SCZ 2018 - Brain_Hippocampus
sheng Qian
2021-2-6
Last updated: 2022-05-12
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] 17848#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1685 1258 1054 701 707 931 1057 622 737 814 1072 981 359 635 616 686
17 18 19 20 21 22
1225 243 1275 611 30 549 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 15888#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8902finish
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
gene snp
0.0079441 0.0003125 gene snp
13.20 10.19 [1] 105318[1] 7021 6309950 gene snp
0.00699 0.19087 [1] 0.01552 1.07050Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3251 LRP8 1_33 1.1226 33.65 0.0003019 4.820 5 5
2405 GIGYF1 7_62 1.0221 34.53 0.0002916 5.266 3 3
2126 FAM177A1 14_9 1.0157 24.42 0.0002097 -4.872 11 13
3236 LPCAT4 15_10 0.9460 25.64 0.0002120 4.892 3 3
3155 LINC00320 21_6 0.9267 29.45 0.0002312 5.336 3 3
587 B3GAT1 11_84 0.8969 23.77 0.0001658 -4.448 8 12
4180 PAK6 15_14 0.8895 30.73 0.0002308 5.588 1 1
1450 CRTAP 3_24 0.8847 20.12 0.0001488 3.929 2 2
3327 MAD1L1 7_3 0.8812 69.62 0.0003687 8.182 6 7
6037 THAP8 19_25 0.8507 19.76 0.0001358 -3.847 2 2
4225 PCBP2 12_33 0.8426 26.30 0.0001773 -4.953 2 2
1660 DGKZ 11_28 0.8422 48.30 0.0003253 7.216 2 2
6256 TPGS2 18_19 0.8198 28.26 0.0001709 -4.088 4 4
1322 COA8 14_54 0.7858 46.02 0.0002582 -7.407 7 10
4042 NT5C2 10_66 0.7796 48.83 0.0002566 -8.541 11 13
112 ACTR1B 2_57 0.7780 20.17 0.0001136 3.978 5 5
298 ANAPC7 12_67 0.7579 38.23 0.0001921 6.385 4 4
5322 SF3B1 2_117 0.7509 46.51 0.0002443 -7.053 2 2
1799 DPYSL3 5_86 0.7392 22.59 0.0001172 -4.157 1 1
6768 ZDHHC20 13_2 0.7325 23.83 0.0001200 -4.615 2 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3327 MAD1L1 7_3 0.8812 69.62 0.0003687 8.182 6 7
1660 DGKZ 11_28 0.8422 48.30 0.0003253 7.216 2 2
3251 LRP8 1_33 1.1226 33.65 0.0003019 4.820 5 5
2405 GIGYF1 7_62 1.0221 34.53 0.0002916 5.266 3 3
1322 COA8 14_54 0.7858 46.02 0.0002582 -7.407 7 10
4042 NT5C2 10_66 0.7796 48.83 0.0002566 -8.541 11 13
5322 SF3B1 2_117 0.7509 46.51 0.0002443 -7.053 2 2
3155 LINC00320 21_6 0.9267 29.45 0.0002312 5.336 3 3
4180 PAK6 15_14 0.8895 30.73 0.0002308 5.588 1 1
3236 LPCAT4 15_10 0.9460 25.64 0.0002120 4.892 3 3
2126 FAM177A1 14_9 1.0157 24.42 0.0002097 -4.872 11 13
298 ANAPC7 12_67 0.7579 38.23 0.0001921 6.385 4 4
4225 PCBP2 12_33 0.8426 26.30 0.0001773 -4.953 2 2
6256 TPGS2 18_19 0.8198 28.26 0.0001709 -4.088 4 4
587 B3GAT1 11_84 0.8969 23.77 0.0001658 -4.448 8 12
1450 CRTAP 3_24 0.8847 20.12 0.0001488 3.929 2 2
2318 FXR1 3_111 0.5794 44.40 0.0001380 6.837 4 4
6037 THAP8 19_25 0.8507 19.76 0.0001358 -3.847 2 2
6768 ZDHHC20 13_2 0.7325 23.83 0.0001200 -4.615 2 2
1667 DHPS 19_10 0.7106 24.82 0.0001190 -4.396 1 1Comparing z scores and PIPs
[1] 0.01837 genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3286 LSM2 6_26 9.745e-05 222.29 2.004e-11 -11.599 1 1
603 BAG6 6_26 1.162e-04 221.94 2.767e-11 -11.590 6 7
1604 DDR1 6_25 1.826e-01 105.59 3.276e-05 11.175 2 2
823 C6orf136 6_24 1.015e-01 82.59 8.072e-06 -11.031 2 2
2263 FLOT1 6_24 2.106e-01 81.23 3.399e-05 -10.981 6 6
724 BTN3A2 6_20 1.394e-01 92.71 8.680e-06 -10.665 5 5
4603 PPT2 6_26 3.691e-05 152.97 1.889e-12 -10.061 5 5
1892 EGFL8 6_26 2.777e-05 142.24 1.029e-12 -9.625 3 4
1014 CCHCR1 6_25 3.239e-02 68.64 3.490e-07 -9.508 6 9
2509 GPSM3 6_26 2.168e-06 124.08 5.539e-15 9.377 1 1
7016 ZSCAN31 6_22 1.725e-02 58.35 9.581e-08 -9.321 2 2
