SCZ 2018 - Brain_Putamen_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] 18714#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1715 1341 1135 747 753 935 1084 644 764 833 1148 1001 362 676 630 762
17 18 19 20 21 22
1314 271 1307 672 31 589 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 16516#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8825INFO: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
0.0072380 0.0003126 gene snp
10.69 10.43 [1] 105318[1] 6949 6309950 gene snp
0.005105 0.195372 [1] 0.00773 1.07231Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
1823 DPYSL3 5_86 0.7896 22.79 1.349e-04 -4.157 1 1
4063 NTRK3 15_41 0.7005 23.92 1.116e-04 -4.457 3 3
5640 SNRPA1 15_50 0.6971 22.06 1.029e-04 -3.967 2 3
256 AKT3 1_128 0.6929 34.91 1.725e-04 -6.350 5 6
6023 THAP8 19_25 0.6774 23.53 1.025e-04 -3.847 1 1
1711 DHPS 19_10 0.6765 24.97 1.085e-04 -4.396 1 1
2431 GIGYF1 7_62 0.6584 27.41 1.241e-04 5.266 2 2
3135 LAMA5 20_36 0.6480 24.08 1.173e-04 -4.335 9 12
2785 HSPA9 5_82 0.6443 26.65 1.051e-04 5.633 1 1
2512 GON4L 1_76 0.6243 26.65 9.861e-05 4.084 1 1
5295 SDCCAG8 1_128 0.5865 27.40 1.003e-04 5.377 6 9
1704 DGKZ 11_28 0.5830 47.17 1.522e-04 7.216 1 1
4522 PP2D1 3_14 0.5698 24.44 8.728e-05 4.056 3 4
982 CASP2 7_89 0.5457 21.12 5.970e-05 -3.889 1 1
616 B9D1 17_16 0.5416 28.14 8.362e-05 5.282 2 2
2149 FAM177A1 14_9 0.5184 23.94 1.052e-04 -4.872 12 15
3201 LINC00320 21_6 0.5044 28.61 1.423e-04 -5.336 5 5
6486 UQCRC2 16_19 0.5021 22.81 5.461e-05 4.716 1 1
1622 DBF4B 17_26 0.4839 22.11 5.213e-05 -3.890 5 5
912 CACNA1G 17_29 0.4733 24.11 5.128e-05 3.916 1 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
256 AKT3 1_128 0.6929 34.91 1.725e-04 -6.350 5 6
1704 DGKZ 11_28 0.5830 47.17 1.522e-04 7.216 1 1
3201 LINC00320 21_6 0.5044 28.61 1.423e-04 -5.336 5 5
5376 SF3B1 2_117 0.4000 44.94 1.384e-04 -7.053 4 4
1823 DPYSL3 5_86 0.7896 22.79 1.349e-04 -4.157 1 1
2431 GIGYF1 7_62 0.6584 27.41 1.241e-04 5.266 2 2
3135 LAMA5 20_36 0.6480 24.08 1.173e-04 -4.335 9 12
402 APOPT1 14_54 0.3448 46.84 1.166e-04 -7.431 6 9
4063 NTRK3 15_41 0.7005 23.92 1.116e-04 -4.457 3 3
1711 DHPS 19_10 0.6765 24.97 1.085e-04 -4.396 1 1
2149 FAM177A1 14_9 0.5184 23.94 1.052e-04 -4.872 12 15
2785 HSPA9 5_82 0.6443 26.65 1.051e-04 5.633 1 1
3373 MAD1L1 7_3 0.4288 54.63 1.046e-04 7.478 4 4
5640 SNRPA1 15_50 0.6971 22.06 1.029e-04 -3.967 2 3
6023 THAP8 19_25 0.6774 23.53 1.025e-04 -3.847 1 1
5295 SDCCAG8 1_128 0.5865 27.40 1.003e-04 5.377 6 9
2512 GON4L 1_76 0.6243 26.65 9.861e-05 4.084 1 1
3297 LRP8 1_33 0.3432 32.81 9.243e-05 -4.820 6 6
4522 PP2D1 3_14 0.5698 24.44 8.728e-05 4.056 3 4
669 BDNF 11_19 0.4443 22.57 8.486e-05 -4.348 3 3Comparing z scores and PIPs
[1] 0.01597 genename region_tag susie_pip mu2 PVE z num_intron
401 APOM 6_26 8.867e-05 215.80 3.305e-11 11.590 3
623 BAG6 6_26 8.867e-05 215.80 1.620e-11 -11.590 5
6535 VARS2 6_25 6.847e-02 101.95 4.538e-06 -11.137 1
868 C6orf136 6_24 3.597e-02 79.99 1.965e-06 11.031 2
2294 FLOT1 6_24 2.962e-02 78.65 3.534e-06 -10.981 6
