SCZ-ME
sheng Qian
2021-8-31
Last updated: 2022-09-02
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
[1] 3575
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
553 224 140 119 137 217 198 136 28 193 205 139 64 78 82 106 235 31 428 112
21 22
50 100 [1] 0.1055INFO: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
gene snp
0.0159385 0.0003225 gene snp
7.567 8.497 [1] 77096[1] 1792 6256830 gene snp
0.002803 0.222358 [1] 0.004542 1.896831Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_methylation
620 GNG12 1_42 0.9136 20.93 2.206e-04 4.447 2
133 ARID1B 6_102 0.9049 19.33 2.013e-04 3.694 2
1531 SYT13 11_28 0.9042 21.95 2.328e-04 -4.427 1
444 DNAJC11 1_5 0.8716 23.90 2.355e-04 4.897 1
880 LZTS2 10_64 0.8146 20.52 1.766e-04 -4.016 1
1109 PDE6B 4_1 0.8127 19.86 1.594e-04 3.761 3
570 FOXO6 1_25 0.8071 20.35 1.637e-04 3.869 3
765 KCNMA1 10_50 0.8024 22.34 1.793e-04 -3.992 3
432 DIP2C 10_1 0.6126 24.17 7.097e-05 -3.132 10
1098 PCBP2 12_33 0.5902 18.77 8.480e-05 -3.984 1
559 FIP1L1 4_39 0.5876 18.95 8.090e-05 4.034 2
115 AP000721.4 11_35 0.5625 21.08 8.651e-05 4.347 1
1391 SEPT4 17_34 0.5568 19.09 7.408e-05 4.103 2
1130 PIK3C2A 11_12 0.5521 21.61 8.546e-05 4.279 1
58 ADARB2 10_2 0.4603 12.95 2.385e-05 1.753 8
1173 PRDM16 1_2 0.4261 22.09 3.508e-05 -2.288 14
450 DOCK1 10_79 0.4224 17.91 4.144e-05 -3.638 1
161 B3GNTL1 17_47 0.4208 13.72 1.985e-05 1.758 9
335 CLYBL 13_50 0.4005 28.62 5.953e-05 3.483 1
499 EPS8L1 19_37 0.3846 22.10 3.380e-05 2.576 4
num_meqtl
620 5
133 7
1531 5
444 5
880 3
1109 7
570 14
765 7
432 32
1098 4
559 10
115 2
1391 8
1130 1
58 33
1173 60
450 8
161 40
335 1
499 12Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_methylation
723 IK 5_83 0.000e+00 2689.75 0.000e+00 -4.4137 1
1100 PCCB 3_84 0.000e+00 849.25 0.000e+00 4.9579 1
1686 WBP1L 10_66 5.096e-06 309.42 1.042e-13 5.9113 1
272 CD276 15_35 0.000e+00 199.27 0.000e+00 0.8631 1
1011 NEURL1 10_66 4.837e-10 89.55 2.718e-22 -2.6489 1
652 GSTO2 10_66 6.052e-09 55.04 2.615e-20 -3.3637 1
1732 ZFP57 6_23 9.758e-02 36.15 2.995e-06 6.7510 3
802 L3MBTL2 22_17 3.832e-01 35.69 6.797e-05 5.6670 1
881 MAD1L1 7_3 3.282e-01 32.14 2.295e-05 -5.7339 5
1135 PLCH2 1_2 2.067e-01 29.87 1.577e-05 3.1367 2
37 AC104534.3 19_26 1.972e-01 28.97 1.461e-05 -2.9367 1
487 EML1 14_52 2.191e-01 28.95 1.803e-05 -3.0444 1
335 CLYBL 13_50 4.005e-01 28.62 5.953e-05 3.4830 1
1114 PFKFB2 1_107 3.033e-01 28.58 3.410e-05 -3.3548 1
740 IREB2 15_37 2.279e-02 28.04 1.888e-07 5.4848 1
1715 YPEL1 22_4 2.575e-01 27.81 2.392e-05 -3.3129 1
633 GPR137C 14_21 1.892e-01 27.61 1.282e-05 -3.4307 1
67 ADRA1D 20_4 2.450e-01 26.90 2.095e-05 -2.9834 1
74 AGO3 1_22 1.270e-01 26.73 5.590e-06 -4.3961 1
