be9	sq-96	2022-05-12

[1] 0.01773

         genename region_tag susie_pip    mu2       PVE       z num_intron
4531        PGBD1       6_22 4.933e-02 161.09 1.444e-06 -13.087          2
6901         VARS       6_26 8.163e-05 217.29 1.375e-11 -11.548          1
441          APOM       6_26 8.357e-05 217.08 1.434e-11 -11.541          2
1747         DDR1       6_25 1.495e-01 101.78 2.106e-05  11.175          2
6902        VARS2       6_25 1.018e-01 100.66 9.907e-06 -11.137          1
914      C6orf136       6_24 9.472e-02  80.92 6.894e-06 -11.031          2
2421        FLOT1       6_24 2.537e-01  79.57 4.851e-05  10.981          7
781        BTN3A2       6_20 1.183e-01  91.37 4.454e-06 -10.659          6
668          BAG6       6_26 3.175e-05 166.08 1.360e-12  10.247          7
2849        HLA-B       6_25 7.365e-02  79.14 1.418e-06  10.150         10
5245         RNF5       6_26 2.893e-05 150.34 1.195e-12 -10.045          1
1123       CCHCR1       6_25 5.652e-02  66.51 1.192e-06   9.508          9
2682        GPSM3       6_26 1.971e-06 122.29 4.509e-15  -9.377          1
4257        NT5C2      10_66 4.641e-01  48.79 9.244e-05  -8.511          8
5383 RP5-874C20.8       6_22 3.788e-02  46.81 4.314e-07   8.304          4
2566       GIGYF2      2_137 9.417e-01  56.62 4.418e-04   8.128          3
3887         MSH5       6_26 1.908e-05  72.41 1.864e-13   7.892          3
814      C12orf65      12_75 2.009e-01  55.60 2.131e-05  -7.754          1
7141      ZKSCAN3       6_22 2.099e-02  36.54 8.960e-08  -7.740          2
780        BTN3A1       6_20 7.314e-02  47.48 7.925e-07   7.490          5
     num_sqtl
4531        3
6901        1
441         2
1747        2
6902        1
914         2
2421        8
781         7
668        10
2849       19
5245        1
1123       13
2682        1
4257       12
5383        5
2566        3
3887        3
814         1
7141        2
780         5

GO enrichment analysis for genes with PIP>0.5

#number of genes for gene set enrichment
length(genes)

[1] 65

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"

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Version	Author	Date
bcaadf3	sq-96	2022-05-19
be614ed	sq-96	2022-05-19
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"

Past versions of unnamed-chunk-19-2.png

Version	Author	Date
bcaadf3	sq-96	2022-05-19
be614ed	sq-96	2022-05-19
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"

Past versions of unnamed-chunk-19-3.png

Version	Author	Date
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[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
73                                              Schizophrenia 0.02013 11/32
49                                                    Measles 0.03821  1/32
90                              Electroencephalogram abnormal 0.03821  1/32
96                                   Congenital absent nipple 0.03821  1/32
155           Congenital absence of breast with absent nipple 0.03821  1/32
212                                 Sporadic Breast Carcinoma 0.03821  1/32
215                              Primary peritoneal carcinoma 0.03821  1/32
221                          Osteogenesis Imperfecta Type VII 0.03821  1/32
222 Familial encephalopathy with neuroserpin inclusion bodies 0.03821  1/32
228     BREAST-OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.03821  1/32
     BgRatio
73  883/9703
49    1/9703
90    1/9703
96    1/9703
155   1/9703
212   1/9703
215   1/9703
221   1/9703
222   1/9703
228   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

Warning: ggrepel: 21 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Past versions of unnamed-chunk-22-1.png

Version	Author	Date
bcaadf3	sq-96	2022-05-19
be614ed	sq-96	2022-05-19
7d08c9b	sq-96	2022-05-18
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] 50

#significance threshold for TWAS
print(sig_thresh)

[1] 4.499

#number of ctwas genes
length(ctwas_genes)

[1] 14

#number of TWAS genes
length(twas_genes)

[1] 130

#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
148    ACTR1B       2_57    0.8316 19.24 0.0001263 -3.978          4        4
712      BDNF      11_19    0.9141 23.31 0.0001849 -4.348          2        2
1545    CRTAP       3_24    0.8764 19.92 0.0001445  3.929          2        2
6335    THAP8      19_25    0.8792 19.47 0.0001426  3.847          2        2
7140     ZIC4       3_91    0.9031 23.46 0.0001755 -4.221          3        4

#sensitivity / recall
print(sensitivity)

  ctwas    TWAS 
0.03846 0.13077 

#specificity
print(specificity)

 ctwas   TWAS 
0.9988 0.9845 

#precision / PPV
print(precision)

 ctwas   TWAS 
0.3571 0.1308 

Session information

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