SCZ 2018 - Brain_Cerebellar_Hemisphere
sheng Qian
2021-2-6
Last updated: 2022-05-19
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] 26564#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2520 1814 1594 973 1137 1377 1526 911 1106 1166 1579 1419 520 921 928 1179
17 18 19 20 21 22
1880 325 1895 891 51 852 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 23201#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8734INFO: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, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionCheck convergence of parameters
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
gene snp
0.0103642 0.0002912 gene snp
12.00 10.12 [1] 105318[1] 7860 6309950 gene snp
0.009283 0.176540 [1] 0.01795 1.05335Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_intron
5293 R3HDM2 12_36 0.9766 43.83 4.119e-04 6.634 9
6205 SLC8B1 12_68 0.9706 28.59 3.598e-04 -4.047 11
3651 LPCAT4 15_10 0.9325 25.36 2.153e-04 4.892 3
842 BUB1B-PAK6 15_14 0.9242 29.86 2.437e-04 -5.588 2
2038 DPYSL3 5_86 0.7910 21.54 1.280e-04 -4.157 1
2719 GIGYF2 2_137 0.7882 56.96 4.059e-04 -8.128 6
4513 NTRK3 15_41 0.7875 24.09 1.426e-04 4.457 3
1631 CRTAP 3_24 0.7796 20.88 1.215e-04 3.929 2
7078 TSNARE1 8_93 0.7781 34.12 1.701e-04 6.364 10
6263 SMYD2 1_108 0.7713 21.62 1.225e-04 -3.952 2
2396 FAM177A1 14_9 0.7657 24.30 1.707e-04 -4.872 12
1039 CAMKK2 12_74 0.7621 35.78 1.702e-04 4.159 6
751 BDNF 11_19 0.7588 23.84 1.316e-04 4.348 3
7567 ZDHHC20 13_2 0.7584 25.00 1.400e-04 -4.832 3
6353 SPECC1 17_16 0.7548 25.87 1.409e-04 -4.822 2
3097 HSPA9 5_82 0.7412 25.57 1.334e-04 5.633 1
294 AKT3 1_128 0.7158 35.12 1.906e-04 6.266 6
2902 GTF2A1 14_39 0.6708 24.76 1.117e-04 4.550 2
1662 CTB-31O20.2 19_3 0.6660 23.34 9.828e-05 4.456 1
4355 NFATC3 16_36 0.6648 28.83 1.219e-04 -5.480 3
num_sqtl
5293 11
6205 12
3651 4
842 2
2038 1
2719 6
4513 3
1631 2
7078 10
6263 2
2396 13
1039 8
751 3
7567 4
6353 2
3097 1
294 6
2902 3
1662 1
4355 3Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_intron
7339 VARS 6_26 0.3832 628.56 0.0008763 -11.620 2
456 APOM 6_26 0.2790 626.01 0.0007298 11.590 3
837 BTN3A1 6_20 0.6226 145.15 0.0005473 13.091 7
5293 R3HDM2 12_36 0.9766 43.83 0.0004119 6.634 9
2719 GIGYF2 2_137 0.7882 56.96 0.0004059 -8.128 6
6205 SLC8B1 12_68 0.9706 28.59 0.0003598 -4.047 11
842 BUB1B-PAK6 15_14 0.9242 29.86 0.0002437 -5.588 2
3651 LPCAT4 15_10 0.9325 25.36 0.0002153 4.892 3
294 AKT3 1_128 0.7158 35.12 0.0001906 6.266 6
5994 SF3B1 2_117 0.4462 45.85 0.0001751 -7.053 3
2396 FAM177A1 14_9 0.7657 24.30 0.0001707 -4.872 12
7005 TRANK1 3_27 0.6544 38.76 0.0001707 -6.365 6
1039 CAMKK2 12_74 0.7621 35.78 0.0001702 4.159 6
7078 TSNARE1 8_93 0.7781 34.12 0.0001701 6.364 10
4513 NTRK3 15_41 0.7875 24.09 0.0001426 4.457 3
6353 SPECC1 17_16 0.7548 25.87 0.0001409 -4.822 2
7567 ZDHHC20 13_2 0.7584 25.00 0.0001400 -4.832 3
3097 HSPA9 5_82 0.7412 25.57 0.0001334 5.633 1
751 BDNF 11_19 0.7588 23.84 0.0001316 4.348 3
2038 DPYSL3 5_86 0.7910 21.54 0.0001280 -4.157 1
num_sqtl
7339 2
456 4
837 8
5293 11
2719 6
6205 12
842 2
3651 4
294 6
5994 3
2396 13
