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] 21263
#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 
1943 1490 1296  830  843 1056 1229  736  851  971 1284 1164  399  806  759  863 
  17   18   19   20   21   22 
1510  310 1515  698   42  668 
#number of imputed weights without missing variants
sum(qclist_all$nmiss==0)
[1] 18726
#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)
[1] 0.8807
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 
0.0095212 0.0003029 
 gene   snp 
10.14 10.49 
[1] 105318
[1]    7589 6309950
    gene      snp 
0.006955 0.190306 
[1] 0.02263 1.04773

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 num_sqtl
2339   FAM177A1       14_9    1.1961 23.35 2.552e-04  4.820         15       16
3586       LRP8       1_33    1.1731 31.79 3.182e-04  4.820          6        6
5170     R3HDM2      12_36    1.0831 42.25 4.412e-04  6.634          5        6
3407      LAMA5      20_36    0.9784 25.18 2.057e-04 -4.341         14       18
5118       PTPA       9_66    0.9768 22.61 1.987e-04  4.650          8       10
3475  LINC00320       21_6    0.9730 28.66 2.418e-04  5.336          4        4
7343    ZDHHC20       13_2    0.9550 24.33 2.005e-04 -4.784          4        5
293        AKT3      1_128    0.9385 34.39 2.613e-04 -6.291          7        7
2459      FEZF1       7_74    0.9335 24.01 1.987e-04 -4.812          1        1
818  BUB1B-PAK6      15_14    0.9306 29.32 2.343e-04 -5.588          3        3
454      APOPT1      14_54    0.9107 42.67 3.249e-04  7.429          7        9
785       BRCA1      17_25    0.9021 30.88 1.315e-04 -3.794         21       23
6150     SNRPA1      15_50    0.8892 22.00 1.516e-04 -3.925          6        7
673      B3GAT1      11_84    0.8677 22.71 1.434e-04  4.348          8       13
7215      WDR27      6_111    0.8638 14.16 6.448e-05 -2.146         20       27
5161    PYROXD2      10_62    0.8241 21.34 1.255e-04  3.755         11       12
2651     GIGYF1       7_62    0.8160 27.02 1.650e-04 -5.266          3        3
1201       CD46      1_105    0.7757 19.49 1.006e-04  3.804         10       10
681        B9D1      17_16    0.7748 27.45 1.554e-04  5.282          3        3
5869       SGCE       7_58    0.7698 20.54 1.084e-04  4.413          6        8

Genes with highest PVE

       genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
5170     R3HDM2      12_36    1.0831 42.25 0.0004412  6.634          5        6
454      APOPT1      14_54    0.9107 42.67 0.0003249  7.429          7        9
3586       LRP8       1_33    1.1731 31.79 0.0003182  4.820          6        6
293        AKT3      1_128    0.9385 34.39 0.0002613 -6.291          7        7
2339   FAM177A1       14_9    1.1961 23.35 0.0002552  4.820         15       16
3475  LINC00320       21_6    0.9730 28.66 0.0002418  5.336          4        4
818  BUB1B-PAK6      15_14    0.9306 29.32 0.0002343 -5.588          3        3
5855      SF3B1      2_117    0.7434 44.53 0.0002219  7.053          3        3
6695    TMEM219      16_24    0.6954 46.18 0.0002114 -7.020          2        2
3407      LAMA5      20_36    0.9784 25.18 0.0002057 -4.341         14       18
7343    ZDHHC20       13_2    0.9550 24.33 0.0002005 -4.784          4        5
5118       PTPA       9_66    0.9768 22.61 0.0001987  4.650          8       10
2459      FEZF1       7_74    0.9335 24.01 0.0001987 -4.812          1        1
4405      NT5C2      10_66    0.6860 46.35 0.0001860  8.475         11       15
2651     GIGYF1       7_62    0.8160 27.02 0.0001650 -5.266          3        3
681        B9D1      17_16    0.7748 27.45 0.0001554  5.282          3        3
6150     SNRPA1      15_50    0.8892 22.00 0.0001516 -3.925          6        7
673      B3GAT1      11_84    0.8677 22.71 0.0001434  4.348          8       13
7349     ZDHHC8       22_4    0.7294 35.56 0.0001336 -4.861          5        5
785       BRCA1      17_25    0.9021 30.88 0.0001315 -3.794         21       23

