Last updated: 2022-05-18

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Knit directory: cTWAS_analysis/

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File Version Author Date Message
Rmd 2749be9 sq-96 2022-05-12 update
html 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] 27353
#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 
2535 1830 1661  982 1135 1371 1536  916 1175 1171 1678 1471  543  971  987 1200 
  17   18   19   20   21   22 
1981  337 2002  917   48  906 
#number of imputed weights without missing variants
sum(qclist_all$nmiss==0)
[1] 23734
#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)
[1] 0.8677
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 
0.0088825 0.0002955 
 gene   snp 
12.43 10.04 
[1] 105318
[1]    8002 6309950
    gene      snp 
0.008389 0.177721 
[1] 0.02987 1.06067

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
3723       LRP8       1_33    1.2179 33.07 0.0003523 -4.820         10       11
5397     R3HDM2      12_36    1.1443 44.06 0.0004924  6.634         10       12
3563      LAMA5      20_36    1.1434 23.61 0.0002517  4.603         24       38
7577      WDR27      6_111    1.0487 17.72 0.0001014 -2.341         29       37
2782     GIGYF1       7_62    0.9748 26.79 0.0002375 -5.266          5        5
854  BUB1B-PAK6      15_14    0.9723 30.33 0.0002604 -5.588          4        5
299        AKT3      1_128    0.9654 35.61 0.0003005  6.350          6        7
4166     MRPS33       7_87    0.9654 20.31 0.0001744 -4.304          6        6
7712    ZDHHC20       13_2    0.9572 24.94 0.0002118 -4.784          3        4
4518     NPIPA1      16_15    0.9556 24.97 0.0002096  4.689          3        3
3632  LINC00320       21_6    0.9542 29.24 0.0002419 -5.336          3        3
6119      SF3B1      2_117    0.9478 45.88 0.0003746  7.053          5        5
1668      CRTAP       3_24    0.9010 19.87 0.0001503  3.929          3        3
1179     CCDC57      17_47    0.8904 20.00 0.0001041  3.022         36       46
7231    TSNARE1       8_93    0.8894 34.70 0.0002087  6.287         10       12
469      APOPT1      14_54    0.8857 43.21 0.0003125  7.429          6        7
765        BDNF      11_19    0.8820 23.81 0.0001695  4.348          3        3
5383    PYROXD2      10_62    0.8732 20.71 0.0001347 -3.755         12       14
4756       PATJ       1_39    0.8686 23.29 0.0001371 -2.798         16       19
360      ANAPC7      12_67    0.8369 37.61 0.0002240  6.385          7        7
640      ATP2B2        3_8    0.8241 26.05 0.0001568  4.229          7        8
698      B3GAT1      11_84    0.8157 23.68 0.0001377  4.324          6        9
4586      NTRK3      15_41    0.8046 24.66 0.0001392  4.457          2        2
1126      CBWD1        9_1    0.8033 20.46 0.0001186  4.060          3        4

Genes with highest PVE

       genename region_tag susie_pip    mu2       PVE      z num_intron
468        APOM       6_26    0.3686 623.03 0.0008033 11.590          3
849      BTN3A1       6_20    0.7393 146.39 0.0006649 13.091          8
5397     R3HDM2      12_36    1.1443  44.06 0.0004924  6.634         10
6119      SF3B1      2_117    0.9478  45.88 0.0003746  7.053          5
3723       LRP8       1_33    1.2179  33.07 0.0003523 -4.820         10
469      APOPT1      14_54    0.8857  43.21 0.0003125  7.429          6
1318      CENPM      22_17    0.7509  57.80 0.0003094 -6.506          1
299        AKT3      1_128    0.9654  35.61 0.0003005  6.350          6
854  BUB1B-PAK6      15_14    0.9723  30.33 0.0002604 -5.588          4
3563      LAMA5      20_36    1.1434  23.61 0.0002517  4.603         24
3632  LINC00320       21_6    0.9542  29.24 0.0002419 -5.336          3
2782     GIGYF1       7_62    0.9748  26.79 0.0002375 -5.266          5
7547       VWA7       6_26    0.1940 627.25 0.0002242 11.553          1
360      ANAPC7      12_67    0.8369  37.61 0.0002240  6.385          7
7712    ZDHHC20       13_2    0.9572  24.94 0.0002118 -4.784          3
4518     NPIPA1      16_15    0.9556  24.97 0.0002096  4.689          3
7231    TSNARE1       8_93    0.8894  34.70 0.0002087  6.287         10
7160     TRANK1       3_27    0.7490  39.04 0.0001917 -6.365          8
1449      CLCN3      4_110    0.7913  29.64 0.0001762  5.470          1
4166     MRPS33       7_87    0.9654  20.31 0.0001744 -4.304          6
     num_sqtl
468         3
849         8
5397       12
6119        5
3723       11
469         7
1318        1
299         7
854         5
3563       38
3632        3
2782        5
7547        1
360         7
7712        4
4518        3
7231       12
7160        8
1449        2
4166        6