2674 HLA-DMA 6_27 1.319e-01 69.70 4.883e-06 8.727 6 10
4042 NT5C2 10_66 7.796e-01 48.83 2.566e-04 -8.541 11 13
3327 MAD1L1 7_3 8.812e-01 69.62 3.687e-04 8.182 6 7
6349 TSNARE1 8_93 3.299e-02 53.87 3.712e-07 7.961 4 6
4314 PGBD1 6_22 3.918e-02 40.35 3.080e-07 -7.746 2 2
722 BTN2A1 6_20 4.151e-02 51.43 3.563e-07 -7.727 3 3
7014 ZSCAN26 6_22 3.226e-02 38.78 2.883e-07 7.631 4 4
723 BTN3A1 6_20 5.359e-02 47.80 4.827e-07 7.490 4 4
700 BRD2 6_27 2.136e-01 46.77 1.498e-05 7.455 6 7GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 50Uploading 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"
Term Overlap Adjusted.P.value
1 protein phosphatase type 2A complex (GO:0000159) 2/17 0.04714
2 microtubule cytoskeleton (GO:0015630) 5/331 0.04714
Genes
1 PPP2R5B;PPP2R2A
2 DYNC1I2;ACTR1B;ANAPC7;KIF21B;MAD1L1
[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
129 Abnormality of head or neck 0.006894 2/22
7 Adenocarcinoma of prostate 0.016828 2/22
29 Measles 0.016828 1/22
48 Electroencephalogram abnormal 0.016828 1/22
97 Sporadic Breast Carcinoma 0.016828 1/22
102 Primary peritoneal carcinoma 0.016828 1/22
104 Osteogenesis Imperfecta Type VII 0.016828 1/22
106 BREAST-OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.016828 1/22
107 BREAST CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.016828 1/22
108 OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.016828 1/22
BgRatio
129 5/9703
7 20/9703
29 1/9703
48 1/9703
97 1/9703
102 1/9703
104 1/9703
106 1/9703
107 1/9703
108 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
Warning: ggrepel: 3 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] 54#significance threshold for TWAS
print(sig_thresh)[1] 4.49#number of ctwas genes
length(ctwas_genes)[1] 13#number of TWAS genes
length(twas_genes)[1] 129#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
587 B3GAT1 11_84 0.8969 23.77 0.0001658 -4.448 8 12
1450 CRTAP 3_24 0.8847 20.12 0.0001488 3.929 2 2
6037 THAP8 19_25 0.8507 19.76 0.0001358 -3.847 2 2
6256 TPGS2 18_19 0.8198 28.26 0.0001709 -4.088 4 4#sensitivity / recall
print(sensitivity) ctwas TWAS
0.03077 0.13846 #specificity
print(specificity) ctwas TWAS
0.9987 0.9841 #precision / PPV
print(precision) ctwas TWAS
0.3077 0.1395
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_1.20 workflowr_1.6.2
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 curl_4.3.2 compiler_4.1.0 git2r_0.28.0
[21] rvest_1.0.0 cli_3.0.0 Cairo_1.5-15 xml2_1.3.2
[25] labeling_0.4.2 sass_0.4.0 scales_1.1.1 systemfonts_1.0.4
[29] apcluster_1.4.9 digest_0.6.27 rmarkdown_2.9 svglite_2.0.0
[33] pkgconfig_2.0.3 htmltools_0.5.1.1 dbplyr_2.1.1 highr_0.9
[37] rlang_1.0.2 rstudioapi_0.13 jquerylib_0.1.4 farver_2.1.0
[41] generics_0.1.0 jsonlite_1.7.2 magrittr_2.0.1 Matrix_1.3-3
[45] ggbeeswarm_0.6.0 Rcpp_1.0.7 munsell_0.5.0 fansi_0.5.0
[49] lifecycle_1.0.0 stringi_1.6.2 whisker_0.4 yaml_2.2.1
[53] plyr_1.8.6 grid_4.1.0 ggrepel_0.9.1 parallel_4.1.0
[57] promises_1.2.0.1 crayon_1.4.1 lattice_0.20-44 haven_2.4.1
[61] hms_1.1.0 knitr_1.33 pillar_1.7.0 igraph_1.2.6
[65] rjson_0.2.20 rngtools_1.5 reshape2_1.4.4 codetools_0.2-18
[69] reprex_2.0.0 glue_1.4.2 evaluate_0.14 data.table_1.14.0
[73] modelr_0.1.8 png_0.1-7 vctrs_0.3.8 httpuv_1.6.1
[77] foreach_1.5.1 cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[81] xfun_0.24 broom_0.7.8 later_1.2.0 iterators_1.0.13
[85] beeswarm_0.4.0 ellipsis_0.3.2