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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| Rmd | 7d08c9b | sq-96 | 2022-05-18 | update |
| html | 7d08c9b | sq-96 | 2022-05-18 | update |
| Rmd | 2749be9 | sq-96 | 2022-05-12 | update |
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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] 0.01403 1.07231Genes with highest PIPs
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3297 LRP8 1_33 1.1442 32.81 0.0003081 -4.820 6 6
3201 LINC00320 21_6 1.0821 28.61 0.0003052 -5.336 5 5
2149 FAM177A1 14_9 1.0339 23.94 0.0002097 -4.872 12 15
669 BDNF 11_19 0.9001 22.57 0.0001719 -4.348 3 3
3135 LAMA5 20_36 0.8624 24.08 0.0001561 -4.335 9 12
5376 SF3B1 2_117 0.8398 44.94 0.0002906 -7.053 4 4
138 ACTR1B 2_57 0.8262 19.35 0.0001232 -3.978 6 6
4411 PLCB2 15_14 0.8102 24.79 0.0001286 -4.470 5 5
1823 DPYSL3 5_86 0.7896 22.79 0.0001349 -4.157 1 1
402 APOPT1 14_54 0.7860 46.84 0.0002658 -7.431 6 9
256 AKT3 1_128 0.7846 34.91 0.0001953 -6.350 5 6
608 B3GAT1 11_84 0.7836 22.65 0.0001170 4.265 7 10
4751 PYROXD2 10_62 0.7830 21.51 0.0001164 3.718 10 11
2982 KAT5 11_36 0.7775 24.18 0.0001362 4.491 7 7
4063 NTRK3 15_41 0.7498 23.92 0.0001194 -4.457 3 3
2431 GIGYF1 7_62 0.7342 27.41 0.0001384 5.266 2 2
4052 NT5C2 10_66 0.7314 47.24 0.0002278 -8.668 9 11
3350 LY6H 8_94 0.7233 22.36 0.0001059 -4.186 4 4
5640 SNRPA1 15_50 0.7159 22.06 0.0001057 -3.967 2 3
4522 PP2D1 3_14 0.7018 24.44 0.0001075 4.056 3 4Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3297 LRP8 1_33 1.1442 32.81 0.0003081 -4.820 6 6
3201 LINC00320 21_6 1.0821 28.61 0.0003052 -5.336 5 5
5376 SF3B1 2_117 0.8398 44.94 0.0002906 -7.053 4 4
402 APOPT1 14_54 0.7860 46.84 0.0002658 -7.431 6 9
4052 NT5C2 10_66 0.7314 47.24 0.0002278 -8.668 9 11
2149 FAM177A1 14_9 1.0339 23.94 0.0002097 -4.872 12 15
256 AKT3 1_128 0.7846 34.91 0.0001953 -6.350 5 6
669 BDNF 11_19 0.9001 22.57 0.0001719 -4.348 3 3
3135 LAMA5 20_36 0.8624 24.08 0.0001561 -4.335 9 12
1704 DGKZ 11_28 0.5830 47.17 0.0001522 7.216 1 1
2431 GIGYF1 7_62 0.7342 27.41 0.0001384 5.266 2 2
2982 KAT5 11_36 0.7775 24.18 0.0001362 4.491 7 7
3373 MAD1L1 7_3 0.5571 54.63 0.0001359 7.478 4 4
1823 DPYSL3 5_86 0.7896 22.79 0.0001349 -4.157 1 1
4411 PLCB2 15_14 0.8102 24.79 0.0001286 -4.470 5 5
138 ACTR1B 2_57 0.8262 19.35 0.0001232 -3.978 6 6
4063 NTRK3 15_41 0.7498 23.92 0.0001194 -4.457 3 3
5295 SDCCAG8 1_128 0.6962 27.40 0.0001191 5.377 6 9
608 B3GAT1 11_84 0.7836 22.65 0.0001170 4.265 7 10
4751 PYROXD2 10_62 0.7830 21.51 0.0001164 3.718 10 11Comparing z scores and PIPs
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
[1] 0.01597 genename region_tag susie_pip mu2 PVE z num_intron
401 APOM 6_26 1.884e-04 215.80 7.023e-11 11.590 3
623 BAG6 6_26 9.194e-05 215.80 1.679e-11 -11.590 5
6535 VARS2 6_25 6.847e-02 101.95 4.538e-06 -11.137 1
868 C6orf136 6_24 7.194e-02 79.99 3.931e-06 11.031 2
2294 FLOT1 6_24 1.607e-01 78.65 1.917e-05 -10.981 6
1602 CYP21A2 6_26 3.944e-06 179.01 2.643e-14 -10.736 1
741 BTN3A2 6_20 6.252e-02 91.47 1.705e-06 -10.717 4
2555 GPSM3 6_26 3.863e-06 120.46 1.706e-14 -9.377 2
1061 CCHCR1 6_25 6.850e-02 61.89 1.309e-06 -9.272 10
1655 DDR1 6_25 1.253e-02 68.37 1.019e-07 9.016 1
1915 EGFL8 6_26 1.227e-05 121.04 1.708e-13 -8.953 2
4052 NT5C2 10_66 7.314e-01 47.24 2.278e-04 -8.668 9
3383 MAIP1 2_118 2.500e-01 44.44 2.637e-05 -7.980 1
494 AS3MT 10_66 3.678e-01 40.78 5.194e-05 7.907 4
5139 RP5-874C20.8 6_22 2.458e-02 37.09 1.450e-07 -7.603 3
3373 MAD1L1 7_3 5.571e-01 54.63 1.359e-04 7.478 4
6941 ZSCAN16 6_22 2.837e-02 53.72 1.616e-07 -7.468 3
402 APOPT1 14_54 7.860e-01 46.84 2.658e-04 -7.431 6
3081 KLC1 14_54 2.052e-01 49.40 1.648e-05 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] 48Uploading 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"
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
Term Overlap
1 positive regulation of neuron projection development (GO:0010976) 4/88
2 positive regulation of phospholipase C activity (GO:0010863) 3/43
Adjusted.P.value Genes
1 0.03183 BDNF;NTRK3;DPYSL3;LRP8
2 0.04056 BDNF;NTRK3;PLCB2
[1] "GO_Cellular_Component_2021"
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
[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
78 Schizophrenia 0.002404 10/22
245 Intellectual Disability 0.005414 7/22
15 Adenocarcinoma of prostate 0.027112 2/22
56 Measles 0.027112 1/22
96 Electroencephalogram abnormal 0.027112 1/22
208 Sporadic Breast Carcinoma 0.027112 1/22
212 Primary peritoneal carcinoma 0.027112 1/22
223 BREAST-OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.027112 1/22
224 BREAST CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.027112 1/22
225 OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.027112 1/22
BgRatio
78 883/9703
245 447/9703
15 20/9703
56 1/9703
96 1/9703
208 1/9703
212 1/9703
223 1/9703
224 1/9703
225 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: 14 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
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] 8#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] genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
138 ACTR1B 2_57 0.8262 19.35 0.0001232 -3.978 6 6
669 BDNF 11_19 0.9001 22.57 0.0001719 -4.348 3 3
3135 LAMA5 20_36 0.8624 24.08 0.0001561 -4.335 9 12
4411 PLCB2 15_14 0.8102 24.79 0.0001286 -4.470 5 5#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.10769 #specificity
print(specificity) ctwas TWAS
0.9993 0.9859 #precision / PPV
print(precision) ctwas TWAS
0.3750 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