Comparing z scores and PIPs

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

Version Author Date
2749be9 sq-96 2022-05-12
[1] 0.02124
         genename region_tag susie_pip    mu2       PVE       z num_intron
849        BTN3A1       6_20 7.393e-01 146.39 6.649e-04  13.091          8
4876        PGBD1       6_22 1.007e-01 160.95 7.079e-06  13.087          5
468          APOM       6_26 3.686e-01 623.03 8.033e-04  11.590          3
7547         VWA7       6_26 1.940e-01 627.25 2.242e-04  11.553          1
7489         VARS       6_26 1.402e-01 623.95 1.165e-04 -11.548          2
4179         MSH5       6_26 1.588e-01 627.91 1.503e-04 -11.538          3
1888         DDR1       6_25 1.570e-01 105.86 2.456e-05 -11.175          3
7490        VARS2       6_25 1.118e-01 104.74 1.206e-05  11.137          2
979      C6orf136       6_25 7.591e-02  87.21 4.771e-06 -11.031          2
2623        FLOT1       6_25 1.547e-01  87.22 1.952e-05 -10.981          7
850        BTN3A2       6_20 1.644e-01  94.96 1.183e-05 -10.694          3
2841         GNL1       6_25 2.920e-03  78.25 6.334e-09 -10.645          1
7183       TRIM39       6_25 7.839e-03  82.27 4.800e-08 -10.616          1
716          BAG6       6_26 1.491e-09 498.08 1.051e-20  10.247          6
5195         PPT2       6_26 7.799e-12 464.25 2.681e-25 -10.061         10
5261        PRRT1       6_26 2.706e-12 462.51 3.216e-26 -10.018          1
2909        GPSM3       6_26 8.360e-14 414.68 2.752e-29  -9.377          1
1203       CCHCR1       6_25 4.718e-02  69.57 6.124e-07  -9.358         17
7105         TNXB       6_26 1.527e-13 452.13 1.000e-28   9.001          6
5832 RP5-874C20.8       6_22 6.731e-02  53.73 1.605e-06   8.672          6
     num_sqtl
849         8
4876        6
468         3
7547        1
7489        2
4179        3
1888        3
7490        2
979         2
2623        7
850         5
2841        1
7183        1
716         7
5195       12
5261        1
2909        1
1203       30
7105        7
5832        6

GO enrichment analysis for genes with PIP>0.5

#number of genes for gene set enrichment
length(genes)
[1] 109
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
                                                  Term Overlap Adjusted.P.value
1 morphogenesis of a polarized epithelium (GO:0001738)    3/12          0.02988
             Genes
1 AHI1;LAMA5;ACTG1
[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  BgRatio
62                              Glioma 0.04106  4/58  87/9703
90                             Measles 0.04106  1/58   1/9703
128                      Schizophrenia 0.04106 12/58 883/9703
156      Electroencephalogram abnormal 0.04106  1/58   1/9703
160                        Polydactyly 0.04106  4/58 117/9703
196                Short upturned nose 0.04106  1/58   1/9703
199                      mixed gliomas 0.04106  4/58  70/9703
218      Hypoglycemia, leucine-induced 0.04106  1/58   1/9703
276 Interfrontal craniofaciosynostosis 0.04106  1/58   1/9703
277            Osteoglophonic dwarfism 0.04106  1/58   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: 66 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

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$genename))
[1] 59
#significance threshold for TWAS
print(sig_thresh)
[1] 4.518
#number of ctwas genes
length(ctwas_genes)
[1] 24
#number of TWAS genes
length(twas_genes)
[1] 170
#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols]
     genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
640    ATP2B2        3_8    0.8241 26.05 0.0001568  4.229          7        8
698    B3GAT1      11_84    0.8157 23.68 0.0001377  4.324          6        9
765      BDNF      11_19    0.8820 23.81 0.0001695  4.348          3        3
1126    CBWD1        9_1    0.8033 20.46 0.0001186  4.060          3        4
1179   CCDC57      17_47    0.8904 20.00 0.0001041  3.022         36       46
1668    CRTAP       3_24    0.9010 19.87 0.0001503  3.929          3        3
4166   MRPS33       7_87    0.9654 20.31 0.0001744 -4.304          6        6
4586    NTRK3      15_41    0.8046 24.66 0.0001392  4.457          2        2
4756     PATJ       1_39    0.8686 23.29 0.0001371 -2.798         16       19
5383  PYROXD2      10_62    0.8732 20.71 0.0001347 -3.755         12       14
7577    WDR27      6_111    1.0487 17.72 0.0001014 -2.341         29       37
#sensitivity / recall
print(sensitivity)
  ctwas    TWAS 
0.03846 0.17692 
#specificity
print(specificity)
 ctwas   TWAS 
0.9976 0.9815 
#precision / PPV
print(precision)
 ctwas   TWAS 
0.2083 0.1353 

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