SCZ - Annotation Analysis
sheng Qian
2022-4-11
Last updated: 2022-04-18
Checks: 5 2
Knit directory: cTWAS_analysis/
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brain_tissue <- list.files("/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/data/SCZ_2014_EUR/")
top_genes <- c()
all_genes <- c()
for(i in brain_tissue){
df <- readRDS(paste0("/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/data/SCZ_2014_EUR/",i,"/SCZ_",i,"_ctwas_gene_res.RDS"))
all_genes <- c(all_genes,df$genename)
df <- df[df$susie_pip>=0.8,]
top_genes <- c(top_genes,df$genename)
}
top_genes <- unique(top_genes)
all_genes <- unique(all_genes)
set.seed(2022)
random_genes <- sample(all_genes,1000)library(readxl)
Supplementary_Table_15_MAGMA <- read_excel("data/Supplementary Table 15 - MAGMA.xlsx",
sheet = "Gene Lists")
summary_known_genes_annotations <- read_excel("data/summary_known_genes_annotations.xlsx",
sheet = "SCZ")
SCHEMA_genes <- Supplementary_Table_15_MAGMA$`SCHEMA (p<0.001)`
SCHEMA_genes <- SCHEMA_genes[!is.na(SCHEMA_genes)]
ASD_genes <- Supplementary_Table_15_MAGMA$`ASD (Satterstrom et al. 2019)`
ASD_genes <- ASD_genes[!is.na(ASD_genes)]
DDD_genes <- Supplementary_Table_15_MAGMA$`DDD (Kaplanis et al. 2019)`
DDD_genes <- DDD_genes[!is.na(DDD_genes)]
PGC3_genes <- summary_known_genes_annotations$`Gene Symbol`
PGC3_genes <- PGC3_genes[!is.na(PGC3_genes)]Supplementary_Table_20_Prioritised_Genes <- read_excel("data/Supplementary Table 20 - Prioritised Genes.xlsx",
sheet = "ST20 all criteria")
SMR_genes <- Supplementary_Table_20_Prioritised_Genes[,c("Symbol.ID","SMRmap","SMRsingleGene","HI.C.SMR")]
SMR_genes["index"] <- (SMR_genes$SMRmap==1) | (SMR_genes$SMRsingleGene==1) | (SMR_genes$HI.C.SMR==1)
SMR_genes <- SMR_genes[SMR_genes$index==1,]$Symbol.IDsum(top_genes %in% SCHEMA_genes)[1] 1sum(top_genes %in% ASD_genes)[1] 0sum(top_genes %in% DDD_genes)[1] 1sum(top_genes %in% PGC3_genes)[1] 5sum(top_genes %in% setdiff(PGC3_genes,SMR_genes))[1] 0sum(random_genes %in% SCHEMA_genes)[1] 3sum(random_genes %in% ASD_genes)[1] 7sum(random_genes %in% DDD_genes)[1] 14sum(random_genes %in% PGC3_genes)[1] 6sum(random_genes %in% setdiff(PGC3_genes,SMR_genes))[1] 4fisher.test(matrix(c(sum(top_genes %in% SCHEMA_genes),length(top_genes)-sum(top_genes %in% SCHEMA_genes),sum(random_genes %in% SCHEMA_genes),1000-sum(random_genes %in% SCHEMA_genes)),ncol=2))
Fisher's Exact Test for Count Data
data:
p-value = 0.1354
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.1711393 117.4160714
sample estimates:
odds ratio
9.175783 fisher.test(matrix(c(sum(top_genes %in% ASD_genes),length(top_genes)-sum(top_genes %in% ASD_genes),sum(random_genes %in% ASD_genes),1000-sum(random_genes %in% ASD_genes)),ncol=2))
Fisher's Exact Test for Count Data
data:
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.00000 19.30366
sample estimates:
odds ratio
0 fisher.test(matrix(c(sum(top_genes %in% DDD_genes),length(top_genes)-sum(top_genes %in% DDD_genes),sum(random_genes %in% DDD_genes),1000-sum(random_genes %in% DDD_genes)),ncol=2))
Fisher's Exact Test for Count Data
data:
p-value = 0.4223
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.04503188 13.53902640
sample estimates:
odds ratio
1.954633 fisher.test(matrix(c(sum(top_genes %in% PGC3_genes),length(top_genes)-sum(top_genes %in% PGC3_genes),sum(random_genes %in% PGC3_genes),1000-sum(random_genes %in% PGC3_genes)),ncol=2))
Fisher's Exact Test for Count Data
data:
p-value = 1.739e-05
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
5.852185 106.928166
sample estimates:
odds ratio
25.56233 fisher.test(matrix(c(sum(top_genes %in% setdiff(PGC3_genes,SMR_genes)),length(top_genes)-sum(top_genes %in% setdiff(PGC3_genes,SMR_genes)),sum(random_genes %in% setdiff(PGC3_genes,SMR_genes)),1000-sum(random_genes %in% setdiff(PGC3_genes,SMR_genes))),ncol=2))
Fisher's Exact Test for Count Data
data:
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.0000 41.8512
sample estimates:
odds ratio
0 library(enrichR)Welcome to enrichR
Checking connection ... Enrichr ... Connection is Live!