1602 CYP21A2 6_26 3.944e-06 179.01 2.643e-14 -10.736 1
741 BTN3A2 6_20 2.139e-02 91.47 5.834e-07 -10.717 4
2555 GPSM3 6_26 1.931e-06 120.46 8.532e-15 -9.377 2
1061 CCHCR1 6_25 1.748e-02 61.89 3.341e-07 -9.272 10
1655 DDR1 6_25 1.253e-02 68.37 1.019e-07 9.016 1
1915 EGFL8 6_26 1.209e-05 121.04 1.683e-13 -8.953 2
4052 NT5C2 10_66 2.050e-01 47.24 6.387e-05 -8.668 9
3383 MAIP1 2_118 2.500e-01 44.44 2.637e-05 -7.980 1
494 AS3MT 10_66 1.817e-01 40.78 2.566e-05 7.907 4
5139 RP5-874C20.8 6_22 8.584e-03 37.09 5.064e-08 -7.603 3
3373 MAD1L1 7_3 4.288e-01 54.63 1.046e-04 7.478 4
6941 ZSCAN16 6_22 1.289e-02 53.72 7.344e-08 -7.468 3
402 APOPT1 14_54 3.448e-01 46.84 1.166e-04 -7.431 6
3081 KLC1 14_54 1.050e-01 49.40 8.432e-06 7.382 6
1704 DGKZ 11_28 5.830e-01 47.17 1.522e-04 7.216 1
num_sqtl
401 3
623 5
6535 1
868 2
2294 6
1602 2
741 5
2555 2
1061 15
1655 1
1915 3
4052 11
3383 1
494 5
5139 4
3373 4
6941 3
402 9
3081 6
1704 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 18Uploading 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
1 U2 snRNA binding (GO:0030620)
2 dihydropyrimidinase activity (GO:0004157)
3 neurotrophin binding (GO:0043121)
4 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amides (GO:0016812)
5 cysteine-type endopeptidase activity involved in apoptotic signaling pathway (GO:0097199)
6 diacylglycerol kinase activity (GO:0004143)
7 filamin binding (GO:0031005)
8 cysteine-type endopeptidase activity involved in execution phase of apoptosis (GO:0097200)
9 cysteine-type endopeptidase activity involved in apoptotic process (GO:0097153)
Overlap Adjusted.P.value Genes
1 1/5 0.03802 SNRPA1
2 1/6 0.03802 DPYSL3
3 1/8 0.03802 NTRK3
4 1/10 0.03802 DPYSL3
5 1/10 0.03802 CASP2
6 1/11 0.03802 DGKZ
7 1/11 0.03802 DPYSL3
8 1/13 0.03929 CASP2
9 1/15 0.04026 CASP2DisGeNET enrichment analysis for genes with PIP>0.5
Description FDR
28 Electroencephalogram abnormal 0.01021
74 SENIOR-LOKEN SYNDROME 7 0.01021
76 MECKEL SYNDROME, TYPE 9 0.01021
77 MITOCHONDRIAL COMPLEX III DEFICIENCY, NUCLEAR TYPE 5 0.01021
79 Intellectual Disability 0.01021
80 BARDET-BIEDL SYNDROME 16 0.01021
82 MEGALENCEPHALY-POLYMICROGYRIA-POLYDACTYLY-HYDROCEPHALUS SYNDROME 2 0.01021
84 EVEN-PLUS SYNDROME 0.01021
85 ANEMIA, SIDEROBLASTIC, 4 0.01021
88 JOUBERT SYNDROME 27 0.01021
Ratio BgRatio
28 1/11 1/9703
74 1/11 1/9703
76 1/11 1/9703
77 1/11 1/9703
79 4/11 447/9703
80 1/11 1/9703
82 1/11 1/9703
84 1/11 1/9703
85 1/11 1/9703
88 1/11 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!NULL
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] 4.488#number of ctwas genes
length(ctwas_genes)[1] 0#number of TWAS genes
length(twas_genes)[1] 111#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols][1] genename region_tag susie_pip mu2 PVE z num_intron
[8] num_sqtl
<0 rows> (or 0-length row.names)#sensitivity / recall
print(sensitivity) ctwas TWAS
0.0000 0.1077 #specificity
print(specificity) ctwas TWAS
1.0000 0.9859 #precision / PPV
print(precision) ctwas TWAS
NaN 0.1261
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