Last updated: 2022-05-19

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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] 15170
#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 
1400 1082  880  623  588  770  877  489  649  714  913  791  301  549  531  626 
  17   18   19   20   21   22 
1040  214 1116  542   34  441 
#number of imputed weights without missing variants
sum(qclist_all$nmiss==0)
[1] 13605
#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)
[1] 0.8968
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 
0.0065597 0.0003205 
  gene    snp 
 9.522 10.339 
[1] 105318
[1]    6278 6309950
    gene      snp 
0.003723 0.198505 
[1] 0.008784 1.088938

Genes with highest PIPs

Version Author Date
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     genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
3022     LRP8       1_33    1.0645 32.61 2.549e-04 -4.624          6        6
1361    CRTAP       3_24    0.8430 20.01 1.343e-04  3.929          2        2
5465    THAP8      19_25    0.8114 20.21 1.261e-04  3.847          2        2
380    APOPT1      14_54    0.8024 44.24 2.642e-04  7.429          6        9
1685   DPYSL3       5_86    0.7628 23.36 1.291e-04  4.157          1        1
619      BDNF      11_19    0.7481 22.63 1.203e-04  4.348          1        1
564    B3GAT1      11_84    0.6722 31.39 1.272e-04 -4.516          6       10
241      AKT3      1_128    0.6713 34.01 1.356e-04 -6.291          6        6
4024    PLCB2      15_14    0.6378 24.42 8.300e-05  4.470          3        4
5949    VPS41       7_28    0.6151 25.12 8.874e-05 -4.509          2        2
129    ACTR1B       2_57    0.5934 22.34 7.367e-05  3.978          3        3
2308    GON4L       1_76    0.5783 27.63 8.773e-05  4.084          1        1
3192      MDK      11_28    0.5743 45.88 1.437e-04  7.159          1        1
2881    LAMA5      20_36    0.5692 32.47 8.011e-05  3.967         10       15
4326  PYROXD2      10_62    0.5608 33.32 7.427e-05 -3.718         10       11
2166     FXR1      3_111    0.5532 42.91 1.221e-04 -6.873          4        4
1022     CD46      1_105    0.5515 18.45 5.268e-05 -3.654          6        6
4280    PTK2B       8_27    0.5318 26.09 6.953e-05  4.730          2        3
2446    HDDC2       6_84    0.5117 19.46 3.262e-05  2.383         15       20
3670     NQO2        6_3    0.5116 25.24 4.071e-05  3.051         16       24

Genes with highest PVE

     genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
380    APOPT1      14_54    0.8024 44.24 2.642e-04  7.429          6        9
3022     LRP8       1_33    1.0645 32.61 2.549e-04 -4.624          6        6
3192      MDK      11_28    0.5743 45.88 1.437e-04  7.159          1        1
241      AKT3      1_128    0.6713 34.01 1.356e-04 -6.291          6        6
1361    CRTAP       3_24    0.8430 20.01 1.343e-04  3.929          2        2
1685   DPYSL3       5_86    0.7628 23.36 1.291e-04  4.157          1        1
564    B3GAT1      11_84    0.6722 31.39 1.272e-04 -4.516          6       10
5465    THAP8      19_25    0.8114 20.21 1.261e-04  3.847          2        2
2166     FXR1      3_111    0.5532 42.91 1.221e-04 -6.873          4        4
619      BDNF      11_19    0.7481 22.63 1.203e-04  4.348          1        1
2203  GATAD2A      19_15    0.4632 45.09 9.073e-05 -6.640          4        4
5949    VPS41       7_28    0.6151 25.12 8.874e-05 -4.509          2        2
2308    GON4L       1_76    0.5783 27.63 8.773e-05  4.084          1        1
4024    PLCB2      15_14    0.6378 24.42 8.300e-05  4.470          3        4
2881    LAMA5      20_36    0.5692 32.47 8.011e-05  3.967         10       15
4326  PYROXD2      10_62    0.5608 33.32 7.427e-05 -3.718         10       11
129    ACTR1B       2_57    0.5934 22.34 7.367e-05  3.978          3        3
4280    PTK2B       8_27    0.5318 26.09 6.953e-05  4.730          2        3
5406    TCAIM       3_31    0.4480 35.10 6.170e-05  4.053          5        5
3090   MAD1L1        7_3    0.3735 62.14 5.662e-05  8.215          4        6

