SCZ 2018 - Brain_Nucleus_accumbens_basal_ganglia
sheng Qian
2021-2-6
Last updated: 2022-05-18
Checks: 5 2
Knit directory: cTWAS_analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 2749be9 | sq-96 | 2022-05-12 | update |
| html | 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] 21642#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1929 1532 1343 876 885 1118 1258 748 873 1021 1288 1205 442 765 754 863
17 18 19 20 21 22
1460 296 1534 750 40 662 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 18965#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8763finish
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
0.0063107 0.0003111 gene snp
10.20 10.34 [1] 105318[1] 7779 6309950 gene snp
0.004756 0.192769 [1] 0.01421 1.10685Genes with highest PIPs
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3588 LRP8 1_33 1.1028 32.85 2.908e-04 -4.820 5 5
7470 ZDHHC20 13_2 0.9020 24.49 1.796e-04 -4.784 5 6
2627 GIGYF1 7_62 0.8896 26.12 1.951e-04 -5.266 4 4
4603 PAK6 15_14 0.8434 29.84 2.016e-04 5.588 1 1
6998 TSNARE1 8_93 0.8350 27.20 1.779e-04 5.555 11 11
3573 LPCAT4 15_10 0.8112 26.23 1.599e-04 4.892 3 5
574 ATP2B2 3_8 0.7968 26.02 1.436e-04 4.229 5 6
6666 THAP8 19_25 0.7784 20.83 1.195e-04 3.847 2 2
253 AKT3 1_128 0.7627 34.93 1.831e-04 -6.350 6 6
861 C2orf80 2_123 0.7584 24.25 9.972e-05 3.053 12 13
1934 DPYSL3 5_86 0.7575 23.63 1.287e-04 4.157 1 1
4310 NGEF 2_137 0.7346 30.69 1.537e-04 7.036 3 3
118 ACTR1B 2_57 0.7234 20.56 1.003e-04 -3.978 5 5
4385 NPIPB14P 16_37 0.7132 17.79 8.171e-05 -3.742 12 12
744 BRCA1 17_25 0.7052 31.00 8.244e-05 -3.837 20 22
3968 MPHOSPH9 12_75 0.6872 60.79 2.709e-04 -8.201 2 4
695 BDNF 11_19 0.6805 23.22 1.021e-04 4.348 1 1
2217 ESAM 11_77 0.6792 35.97 1.325e-04 5.889 2 2
7331 WDR27 6_111 0.6484 16.96 3.988e-05 2.235 21 33
1792 DHPS 19_10 0.6472 25.49 1.014e-04 -4.396 1 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3588 LRP8 1_33 1.1028 32.85 0.0002908 -4.820 5 5
3968 MPHOSPH9 12_75 0.6872 60.79 0.0002709 -8.201 2 4
4603 PAK6 15_14 0.8434 29.84 0.0002016 5.588 1 1
415 APOM 6_26 0.1818 627.31 0.0001967 11.590 2 2
2627 GIGYF1 7_62 0.8896 26.12 0.0001951 -5.266 4 4
253 AKT3 1_128 0.7627 34.93 0.0001831 -6.350 6 6
7470 ZDHHC20 13_2 0.9020 24.49 0.0001796 -4.784 5 6
6998 TSNARE1 8_93 0.8350 27.20 0.0001779 5.555 11 11
3573 LPCAT4 15_10 0.8112 26.23 0.0001599 4.892 3 5
3627 LSM2 6_26 0.1619 635.43 0.0001581 -11.599 1 1
6542 TAOK2 16_24 0.6049 46.28 0.0001540 7.024 5 6
4310 NGEF 2_137 0.7346 30.69 0.0001537 7.036 3 3
7237 VARS1 6_26 0.1563 629.91 0.0001462 -11.620 1 1
4445 NT5C2 10_66 0.5910 46.04 0.0001450 -8.511 11 15
574 ATP2B2 3_8 0.7968 26.02 0.0001436 4.229 5 6
2217 ESAM 11_77 0.6792 35.97 0.0001325 5.889 2 2
1934 DPYSL3 5_86 0.7575 23.63 0.0001287 4.157 1 1
6666 THAP8 19_25 0.7784 20.83 0.0001195 3.847 2 2
695 BDNF 11_19 0.6805 23.22 0.0001021 4.348 1 1
1431 COA8 14_54 0.5012 43.66 0.0001015 7.265 4 7Comparing z scores and PIPs
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
[1] 0.01825 genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
7237 VARS1 6_26 0.15633 629.91 1.462e-04 -11.620 1 1
3627 LSM2 6_26 0.16187 635.43 1.581e-04 -11.599 1 1
415 APOM 6_26 0.18177 627.31 1.967e-04 11.590 2 2
645 BAG6 6_26 0.11475 627.31 7.843e-05 -11.590 7 7
1656 CYP21A2 6_26 0.01511 659.18 1.430e-06 -11.340 1 1
7238 VARS2 6_25 0.05583 101.38 3.000e-06 -11.137 1 1
877 C6orf136 6_24 0.06069 79.63 2.785e-06 -11.031 2 2
2465 FLOT1 6_24 0.14500 78.29 1.560e-05 10.981 6 7
776 BTN3A2 6_20 0.08647 90.16 2.598e-06 -10.659 3 3
773 BTN2A1 6_20 0.08574 82.29 3.455e-06 10.110 5 6
1087 CCHCR1 6_25 0.05360 62.58 9.944e-07 -9.358 10 14
1728 DDR1 6_25 0.01101 67.83 7.808e-08 9.016 1 1
2927 HLA-DMA 6_27 0.05258 65.16 9.905e-07 8.596 4 7
4445 NT5C2 10_66 0.59099 46.04 1.450e-04 -8.511 11 15
3671 MAD1L1 7_3 0.31932 63.77 4.772e-05 -8.215 3 3
3968 MPHOSPH9 12_75 0.68723 60.79 2.709e-04 -8.201 2 4
512 AS3MT 10_66 0.21828 44.51 1.947e-05 8.051 6 7
4055 MSH5 6_26 0.00000 236.73 0.000e+00 -7.892 3 3
802 C12orf65 12_75 0.04490 54.18 9.788e-07 -7.754 2 2
7762 ZSCAN16 6_22 0.01873 53.24 8.925e-08 -7.468 2 2GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 38Uploading 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 |
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[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
48 Measles 0.02668 1/20
69 Schizophrenia 0.02668 7/20
86 Electroencephalogram abnormal 0.02668 1/20
181 Sporadic Breast Carcinoma 0.02668 1/20
184 Primary peritoneal carcinoma 0.02668 1/20
193 BREAST-OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.02668 1/20
194 BREAST CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.02668 1/20
195 OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.02668 1/20
196 HEMOLYTIC UREMIC SYNDROME, ATYPICAL, SUSCEPTIBILITY TO, 2 0.02668 1/20
201 SENIOR-LOKEN SYNDROME 7 0.02668 1/20
BgRatio
48 1/9703
69 883/9703
86 1/9703
181 1/9703
184 1/9703
193 1/9703
194 1/9703
195 1/9703
196 1/9703
201 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: 3 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] 4.512#number of ctwas genes
length(ctwas_genes)[1] 6#number of TWAS genes
length(twas_genes)[1] 142#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
0.01538 0.10769 #specificity
print(specificity) ctwas TWAS
0.9995 0.9834 #precision / PPV
print(precision) ctwas TWAS
0.33333 0.09859
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