FlyEnrichr ... Connection is available!
WormEnrichr ... Connection is available!
YeastEnrichr ... Connection is available!
FishEnrichr ... Connection is available!dbs <- c("GO_Biological_Process_2021", "GO_Cellular_Component_2021", "GO_Molecular_Function_2021")
if (length(top_genes)>0){
GO_enrichment <- enrichr(top_genes, dbs)
for (db in dbs){
print(db)
df <- GO_enrichment[[db]]
print(plotEnrich(GO_enrichment[[db]]))
df <- df[df$Adjusted.P.value<0.05,c("Term", "Overlap", "Adjusted.P.value", "Genes")]
print(df)
}
}Uploading 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 Adjusted.P.value
1 mitochondrial tRNA processing (GO:0090646) 2/8 0.02165807
2 signal peptide processing (GO:0006465) 2/11 0.02165807
Genes
1 ELAC2;TRIT1
2 SPCS1;FURIN
[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) Description FDR Ratio
68 Amaurosis hypertrichosis 0.01183261 1/14
69 Familial encephalopathy with neuroserpin inclusion bodies 0.01183261 1/14
72 Cone rod dystrophy amelogenesis imperfecta 0.01183261 1/14
75 Jalili syndrome 0.01183261 1/14
76 PROSTATE CANCER, HEREDITARY, 2 0.01183261 1/14
77 SPASTIC PARAPLEGIA 53, AUTOSOMAL RECESSIVE 0.01183261 1/14
79 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17 0.01183261 1/14
80 SPASTIC PARAPLEGIA 45, AUTOSOMAL RECESSIVE 0.01183261 1/14
81 HYPOGONADOTROPIC HYPOGONADISM 22 WITH OR WITHOUT ANOSMIA 0.01183261 1/14
82 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 35 0.01183261 1/14
BgRatio
68 1/9703
69 1/9703
72 1/9703
75 1/9703
76 1/9703
77 1/9703
79 1/9703
80 1/9703
81 1/9703
82 1/9703
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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] disgenet2r_0.99.2 enrichR_3.0 readxl_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] tidyselect_1.1.1 xfun_0.29 reshape2_1.4.4 purrr_0.3.4
[5] colorspace_2.0-2 vctrs_0.3.8 generics_0.1.1 htmltools_0.5.2
[9] yaml_2.2.1 utf8_1.2.2 rlang_1.0.1 later_0.8.0
[13] pillar_1.6.4 jquerylib_0.1.4 glue_1.6.2 DBI_1.1.2
[17] plyr_1.8.6 lifecycle_1.0.1 stringr_1.4.0 munsell_0.5.0
[21] gtable_0.3.0 cellranger_1.1.0 evaluate_0.14 labeling_0.4.2
[25] knitr_1.36 callr_3.7.0 fastmap_1.1.0 httpuv_1.5.1
[29] ps_1.6.0 curl_4.3.2 fansi_1.0.2 highr_0.9
[33] Rcpp_1.0.8 promises_1.0.1 scales_1.1.1 jsonlite_1.7.2
[37] farver_2.1.0 fs_1.5.2 rjson_0.2.20 ggplot2_3.3.5
[41] digest_0.6.29 stringi_1.7.6 processx_3.5.2 dplyr_1.0.7
[45] getPass_0.2-2 rprojroot_2.0.2 grid_3.6.1 cli_3.1.0
[49] tools_3.6.1 magrittr_2.0.2 tibble_3.1.6 crayon_1.5.0
[53] whisker_0.3-2 pkgconfig_2.0.3 ellipsis_0.3.2 assertthat_0.2.1
[57] rmarkdown_2.11 httr_1.4.2 rstudioapi_0.13 R6_2.5.1
[61] git2r_0.26.1 compiler_3.6.1