SCZ - Brain Cortex
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] 23372#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2106 1670 1401 900 973 1205 1349 834 978 1043 1384 1297 477 810 795 919
17 18 19 20 21 22
1718 307 1661 776 45 724 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 20390#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8724INFO: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
0.0082338 0.0003052 gene snp
10.48 10.44 [1] 105318[1] 7742 6309950 gene snp
0.00634 0.19088 [1] 0.02217 1.09222Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3623 LRP8 1_33 1.2198 32.31 0.0003555 -5.003 10 10
6190 SLC8B1 12_68 1.2142 22.26 0.0002788 -4.047 10 10
3510 LINC00320 21_6 1.0957 28.55 0.0003103 5.336 5 5
5276 R3HDM2 12_36 1.0921 42.83 0.0004461 -6.634 7 9
2356 FAM177A1 14_9 1.0212 23.31 0.0001933 4.849 13 16
3436 LAMA5 20_36 0.9539 28.66 0.0002078 -4.269 16 21
838 BUB1B-PAK6 15_14 0.9292 29.77 0.0002353 5.588 4 4
749 BDNF 11_19 0.9142 23.00 0.0001788 4.348 3 4
1042 CAMKK2 12_74 0.9028 35.27 0.0002014 4.060 8 10
5228 PTPRF 1_27 0.8882 35.71 0.0002541 6.680 6 6
2682 GIGYF1 7_62 0.8820 26.71 0.0001958 -5.266 3 3
1613 CRTAP 3_24 0.8650 19.74 0.0001395 3.929 2 2
6274 SNRPA1 15_50 0.8489 20.88 0.0001372 -4.098 4 5
623 ATP2B2 3_8 0.8284 25.09 0.0001526 4.229 5 7
5264 PYROXD2 10_62 0.8166 23.48 0.0001259 3.755 11 15
1215 CD46 1_105 0.8156 19.39 0.0001137 -3.933 11 14
4482 NTRK3 15_41 0.7867 23.89 0.0001304 4.457 3 3
352 ANAPC7 12_67 0.7853 39.18 0.0002002 6.385 6 6
154 ACTR1B 2_57 0.7830 20.03 0.0001145 3.978 5 5
674 B3GAT1 11_84 0.7565 23.78 0.0001131 4.248 8 13Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_intron
5276 R3HDM2 12_36 1.0921 42.83 0.0004461 -6.634 7
3623 LRP8 1_33 1.2198 32.31 0.0003555 -5.003 10
3510 LINC00320 21_6 1.0957 28.55 0.0003103 5.336 5
6190 SLC8B1 12_68 1.2142 22.26 0.0002788 -4.047 10
693 BAG6 6_26 0.2068 637.42 0.0002588 11.590 9
455 APOM 6_26 0.2068 637.42 0.0002588 11.590 2
5228 PTPRF 1_27 0.8882 35.71 0.0002541 6.680 6
838 BUB1B-PAK6 15_14 0.9292 29.77 0.0002353 5.588 4
456 APOPT1 14_54 0.7266 43.77 0.0002146 7.429 4
5969 SF3B1 2_117 0.7200 45.12 0.0002121 7.053 3
3436 LAMA5 20_36 0.9539 28.66 0.0002078 -4.269 16
1042 CAMKK2 12_74 0.9028 35.27 0.0002014 4.060 8
352 ANAPC7 12_67 0.7853 39.18 0.0002002 6.385 6
2682 GIGYF1 7_62 0.8820 26.71 0.0001958 -5.266 3
2356 FAM177A1 14_9 1.0212 23.31 0.0001933 4.849 13
3193 IRF3 19_34 0.7132 39.57 0.0001907 -6.461 2
1871 DGKZ 11_28 0.6445 46.77 0.0001845 7.216 2
749 BDNF 11_19 0.9142 23.00 0.0001788 4.348 3
292 AKT3 1_128 0.7447 34.40 0.0001716 -6.291 5
2636 GATAD2A 19_15 0.6131 46.80 0.0001634 -6.668 5
num_sqtl
5276 9
3623 10
3510 5
6190 10
693 9
455 2
5228 6
838 4
456 7
5969 3
3436 21
1042 10
352 6
2682 3
2356 16
3193 2
1871 2
749 4
292 5
2636 5Comparing z scores and PIPs
[1] 0.01899 genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
4777 PGBD1 6_22 6.697e-02 158.26 2.166e-06 -13.087 3 5
