SCZ - Brain Cortex
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] 23372#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2106 1670 1401 900 973 1205 1349 834 978 1043 1384 1297 477 810 795 919
17 18 19 20 21 22
1718 307 1661 776 45 724 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 20390#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8724finish
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
0.0082338 0.0003052 gene snp
10.48 10.44 [1] 105318[1] 7921 6309950 gene snp
0.006487 0.190885 [1] 0.02274 1.09222Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3637 LRP8 1_33 1.2198 32.31 0.0003555 -5.003 10 10
6207 SLC8B1 12_68 1.2142 22.26 0.0002788 -4.047 10 10
3503 LINC00320 21_6 1.0957 28.55 0.0003103 5.336 5 5
5376 R3HDM2 12_36 1.0921 42.83 0.0004461 -6.634 7 9
2336 FAM177A1 14_9 0.9825 23.31 0.0001838 4.849 11 14
3423 LAMA5 20_36 0.9539 28.66 0.0002078 -4.269 16 21
4682 PAK6 15_14 0.9137 29.77 0.0002303 5.588 3 3
983 CAMKK2 12_74 0.9028 35.27 0.0002014 4.060 8 10
5328 PTPRF 1_27 0.8882 35.71 0.0002541 6.680 6 6
2665 GIGYF1 7_62 0.8820 26.71 0.0001958 -5.266 3 3
1600 CRTAP 3_24 0.8650 19.74 0.0001395 3.929 2 2
592 ATP2B2 3_8 0.8284 25.09 0.0001526 4.229 5 7
5364 PYROXD2 10_62 0.8166 23.48 0.0001259 3.755 11 15
1165 CD46 1_105 0.8156 19.39 0.0001137 -3.933 11 14
6316 SNRPA1 15_50 0.8033 20.88 0.0001262 -4.098 2 3
1899 DNAJB1 19_12 0.7951 20.11 0.0001097 4.009 5 7
4540 NTRK3 15_41 0.7867 23.89 0.0001304 4.457 3 3
317 ANAPC7 12_67 0.7853 39.18 0.0002002 6.385 6 6
115 ACTR1B 2_57 0.7830 20.03 0.0001145 3.978 5 5
4465 NPIPB14P 16_37 0.7707 18.23 0.0000945 3.742 13 17Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
5376 R3HDM2 12_36 1.0921 42.83 0.0004461 -6.634 7 9
3637 LRP8 1_33 1.2198 32.31 0.0003555 -5.003 10 10
3503 LINC00320 21_6 1.0957 28.55 0.0003103 5.336 5 5
6207 SLC8B1 12_68 1.2142 22.26 0.0002788 -4.047 10 10
666 BAG6 6_26 0.2068 637.42 0.0002588 11.590 9 9
417 APOM 6_26 0.2068 637.42 0.0002588 11.590 2 2
5328 PTPRF 1_27 0.8882 35.71 0.0002541 6.680 6 6
4682 PAK6 15_14 0.9137 29.77 0.0002303 5.588 3 3
1465 COA8 14_54 0.7266 43.77 0.0002146 7.429 4 7
5969 SF3B1 2_117 0.7200 45.12 0.0002121 7.053 3 3
3423 LAMA5 20_36 0.9539 28.66 0.0002078 -4.269 16 21
983 CAMKK2 12_74 0.9028 35.27 0.0002014 4.060 8 10
317 ANAPC7 12_67 0.7853 39.18 0.0002002 6.385 6 6
2665 GIGYF1 7_62 0.8820 26.71 0.0001958 -5.266 3 3
3191 IRF3 19_34 0.7132 39.57 0.0001907 -6.461 2 2
1825 DGKZ 11_28 0.6445 46.77 0.0001845 7.216 2 2
2336 FAM177A1 14_9 0.9825 23.31 0.0001838 4.849 11 14
256 AKT3 1_128 0.7349 34.40 0.0001688 -6.291 4 4
2616 GATAD2A 19_15 0.6131 46.80 0.0001634 -6.668 5 5
592 ATP2B2 3_8 0.8284 25.09 0.0001526 4.229 5 7Comparing z scores and PIPs
[1] 0.01944 genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
4849 PGBD1 6_22 6.697e-02 158.26 2.166e-06 -13.087 3 5
417 APOM 6_26 2.068e-01 637.42 2.588e-04 11.590 2 2
