raw_scripts/README.md
In thmourikis/sysSVM: sysSVM
This directory was created to automate the training and prediction steps of sysSVM
***********
Parsing of data / Mutation annotation
***********
------------------------------
Sample parsing and annotation
------------------------------
Five scripts involved in this step
functions.R ## Functions for mutation annotation, gene annotation etc
data_parsing.R ## R script that does the data manipulation and mutation annotation
data_parsing.sh ## Submission script - calls the R script
parse_dir_str.R ## To make the commands - see below
runOncodriveClust.R ## To run GOF prediction (separate because I need to aggregate mutations form all the samples together) - I usually run this via Rscript in rosalind (activating oncodroveClust pyenv first)
summary_plotting.R ## Use this to get the overall data (aggregation of all patients) and save mut, cnv, sv data for all samples
Example command (I usually make all these commands with R and write them in a file)
qsub -N annot -pe threaded 3 -q FCgroup.q -l h_vmem=12G -o /home/mourikisa/data/OAC/71_OAC/strelka/LP6005334-DNA_B01_vs_SM-4JPUU/test.out -e /home/mourikisa/data/OAC/71_OAC/strelka/LP6005334-DNA_B01_vs_SM-4JPUU/test.err ./mutation_annotation.sh -s /home/mourikisa/data/OAC/71_OAC/strelka/LP6005334-DNA_B01_vs_SM-4JPUU -m /home/mourikisa/data/OAC/71_OAC/strelka/LP6005334-DNA_B01_vs_SM-4JPUU/pv.1.3rg.grch37_e71al.bwa/strelka/filtered_results/LP6005334-DNA_B01_vs_SM-4JPUU.snp.pass.vcf -i /home/mourikisa/data/OAC/71_OAC/strelka/LP6005334-DNA_B01_vs_SM-4JPUU/pv.1.3rg.grch37_e71al.bwa/strelka/results/passed.somatic.indels.vcf -g hg19
As soon as you have the overall tables use createTotalTable function in the functions.R to create the total table
and then the getMLinput function to bring in systems-level properties
***********
Main pipeline (sysSVM)
***********
------------------------------------------------------------------------------------------------
STEP 01: Create the training and prediction set and the command files for training (prepare.sh)
------------------------------------------------------------------------------------------------
The first part of the pipeline is to prepare the training and prediction set of the 4 kernels for all the cancer types
To do that run the following command for all the cancer types (change the name of the cancer type and the parameters accordingly)
Commands
More command-line arguments were added to address both input from file and from a mysql database
qsub -N OAC -pe threaded 3 -q FCgroup.q -l h_vmem=12G ./prepare.sh OAC F /home/mourikisa/data/OAC/129_OAC/ML/oac_ML_input.tsv young,reptime T /home/mourikisa/data/OAC /home/mourikisa/data/OAC/129_OAC/ML/OAC_false_positives_included /home/mourikisa/data/OAC/config.R F
qsub -N OAC -l h_vmem=12G ./prepare.sh OAC F /mnt/lustre/users/k1469280/mourikisa/data/OAC/validation_cohort_OAC_TCGA/oac_TCGA_ML_input.tsv young,reptime T /mnt/lustre/users/k1469280/mourikisa/data/OAC /mnt/lustre/users/k1469280/mourikisa/data/OAC/validation_cohort_OAC_TCGA/sysSVM/ /mnt/lustre/users/k1469280/mourikisa/data/OAC/config.R /mnt/lustre/users/k1469280/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/mean_sd_of_training_validation_features.Rdata
qsub -N OAC -l h_vmem=12G ./prepare.sh OAC F /mnt/lustre/users/k1469280/mourikisa/data/OAC/18_OAC/sysSVM/oac_18_ML_input.tsv young,reptime T /mnt/lustre/users/k1469280/mourikisa/data/OAC /mnt/lustre/users/k1469280/mourikisa/data/OAC/18_OAC/sysSVM/ /mnt/lustre/users/k1469280/mourikisa/data/OAC/config.R F
------------------------------
STEP 02: Run the command files
------------------------------
Change to the directory where the cancer type is and submit the files to the cluster (i.e jobs_linear.txt etc...)
