SCZ 2018 - Brain_Putamen_basal_ganglia

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Last updated: 2022-05-18

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] 18714

#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 
1715 1341 1135  747  753  935 1084  644  764  833 1148 1001  362  676  630  762 
  17   18   19   20   21   22 
1314  271 1307  672   31  589 

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

[1] 16516

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

[1] 0.8825

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 
0.0072380 0.0003126 

 gene   snp 
10.69 10.43 

[1] 105318

[1]    7109 6309950

    gene      snp 
0.005222 0.195372 

[1] 0.01435 1.07231

Genes with highest PIPs

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

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

      genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
3298      LRP8       1_33    1.1442 32.81 0.0003081 -4.820          6        6
3183 LINC00320       21_6    1.0821 28.61 0.0003052 -5.336          5        5
2119  FAM177A1       14_9    1.0113 23.94 0.0002035 -4.872         11       14
3112     LAMA5      20_36    0.8624 24.08 0.0001561 -4.335          9       12
242   ALG1L13P       8_11    0.8561 23.49 0.0001531  4.461          4        4
5379     SF3B1      2_117    0.8398 44.94 0.0002906 -7.053          4        4
107     ACTR1B       2_57    0.8262 19.35 0.0001232 -3.978          6        6
4477     PLCB2      15_14    0.8102 24.79 0.0001286 -4.470          5        5
1776    DPYSL3       5_86    0.7896 22.79 0.0001349 -4.157          1        1
1322      COA8      14_54    0.7860 46.84 0.0002658 -7.431          6        9
589     B3GAT1      11_84    0.7836 22.65 0.0001170  4.265          7       10
4839   PYROXD2      10_62    0.7830 21.51 0.0001164  3.718         10       11
228       AKT3      1_128    0.7781 34.91 0.0001934 -6.350          4        4
2967      KAT5      11_36    0.7775 24.18 0.0001362  4.491          7        7
4101     NTRK3      15_41    0.7498 23.92 0.0001194 -4.457          3        3
2398    GIGYF1       7_62    0.7342 27.41 0.0001384  5.266          2        2
4088     NT5C2      10_66    0.7314 47.24 0.0002278 -8.668          9       11
3357      LY6H       8_94    0.7233 22.36 0.0001059 -4.186          4        4
4294     PCSK6      15_50    0.7178 22.06 0.0001061 -3.967          2        3
5292   SDCCAG8      1_128    0.7027 27.40 0.0001204  5.377          7       11

Genes with highest PVE

      genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
3298      LRP8       1_33    1.1442 32.81 0.0003081 -4.820          6        6
3183 LINC00320       21_6    1.0821 28.61 0.0003052 -5.336          5        5
5379     SF3B1      2_117    0.8398 44.94 0.0002906 -7.053          4        4
1322      COA8      14_54    0.7860 46.84 0.0002658 -7.431          6        9
4088     NT5C2      10_66    0.7314 47.24 0.0002278 -8.668          9       11
2119  FAM177A1       14_9    1.0113 23.94 0.0002035 -4.872         11       14
228       AKT3      1_128    0.7781 34.91 0.0001934 -6.350          4        4
3112     LAMA5      20_36    0.8624 24.08 0.0001561 -4.335          9       12
242   ALG1L13P       8_11    0.8561 23.49 0.0001531  4.461          4        4
1646      DGKZ      11_28    0.5830 47.17 0.0001522  7.216          1        1
2398    GIGYF1       7_62    0.7342 27.41 0.0001384  5.266          2        2
2967      KAT5      11_36    0.7775 24.18 0.0001362  4.491          7        7
3380    MAD1L1        7_3    0.5571 54.63 0.0001359  7.478          4        4
1776    DPYSL3       5_86    0.7896 22.79 0.0001349 -4.157          1        1
4477     PLCB2      15_14    0.8102 24.79 0.0001286 -4.470          5        5
107     ACTR1B       2_57    0.8262 19.35 0.0001232 -3.978          6        6
5292   SDCCAG8      1_128    0.7027 27.40 0.0001204  5.377          7       11
4101     NTRK3      15_41    0.7498 23.92 0.0001194 -4.457          3        3
589     B3GAT1      11_84    0.7836 22.65 0.0001170  4.265          7       10
4839   PYROXD2      10_62    0.7830 21.51 0.0001164  3.718         10       11