Comparing z scores and PIPs

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

Version Author Date
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[1] 0.01434
         genename region_tag susie_pip    mu2       PVE       z num_intron
3948        PGBD1       6_22 4.087e-02 155.68 9.882e-07 -13.087          2
1537         DDR1       6_25 1.261e-01 100.79 1.477e-05  11.175          3
2118        FLOT1       6_24 1.104e-01  77.49 8.920e-06 -10.944          5
692        BTN3A2       6_20 1.060e-01  88.48 4.796e-06 -10.665          4
574          BAG6       6_26 3.796e-05 160.21 1.643e-12 -10.247          5
984        CCHCR1       6_25 2.958e-02  62.23 3.774e-07  -9.378          5
2347        GPSM3       6_26 1.690e-06 118.03 3.202e-15  -9.377          1
6126      ZKSCAN3       6_22 2.955e-02  55.20 1.934e-07  -9.321          3
3706        NT5C2      10_66 3.297e-01  46.96 4.551e-05  -8.541          8
4651 RP5-874C20.8       6_22 3.391e-02  45.29 3.198e-07  -8.313          4
3090       MAD1L1        7_3 3.735e-01  62.14 5.662e-05   8.215          4
463         AS3MT      10_66 2.402e-01  44.47 2.402e-05   8.051          3
6270      ZSCAN16       6_22 2.759e-02  52.38 2.415e-07   7.468          3
380        APOPT1      14_54 8.024e-01  44.24 2.642e-04   7.429          6
3192          MDK      11_28 5.743e-01  45.88 1.437e-04   7.159          1
209          AIF1       6_26 1.311e-02  59.25 9.670e-08  -7.131          5
5564      TMEM219      16_24 3.359e-01  45.65 4.891e-05  -7.020          1
1581         DGKZ      11_28 1.388e-01  43.55 7.970e-06  -6.964          1
2643       INO80E      16_24 2.253e-01  44.02 1.928e-05  -6.917          4
2166         FXR1      3_111 5.532e-01  42.91 1.221e-04  -6.873          4
     num_sqtl
3948        2
1537        3
2118        5
692         4
574         7
984         8
2347        1
6126        3
3706       11
4651        5
3090        6
463         3
6270        3
380         9
3192        1
209         5
5564        1
1581        2
2643        5
2166        4