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] 0.01884
     genename region_tag susie_pip    mu2       PVE       z num_intron num_sqtl
811    BTN2A1       6_20 1.021e-01 107.10 4.502e-06 -11.606          5        5
3622     LSM2       6_26 9.237e-05 214.43 1.737e-11 -11.599          1        1
453      APOM       6_26 1.969e-04 214.01 7.828e-11  11.590          3        3
7130     VARS       6_26 8.850e-05 212.38 1.580e-11 -11.548          1        1
4031     MSH5       6_26 1.671e-04 212.05 5.316e-11  11.538          5        5
690      BAG6       6_26 1.071e-04 208.16 2.197e-11 -11.525          5        7
1742  CYP21A2       6_26 1.973e-05 205.68 7.605e-13 -11.340          1        1
7131    VARS2       6_25 7.654e-02 101.41 5.642e-06 -11.137          1        1
950  C6orf136       6_24 8.935e-02  78.68 5.963e-06  11.031          2        2
2500    FLOT1       6_24 2.163e-01  77.34 3.429e-05  10.981          6        6
814    BTN3A2       6_20 2.398e-01  91.77 2.183e-05 -10.743          6        6
4989     PPT2       6_26 3.405e-05 147.91 1.534e-12 -10.061          5        5
2093    EGFL8       6_26 3.178e-05 147.12 1.305e-12  10.036          6        6
5049    PRRT1       6_26 2.733e-05 146.36 1.038e-12 -10.018          1        1
2773    GPSM3       6_26 2.302e-06 119.97 6.034e-15   9.377          1        1
1157   CCHCR1       6_25 1.115e-01  63.28 2.757e-06  -9.358         11       14
7382  ZKSCAN3       6_22 3.568e-02  54.53 4.183e-07  -9.230          3        3
1805     DDR1       6_25 1.590e-02  67.83 1.627e-07   9.016          1        1
2944  HLA-DMA       6_27 1.391e-01  66.33 4.945e-06   8.781          6       10
4405    NT5C2      10_66 6.860e-01  46.35 1.860e-04   8.475         11       15

GO enrichment analysis for genes with PIP>0.5

#number of genes for gene set enrichment
length(genes)
[1] 85
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
[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)
[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  BgRatio
10               Balo's Concentric Sclerosis 0.04698  1/46   1/9703
19                       Malignant Neoplasms 0.04698  4/46 128/9703
28    Diffuse Cerebral Sclerosis of Schilder 0.04698  1/46   1/9703
73                                   Measles 0.04698  1/46   1/9703
104                            Schizophrenia 0.04698 12/46 883/9703
130            Electroencephalogram abnormal 0.04698  1/46   1/9703
149                              gliosarcoma 0.04698  2/46  16/9703
175                   Dyskeratosis Congenita 0.04698  2/46  16/9703
177          Gastric Antral Vascular Ectasia 0.04698  1/46   1/9703
207 Idiopathic hypogonadotropic hypogonadism 0.04698  2/46  18/9703

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: 45 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$genename))
[1] 50
#significance threshold for TWAS
print(sig_thresh)
[1] 4.507
#number of ctwas genes
length(ctwas_genes)
[1] 17
#number of TWAS genes
length(twas_genes)
[1] 143
#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols]
     genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
673    B3GAT1      11_84    0.8677 22.71 1.434e-04  4.348          8       13
785     BRCA1      17_25    0.9021 30.88 1.315e-04 -3.794         21       23
3407    LAMA5      20_36    0.9784 25.18 2.057e-04 -4.341         14       18
5161  PYROXD2      10_62    0.8241 21.34 1.255e-04  3.755         11       12
6150   SNRPA1      15_50    0.8892 22.00 1.516e-04 -3.925          6        7
7215    WDR27      6_111    0.8638 14.16 6.448e-05 -2.146         20       27
#sensitivity / recall
print(sensitivity)
  ctwas    TWAS 
0.02308 0.09231 
#specificity
print(specificity)
 ctwas   TWAS 
0.9981 0.9826 
#precision / PPV
print(precision)
  ctwas    TWAS 
0.17647 0.08392 

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