Comparing z scores and PIPs

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

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

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

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

[1] 0.01618

     genename region_tag susie_pip    mu2       PVE       z num_intron num_sqtl
374      APOM       6_26 1.639e-04 215.80 5.484e-11  11.590          2        2
605      BAG6       6_26 1.164e-04 215.80 2.571e-11 -11.590          6        6
6621    VARS2       6_25 6.847e-02 101.95 4.538e-06 -11.137          1        1
819  C6orf136       6_24 7.194e-02  79.99 3.931e-06  11.031          2        2
2252    FLOT1       6_24 1.607e-01  78.65 1.917e-05 -10.981          6        6
1535  CYP21A2       6_26 3.944e-06 179.01 2.643e-14 -10.736          1        2
726    BTN3A2       6_20 6.252e-02  91.47 1.705e-06 -10.717          4        5
2519    GPSM3       6_26 3.863e-06 120.46 1.706e-14  -9.377          2        2
1006   CCHCR1       6_25 6.850e-02  61.89 1.309e-06  -9.272         10       15
1591     DDR1       6_25 1.253e-02  68.37 1.019e-07   9.016          1        1
1878    EGFL8       6_26 1.227e-05 121.04 1.708e-13  -8.953          2        3
4088    NT5C2      10_66 7.314e-01  47.24 2.278e-04  -8.668          9       11
3392    MAIP1      2_118 2.500e-01  44.44 2.637e-05  -7.980          1        1
472     AS3MT      10_66 3.678e-01  40.78 5.194e-05   7.907          4        5
7102  ZSCAN26       6_22 2.458e-02  37.09 1.450e-07  -7.603          3        4
3380   MAD1L1        7_3 5.571e-01  54.63 1.359e-04   7.478          4        4
7098  ZSCAN16       6_22 2.059e-02  53.72 1.087e-07  -7.468          2        2
1322     COA8      14_54 7.860e-01  46.84 2.658e-04  -7.431          6        9
6912 ZNF192P1       6_22 7.746e-03  53.28 3.035e-08  -7.429          1        1
3060     KLC1      14_54 2.052e-01  49.40 1.648e-05   7.382          6        6

GO enrichment analysis for genes with PIP>0.5

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

[1] 48

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"

Past versions of unnamed-chunk-19-1.png

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"

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

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"

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

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

                               Term Overlap Adjusted.P.value       Genes
1 neurotrophin binding (GO:0043121)     2/8          0.01252 NTRK3;PCSK6

DisGeNET enrichment analysis for genes with PIP>0.5

                                                  Description     FDR Ratio
49                                              Schizophrenia 0.01231  9/22
7                                  Adenocarcinoma of prostate 0.01755  2/22
34                                                    Measles 0.01755  1/22
56                              Electroencephalogram abnormal 0.01755  1/22
125                                 Sporadic Breast Carcinoma 0.01755  1/22
128                              Primary peritoneal carcinoma 0.01755  1/22
137     BREAST-OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.01755  1/22
138             BREAST CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.01755  1/22
139            OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.01755  1/22
141 HEMOLYTIC UREMIC SYNDROME, ATYPICAL, SUSCEPTIBILITY TO, 2 0.01755  1/22
     BgRatio
49  883/9703
7    20/9703
34    1/9703
56    1/9703
125   1/9703
128   1/9703
137   1/9703
138   1/9703
139   1/9703
141   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: 13 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

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

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] 56

#significance threshold for TWAS
print(sig_thresh)

[1] 4.493

#number of ctwas genes
length(ctwas_genes)

[1] 8

#number of TWAS genes
length(twas_genes)

[1] 115

#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
107    ACTR1B       2_57    0.8262 19.35 0.0001232 -3.978          6        6
242  ALG1L13P       8_11    0.8561 23.49 0.0001531  4.461          4        4
3112    LAMA5      20_36    0.8624 24.08 0.0001561 -4.335          9       12
4477    PLCB2      15_14    0.8102 24.79 0.0001286 -4.470          5        5

#sensitivity / recall
print(sensitivity)

  ctwas    TWAS 
0.02308 0.10769 

#specificity
print(specificity)

 ctwas   TWAS 
0.9993 0.9857 

#precision / PPV
print(precision)

 ctwas   TWAS 
0.3750 0.1217 

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_1.20   workflowr_1.6.2  

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  curl_4.3.2        compiler_4.1.0    git2r_0.28.0     
[21] rvest_1.0.0       cli_3.0.0         Cairo_1.5-15      xml2_1.3.2       
[25] labeling_0.4.2    sass_0.4.0        scales_1.1.1      systemfonts_1.0.4
[29] apcluster_1.4.9   digest_0.6.27     rmarkdown_2.9     svglite_2.0.0    
[33] pkgconfig_2.0.3   htmltools_0.5.1.1 dbplyr_2.1.1      highr_0.9        
[37] rlang_1.0.2       rstudioapi_0.13   jquerylib_0.1.4   farver_2.1.0     
[41] generics_0.1.0    jsonlite_1.7.2    magrittr_2.0.1    Matrix_1.3-3     
[45] ggbeeswarm_0.6.0  Rcpp_1.0.7        munsell_0.5.0     fansi_0.5.0      
[49] lifecycle_1.0.0   stringi_1.6.2     whisker_0.4       yaml_2.2.1       
[53] plyr_1.8.6        grid_4.1.0        ggrepel_0.9.1     parallel_4.1.0   
[57] promises_1.2.0.1  crayon_1.4.1      lattice_0.20-44   haven_2.4.1      
[61] hms_1.1.0         knitr_1.33        pillar_1.7.0      igraph_1.2.6     
[65] rjson_0.2.20      rngtools_1.5      reshape2_1.4.4    codetools_0.2-18 
[69] reprex_2.0.0      glue_1.4.2        evaluate_0.14     data.table_1.14.0
[73] modelr_0.1.8      png_0.1-7         vctrs_0.3.8       httpuv_1.6.1     
[77] foreach_1.5.1     cellranger_1.1.0  gtable_0.3.0      assertthat_0.2.1 
[81] xfun_0.24         broom_0.7.8       later_1.2.0       iterators_1.0.13 
[85] beeswarm_0.4.0    ellipsis_0.3.2