SCZ 2018 - Brain_Cerebellar_Hemisphere
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] 26564#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2520 1814 1594 973 1137 1377 1526 911 1106 1166 1579 1419 520 921 928 1179
17 18 19 20 21 22
1880 325 1895 891 51 852 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 23201#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8734finish
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
0.0103642 0.0002912 gene snp
12.00 10.12 [1] 105318[1] 8010 6309950 gene snp
0.00946 0.17654 [1] 0.03556 1.05335Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
6205 SLC8B1 12_68 1.5477 28.59 5.738e-04 -4.047 11 12
3664 LRP8 1_33 1.2607 32.55 3.761e-04 4.820 11 11
7510 WDR27 6_111 1.1795 17.37 1.185e-04 2.338 30 41
2370 FAM177A1 14_9 1.1089 24.30 2.444e-04 -4.872 10 11
2694 GIGYF2 2_137 1.0940 56.96 5.633e-04 -8.128 6 6
5370 R3HDM2 12_36 1.0731 43.83 4.526e-04 6.634 9 11
3648 LPCAT4 15_10 0.9950 25.36 2.297e-04 4.892 3 4
2693 GIGYF1 7_62 0.9927 26.42 2.425e-04 -5.266 5 5
1109 CCDC57 17_47 0.9837 18.98 1.239e-04 -3.061 34 44
3494 LAMA5 20_37 0.9462 28.70 1.979e-04 -4.211 25 32
118 ACTR1B 2_57 0.9438 19.16 1.576e-04 3.978 9 9
4732 PATJ 1_39 0.9400 22.53 1.571e-04 2.798 15 17
987 CAMKK2 12_74 0.9338 35.78 2.086e-04 4.159 6 8
4808 PDXDC1 16_15 0.9292 29.62 1.423e-04 3.879 17 18
4700 PAK6 15_14 0.9242 29.86 2.422e-04 -5.588 1 1
5975 SF3B1 2_117 0.9204 45.85 3.612e-04 -7.053 3 3
4121 MRPS33 7_87 0.9200 20.70 1.602e-04 -4.304 5 5
1453 CNOT1 16_31 0.9167 35.98 2.567e-04 6.282 10 11
6804 THAP8 19_25 0.9103 19.03 1.497e-04 3.847 2 2
5358 PYROXD2 10_62 0.9087 21.98 1.517e-04 -3.852 9 10
4480 NPIPB14P 16_37 0.8947 18.17 1.248e-04 3.742 16 19
4586 NUP50 22_20 0.8804 18.64 1.329e-04 -3.850 5 5
5991 SGCE 7_58 0.8780 20.72 1.455e-04 4.413 6 6
2494 FGFR1 8_34 0.8758 36.56 2.276e-04 -6.046 10 11
1230 CECR2 22_2 0.8629 18.61 1.295e-04 -3.928 4 4
263 AKT3 1_128 0.8604 35.12 2.284e-04 6.266 5 5
4550 NTRK3 15_41 0.8602 24.09 1.557e-04 4.457 3 3
2899 GUSBP11 22_6 0.8598 21.55 9.607e-05 -2.922 21 34
1683 CUL9 6_33 0.8557 31.85 1.747e-04 4.961 11 12
6995 TNK2 3_120 0.8483 27.71 1.251e-04 3.409 16 16
7146 TSNARE1 8_93 0.8348 34.12 1.825e-04 6.364 10 10
654 B3GAT1 11_84 0.8323 23.80 1.477e-04 4.394 4 6
7650 ZDHHC20 13_2 0.8099 25.00 1.495e-04 -4.832 3 4
1919 DNAJB1 19_12 0.8080 19.58 1.102e-04 3.972 5 6
1612 CRTAP 3_24 0.8018 20.88 1.249e-04 3.929 2 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
420 APOM 6_26 0.4404 626.01 0.0011520 11.590 2 2
