Last updated: 2020-04-17
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Knit directory: bigquery-covid19/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 2199019 | Hae Kyung Im | 2020-04-17 | built |
| html | 2199019 | Hae Kyung Im | 2020-04-17 | built |
| Rmd | 19c2011 | Hae Kyung Im | 2020-04-17 | rmd querying bigquery and postgres |
| Rmd | 7fb3256 | Hae Kyung Im | 2020-04-16 | bigqueries |
| Rmd | 3bb3017 | Hae Kyung Im | 2020-04-16 | edits |
| html | 3bb3017 | Hae Kyung Im | 2020-04-16 | edits |
| Rmd | b96fa1c | Hae Kyung Im | 2020-04-16 | query tables |
## install.packages("bigrquery")
## suppressPackageStartupMessages(library(bigrquery))
library(tidyverse)── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──✓ ggplot2 3.3.0 ✓ purrr 0.3.3
✓ tibble 3.0.0 ✓ dplyr 0.8.5
✓ tidyr 1.0.2 ✓ stringr 1.4.0
✓ readr 1.3.1 ✓ forcats 0.5.0── Conflicts ────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()## connect tp phenomexcan postgres database, define querying functions
suppressPackageStartupMessages(source("code/helpers.R", chdir = TRUE))
phenomexcan_con <- get_db()
dbListTables(phenomexcan_con)[1] "clinvar_phenotypes" "genes" "phenotypes"
[4] "smultixcan" "spredixcan" "tissues"
[7] "ukb_clinvar" ## connect to dapg dataset
dapg_dataset = "GTEx_V8_DAPG"
dapg_con <- DBI::dbConnect(
bigrquery::bigquery(),
project = "gtex-awg-im",
dataset = dapg_dataset,
billing = "gtex-awg-im")
##
dbListTables(dapg_con)Using an auto-discovered, cached token.
To suppress this message, modify your code or options to clearly consent to the use of a cached token.
See gargle's "Non-interactive auth" vignette for more details:
https://gargle.r-lib.org/articles/non-interactive-auth.html
The bigrquery package is using a cached token for imlab2015@gmail.com.Auto-refreshing stale OAuth token. [1] "clusters_eqtl" "clusters_eqtl_eur"
[3] "clusters_sqtl" "model_summary_eqtl_eur"
[5] "model_variants_eqtl_eur" "models_eqtl"
[7] "models_eqtl_eur" "variants_pip_eqtl"
[9] "variants_pip_eqtl_eur" "variants_pip_sqtl" ## connect to gtex-awg-im:annotations.gencode_v26_all
gencode_con <- DBI::dbConnect(
bigrquery::bigquery(),
project = "gtex-awg-im",
dataset = "annotations",
billing = "gtex-awg-im")
gencode_tbl <- tbl(gencode_con, "gencode_v26_all")
gencode_df <- gencode_tbl %>% collect()
## connect to prediction weights gtex-awg-im:GTEx_V8_PF_MASHR_EUR_v1.weights_eqtl
mashrpred_con <- DBI::dbConnect(
bigrquery::bigquery(),
project = "gtex-awg-im",
dataset = "GTEx_V8_PF_MASHR_EUR_v1",
billing = "gtex-awg-im")
##
dbListTables(mashrpred_con) [1] "extra_eqtl" "extra_sqtl"
[3] "smultixcan_eqtl" "smultixcan_eqtl_count"
[5] "smultixcan_sqtl" "smultixcan_sqtl_count"
[7] "spredixcan_eqtl" "spredixcan_eqtl_count"
[9] "spredixcan_eqtl_hn" "spredixcan_eqtl_hn_count"
[11] "spredixcan_eqtl_hq" "spredixcan_eqtl_hq_count"
[13] "spredixcan_sqtl" "spredixcan_sqtl_count"
[15] "weights_eqtl" "weights_sqtl" weights_tbl = tbl(mashrpred_con,"weights_eqtl")enslist = c("ENSG00000184012", "ENSG00000132842","ENSG00000130234")
## using the table from phenomexcan (sex chr missing)
genes_tbl <- tbl(phenomexcan_con,"genes")
colnames(genes_tbl)[1] "id" "gene_id" "gene_id_version" "gene_name"
[5] "gene_type" "band" genes_tbl %>% filter(gene_id %in% enslist) %>% collect() %>% .[["gene_name"]][1] "AP3B1" "TMPRSS2"## using gencode table
colnames(gencode_tbl)[1] "chromosome" "start_location" "end_location" "feature_type"