157 ATPAF2 17_15 2.290e-01 26.66 1.814e-05 5.3110 1
num_meqtl
723 7
1100 2
1686 6
272 15
1011 6
652 2
1732 42
802 1
881 22
1135 7
37 2
487 2
335 1
1114 1
740 1
1715 7
633 1
67 6
74 5
157 3Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_methylation
444 DNAJC11 1_5 0.8716 23.90 2.355e-04 4.897 1
1531 SYT13 11_28 0.9042 21.95 2.328e-04 -4.427 1
620 GNG12 1_42 0.9136 20.93 2.206e-04 4.447 2
133 ARID1B 6_102 0.9049 19.33 2.013e-04 3.694 2
765 KCNMA1 10_50 0.8024 22.34 1.793e-04 -3.992 3
880 LZTS2 10_64 0.8146 20.52 1.766e-04 -4.016 1
570 FOXO6 1_25 0.8071 20.35 1.637e-04 3.869 3
1109 PDE6B 4_1 0.8127 19.86 1.594e-04 3.761 3
115 AP000721.4 11_35 0.5625 21.08 8.651e-05 4.347 1
1130 PIK3C2A 11_12 0.5521 21.61 8.546e-05 4.279 1
1098 PCBP2 12_33 0.5902 18.77 8.480e-05 -3.984 1
559 FIP1L1 4_39 0.5876 18.95 8.090e-05 4.034 2
1391 SEPT4 17_34 0.5568 19.09 7.408e-05 4.103 2
432 DIP2C 10_1 0.6126 24.17 7.097e-05 -3.132 10
802 L3MBTL2 22_17 0.3832 35.69 6.797e-05 5.667 1
335 CLYBL 13_50 0.4005 28.62 5.953e-05 3.483 1
450 DOCK1 10_79 0.4224 17.91 4.144e-05 -3.638 1
123 APOC2 19_31 0.3765 20.86 3.574e-05 3.035 3
1173 PRDM16 1_2 0.4261 22.09 3.508e-05 -2.288 14
1292 RP11-338H14.1 11_54 0.3274 24.80 3.448e-05 -3.556 1
num_meqtl
444 5
1531 5
620 5
133 7
765 7
880 3
570 14
1109 7
115 2
1130 1
1098 4
559 10
1391 8
432 32
802 1
335 1
450 8
123 11
1173 60
1292 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_methylation
1732 ZFP57 6_23 9.758e-02 36.15 2.995e-06 6.751 3
1789 ZSCAN16 6_22 2.273e-02 19.49 1.306e-07 6.250 1
1686 WBP1L 10_66 5.096e-06 309.42 1.042e-13 5.911 1
881 MAD1L1 7_3 3.282e-01 32.14 2.295e-05 -5.734 5
802 L3MBTL2 22_17 3.832e-01 35.69 6.797e-05 5.667 1
1068 OR2J2 6_23 3.438e-02 24.85 3.810e-07 -5.544 1
740 IREB2 15_37 2.279e-02 28.04 1.888e-07 5.485 1
157 ATPAF2 17_15 2.290e-01 26.66 1.814e-05 5.311 1
1100 PCCB 3_84 0.000e+00 849.25 0.000e+00 4.958 1
444 DNAJC11 1_5 8.716e-01 23.90 2.355e-04 4.897 1
1504 STAB1 3_36 3.868e-02 18.13 3.517e-07 4.586 1
457 DST 6_42 2.755e-01 20.61 2.029e-05 -4.463 1
660 HAPLN4 19_15 8.882e-02 19.90 1.901e-06 4.463 2
620 GNG12 1_42 9.136e-01 20.93 2.206e-04 4.447 2
1531 SYT13 11_28 9.042e-01 21.95 2.328e-04 -4.427 1
723 IK 5_83 0.000e+00 2689.75 0.000e+00 -4.414 1
369 CRELD2 22_24 6.797e-02 19.46 1.166e-06 -4.409 1
74 AGO3 1_22 1.270e-01 26.73 5.590e-06 -4.396 1
115 AP000721.4 11_35 5.625e-01 21.08 8.651e-05 4.347 1
168 BCL11B 14_52 1.003e-01 25.55 3.333e-06 4.310 1
num_meqtl
1732 42
1789 4
1686 6
881 22
802 1
1068 1
740 1
157 3
1100 2
444 5
1504 5
457 2
660 8
620 5
1531 5
723 7
369 5
74 5
115 2
168 3Comparing z scores and PIPs
[1] 0.01283 genename region_tag susie_pip mu2 PVE z num_methylation
1732 ZFP57 6_23 9.758e-02 36.15 2.995e-06 6.751 3
