SCZ 2018 - Brain_Amygdala
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] 15774#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1480 1078 891 631 654 819 931 556 642 735 948 858 322 583 532 618
17 18 19 20 21 22
1087 198 1146 543 34 488 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 14087#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8931finish
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
0.0048321 0.0003214 gene snp
9.84 10.49 [1] 105318[1] 6521 6309950 gene snp
0.002944 0.202078 [1] 0.007243 1.090103Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
2879 LINC00320 21_6 1.0610 28.32 2.926e-04 5.336 6 6
2988 LRP8 1_33 0.9257 24.92 1.962e-04 -4.820 3 3
1218 COA8 14_54 0.8221 43.94 2.808e-04 -7.429 5 6
6271 ZDHHC20 13_2 0.7562 24.63 1.313e-04 -4.784 3 4
2825 LAMA5 20_37 0.7084 30.04 1.381e-04 -4.371 10 14
1635 DPYSL3 5_86 0.7025 25.41 1.190e-04 -4.157 1 1
597 BDNF-AS 11_19 0.6948 23.25 1.066e-04 -4.348 1 1
4083 PLCB2 15_14 0.6903 25.17 9.585e-05 -4.470 5 5
1513 DGKZ 11_28 0.6889 46.65 2.102e-04 7.216 2 2
537 B3GAT1 11_84 0.6636 25.37 9.490e-05 4.345 7 11
4406 PYROXD2 10_62 0.6365 24.28 8.561e-05 3.755 10 10
98 ACTR1B 2_57 0.6179 21.59 7.826e-05 3.978 3 3
1433 DBF4B 17_26 0.5949 20.61 6.733e-05 -3.890 4 4
3740 NTRK3 15_41 0.5946 22.72 7.628e-05 -4.457 1 1
5637 TMED4 7_32 0.5724 22.25 6.781e-05 -4.862 3 3
1574 DNAJB1 19_12 0.5641 19.50 5.863e-05 3.988 2 2
748 C2orf80 2_123 0.5393 25.65 5.402e-05 -3.011 10 12
214 AKT3 1_128 0.5206 34.14 8.239e-05 6.266 4 4
6345 ZNF211 19_39 0.5206 22.95 5.737e-05 -3.624 4 5
2350 GUSBP11 22_6 0.4888 19.24 3.703e-05 2.862 13 16Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
2879 LINC00320 21_6 1.0610 28.32 2.926e-04 5.336 6 6
1218 COA8 14_54 0.8221 43.94 2.808e-04 -7.429 5 6
1513 DGKZ 11_28 0.6889 46.65 2.102e-04 7.216 2 2
2988 LRP8 1_33 0.9257 24.92 1.962e-04 -4.820 3 3
2825 LAMA5 20_37 0.7084 30.04 1.381e-04 -4.371 10 14
6271 ZDHHC20 13_2 0.7562 24.63 1.313e-04 -4.784 3 4
1635 DPYSL3 5_86 0.7025 25.41 1.190e-04 -4.157 1 1
597 BDNF-AS 11_19 0.6948 23.25 1.066e-04 -4.348 1 1
4083 PLCB2 15_14 0.6903 25.17 9.585e-05 -4.470 5 5
537 B3GAT1 11_84 0.6636 25.37 9.490e-05 4.345 7 11
4406 PYROXD2 10_62 0.6365 24.28 8.561e-05 3.755 10 10
3729 NT5C2 10_66 0.4472 46.77 8.490e-05 -8.511 7 9
214 AKT3 1_128 0.5206 34.14 8.239e-05 6.266 4 4
4907 SF3B1 2_117 0.4467 43.83 8.117e-05 7.002 2 2
98 ACTR1B 2_57 0.6179 21.59 7.826e-05 3.978 3 3
3740 NTRK3 15_41 0.5946 22.72 7.628e-05 -4.457 1 1
5637 TMED4 7_32 0.5724 22.25 6.781e-05 -4.862 3 3
492 ATP2B2 3_8 0.4886 31.83 6.736e-05 4.229 3 3
1433 DBF4B 17_26 0.5949 20.61 6.733e-05 -3.890 4 4
