SCZ 2018 - Brain_Caudate_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] 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] 0.8807INFO: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
0.0095212 0.0003029 gene snp
10.14 10.49 [1] 105318[1] 7589 6309950 gene snp
0.006955 0.190306 [1] 0.02263 1.04773Genes with highest PIPs
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 8Genes 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 23Comparing z scores and PIPs
[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 15GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 85Uploading 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 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 18/9703WebGestalt 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: 45 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] 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