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initial_benchmark_framework_ukb10K_streamsnps.Rmd
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initial_benchmark_framework_ukb10K_streamsnps.Rmd
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---
title: "Initial Benchmark Framework"
output:
html_notebook:
toc: true
---
```{r setup}
source("project_settings.R")
source("functions.R")
```
```{r}
stream_snps_20 <- NA
stream_snps_100 <- NA
stream_snps_200 <- NA
```
December 28, 2019
https://github.com/large-scale-gxe-methods/gem-workflow/blob/master/gem_workflow.wdl
# Current WDL
Note, in google cloud, the size of the local-disk is ignored. 'local-disk' is the best option for I/O intensive applications.
```
task run_tests {
File genofile
Float? maf = 0.001
File? samplefile
File phenofile
String sample_id_header
String outcome
Boolean binary_outcome
String covar_headers
String int_covar_num
String? delimiter = ","
String? missing = "NaN"
Boolean robust
Int? stream_snps = 20
Float? tol = 0.000001
Int? memory = 10
Int? cpu = 4
Int? disk = 20
String pheno = if binary_outcome then "1" else "0"
String robust01 = if robust then "1" else "0"
command {
echo -e "SAMPLE_ID_HEADER\n${sample_id_header}\n"\
"PHENOTYPE\n${pheno}\n"\
"PHENO_HEADER\n${outcome}\n"\
"COVARIATES_HEADERS\n${covar_headers}\n"\
"MISSING\n${missing}\n"\
"ROBUST\n${robust01}\n"\
"STREAM_SNPS\n${stream_snps}\n"\
"NUM_OF_INTER_COVARIATE\n${int_covar_num}\n"\
"LOGISTIC_CONVERG_TOL\n${tol}\n"\
"DELIMINATOR\n${delimiter}\n"\
"GENO_FILE_PATH\n${genofile}\n"\
"PHENO_FILE_PATH\n${phenofile}\n"\
"SAMPLE_FILE_PATH\n${samplefile}\n"\
"OUTPUT_PATH\ngem_res"\
> GEM_Input.param
echo "" > resource_usage.log
dstat -c -d -m --nocolor 10 1>>resource_usage.log &
/GEM/GEM -param GEM_Input.param -maf ${maf}
}
runtime {
docker: "quay.io/large-scale-gxe-methods/gem-workflow"
memory: "${memory} GB"
cpu: "${cpu}"
disks: "local-disk ${disk} HDD"
}
output {
File param_file = "GEM_Input.param"
File out = "gem_res"
File resource_usage = "resource_usage.log"
}
}
```
# UK Biobank - chromosome 22 - subset 10K
https://app.terra.bio/#workspaces/largescale-gxe-implementation/ukb_gene_diet/job_history/09122736-c836-4add-9e6f-b1e20887dd4b
## Stream SNPs: 20
https://job-manager.dsde-prod.broadinstitute.org/jobs/9e273f41-4163-4b51-abf7-1de03a03ad5b
```
****************************************************************************
Starting GWAS.
Streaming SNPs for speeding up GWAS analysis in parallel.
Number of SNPs in each batch is: 20
*********************************************************
Total Wall Time = 259.916 Seconds
Total CPU Time = 530.214 Seconds
Execution Wall Time = 41.0762 Seconds
*********************************************************
```
```{r fig.height=8,fig.width=10}
stream_snps_20 <- do_plots(path_to_workflow = "gs://fc-secure-006196ce-f92c-4096-80cf-80a7277524dc/09122736-c836-4add-9e6f-b1e20887dd4b/run_GEM/9e273f41-4163-4b51-abf7-1de03a03ad5b/call-run_tests",
cpus=8,
disk="25G",
memory="16G",
maf=0.005,
stream_snps=20,
run_tests_runtime="0h 7m",
plot_description="UK Biobank Chr22 - subset 10K\nlargescale-gxe-implementation/ukb_gene_diet\nExploring stream_snps\nSubmitted:Dec 30, 2019, 5:20 PM",plot_data=stream_snps_20)
```
## Stream SNPs: 100
https://job-manager.dsde-prod.broadinstitute.org/jobs/c93d16a0-6806-4ac9-8bfa-c273ddd36595
```
****************************************************************************
Starting GWAS.
Streaming SNPs for speeding up GWAS analysis in parallel.
Number of SNPs in each batch is: 100
*********************************************************
Total Wall Time = 265.331 Seconds
Total CPU Time = 547.377 Seconds
Execution Wall Time = 45.17 Seconds
*********************************************************
```
```{r fig.height=8,fig.width=10}
stream_snps_100 <- do_plots(path_to_workflow = "gs://fc-secure-006196ce-f92c-4096-80cf-80a7277524dc/09122736-c836-4add-9e6f-b1e20887dd4b/run_GEM/c93d16a0-6806-4ac9-8bfa-c273ddd36595/call-run_tests",
cpus=8,
disk="25G",
memory="16G",
maf=0.005,
stream_snps=100,
run_tests_runtime="0h 8m",
plot_description="UK Biobank Chr22 - subset 10K\nlargescale-gxe-implementation/ukb_gene_diet\nExploring stream_snps\nSubmitted:Dec 30, 2019, 5:20 PM",plot_data = stream_snps_100)
```
## Stream SNPs: 200
https://job-manager.dsde-prod.broadinstitute.org/jobs/f885d047-526d-4976-a672-a088285cf12d
```
****************************************************************************
Starting GWAS.
