Last updated: 2022-05-19

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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] 21088
#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 
1945 1489 1242  813  857 1099 1218  761  872  971 1260 1129  427  730  701  875 
  17   18   19   20   21   22 
1505  291 1506  706   43  648 
#number of imputed weights without missing variants
sum(qclist_all$nmiss==0)
[1] 18528
#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)
[1] 0.8786
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 
0.0079195 0.0003085 
  gene    snp 
15.345  9.982 
[1] 105318
[1]    7501 6309950
    gene      snp 
0.008655 0.184514 
[1] 0.02202 1.09251

Genes with highest PIPs

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
3525      LRP8       1_33    1.1351  34.30 0.0003112   4.820          6
791     BTN2A1       6_20    1.0285 157.11 0.0015234 -13.238          4
5082   PYROXD2      10_62    0.9822  23.05 0.0001954  -4.285          9
3424 LINC00320       21_6    0.9439  29.89 0.0002422  -5.336          4
719       BDNF      11_19    0.9220  23.77 0.0001901  -4.348          3
3360     LAMA5      20_36    0.9116  28.08 0.0001949  -4.299         14
1557     CRTAP       3_24    0.8882  20.38 0.0001502   3.929          3
652     B3GAT1      11_84    0.8763  22.16 0.0001522   4.287          7
6490     THAP8      19_25    0.8541  20.59 0.0001383  -3.802          2
6054    SNRPA1      15_50    0.8325  23.29 0.0001424  -3.925          6
4710     PLCB2      15_14    0.8308  25.63 0.0001382   4.470          5
5772     SF3B1      2_117    0.8056  47.59 0.0002835  -7.053          3
2595    GIGYF1       7_62    0.7930  28.66 0.0001691   5.266          2
3600    MAD1L1        7_3    0.7856  55.70 0.0003067   7.478          3
7044     VARS2       6_25    0.7827  95.93 0.0005580 -11.413          1
3576      LY6H       8_94    0.7826  22.30 0.0001256  -4.186          4
152     ACTR1B       2_57    0.7573  20.67 0.0001104   3.978          5
5829     SIPA1      11_36    0.7516  28.32 0.0001507  -4.893          2
6034     SMYD2      1_108    0.7427  23.47 0.0001207   3.952          3
1937    DPYSL3       5_86    0.7423  22.76 0.0001191   4.157          1
     num_sqtl
3525        6
791         5
5082       10
3424        4
719         3
3360       21
1557        3
652        12
6490        4
6054        7
4710        5
5772        3
2595        2
3600        5
7044        1
3576        4
152         5
5829        2
6034        3
1937        1

Genes with highest PVE

      genename region_tag susie_pip    mu2       PVE       z num_intron
791     BTN2A1       6_20    1.0285 157.11 0.0015234 -13.238          4
439       APOM       6_26    0.4871 631.82 0.0014228  11.590          3
7043      VARS       6_26    0.4360 634.35 0.0011450 -11.620          1
7044     VARS2       6_25    0.7827  95.93 0.0005580 -11.413          1
3525      LRP8       1_33    1.1351  34.30 0.0003112   4.820          6
3600    MAD1L1        7_3    0.7856  55.70 0.0003067   7.478          3
5772     SF3B1      2_117    0.8056  47.59 0.0002835  -7.053          3
3424 LINC00320       21_6    0.9439  29.89 0.0002422  -5.336          4
2548   GATAD2A      19_15    0.6956  48.44 0.0002193  -6.668          4
2596    GIGYF2      2_137    0.7039  54.88 0.0002153   7.841          5
1816      DGKZ      11_28    0.6718  49.23 0.0002110  -7.216          2
5082   PYROXD2      10_62    0.9822  23.05 0.0001954  -4.285          9
3360     LAMA5      20_36    0.9116  28.08 0.0001949  -4.299         14
719       BDNF      11_19    0.9220  23.77 0.0001901  -4.348          3
2595    GIGYF1       7_62    0.7930  28.66 0.0001691   5.266          2
4135     NDRG4      16_31    0.6541  38.82 0.0001527  -6.343          4
6823   TSNARE1       8_93    0.7412  33.80 0.0001523   6.367          7
652     B3GAT1      11_84    0.8763  22.16 0.0001522   4.287          7
5829     SIPA1      11_36    0.7516  28.32 0.0001507  -4.893          2
1557     CRTAP       3_24    0.8882  20.38 0.0001502   3.929          3
     num_sqtl
791         5
439         3
7043        1
7044        1
3525        6
3600        5
5772        3
3424        4
2548        4
2596        5
1816        2
5082       10
3360       21
719         3
2595        2
4135        4
6823        7
652        12
5829        2
1557        3