1789 ZSCAN16 6_22 2.273e-02 19.49 1.306e-07 6.250 1
1686 WBP1L 10_66 5.096e-06 309.42 1.042e-13 5.911 1
881 MAD1L1 7_3 3.282e-01 32.14 2.295e-05 -5.734 5
802 L3MBTL2 22_17 3.832e-01 35.69 6.797e-05 5.667 1
1068 OR2J2 6_23 3.438e-02 24.85 3.810e-07 -5.544 1
740 IREB2 15_37 2.279e-02 28.04 1.888e-07 5.485 1
157 ATPAF2 17_15 2.290e-01 26.66 1.814e-05 5.311 1
1100 PCCB 3_84 0.000e+00 849.25 0.000e+00 4.958 1
444 DNAJC11 1_5 8.716e-01 23.90 2.355e-04 4.897 1
1504 STAB1 3_36 3.868e-02 18.13 3.517e-07 4.586 1
457 DST 6_42 2.755e-01 20.61 2.029e-05 -4.463 1
660 HAPLN4 19_15 8.882e-02 19.90 1.901e-06 4.463 2
620 GNG12 1_42 9.136e-01 20.93 2.206e-04 4.447 2
1531 SYT13 11_28 9.042e-01 21.95 2.328e-04 -4.427 1
723 IK 5_83 0.000e+00 2689.75 0.000e+00 -4.414 1
369 CRELD2 22_24 6.797e-02 19.46 1.166e-06 -4.409 1
74 AGO3 1_22 1.270e-01 26.73 5.590e-06 -4.396 1
115 AP000721.4 11_35 5.625e-01 21.08 8.651e-05 4.347 1
168 BCL11B 14_52 1.003e-01 25.55 3.333e-06 4.310 1
num_meqtl
1732 42
1789 4
1686 6
881 22
802 1
1068 1
740 1
157 3
1100 2
444 5
1504 5
457 2
660 8
620 5
1531 5
723 7
369 5
74 5
115 2
168 3GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 14Uploading 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"
Term Overlap
1 1-phosphatidylinositol-4-phosphate 3-kinase activity (GO:0035005) 1/7
2 1-phosphatidylinositol-3-kinase activity (GO:0016303) 1/10
3 calcium-activated potassium channel activity (GO:0015269) 1/12
4 phosphatidylinositol 3-kinase activity (GO:0035004) 1/12
5 phosphatidylinositol phosphate kinase activity (GO:0016307) 1/13
6 phosphatidylinositol kinase activity (GO:0052742) 1/15
7 calcium activated cation channel activity (GO:0005227) 1/19
8 3',5'-cyclic-nucleotide phosphodiesterase activity (GO:0004114) 1/21
9 cyclic-nucleotide phosphodiesterase activity (GO:0004112) 1/23
Adjusted.P.value Genes
1 0.04878 PIK3C2A
2 0.04878 PIK3C2A
3 0.04878 KCNMA1
4 0.04878 PIK3C2A
5 0.04878 PIK3C2A
6 0.04878 PIK3C2A
7 0.04973 KCNMA1
8 0.04973 PDE6B
9 0.04973 PDE6BDisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio
40 Neonatal Death 0.008538 1/7
44 Perinatal death 0.008538 1/7
56 Generalized Epilepsy and Paroxysmal Dyskinesia 0.008538 1/7
60 NIGHT BLINDNESS, CONGENITAL STATIONARY, AUTOSOMAL DOMINANT 2 0.008538 1/7
65 RETINITIS PIGMENTOSA 40 (disorder) 0.008538 1/7
71 CEREBELLAR ATROPHY, DEVELOPMENTAL DELAY, AND SEIZURES 0.008538 1/7
27 Idiopathic Hypereosinophilic Syndrome 0.009311 1/7
28 Eosinophilic leukemia 0.009311 1/7
29 Loeffler's Endocarditis 0.009311 1/7
38 Chronic eosinophilic leukemia 0.009311 1/7
BgRatio
40 1/9703
44 1/9703
56 1/9703
60 1/9703
65 1/9703
71 1/9703
27 2/9703
28 2/9703
29 2/9703