Streaming SNPs for speeding up GWAS analysis in parallel.
Number of SNPs in each batch is: 200
*********************************************************
Total Wall Time = 285.098 Seconds
Total CPU Time = 616.202 Seconds
Execution Wall Time = 58.2076 Seconds
*********************************************************
```
```{r fig.height=8,fig.width=10}
stream_snps_200 <- do_plots(path_to_workflow = "gs://fc-secure-006196ce-f92c-4096-80cf-80a7277524dc/09122736-c836-4add-9e6f-b1e20887dd4b/run_GEM/f885d047-526d-4976-a672-a088285cf12d/call-run_tests",
cpus=8,
disk="25G",
memory="16G",
maf=0.005,
stream_snps=200,
run_tests_runtime="0h 9m",
plot_description="UK Biobank Chr22 - subset 10K\nlargescale-gxe-implementation/ukb_gene_diet\nExploring stream_snps\nSubmitted:Dec 30, 2019, 5:20 PM",plot_data = stream_snps_200)
```
# Explore the performance impact of covariates
```{r}
stream_20_5PCs <- NA
stream_20_noPCs <- NA
stream_100_5PCs <- NA
stream_100_noPCs <- NA
```
## Stream 20, 5 PCs
https://job-manager.dsde-prod.broadinstitute.org/jobs/ad25d213-e3ae-447e-ac5f-9854b8e66d50
```{r fig.height=8,fig.width=10}
stream_20_5PCs <- do_plots(path_to_workflow = "gs://fc-secure-006196ce-f92c-4096-80cf-80a7277524dc/d45966a9-b909-4935-b0ab-7b68cbae55f8/run_GEM/ad25d213-e3ae-447e-ac5f-9854b8e66d50/call-run_tests",
cpus=8,
disk="25G",
memory="16G",
maf=0.005,
stream_snps=20,
run_tests_runtime="0h 10m",
plot_description="UK Biobank Chr22 - subset 10K\nlargescale-gxe-implementation/ukb_gene_diet\nExploring stream_snps & covariates\nSubmitted:January 2, 2020, 1:43 PM",plot_data=stream_20_5PCs)
```
## Stream 20 No PCs
https://job-manager.dsde-prod.broadinstitute.org/jobs/3834fecb-b154-457b-a1d7-21f8199103bb
```{r fig.height=8,fig.width=10}
stream_20_noPCs <- do_plots(path_to_workflow = "gs://fc-secure-006196ce-f92c-4096-80cf-80a7277524dc/d45966a9-b909-4935-b0ab-7b68cbae55f8/run_GEM/3834fecb-b154-457b-a1d7-21f8199103bb/call-run_tests",
cpus=8,
disk="25G",
memory="16G",
maf=0.005,
stream_snps=20,
run_tests_runtime="0h 6m",
plot_description="UK Biobank Chr22 - subset 10K\nlargescale-gxe-implementation/ukb_gene_diet\nExploring stream_snps & covariates\nSubmitted:January 2, 2020, 1:43 PM",plot_data=stream_20_noPCs)
```
## Stream 100 5 PCs
https://job-manager.dsde-prod.broadinstitute.org/jobs/f13761d8-78ce-41c3-b922-776049cabfc5
```{r fig.height=8,fig.width=10}
stream_100_5PCs <- do_plots(path_to_workflow = "gs://fc-secure-006196ce-f92c-4096-80cf-80a7277524dc/d45966a9-b909-4935-b0ab-7b68cbae55f8/run_GEM/f13761d8-78ce-41c3-b922-776049cabfc5/call-run_tests",
cpus=8,
disk="25G",
memory="16G",
maf=0.005,
stream_snps=100,
run_tests_runtime="0h 7m",
plot_description="UK Biobank Chr22 - subset 10K\nlargescale-gxe-implementation/ukb_gene_diet\nExploring stream_snps & covariates\nSubmitted:January 2, 2020, 1:43 PM",plot_data=stream_100_5PCs)
```
## Stream 100 No PCs
https://job-manager.dsde-prod.broadinstitute.org/jobs/04453f06-d4c9-4ad1-ad7e-a800bbbf166e
```{r fig.height=8,fig.width=10}
stream_100_noPCs <- do_plots(path_to_workflow = "gs://fc-secure-006196ce-f92c-4096-80cf-80a7277524dc/d45966a9-b909-4935-b0ab-7b68cbae55f8/run_GEM/04453f06-d4c9-4ad1-ad7e-a800bbbf166e/call-run_tests",
cpus=8,
disk="25G",
memory="16G",
maf=0.005,
stream_snps=100,
run_tests_runtime="0h 8m",
plot_description="UK Biobank Chr22 - subset 10K\nlargescale-gxe-implementation/ukb_gene_diet\nExploring stream_snps & covariates\nSubmitted:January 2, 2020, 1:43 PM",plot_data=stream_100_noPCs)
```
```{r}
save(stream_snps_20,stream_snps_100,stream_snps_200, stream_20_5PCs,stream_20_noPCs,stream_100_5PCs,stream_100_noPCs, file="initial_benchmark_framework_ukb10K_streamsnps.RData")
```