666 BAG6 6_26 2.068e-01 637.42 2.588e-04 11.590 9 9
7388 VARS1 6_26 1.246e-01 638.44 9.411e-05 -11.548 1 1
1767 DDR1 6_25 1.135e-01 101.67 1.195e-05 -11.175 2 2
912 C6orf136 6_24 8.036e-02 79.81 4.893e-06 11.031 2 2
2492 FLOT1 6_24 1.604e-01 78.48 1.914e-05 10.981 5 6
811 BTN3A2 6_20 8.836e-02 91.92 4.950e-06 -10.759 5 5
808 BTN2A1 6_20 1.160e-01 83.45 4.270e-06 10.185 6 7
5168 PPT2 6_26 9.858e-12 474.58 4.379e-25 -10.061 5 5
2079 EGFL8 6_26 4.045e-12 473.96 7.332e-26 10.036 6 7
5236 PRRT1 6_26 3.440e-12 472.86 5.315e-26 -10.018 1 1
5674 RNF5 6_26 7.171e-13 467.32 2.282e-27 -9.714 1 1
2785 GPSM3 6_26 2.278e-13 424.06 2.090e-28 9.377 2 2
1121 CCHCR1 6_25 9.482e-02 62.94 2.689e-06 -9.376 11 15
7913 ZSCAN31 6_22 3.256e-02 55.91 2.849e-07 9.321 3 4
2960 HLA-DMB 6_27 4.018e-02 68.96 8.983e-07 8.860 2 2
7910 ZSCAN23 6_22 1.033e-02 45.88 4.652e-08 -8.541 1 1
4526 NT5C2 10_66 5.965e-01 47.55 1.509e-04 -8.511 11 15
7699 ZNF192P1 6_22 9.360e-03 51.75 4.305e-08 8.024 1 2GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 79Uploading 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 Overlap Adjusted.P.value
1 cadherin binding (GO:0045296) 7/322 0.02739
Genes
1 CAST;DNAJB1;DLG1;PDXDC1;PAK6;CD46;KTN1DisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio
38 Measles 0.03514 1/43
73 Congenital absent nipple 0.03514 1/43
117 Congenital absence of breast with absent nipple 0.03514 1/43
155 Sporadic Breast Carcinoma 0.03514 1/43
159 Primary peritoneal carcinoma 0.03514 1/43
167 Osteogenesis Imperfecta Type VII 0.03514 1/43
168 Familial encephalopathy with neuroserpin inclusion bodies 0.03514 1/43
173 Retinitis Pigmentosa 46 0.03514 1/43
174 BREAST-OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.03514 1/43
175 BREAST CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.03514 1/43
BgRatio
38 1/9703
73 1/9703
117 1/9703
155 1/9703
159 1/9703
167 1/9703
168 1/9703
173 1/9703
174 1/9703
175 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: 42 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] 55#significance threshold for TWAS
print(sig_thresh)[1] 4.516#number of ctwas genes
length(ctwas_genes)[1] 15#number of TWAS genes
length(twas_genes)[1] 154#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
592 ATP2B2 3_8 0.8284 25.09 0.0001526 4.229 5 7
983 CAMKK2 12_74 0.9028 35.27 0.0002014 4.060 8 10
1165 CD46 1_105 0.8156 19.39 0.0001137 -3.933 11 14
1600 CRTAP 3_24 0.8650 19.74 0.0001395 3.929 2 2
3423 LAMA5 20_36 0.9539 28.66 0.0002078 -4.269 16 21
5364 PYROXD2 10_62 0.8166 23.48 0.0001259 3.755 11 15
6207 SLC8B1 12_68 1.2142 22.26 0.0002788 -4.047 10 10
6316 SNRPA1 15_50 0.8033 20.88 0.0001262 -4.098 2 3#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.11538 #specificity
print(specificity) ctwas TWAS
0.9985 0.9823 #precision / PPV
print(precision) ctwas TWAS
0.2000 0.0974
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