Comparing z scores and PIPs

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

Version Author Date
bcaadf3 sq-96 2022-05-19
be614ed sq-96 2022-05-19
2749be9 sq-96 2022-05-12 
[1] 0.01866
     genename region_tag susie_pip    mu2       PVE       z num_intron num_sqtl
791    BTN2A1       6_20 1.028e+00 157.11 1.523e-03 -13.238          4        5
7303  ZKSCAN3       6_22 4.155e-02 166.82 1.595e-06 -13.135          4        4
4612    PGBD1       6_22 4.774e-02 165.70 2.426e-06 -13.087          3        4
7043     VARS       6_26 4.360e-01 634.35 1.145e-03 -11.620          1        1
439      APOM       6_26 4.871e-01 631.82 1.423e-03  11.590          3        3
7044    VARS2       6_25 7.827e-01  95.93 5.580e-04 -11.413          1        1
561     ATAT1       6_24 6.167e-02  84.80 3.063e-06  11.039          1        1
928  C6orf136       6_24 1.177e-01  84.50 1.112e-05 -11.031          2        2
2438    FLOT1       6_24 2.516e-01  83.18 4.983e-05  10.981          6        7
793    BTN3A2       6_20 8.199e-02 103.10 2.442e-06 -10.759          4        5
667      BAG6       6_26 2.191e-09 504.13 2.299e-20  10.247          7        8
5369     RNF5       6_26 8.494e-13 470.42 3.223e-27 -10.045          1        1
1128   CCHCR1       6_25 8.454e-02  63.68 1.907e-06   9.508         14       20
2724    GPSM3       6_26 2.132e-14 419.92 1.812e-30   9.377          1        1
1768     DDR1       6_25 6.371e-03  62.32 2.402e-08   9.016          1        1
2897  HLA-DMA       6_27 6.303e-02  69.97 1.354e-06   8.781          5        7
4333    NT5C2      10_66 5.360e-01  50.29 1.300e-04  -8.541         10       14
535     AS3MT      10_66 3.162e-01  43.68 4.061e-05   7.907          4        4
3960     MSH5       6_26 0.000e+00 238.50 0.000e+00  -7.892          2        2
2596   GIGYF2      2_137 7.039e-01  54.88 2.153e-04   7.841          5        5

GO enrichment analysis for genes with PIP>0.5

#number of genes for gene set enrichment
length(genes)
[1] 71
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
bcaadf3 sq-96 2022-05-19
be614ed sq-96 2022-05-19
2749be9 sq-96 2022-05-12 
                                                               Term Overlap
1 positive regulation of neuron projection development (GO:0010976)    6/88
2  positive regulation of cell projection organization (GO:0031346)   5/117
  Adjusted.P.value                                 Genes
1        0.0003941 NDRG4;BDNF;DPYSL3;PRKD1;SERPINI1;LRP8
2        0.0170544      NDRG4;BDNF;DPYSL3;PRKD1;SERPINI1
[1] "GO_Cellular_Component_2021"

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"

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)

DisGeNET enrichment analysis for genes with PIP>0.5

                                                  Description     FDR Ratio
52                                                    Measles 0.05983  1/36
72                                              Schizophrenia 0.05983 10/36
209                          Osteogenesis Imperfecta Type VII 0.05983  1/36
210                        Maple Syrup Urine Disease, Type IA 0.05983  1/36
211 Familial encephalopathy with neuroserpin inclusion bodies 0.05983  1/36
217 HEMOLYTIC UREMIC SYNDROME, ATYPICAL, SUSCEPTIBILITY TO, 2 0.05983  1/36
221                MENTAL RETARDATION, AUTOSOMAL RECESSIVE 14 0.05983  1/36
224                                   MECKEL SYNDROME, TYPE 9 0.05983  1/36
228      MITOCHONDRIAL COMPLEX III DEFICIENCY, NUCLEAR TYPE 5 0.05983  1/36
236                SPASTIC PARAPLEGIA 45, AUTOSOMAL RECESSIVE 0.05983  1/36
     BgRatio
52    1/9703
72  883/9703
209   1/9703
210   1/9703
211   1/9703
217   1/9703
221   1/9703
224   1/9703
228   1/9703
236   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: 26 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

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$genename))
[1] 59
#significance threshold for TWAS
print(sig_thresh)
[1] 4.504
#number of ctwas genes
length(ctwas_genes)
[1] 12
#number of TWAS genes
length(twas_genes)
[1] 140
#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols]
     genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
652    B3GAT1      11_84    0.8763 22.16 0.0001522  4.287          7       12
719      BDNF      11_19    0.9220 23.77 0.0001901 -4.348          3        3
1557    CRTAP       3_24    0.8882 20.38 0.0001502  3.929          3        3
3360    LAMA5      20_36    0.9116 28.08 0.0001949 -4.299         14       21
4710    PLCB2      15_14    0.8308 25.63 0.0001382  4.470          5        5
5082  PYROXD2      10_62    0.9822 23.05 0.0001954 -4.285          9       10
6054   SNRPA1      15_50    0.8325 23.29 0.0001424 -3.925          6        7
6490    THAP8      19_25    0.8541 20.59 0.0001383 -3.802          2        4
#sensitivity / recall
print(sensitivity)
  ctwas    TWAS 
0.02308 0.13077 
#specificity
print(specificity)
 ctwas   TWAS 
0.9988 0.9835 
#precision / PPV
print(precision)
 ctwas   TWAS 
0.2500 0.1214 

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