455 APOM 6_26 2.068e-01 637.42 2.588e-04 11.590 2 2
693 BAG6 6_26 2.068e-01 637.42 2.588e-04 11.590 9 9
7287 VARS 6_26 1.246e-01 638.44 9.411e-05 -11.548 1 1
1821 DDR1 6_25 1.135e-01 101.67 1.195e-05 -11.175 2 2
966 C6orf136 6_24 8.036e-02 79.81 4.893e-06 11.031 2 2
2518 FLOT1 6_24 1.604e-01 78.48 1.914e-05 10.981 5 6
834 BTN3A2 6_20 8.836e-02 91.92 4.950e-06 -10.759 5 5
831 BTN2A1 6_20 1.160e-01 83.45 4.270e-06 10.185 6 7
2975 HLA-B 6_25 8.535e-02 77.13 1.824e-06 10.155 12 31
5078 PPT2 6_26 9.858e-12 474.58 4.379e-25 -10.061 5 5
2105 EGFL8 6_26 4.045e-12 473.96 7.332e-26 10.036 7 8
5142 PRRT1 6_26 3.440e-12 472.86 5.315e-26 -10.018 1 1
5558 RNF5 6_26 7.171e-13 467.32 2.282e-27 -9.714 1 1
2806 GPSM3 6_26 2.278e-13 424.06 2.090e-28 9.377 2 2
1176 CCHCR1 6_25 9.482e-02 62.94 2.689e-06 -9.376 11 15
7545 ZKSCAN3 6_22 2.297e-02 55.91 1.900e-07 9.321 2 3
2977 HLA-DMB 6_27 4.018e-02 68.96 8.983e-07 8.860 2 2
7734 ZSCAN23 6_22 1.033e-02 45.88 4.652e-08 -8.541 1 1
4470 NT5C2 10_66 5.993e-01 47.55 1.518e-04 -8.511 12 16GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 76Uploading 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 Overlap
1 positive regulation of neuron projection development (GO:0010976) 5/88
Adjusted.P.value Genes
1 0.01519 BDNF;NTRK3;DPYSL3;SERPINI1;LRP8
[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
11 Body Weight 0.0486 2/41
60 Measles 0.0486 1/41
88 Schizophrenia 0.0486 11/41
116 Congenital absent nipple 0.0486 1/41
181 Congenital absence of breast with absent nipple 0.0486 1/41
244 Sporadic Breast Carcinoma 0.0486 1/41
249 Primary peritoneal carcinoma 0.0486 1/41
257 Osteogenesis Imperfecta Type VII 0.0486 1/41
258 Familial encephalopathy with neuroserpin inclusion bodies 0.0486 1/41
264 Retinitis Pigmentosa 46 0.0486 1/41
BgRatio
11 15/9703
60 1/9703
88 883/9703
116 1/9703
181 1/9703
244 1/9703
249 1/9703
257 1/9703
258 1/9703
264 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: 37 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] 53#significance threshold for TWAS
print(sig_thresh)[1] 4.511#number of ctwas genes
length(ctwas_genes)[1] 16#number of TWAS genes
length(twas_genes)[1] 147#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
623 ATP2B2 3_8 0.8284 25.09 0.0001526 4.229 5 7
749 BDNF 11_19 0.9142 23.00 0.0001788 4.348 3 4
1042 CAMKK2 12_74 0.9028 35.27 0.0002014 4.060 8 10
1215 CD46 1_105 0.8156 19.39 0.0001137 -3.933 11 14
1613 CRTAP 3_24 0.8650 19.74 0.0001395 3.929 2 2
3436 LAMA5 20_36 0.9539 28.66 0.0002078 -4.269 16 21
5264 PYROXD2 10_62 0.8166 23.48 0.0001259 3.755 11 15
6190 SLC8B1 12_68 1.2142 22.26 0.0002788 -4.047 10 10
6274 SNRPA1 15_50 0.8489 20.88 0.0001372 -4.098 4 5#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.10769 #specificity
print(specificity) ctwas TWAS
0.9982 0.9827 #precision / PPV
print(precision) ctwas TWAS
0.12500 0.09524
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