SCZ 2018 - Brain_Cerebellum
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] 27353#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2535 1830 1661 982 1135 1371 1536 916 1175 1171 1678 1471 543 971 987 1200
17 18 19 20 21 22
1981 337 2002 917 48 906 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 23734#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8677finish
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
0.0088825 0.0002955 gene snp
12.43 10.04 [1] 105318[1] 8177 6309950 gene snp
0.008573 0.177721 [1] 0.0304 1.0607Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3737 LRP8 1_33 1.2179 33.07 0.0003523 -4.820 10 11
5502 R3HDM2 12_36 1.1443 44.06 0.0004924 6.634 10 12
3555 LAMA5 20_36 1.1434 23.61 0.0002517 4.603 24 38
7678 WDR27 6_111 1.0487 17.72 0.0001014 -2.341 29 37
2759 GIGYF1 7_62 0.9748 26.79 0.0002375 -5.266 5 5
4202 MRPS33 7_87 0.9654 20.31 0.0001744 -4.304 6 6
262 AKT3 1_128 0.9580 35.61 0.0002979 6.350 5 5
7812 ZDHHC20 13_2 0.9572 24.94 0.0002118 -4.784 3 4
4567 NPIPA1 16_15 0.9556 24.97 0.0002096 4.689 3 3
3628 LINC00320 21_6 0.9542 29.24 0.0002419 -5.336 3 3
6117 SF3B1 2_117 0.9478 45.88 0.0003746 7.053 5 5
4791 PAK6 15_14 0.9449 30.33 0.0002506 -5.588 3 3
1654 CRTAP 3_24 0.9010 19.87 0.0001503 3.929 3 3
1128 CCDC57 17_47 0.8904 20.00 0.0001041 3.022 36 46
7314 TSNARE1 8_93 0.8894 34.70 0.0002087 6.287 10 12
1517 COA8 14_54 0.8857 43.21 0.0003125 7.429 6 7
5488 PYROXD2 10_62 0.8732 20.71 0.0001347 -3.755 12 14
4823 PATJ 1_39 0.8686 23.29 0.0001371 -2.798 16 19
324 ANAPC7 12_67 0.8369 37.61 0.0002240 6.385 7 7
4569 NPIPB14P 16_37 0.8337 18.72 0.0001125 -3.795 15 19
603 ATP2B2 3_8 0.8241 26.05 0.0001568 4.229 7 8
666 B3GAT1 11_84 0.8157 23.68 0.0001377 4.324 6 9
4643 NTRK3 15_41 0.8046 24.66 0.0001392 4.457 2 2
1073 CBWD1 9_1 0.8033 20.46 0.0001186 4.060 3 4
2554 FGFR1 8_34 0.8002 37.26 0.0001970 -6.046 10 12Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
425 APOM 6_26 0.3686 623.03 0.0008033 11.590 2 2
826 BTN3A1 6_20 0.7393 146.39 0.0006649 13.091 8 8
5502 R3HDM2 12_36 1.1443 44.06 0.0004924 6.634 10 12
6117 SF3B1 2_117 0.9478 45.88 0.0003746 7.053 5 5
3737 LRP8 1_33 1.2179 33.07 0.0003523 -4.820 10 11
1517 COA8 14_54 0.8857 43.21 0.0003125 7.429 6 7
1275 CENPM 22_17 0.7509 57.80 0.0003094 -6.506 1 1
262 AKT3 1_128 0.9580 35.61 0.0002979 6.350 5 5
3555 LAMA5 20_36 1.1434 23.61 0.0002517 4.603 24 38
4791 PAK6 15_14 0.9449 30.33 0.0002506 -5.588 3 3
3628 LINC00320 21_6 0.9542 29.24 0.0002419 -5.336 3 3
2759 GIGYF1 7_62 0.9748 26.79 0.0002375 -5.266 5 5
7645 VWA7 6_26 0.1940 627.25 0.0002242 11.553 1 1
324 ANAPC7 12_67 0.8369 37.61 0.0002240 6.385 7 7
7812 ZDHHC20 13_2 0.9572 24.94 0.0002118 -4.784 3 4
4567 NPIPA1 16_15 0.9556 24.97 0.0002096 4.689 3 3
7314 TSNARE1 8_93 0.8894 34.70 0.0002087 6.287 10 12
2554 FGFR1 8_34 0.8002 37.26 0.0001970 -6.046 10 12
7240 TRANK1 3_27 0.7490 39.04 0.0001917 -6.365 8 8
1421 CLCN3 4_110 0.7913 29.64 0.0001762 5.470 1 2Comparing z scores and PIPs