7415 VARS1 6_26 0.3832 628.56 0.0008763 -11.620 2 2
821 BTN3A1 6_20 0.7245 145.15 0.0006369 13.091 7 8
6205 SLC8B1 12_68 1.5477 28.59 0.0005738 -4.047 11 12
2694 GIGYF2 2_137 1.0940 56.96 0.0005633 -8.128 6 6
5370 R3HDM2 12_36 1.0731 43.83 0.0004526 6.634 9 11
3664 LRP8 1_33 1.2607 32.55 0.0003761 4.820 11 11
5975 SF3B1 2_117 0.9204 45.85 0.0003612 -7.053 3 3
1453 CNOT1 16_31 0.9167 35.98 0.0002567 6.282 10 11
2370 FAM177A1 14_9 1.1089 24.30 0.0002444 -4.872 10 11
2693 GIGYF1 7_62 0.9927 26.42 0.0002425 -5.266 5 5
4700 PAK6 15_14 0.9242 29.86 0.0002422 -5.588 1 1
3648 LPCAT4 15_10 0.9950 25.36 0.0002297 4.892 3 4
263 AKT3 1_128 0.8604 35.12 0.0002284 6.266 5 5
2494 FGFR1 8_34 0.8758 36.56 0.0002276 -6.046 10 11
987 CAMKK2 12_74 0.9338 35.78 0.0002086 4.159 6 8
3494 LAMA5 20_37 0.9462 28.70 0.0001979 -4.211 25 32
7070 TRANK1 3_27 0.7567 38.76 0.0001973 -6.365 6 6
7146 TSNARE1 8_93 0.8348 34.12 0.0001825 6.364 10 10
1683 CUL9 6_33 0.8557 31.85 0.0001747 4.961 11 12Comparing z scores and PIPs
[1] 0.02197 genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
7999 ZSCAN31 6_22 3.695e-02 160.21 1.569e-06 -13.135 2 2
821 BTN3A1 6_20 7.245e-01 145.15 6.369e-04 13.091 7 8
4852 PGBD1 6_22 1.027e-01 159.13 7.726e-06 -13.087 5 7
7415 VARS1 6_26 3.832e-01 628.56 8.763e-04 -11.620 2 2
420 APOM 6_26 4.404e-01 626.01 1.152e-03 11.590 2 2
1789 DDR1 6_25 3.515e-01 101.78 1.165e-04 -11.175 4 4
915 C6orf136 6_24 1.205e-01 80.18 1.105e-05 -11.031 2 2
2523 FLOT1 6_24 3.515e-01 78.83 9.198e-05 10.981 8 8
822 BTN3A2 6_20 1.534e-01 94.90 1.064e-05 -10.743 5 7
1712 CYP21A2 6_26 5.976e-06 607.99 2.062e-13 -10.513 1 2
674 BAG6 6_26 1.124e-08 500.57 5.992e-19 10.342 10 11
819 BTN2A1 6_20 1.490e-01 84.19 6.335e-06 10.110 7 7
5179 PPT2 6_26 5.412e-12 466.36 1.297e-25 10.061 7 9
2107 EGFL8 6_26 4.315e-12 465.72 8.227e-26 10.036 5 6
5241 PRRT1 6_26 3.762e-12 464.63 6.243e-26 -10.018 1 1
2820 GPSM3 6_26 2.356e-13 416.63 2.196e-28 -9.377 2 2
1130 CCHCR1 6_25 9.102e-02 59.77 1.948e-06 -9.032 11 18
7007 TNXB 6_26 2.108e-13 454.39 1.918e-28 9.001 4 5
3002 HLA-DMA 6_27 1.797e-01 70.57 1.141e-05 8.860 5 6
7995 ZSCAN23 6_22 1.294e-02 46.07 7.324e-08 -8.541 1 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 138Uploading 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 Adjusted.P.value
1 cadherin binding (GO:0045296) 10/322 0.01731
Genes
1 CAST;DNAJB1;DLG1;PDXDC1;SRC;PAK6;LRRFIP1;CD46;GIGYF2;KTN1DisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio BgRatio