---------------------------------------------------------------------------------------
STEP 03: Pool the result, select the best model and calculate the scores for each gene
---------------------------------------------------------------------------------------
Before this stewp remember to check GTEx tissue name for the corresponding cancer type
qsub -N OAC_score -pe threaded 3 -q FCgroup.q -l h_vmem=30G ./score_genes.sh OAC esophagus Esophagus T /home/mourikisa/data/OAC/129_OAC/ML /home/mourikisa/data/OAC/config.R F
Use this command to predict in a new cohort
qsub -N OAC_score -l h_vmem=30G ./predict_new_samples.sh OAC esophagus Esophagus T /mnt/lustre/users/k1469280/mourikisa/data/OAC/validation_cohort_OAC_TCGA /mnt/lustre/users/k1469280/mourikisa/data/OAC/config.R /mnt/lustre/users/k1469280/mourikisa/data/OAC/validation_cohort_OAC_TCGA/prediction_set.Rdata /mnt/lustre/users/k1469280/mourikisa/data/OAC/validation_cohort_OAC_TCGA/prediction_set_noScale.Rdata /mnt/lustre/users/k1469280/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/best_models.tsv /mnt/lustre/users/k1469280/mourikisa/data/OAC/validation_cohort_OAC_TCGA/sysSVM/OAC/config.txt
---------------------------------------
Get the ranks in each sample
---------------------------------------
Add refined CGCs, exclude "Not expressed" genes and define patient ranks
Then I use the getSysCans (source it from config.R) to get a data frame with the ranks. Command as follows:
syscan = getSysCans(path = "~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/", cancer_type = "OAC", tissue = "Esophagus")
And then save it
save(syscan, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/syscan.Rdata")
---------------------------------------
gene set enrichment analysis
---------------------------------------
gsea.noAmp.top10.plusCGC = pathwayEnrichmentTopGenes() ## With default parameters
save(gsea.noAmp.top10.plusCGC, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.noAmp.top10.plusCGC.Rdata")
gsea.withAmp.top10.plusCGC = pathwayEnrichmentTopGenes(remove.amplifications=F)
save(gsea.withAmp.top10.plusCGC, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.top10.plusCGC.Rdata")
-------------------------------------------------------------
Sys-candidates + Refined CGCs to enriched pathways
-------------------------------------------------------------
This function returns the gsea list above with an extra element which is the genes mapped to pathways (pathways also annotated as Level 1/2 and also with the number of samples/genes they cover)
gsea.noAmp.top10.plusCGC.pathways = mapPathways2Samples(gsea=gsea.noAmp.top10.plusCGC)
save(gsea.noAmp.top10.plusCGC.pathways, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.noAmp.top10.plusCGC.pathways.Rdata")
gsea.withAmp.top10.plusCGC.pathways = mapPathways2Samples(gsea=gsea.withAmp.top10.plusCGC)
save(gsea.withAmp.top10.plusCGC.pathways, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.top10.plusCGC.pathways.Rdata")
---------------------------------------
Gene sets to containers
---------------------------------------
gsea.noAmp.top10.plusCGC.containers = getContainers(gsea=gsea.noAmp.top10.plusCGC.pathways[["genes2paths"]])
save(gsea.noAmp.top10.plusCGC.containers, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.noAmp.top10.plusCGC.containers.Rdata")
gsea.withAmp.top10.plusCGC.containers = getContainers(gsea=gsea.withAmp.top10.plusCGC.pathways[["genes2paths"]])
save(gsea.withAmp.top10.plusCGC.containers, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.top10.plusCGC.containers.Rdata")
--------------------------------------------------------
Gene patient groups using shared containers
--------------------------------------------------------
common_processes_top5_plusCGC = getPatientCommonProcesses(genes2paths=gsea.withAmp.onlyTop5.containers[["60"]][["genes2paths"]])