GO enrichment analysis for genes with PIP>0.5

#number of genes for gene set enrichment
length(genes)
[1] 21
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
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                                                                          Term
1            positive regulation of neuron projection development (GO:0010976)
2             positive regulation of cell projection organization (GO:0031346)
3                     regulation of neuron projection development (GO:0010975)
4                 negative regulation of neuron apoptotic process (GO:0043524)
5  positive regulation of vascular endothelial cell proliferation (GO:1905564)
6                             negative regulation of neuron death (GO:1901215)
7                          regulation of neuron apoptotic process (GO:0043523)
8           regulation of vascular endothelial cell proliferation (GO:1905562)
9                              regulation of leukocyte chemotaxis (GO:0002688)
10                            regulation of macrophage chemotaxis (GO:0010758)
11                            negative regulation of ossification (GO:0030279)
12                regulation of regulatory T cell differentiation (GO:0045589)
13                         activation of phospholipase C activity (GO:0007202)
14                 positive regulation of protein phosphorylation (GO:0001934)
15                         regulation of trans-synaptic signaling (GO:0099177)
16                regulation of actin cytoskeleton reorganization (GO:2000249)
17                              regulation of filopodium assembly (GO:0051489)
18        positive regulation of protein tyrosine kinase activity (GO:0061098)
19                positive regulation of phospholipase C activity (GO:0010863)
20                  positive regulation of T cell differentiation (GO:0045582)
21                       negative regulation of apoptotic process (GO:0043066)
22                                              apoptotic process (GO:0006915)
23                 positive regulation of cell-substrate adhesion (GO:0010811)
24                            integrin-mediated signaling pathway (GO:0007229)
25                       positive regulation of T cell activation (GO:0050870)
26          positive regulation of endothelial cell proliferation (GO:0001938)
   Overlap Adjusted.P.value                      Genes
1     5/88        1.185e-05 BDNF;MDK;DPYSL3;PTK2B;LRP8
2    4/117        1.291e-03      BDNF;MDK;DPYSL3;PTK2B
3    4/165        3.346e-03      BDNF;MDK;DPYSL3;PTK2B
4     3/71        5.705e-03             BDNF;MDK;PTK2B
5     2/13        6.816e-03                   MDK;AKT3
6     3/98        8.270e-03             BDNF;MDK;PTK2B
7     3/98        8.270e-03             BDNF;MDK;PTK2B
8     2/18        8.270e-03                   MDK;AKT3
9     2/19        8.270e-03                  MDK;PTK2B
10    2/22        1.004e-02                  MDK;PTK2B
11    2/24        1.089e-02                  MDK;PTK2B
12    2/26        1.174e-02                   MDK;CD46
13    2/32        1.625e-02                 BDNF;PLCB2
14   4/371        1.625e-02       FXR1;BDNF;PTK2B;LRP8
15    2/35        1.709e-02                  BDNF;LRP8
16    2/37        1.791e-02                  MDK;PTK2B
17    2/41        1.936e-02                FXR1;DPYSL3
18    2/42        1.936e-02                  BDNF;LRP8
19    2/43        1.936e-02                 BDNF;PLCB2
20    2/43        1.936e-02                   MDK;CD46
21   4/485        2.938e-02       BDNF;MDK;CASP2;PTK2B
22   3/231        3.303e-02           FXR1;CASP2;PTK2B
23    2/70        4.425e-02                  MDK;PTK2B
24    2/75        4.663e-02                LAMA5;PTK2B
25    2/75        4.663e-02                   MDK;CD46
26    2/77        4.722e-02                   MDK;AKT3
[1] "GO_Cellular_Component_2021"

Version Author Date
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[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"

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  BgRatio
3    Alcoholic Intoxication, Chronic 0.01997  4/13 268/9703
34       Profound Mental Retardation 0.01997  3/13 139/9703
41                           Measles 0.01997  1/13   1/9703
44                  Memory Disorders 0.01997  2/13  43/9703
45  Mental Retardation, Psychosocial 0.01997  3/13 139/9703
77                 Memory impairment 0.01997  2/13  44/9703
134     Age-Related Memory Disorders 0.01997  2/13  43/9703
135        Memory Disorder, Semantic 0.01997  2/13  43/9703
136         Memory Disorder, Spatial 0.01997  2/13  43/9703
137                      Memory Loss 0.01997  2/13  43/9703

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
bcaadf3 sq-96 2022-05-19
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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$genename))
[1] 42
#significance threshold for TWAS
print(sig_thresh)
[1] 4.466
#number of ctwas genes
length(ctwas_genes)
[1] 4
#number of TWAS genes
length(twas_genes)
[1] 90
#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols]
     genename region_tag susie_pip   mu2       PVE     z num_intron num_sqtl
1361    CRTAP       3_24    0.8430 20.01 0.0001343 3.929          2        2
5465    THAP8      19_25    0.8114 20.21 0.0001261 3.847          2        2
#sensitivity / recall
print(sensitivity)
   ctwas     TWAS 
0.007692 0.076923 
#specificity
print(specificity)
 ctwas   TWAS 
0.9995 0.9872 
#precision / PPV
print(precision)
 ctwas   TWAS 
0.2500 0.1111 

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