38 2/9703WebGestalt enrichment analysis for genes with PIP>0.5
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: 'timedatectl' indicates the non-existent timezone name 'n/a'Warning: Your system is mis-configured: '/etc/localtime' is not a symlinkWarning: It is strongly recommended to set envionment variable TZ to 'America/
Chicago' (or equivalent)
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$genename))[1] 16#significance threshold for TWAS
print(sig_thresh)[1] 4.19#number of ctwas genes
length(ctwas_genes)[1] 8#number of TWAS genes
length(twas_genes)[1] 23#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_methylation
133 ARID1B 6_102 0.9049 19.33 0.0002013 3.694 2
570 FOXO6 1_25 0.8071 20.35 0.0001637 3.869 3
765 KCNMA1 10_50 0.8024 22.34 0.0001793 -3.992 3
880 LZTS2 10_64 0.8146 20.52 0.0001766 -4.016 1
1109 PDE6B 4_1 0.8127 19.86 0.0001594 3.761 3
num_meqtl
133 7
570 14
765 7
880 3
1109 7#sensitivity / recall
print(sensitivity) ctwas TWAS
0.00000 0.02308 #specificity
print(specificity) ctwas TWAS
0.9955 0.9887 #precision / PPV
print(precision) ctwas TWAS
0.0000 0.1304
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_2.1.2 tidyr_1.2.0 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_3.3.6 dplyr_1.0.9 reticulate_1.26 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 lubridate_1.7.10 bit64_4.0.5 doParallel_1.0.17
[5] httr_1.4.3 rprojroot_2.0.3 backports_1.2.1 tools_4.1.0
[9] doRNG_1.8.2 bslib_0.4.0 utf8_1.2.2 R6_2.5.1
[13] vipor_0.4.5 DBI_1.1.2 colorspace_2.0-3 withr_2.5.0
[17] ggrastr_1.0.1 tidyselect_1.1.2 processx_3.5.3 bit_4.0.4
[21] curl_4.3.2 compiler_4.1.0 git2r_0.28.0 rvest_1.0.0
[25] cli_3.3.0 Cairo_1.5-15 xml2_1.3.2 labeling_0.4.2
[29] sass_0.4.0 scales_1.2.0 callr_3.7.0 systemfonts_1.0.4
[33] apcluster_1.4.9 digest_0.6.29 rmarkdown_2.9 svglite_2.1.0
[37] pkgconfig_2.0.3 htmltools_0.5.3 dbplyr_2.1.1 fastmap_1.1.0
[41] highr_0.9 rlang_1.0.4 rstudioapi_0.13 jquerylib_0.1.4
[45] farver_2.1.0 generics_0.1.2 jsonlite_1.8.0 vroom_1.5.7
[49] magrittr_2.0.3 Matrix_1.3-3 ggbeeswarm_0.6.0 Rcpp_1.0.9
[53] munsell_0.5.0 fansi_1.0.3 lifecycle_1.0.1 stringi_1.7.6
[57] whisker_0.4 yaml_2.2.1 plyr_1.8.7 grid_4.1.0
[61] ggrepel_0.9.1 parallel_4.1.0 promises_1.2.0.1 crayon_1.5.1
[65] lattice_0.20-44 haven_2.4.1 hms_1.1.1 knitr_1.33
[69] ps_1.7.0 pillar_1.7.0 igraph_1.3.1 rjson_0.2.20
[73] rngtools_1.5.2 reshape2_1.4.4 codetools_0.2-18 reprex_2.0.0
[77] glue_1.6.2 evaluate_0.15 getPass_0.2-2 modelr_0.1.8
[81] data.table_1.14.2 png_0.1-7 vctrs_0.4.1 tzdb_0.3.0
[85] httpuv_1.6.1 foreach_1.5.2 cellranger_1.1.0 gtable_0.3.0
[89] assertthat_0.2.1 cachem_1.0.6 xfun_0.24 broom_0.7.8
[93] later_1.2.0 iterators_1.0.14 beeswarm_0.4.0 ellipsis_0.3.2
[97] here_1.0.1