SCZ 2018 - Brain_Nucleus_accumbens_basal_ganglia
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] 21642#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1929 1532 1343 876 885 1118 1258 748 873 1021 1288 1205 442 765 754 863
17 18 19 20 21 22
1460 296 1534 750 40 662 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 18965#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8763INFO: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
0.0063107 0.0003111 gene snp
10.20 10.34 [1] 105318[1] 7622 6309950 gene snp
0.00466 0.19277 [1] 0.01395 1.10685Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3585 LRP8 1_33 1.1028 32.85 2.908e-04 -4.820 5 5
7379 ZDHHC20 13_2 0.9020 24.49 1.796e-04 -4.784 5 6
2652 GIGYF1 7_62 0.8896 26.12 1.951e-04 -5.266 4 4
6179 SNRPA1 15_50 0.8796 20.85 1.460e-04 -4.098 4 6
811 BUB1B-PAK6 15_14 0.8551 29.84 2.051e-04 5.588 2 2
6923 TSNARE1 8_93 0.8350 27.20 1.779e-04 5.555 11 11
3570 LPCAT4 15_10 0.8112 26.23 1.599e-04 4.892 3 5
731 BDNF 11_19 0.8017 23.22 1.365e-04 4.348 3 4
613 ATP2B2 3_8 0.7968 26.02 1.436e-04 4.229 5 6
6602 THAP8 19_25 0.7784 20.83 1.195e-04 3.847 2 2
292 AKT3 1_128 0.7682 34.93 1.847e-04 -6.350 7 8
924 C2orf80 2_123 0.7584 24.25 9.972e-05 3.053 12 13
1985 DPYSL3 5_86 0.7575 23.63 1.287e-04 4.157 1 1
4279 NGEF 2_137 0.7346 30.69 1.537e-04 7.036 3 3
159 ACTR1B 2_57 0.7234 20.56 1.003e-04 -3.978 5 5
775 BRCA1 17_25 0.7052 31.00 8.244e-05 -3.837 20 22
3953 MPHOSPH9 12_75 0.6872 60.79 2.709e-04 -8.201 2 4
2244 ESAM 11_77 0.6792 35.97 1.325e-04 5.889 2 2
7239 WDR27 6_111 0.6484 16.96 3.988e-05 2.235 21 33
1856 DHPS 19_10 0.6472 25.49 1.014e-04 -4.396 1 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_intron
3585 LRP8 1_33 1.1028 32.85 0.0002908 -4.820 5
3953 MPHOSPH9 12_75 0.6872 60.79 0.0002709 -8.201 2
811 BUB1B-PAK6 15_14 0.8551 29.84 0.0002051 5.588 2
459 APOM 6_26 0.1818 627.31 0.0001967 11.590 3
2652 GIGYF1 7_62 0.8896 26.12 0.0001951 -5.266 4
292 AKT3 1_128 0.7682 34.93 0.0001847 -6.350 7
7379 ZDHHC20 13_2 0.9020 24.49 0.0001796 -4.784 5
6923 TSNARE1 8_93 0.8350 27.20 0.0001779 5.555 11
3570 LPCAT4 15_10 0.8112 26.23 0.0001599 4.892 3
3622 LSM2 6_26 0.1619 635.43 0.0001581 -11.599 1
6484 TAOK2 16_24 0.6049 46.28 0.0001540 7.024 5
4279 NGEF 2_137 0.7346 30.69 0.0001537 7.036 3
7159 VARS 6_26 0.1563 629.91 0.0001462 -11.620 1
6179 SNRPA1 15_50 0.8796 20.85 0.0001460 -4.098 4
4405 NT5C2 10_66 0.5910 46.04 0.0001450 -8.511 11
613 ATP2B2 3_8 0.7968 26.02 0.0001436 4.229 5
731 BDNF 11_19 0.8017 23.22 0.0001365 4.348 3
2244 ESAM 11_77 0.6792 35.97 0.0001325 5.889 2
1985 DPYSL3 5_86 0.7575 23.63 0.0001287 4.157 1
6602 THAP8 19_25 0.7784 20.83 0.0001195 3.847 2
num_sqtl
3585 5
3953 4
811 2
459 3
2652 4
292 8
7379 6
6923 11
3570 5
3622 1
6484 6
4279 3
7159 1
6179 6
4405 15
613 6
731 4
2244 2
1985 1