3677 NPEPL1 20_34 0.4774 34.73 6.443e-05 3.996 11 13Comparing z scores and PIPs
[1] 0.01641 genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3990 PGBD1 6_22 1.867e-02 157.22 2.841e-07 -13.087 2 3
348 APOM 6_26 2.029e-01 123.81 4.840e-05 11.541 1 1
1459 DDR1 6_26 1.524e-02 119.73 2.641e-07 -11.175 2 2
464 ATAT1 6_24 2.265e-02 79.41 3.868e-07 11.039 1 1
761 C6orf136 6_24 4.367e-02 79.12 1.433e-06 -11.031 2 2
2059 FLOT1 6_24 1.053e-01 77.80 8.177e-06 -10.981 6 6
554 BAG6 6_26 5.880e-04 108.10 3.549e-10 -10.247 5 7
4657 RNF5 6_26 6.016e-05 96.37 3.312e-12 -9.714 1 1
932 CCHCR1 6_26 8.126e-10 89.72 5.578e-22 -9.376 8 12
3729 NT5C2 10_66 4.472e-01 46.77 8.490e-05 -8.511 7 9
3053 MAD1L1 7_3 2.348e-01 63.35 2.501e-05 -8.215 3 3
2454 HLA-F 6_23 3.791e-02 61.03 6.413e-07 -8.066 2 3
2196 GIGYF2 2_137 3.745e-01 50.80 5.226e-05 -7.841 4 4
6514 ZSCAN26 6_22 2.206e-02 37.39 1.296e-07 7.631 4 4
6510 ZSCAN16 6_22 1.933e-02 52.88 1.201e-07 7.468 3 3
1218 COA8 14_54 8.221e-01 43.94 2.808e-04 -7.429 5 6
1513 DGKZ 11_28 6.889e-01 46.65 2.102e-04 7.216 2 2
4970 SKIV2L 6_26 2.813e-08 77.27 5.804e-19 7.101 4 5
1404 CYP2D7 22_17 5.014e-02 34.90 8.329e-07 7.071 1 2
4907 SF3B1 2_117 4.467e-01 43.83 8.117e-05 7.002 2 2GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 19Uploading 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
1 positive regulation of neuron projection development (GO:0010976) 3/88
Adjusted.P.value Genes
1 0.01675 NTRK3;DPYSL3;LRP8
[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
4 Fibrosarcoma 0.02003
10 Myocardial Infarction 0.02003
25 Fibrolamellar Hepatocellular Carcinoma 0.02003
38 Congenital Mesoblastic Nephroma 0.02003
45 MEGALENCEPHALY-POLYMICROGYRIA-POLYDACTYLY-HYDROCEPHALUS SYNDROME 1 0.02003
46 MEGALENCEPHALY-POLYMICROGYRIA-POLYDACTYLY-HYDROCEPHALUS SYNDROME 2 0.02003
40 Megalanecephaly Polymicrogyria-Polydactyly Hydrocephalus Syndrome 0.02212
30 Hemimegalencephaly 0.02901
29 Cortical Dysplasia 0.05400
42 Malformations of Cortical Development 0.05400
Ratio BgRatio
4 1/8 3/9703
10 2/8 95/9703
25 1/8 2/9703
38 1/8 2/9703
45 1/8 3/9703
46 1/8 1/9703
40 1/8 4/9703
30 1/8 6/9703
29 1/8 14/9703
42 1/8 14/9703WebGestalt 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
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] 51#significance threshold for TWAS
print(sig_thresh)[1] 4.474#number of ctwas genes
length(ctwas_genes)[1] 3#number of TWAS genes
length(twas_genes)[1] 107#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
0.007692 0.100000 #specificity
print(specificity) ctwas TWAS
0.9997 0.9855 #precision / PPV
print(precision) ctwas TWAS
0.3333 0.1215
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