7005 6
1039 8
7078 10
4513 3
6353 2
7567 4
3097 1
751 3
2038 1Comparing z scores and PIPs
[1] 0.02176 genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
7606 ZKSCAN3 6_22 2.302e-02 160.21 1.217e-06 -13.135 4 4
837 BTN3A1 6_20 6.226e-01 145.15 5.473e-04 13.091 7 8
4797 PGBD1 6_22 2.270e-02 159.13 1.708e-06 -13.087 5 7
7339 VARS 6_26 3.832e-01 628.56 8.763e-04 -11.620 2 2
456 APOM 6_26 2.790e-01 626.01 7.298e-04 11.590 3 4
1851 DDR1 6_25 1.708e-01 101.78 5.658e-05 -11.175 4 4
964 C6orf136 6_24 6.024e-02 80.18 5.525e-06 -11.031 2 2
2559 FLOT1 6_24 4.864e-02 78.83 1.273e-05 10.981 8 8
838 BTN3A2 6_20 6.427e-02 94.90 4.458e-06 -10.743 5 7
1781 CYP21A2 6_26 5.976e-06 607.99 2.062e-13 -10.513 1 2
699 BAG6 6_26 1.969e-09 500.57 5.529e-20 10.247 9 9
835 BTN2A1 6_20 4.016e-02 84.19 1.707e-06 10.110 7 7
5104 PPT2 6_26 5.412e-12 466.36 1.297e-25 10.061 7 9
2138 EGFL8 6_26 4.301e-12 465.72 8.201e-26 10.036 6 7
5165 PRRT1 6_26 3.762e-12 464.63 6.243e-26 -10.018 1 1
2850 GPSM3 6_26 1.178e-13 416.63 1.098e-28 -9.377 2 2
1176 CCHCR1 6_25 2.559e-02 59.77 5.477e-07 -9.032 11 18
6952 TNXB 6_26 2.108e-13 454.39 1.918e-28 9.001 4 5
3026 HLA-DMA 6_27 9.405e-02 70.57 5.975e-06 8.860 5 6
7849 ZSCAN23 6_22 1.294e-02 46.07 7.324e-08 -8.541 1 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 34Uploading 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"
Term
1 positive regulation of non-membrane spanning protein tyrosine kinase activity (GO:1903997)
2 regulation of non-membrane spanning protein tyrosine kinase activity (GO:1903995)
Overlap Adjusted.P.value Genes
1 2/6 0.01482 BDNF;SRC
2 2/7 0.01482 BDNF;SRC
[1] "GO_Cellular_Component_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
[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
73 Status Epilepticus 0.01241 3/21 68/9703
110 Petit mal status 0.01241 3/21 67/9703
118 Grand Mal Status Epilepticus 0.01241 3/21 67/9703
126 Complex Partial Status Epilepticus 0.01241 3/21 67/9703
166 Status Epilepticus, Subclinical 0.01241 3/21 67/9703
167 Non-Convulsive Status Epilepticus 0.01241 3/21 67/9703
168 Simple Partial Status Epilepticus 0.01241 3/21 67/9703
199 TOBACCO ADDICTION, SUSCEPTIBILITY TO (finding) 0.01241 2/21 12/9703
70 Schizophrenia 0.02137 7/21 883/9703
197 AICARDI-GOUTIERES SYNDROME 3 0.02137 1/21 1/9703WebGestalt 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...
description size overlap FDR database
1 Bipolar Disorder 136 7 0.003887 disease_GLAD4U
userId
1 BDNF;CAMKK2;GABBR2;NTRK3;SDCCAG8;TRANK1;TSNARE1PIP Manhattan Plot
Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
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] 4.514#number of ctwas genes
length(ctwas_genes)[1] 4#number of TWAS genes
length(twas_genes)[1] 171#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
6205 SLC8B1 12_68 0.9706 28.59 0.0003598 -4.047 11 12#sensitivity / recall
print(sensitivity) ctwas TWAS
0.007692 0.130769 #specificity
print(specificity) ctwas TWAS
0.9996 0.9803 #precision / PPV
print(precision) ctwas TWAS
0.25000 0.09942
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