sample_combns = common_processes_top5_plusCGC
save(sample_combns, file="~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/common_processes_top5_plusCGC.Rdata")
-------------------------------------------------------------------
GSEA/processes/patient groups on additional datasets
-------------------------------------------------------------------
Only CGCs
gsea.withAmp.onlyCGC = pathwayEnrichmentTopGenes(remove.amplifications = F, genes.per.patient = NULL)
save(gsea.withAmp.onlyCGC, file="~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.onlyCGC.Rdata")
gsea.withAmp.onlyCGC.pathways = mapPathways2Samples(gsea=gsea.withAmp.onlyCGC)
save(gsea.withAmp.onlyCGC.pathways, file="~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.onlyCGC.pathways.Rdata")
gsea.withAmp.onlyCGC.containers = getContainers(gsea=gsea.withAmp.onlyCGC.pathways[["genes2paths"]])
save(gsea.withAmp.onlyCGC.containers, file = "~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.onlyCGC.containers.Rdata")
Only Top 10 syscans
gsea.withAmp.onlyTop10 = pathwayEnrichmentTopGenes(remove.amplifications = F, genes.per.patient = 10, CGC.plus = F, remove.cgc = T)
save(gsea.withAmp.onlyTop10, file="~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.onlyTop10.Rdata")
gsea.withAmp.onlyTop10.pathways = mapPathways2Samples(gsea=gsea.withAmp.onlyTop10)
save(gsea.withAmp.onlyTop10.pathways, file="~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.onlyTop10.pathways.Rdata")
gsea.withAmp.onlyTop10.containers = getContainers(gsea=gsea.withAmp.onlyTop10.pathways[["genes2paths"]])
save(gsea.withAmp.onlyTop10.containers, file = "~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.onlyTop10.containers.Rdata")
common_processes_onlyTop10 = getPatientCommonProcesses(genes2paths=gsea.withAmp.onlyTop10.containers[["60"]][["genes2paths"]])
save(common_processes_onlyTop10, file = "~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/common_processes_onlyTop10.Rdata")
All predictions
gsea.withAmp.allPredictionSet = pathwayEnrichmentTopGenes(remove.amplifications = F, genes.per.patient = NULL, CGC.plus = NULL, given_tb = validation_ns)
save(gsea.withAmp.allPredictionSet, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.allPredictionSet.Rdata")
gsea.withAmp.allPredictionSet.pathways = mapPathways2Samples(gsea=gsea.withAmp.allPredictionSet)
save(gsea.withAmp.allPredictionSet.pathways, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.allPredictionSet.pathways.Rdata")
gsea.withAmp.allPredictionSet.containers = getContainers(gsea=gsea.withAmp.allPredictionSet.pathways[["genes2paths"]])
save(gsea.withAmp.allPredictionSet.containers, file = "~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.allPredictionSet.containers.Rdata")
All predictions + CGCs (validation+training sets essentially)
cohort = rbind(training_ns%>%select(-type), validation_ns)
gsea.withAmp.allPredictionSet.plusCGC = pathwayEnrichmentTopGenes(remove.amplifications = F, genes.per.patient = NULL, CGC.plus = NULL, given_tb = cohort)
save(gsea.withAmp.allPredictionSet.plusCGC, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.allPredictionSet.plusCGC.Rdata")
gsea.withAmp.allPredictionSet.plusCGC.pathways = mapPathways2Samples(gsea=gsea.withAmp.allPredictionSet.plusCGC)
save(gsea.withAmp.allPredictionSet.plusCGC.pathways, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.allPredictionSet.plusCGC.pathways.Rdata")
gsea.withAmp.allPredictionSet.plusCGC.containers = getContainers(gsea=gsea.withAmp.allPredictionSet.plusCGC.pathways[["genes2paths"]])
save(gsea.withAmp.allPredictionSet.plusCGC.containers, file = "~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.allPredictionSet.plusCGC.containers.Rdata")
thmourikis/sysSVM documentation built on May 30, 2019, 4:05 a.m.