13 Balo's Concentric Sclerosis 0.0579 1/86 1/9703
40 Diffuse Cerebral Sclerosis of Schilder 0.0579 1/86 1/9703
90 Profound Mental Retardation 0.0579 5/86 139/9703
100 Acute monocytic leukemia 0.0579 3/86 26/9703
101 Leukemia, Myelocytic, Acute 0.0579 6/86 173/9703
112 Measles 0.0579 1/86 1/9703
116 Mental Retardation, Psychosocial 0.0579 5/86 139/9703
132 Nicotine Dependence 0.0579 2/86 14/9703
153 Schizophrenia 0.0579 17/86 883/9703
157 Status Epilepticus 0.0579 4/86 68/9703WebGestalt 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...
description size overlap FDR database
1 Bipolar Disorder 139 12 0.01063 disease_GLAD4U
2 Schizophrenia 171 12 0.04340 disease_GLAD4U
userId
1 AS3MT;BDNF;CAMKK2;DLG1;GABBR2;NT5C2;NTRK3;SDCCAG8;SYNE1;TCF4;TRANK1;TSNARE1
2 AHI1;AS3MT;BDNF;CAMKK2;DLG1;NT5C2;NTRK3;SDCCAG8;SYNE1;TCF4;TRANK1;TSNARE1PIP Manhattan Plot
Warning: Removed 2 rows containing missing values (geom_point).
Warning: Removed 2 rows containing missing values (geom_point).Warning: Removed 2 rows containing missing values (geom_label_repel).Warning: ggrepel: 106 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] 60#significance threshold for TWAS
print(sig_thresh)[1] 4.518#number of ctwas genes
length(ctwas_genes)[1] 35#number of TWAS genes
length(twas_genes)[1] 176#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
118 ACTR1B 2_57 0.9438 19.16 1.576e-04 3.978 9 9
654 B3GAT1 11_84 0.8323 23.80 1.477e-04 4.394 4 6
987 CAMKK2 12_74 0.9338 35.78 2.086e-04 4.159 6 8
1109 CCDC57 17_47 0.9837 18.98 1.239e-04 -3.061 34 44
1230 CECR2 22_2 0.8629 18.61 1.295e-04 -3.928 4 4
1612 CRTAP 3_24 0.8018 20.88 1.249e-04 3.929 2 2
1919 DNAJB1 19_12 0.8080 19.58 1.102e-04 3.972 5 6
2899 GUSBP11 22_6 0.8598 21.55 9.607e-05 -2.922 21 34
3494 LAMA5 20_37 0.9462 28.70 1.979e-04 -4.211 25 32
4121 MRPS33 7_87 0.9200 20.70 1.602e-04 -4.304 5 5
4480 NPIPB14P 16_37 0.8947 18.17 1.248e-04 3.742 16 19
4550 NTRK3 15_41 0.8602 24.09 1.557e-04 4.457 3 3
4586 NUP50 22_20 0.8804 18.64 1.329e-04 -3.850 5 5
4732 PATJ 1_39 0.9400 22.53 1.571e-04 2.798 15 17
4808 PDXDC1 16_15 0.9292 29.62 1.423e-04 3.879 17 18
5358 PYROXD2 10_62 0.9087 21.98 1.517e-04 -3.852 9 10
5991 SGCE 7_58 0.8780 20.72 1.455e-04 4.413 6 6
6205 SLC8B1 12_68 1.5477 28.59 5.738e-04 -4.047 11 12
6804 THAP8 19_25 0.9103 19.03 1.497e-04 3.847 2 2
6995 TNK2 3_120 0.8483 27.71 1.251e-04 3.409 16 16
7510 WDR27 6_111 1.1795 17.37 1.185e-04 2.338 30 41#sensitivity / recall
print(sensitivity) ctwas TWAS
0.06154 0.13846 #specificity
print(specificity) ctwas TWAS
0.9966 0.9801 #precision / PPV
print(precision) ctwas TWAS
0.2286 0.1023
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