6602 2Comparing z scores and PIPs
[1] 0.01719 genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
7159 VARS 6_26 0.15633 629.91 1.462e-04 -11.620 1 1
3622 LSM2 6_26 0.16187 635.43 1.581e-04 -11.599 1 1
459 APOM 6_26 0.18177 627.31 1.967e-04 11.590 3 3
682 BAG6 6_26 0.11475 627.31 7.843e-05 -11.590 6 6
1732 CYP21A2 6_26 0.01511 659.18 1.430e-06 -11.340 1 1
7160 VARS2 6_25 0.05583 101.38 3.000e-06 -11.137 1 1
941 C6orf136 6_24 0.06069 79.63 2.785e-06 -11.031 2 2
2501 FLOT1 6_24 0.14500 78.29 1.560e-05 10.981 6 7
808 BTN3A2 6_20 0.08647 90.16 2.598e-06 -10.659 3 3
2949 HLA-B 6_25 0.05761 76.72 9.724e-07 10.150 11 21
805 BTN2A1 6_20 0.08574 82.29 3.455e-06 10.110 5 6
1153 CCHCR1 6_25 0.05360 62.58 9.944e-07 -9.358 10 14
1799 DDR1 6_25 0.01101 67.83 7.808e-08 9.016 1 1
2950 HLA-DMA 6_27 0.05258 65.16 9.905e-07 8.596 4 7
4405 NT5C2 10_66 0.59099 46.04 1.450e-04 -8.511 11 15
3664 MAD1L1 7_3 0.31932 63.77 4.772e-05 -8.215 3 3
3953 MPHOSPH9 12_75 0.68723 60.79 2.709e-04 -8.201 2 4
554 AS3MT 10_66 0.21828 44.51 1.947e-05 8.051 6 7
4030 MSH5 6_26 0.00000 236.73 0.000e+00 -7.892 3 3
841 C12orf65 12_75 0.04490 54.18 9.788e-07 -7.754 2 2GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 35Uploading 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"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[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
48 Measles 0.02256 1/18
69 Schizophrenia 0.02256 7/18
85 Electroencephalogram abnormal 0.02256 1/18
178 Sporadic Breast Carcinoma 0.02256 1/18
181 Primary peritoneal carcinoma 0.02256 1/18
190 BREAST-OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.02256 1/18
191 BREAST CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.02256 1/18
192 OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.02256 1/18
193 HEMOLYTIC UREMIC SYNDROME, ATYPICAL, SUSCEPTIBILITY TO, 2 0.02256 1/18
198 SENIOR-LOKEN SYNDROME 7 0.02256 1/18
BgRatio
48 1/9703
69 883/9703
85 1/9703
178 1/9703
181 1/9703
190 1/9703
191 1/9703
192 1/9703
193 1/9703
198 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...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet, minNum =
minNum, : No significant gene set is identified based on FDR 0.05!NULLPIP Manhattan Plot
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] 51#significance threshold for TWAS
print(sig_thresh)[1] 4.507#number of ctwas genes
length(ctwas_genes)[1] 8#number of TWAS genes
length(twas_genes)[1] 131#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
731 BDNF 11_19 0.8017 23.22 0.0001365 4.348 3 4
6179 SNRPA1 15_50 0.8796 20.85 0.0001460 -4.098 4 6#sensitivity / recall
print(sensitivity) ctwas TWAS
0.007692 0.100000 #specificity
print(specificity) ctwas TWAS
0.9991 0.9844 #precision / PPV
print(precision) ctwas TWAS
0.12500 0.09924
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