[5] "strand" "gene_id" "gene_name" "gene_type" gencode_tbl %>% filter(substr(gene_id,1,15) %in% enslist) %>% collect() %>% .[["gene_name"]][1] "AP3B1" "TMPRSS2" "ACE2" input = list()
input$gene_name = c("TMPRSS2", "AP3B1")
input$limit = 100
tempo <- get_results_from_data_db(input)results:
SELECT
p.unique_description as phenotype, p.source as phenotype_source, g.gene_name, g.band as gene_band,
sm.pvalue, sm.rcp,
(case when sm.dir_effect_most_signif = 1 then '+'
when sm.dir_effect_most_signif = -1 then '-'
else '' end) as best_sign,
(case when sm.dir_effect_consensus = 1 then '+'
when sm.dir_effect_consensus = -1 then '-'
else '' end) as consensus_sign,
sm.n as n_tissues, sm.n_indep
FROM smultixcan sm inner join phenotypes p on (sm.pheno_id = p.id) inner join genes g on (sm.gene_num_id = g.id)
WHERE 1=1
AND g.gene_name IN (values ('TMPRSS2'), ('AP3B1'))
ORDER BY sm.pvalue
LIMIT 100Query completedhead(tempo) phenotype phenotype_source gene_name
1 Sitting height UK Biobank AP3B1
2 Leg fat-free mass (left) UK Biobank AP3B1
3 Standing height UK Biobank AP3B1
4 Leg predicted mass (left) UK Biobank AP3B1
5 Forced vital capacity (FVC), Best measure UK Biobank AP3B1
6 Forced vital capacity (FVC) UK Biobank AP3B1
gene_band pvalue rcp best_sign consensus_sign n_tissues n_indep
1 5q14.1 5.3877e-37 0.0891 - - 19 5
2 5q14.1 8.0496e-26 0.1424 - - 19 5
3 5q14.1 9.8699e-26 0.2496 - - 19 5
4 5q14.1 1.0095e-25 0.1397 - - 19 5
5 5q14.1 1.9335e-25 0.3197 - - 19 5
6 5q14.1 3.2199e-25 0.3423 - - 19 5input = list()
input$sp_gene_name = "TMPRSS2"
input$sp_limit = 100
if(T) tempop <- get_sp_results_from_data_db(input)results:
SELECT
p.unique_description as phenotype, p.source as phenotype_source, t.name as tissue_name, g.gene_name, g.band as gene_band,
sp.pvalue, sp.zscore, sp.effect_size
FROM spredixcan sp inner join phenotypes p on (sp.pheno_id = p.id)
inner join tissues t on (sp.tissue_id = t.id)
inner join genes g on (sp.gene_num_id = g.id)
WHERE 1=1
AND g.gene_name IN (values ('TMPRSS2'))
ORDER BY sp.pvalue
LIMIT 100Query completedhead(tempop) phenotype phenotype_source
1 C_MALE_GENITAL UK Biobank
2 malignant neoplasm of male genital organs UK Biobank
3 Malignant neoplasm of prostate UK Biobank
4 Diagnoses - main ICD10: J43 Emphysema UK Biobank
5 Diagnoses - main ICD10: C61 Malignant neoplasm of prostate UK Biobank
6 Diagnoses - main ICD10: C61 Malignant neoplasm of prostate UK Biobank
tissue_name gene_name gene_band pvalue zscore effect_size
1 Prostate TMPRSS2 21q22.3 8.6656e-08 5.3527 0.0138880
2 Prostate TMPRSS2 21q22.3 8.6656e-08 5.3527 0.0138880
3 Prostate TMPRSS2 21q22.3 1.3051e-07 5.2781 0.0132060
4 Lung TMPRSS2 21q22.3 5.6411e-07 5.0031 0.0022190
5 Prostate TMPRSS2 21q22.3 4.0875e-05 4.1025 0.0088149
6 Esophagus_Mucosa TMPRSS2 21q22.3 5.0152e-05 -4.0549 -0.3784200tbl(phenomexcan_con,"genes") %>% tally()# Source: lazy query [?? x 1]
# Database: postgres [metaxcan_ro@35.202.243.187:5432/phenomexcan]
n
<int64>
1 22515 ## TODO write function genename2ensid and ensid2genename
genelist = c("TMPRSS2", "AP3B1","ACE2")
ensverlist = gencode_df %>% filter(gene_name %in% genelist) %>% collect() %>% .[["gene_id"]]
dbListTables(dapg_con) [1] "clusters_eqtl" "clusters_eqtl_eur"
[3] "clusters_sqtl" "model_summary_eqtl_eur"
[5] "model_variants_eqtl_eur" "models_eqtl"
[7] "models_eqtl_eur" "variants_pip_eqtl"
[9] "variants_pip_eqtl_eur" "variants_pip_sqtl" dapg_pip_tbl <- tbl(dapg_con,"variants_pip_eqtl")
tempof <- dapg_pip_tbl %>% filter(gene %in% ensverlist) %>% collect()