[1] 0.02091 genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
826 BTN3A1 6_20 7.393e-01 146.39 6.649e-04 13.091 8 8
4952 PGBD1 6_22 1.007e-01 160.95 7.079e-06 13.087 5 6
425 APOM 6_26 3.686e-01 623.03 8.033e-04 11.590 2 2
7645 VWA7 6_26 1.940e-01 627.25 2.242e-04 11.553 1 1
7578 VARS1 6_26 1.402e-01 623.95 1.165e-04 -11.548 2 2
4216 MSH5 6_26 1.588e-01 627.91 1.503e-04 -11.538 3 3
1834 DDR1 6_25 1.570e-01 105.86 2.456e-05 -11.175 3 3
7579 VARS2 6_25 1.118e-01 104.74 1.206e-05 11.137 2 2
925 C6orf136 6_25 7.591e-02 87.21 4.771e-06 -11.031 2 2
2587 FLOT1 6_25 1.547e-01 87.22 1.952e-05 -10.981 7 7
827 BTN3A2 6_20 1.644e-01 94.96 1.183e-05 -10.694 3 5
2816 GNL1 6_25 2.920e-03 78.25 6.334e-09 -10.645 1 1
7265 TRIM39 6_25 7.839e-03 82.27 4.800e-08 -10.616 1 1
686 BAG6 6_26 2.982e-09 498.08 4.206e-20 10.247 7 8
5293 PPT2 6_26 7.799e-12 464.25 2.681e-25 -10.061 10 12
5362 PRRT1 6_26 2.706e-12 462.51 3.216e-26 -10.018 1 1
2884 GPSM3 6_26 8.360e-14 414.68 2.752e-29 -9.377 1 1
1152 CCHCR1 6_25 4.718e-02 69.57 6.124e-07 -9.358 17 30
7175 TNXB 6_26 1.527e-13 452.13 1.000e-28 9.001 6 7
8165 ZSCAN26 6_22 6.731e-02 53.73 1.605e-06 8.672 6 6GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 109Uploading 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 Adjusted.P.value
1 morphogenesis of a polarized epithelium (GO:0001738) 3/12 0.03045
Genes
1 AHI1;LAMA5;ACTG1
[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
62 Glioma 0.04401 4/61 87/9703
90 Measles 0.04401 1/61 1/9703
156 Electroencephalogram abnormal 0.04401 1/61 1/9703
160 Polydactyly 0.04401 4/61 117/9703
196 Short upturned nose 0.04401 1/61 1/9703
199 mixed gliomas 0.04401 4/61 70/9703
219 Hypoglycemia, leucine-induced 0.04401 1/61 1/9703
278 Interfrontal craniofaciosynostosis 0.04401 1/61 1/9703
279 Osteoglophonic dwarfism 0.04401 1/61 1/9703
291 Malignant Glioma 0.04401 4/61 70/9703WebGestalt 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: 67 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] 4.522#number of ctwas genes
length(ctwas_genes)[1] 25#number of TWAS genes
length(twas_genes)[1] 171#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
603 ATP2B2 3_8 0.8241 26.05 0.0001568 4.229 7 8
666 B3GAT1 11_84 0.8157 23.68 0.0001377 4.324 6 9
1073 CBWD1 9_1 0.8033 20.46 0.0001186 4.060 3 4
1128 CCDC57 17_47 0.8904 20.00 0.0001041 3.022 36 46
1654 CRTAP 3_24 0.9010 19.87 0.0001503 3.929 3 3
4202 MRPS33 7_87 0.9654 20.31 0.0001744 -4.304 6 6
4569 NPIPB14P 16_37 0.8337 18.72 0.0001125 -3.795 15 19
4643 NTRK3 15_41 0.8046 24.66 0.0001392 4.457 2 2
4823 PATJ 1_39 0.8686 23.29 0.0001371 -2.798 16 19
5488 PYROXD2 10_62 0.8732 20.71 0.0001347 -3.755 12 14
7678 WDR27 6_111 1.0487 17.72 0.0001014 -2.341 29 37#sensitivity / recall
print(sensitivity) ctwas TWAS
0.04615 0.18462 #specificity
print(specificity) ctwas TWAS
0.9977 0.9819 #precision / PPV
print(precision) ctwas TWAS
0.2400 0.1404
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