tempof <- tempof %>% left_join(gencode_df %>% select(gene_id,gene_name),by=c("gene"="gene_id"))
head(tempof)# A tibble: 6 x 8
tissue gene rank variant_id pip log10_abvf cluster_id gene_name
<chr> <chr> <int> <chr> <dbl> <dbl> <int> <chr>
1 Lung ENSG00000… 95 chr5_78141447… 2.04e-3 3.23 1 AP3B1
2 Lung ENSG00000… 2 chr5_78089857… 3.83e-2 4.50 1 AP3B1
3 Lung ENSG00000… 79 chr5_78050844… 3.01e-3 3.40 1 AP3B1
4 Lung ENSG00000… 39 chr5_78242771… 6.25e-3 2.94 1 AP3B1
5 Lung ENSG00000… 107 chr5_78053912… 1.94e-3 3.22 1 AP3B1
6 Lung ENSG00000… 155 chr5_78192860… 7.68e-4 2.81 1 AP3B1 genelist = c("TMPRSS2", "AP3B1")
ensidverlist = gencode_df %>% filter(gene_name %in% genelist) %>% collect() %>% .[["gene_id"]]
tempow <- weights_tbl %>% filter(gene %in% ensverlist) %>% collect()
tempow <- tempow %>% left_join(gencode_df %>% select(gene_id,gene_name),by=c("gene"="gene_id"))
head(tempow)# A tibble: 6 x 8
gene rsid varID ref_allele eff_allele weight tissue gene_name
<chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
1 ENSG0000… rs3507… chr21_414… AC A 0.115 Lung TMPRSS2
2 ENSG0000… rs7881… chr5_7828… A C -0.0278 Brain_C… AP3B1
3 ENSG0000… rs7881… chr5_7828… A C -0.0631 Muscle_… AP3B1
4 ENSG0000… rs9293… chr5_7820… A C 0.116 Whole_B… AP3B1
5 ENSG0000… rs7390… chr21_415… A G -0.00317 Esophag… TMPRSS2
6 ENSG0000… rs7390… chr21_415… A G 0.00957 Spleen TMPRSS2 # server <- function(input, output, session) {
# updateSelectizeInput(session = session, inputId = 'gene_name', choices = c(genes_), server = TRUE)
# updateSelectizeInput(session = session, inputId = 'sp_gene_name', choices = c(genes_), server = TRUE)
# updateSelectizeInput(session = session, inputId = 'pheno', choices = c(phenotypes_), server = TRUE)
# updateSelectizeInput(session = session, inputId = 'sp_pheno', choices = c(phenotypes_), server = TRUE)
# updateSelectizeInput(session = session, inputId = 'sp_tissue', choices = c(tissues_), server = TRUE)
# updateSelectizeInput(session = session, inputId = 'uc_ukb_trait', choices = c(phenotypes_), server = TRUE)
# updateSelectizeInput(session = session, inputId = 'uc_clinvar_trait', choices = c(clinvar_phenotypes_), server = TRUE)
#
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.2
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] RPostgres_1.2.0 forcats_0.5.0 stringr_1.4.0 dplyr_0.8.5
[5] purrr_0.3.3 readr_1.3.1 tidyr_1.0.2 tibble_3.0.0
[9] ggplot2_3.3.0 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 lubridate_1.7.8 lattice_0.20-41 utf8_1.1.4
[5] assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.25 R6_2.4.1
[9] cellranger_1.1.0 backports_1.1.6 reprex_0.3.0 evaluate_0.14
[13] httr_1.4.1 pillar_1.4.3 rlang_0.4.5 curl_4.3
[17] readxl_1.3.1 rstudioapi_0.11 whisker_0.4 blob_1.2.1
[21] rmarkdown_2.1 bit_1.1-15.2 munsell_0.5.0 broom_0.5.5
[25] compiler_3.6.3 httpuv_1.5.2 modelr_0.1.6 xfun_0.13
[29] askpass_1.1 pkgconfig_2.0.3 htmltools_0.4.0 openssl_1.4.1
[33] tidyselect_1.0.0 workflowr_1.6.1 fansi_0.4.1 crayon_1.3.4
[37] dbplyr_1.4.2 withr_2.1.2 later_1.0.0 grid_3.6.3
[41] bigrquery_1.2.0 nlme_3.1-147 jsonlite_1.6.1 gtable_0.3.0
[45] lifecycle_0.2.0 DBI_1.1.0 git2r_0.26.1 magrittr_1.5
[49] scales_1.1.0 cli_2.0.2 stringi_1.4.6 fs_1.4.1
[53] promises_1.1.0 xml2_1.3.1 ellipsis_0.3.0 generics_0.0.2
[57] vctrs_0.2.4 tools_3.6.3 bit64_0.9-7 glue_1.4.0
[61] hms_0.5.3 yaml_2.2.1 gargle_0.4.0 colorspace_1.4-1
[65] rvest_0.3.5 knitr_1.28 haven_2.2.0