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This directory was created to automate the training and prediction steps of sysSVM
***********
Parsing of data / Mutation annotation
***********
------------------------------
Sample parsing and annotation
------------------------------
Five scripts involved in this step functions.R ## Functions for mutation annotation, gene annotation etc data_parsing.R ## R script that does the data manipulation and mutation annotation data_parsing.sh ## Submission script - calls the R script parse_dir_str.R ## To make the commands - see below runOncodriveClust.R ## To run GOF prediction (separate because I need to aggregate mutations form all the samples together) - I usually run this via Rscript in rosalind (activating oncodroveClust pyenv first) summary_plotting.R ## Use this to get the overall data (aggregation of all patients) and save mut, cnv, sv data for all samples
Example command (I usually make all these commands with R and write them in a file)
qsub -N annot -pe threaded 3 -q FCgroup.q -l h_vmem=12G -o /home/mourikisa/data/OAC/71_OAC/strelka/LP6005334-DNA_B01_vs_SM-4JPUU/test.out -e /home/mourikisa/data/OAC/71_OAC/strelka/LP6005334-DNA_B01_vs_SM-4JPUU/test.err ./mutation_annotation.sh -s /home/mourikisa/data/OAC/71_OAC/strelka/LP6005334-DNA_B01_vs_SM-4JPUU -m /home/mourikisa/data/OAC/71_OAC/strelka/LP6005334-DNA_B01_vs_SM-4JPUU/pv.1.3rg.grch37_e71al.bwa/strelka/filtered_results/LP6005334-DNA_B01_vs_SM-4JPUU.snp.pass.vcf -i /home/mourikisa/data/OAC/71_OAC/strelka/LP6005334-DNA_B01_vs_SM-4JPUU/pv.1.3rg.grch37_e71al.bwa/strelka/results/passed.somatic.indels.vcf -g hg19
As soon as you have the overall tables use createTotalTable function in the functions.R to create the total table and then the getMLinput function to bring in systems-level properties
***********
Main pipeline (sysSVM)
***********
------------------------------------------------------------------------------------------------
STEP 01: Create the training and prediction set and the command files for training (prepare.sh)
------------------------------------------------------------------------------------------------
The first part of the pipeline is to prepare the training and prediction set of the 4 kernels for all the cancer types
To do that run the following command for all the cancer types (change the name of the cancer type and the parameters accordingly)
Commands
More command-line arguments were added to address both input from file and from a mysql database
qsub -N OAC -pe threaded 3 -q FCgroup.q -l h_vmem=12G ./prepare.sh OAC F /home/mourikisa/data/OAC/129_OAC/ML/oac_ML_input.tsv young,reptime T /home/mourikisa/data/OAC /home/mourikisa/data/OAC/129_OAC/ML/OAC_false_positives_included /home/mourikisa/data/OAC/config.R F
qsub -N OAC -l h_vmem=12G ./prepare.sh OAC F /mnt/lustre/users/k1469280/mourikisa/data/OAC/validation_cohort_OAC_TCGA/oac_TCGA_ML_input.tsv young,reptime T /mnt/lustre/users/k1469280/mourikisa/data/OAC /mnt/lustre/users/k1469280/mourikisa/data/OAC/validation_cohort_OAC_TCGA/sysSVM/ /mnt/lustre/users/k1469280/mourikisa/data/OAC/config.R /mnt/lustre/users/k1469280/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/mean_sd_of_training_validation_features.Rdata
qsub -N OAC -l h_vmem=12G ./prepare.sh OAC F /mnt/lustre/users/k1469280/mourikisa/data/OAC/18_OAC/sysSVM/oac_18_ML_input.tsv young,reptime T /mnt/lustre/users/k1469280/mourikisa/data/OAC /mnt/lustre/users/k1469280/mourikisa/data/OAC/18_OAC/sysSVM/ /mnt/lustre/users/k1469280/mourikisa/data/OAC/config.R F
------------------------------
STEP 02: Run the command files
------------------------------
Change to the directory where the cancer type is and submit the files to the cluster (i.e jobs_linear.txt etc...)
---------------------------------------------------------------------------------------
STEP 03: Pool the result, select the best model and calculate the scores for each gene
---------------------------------------------------------------------------------------
Before this stewp remember to check GTEx tissue name for the corresponding cancer type
qsub -N OAC_score -pe threaded 3 -q FCgroup.q -l h_vmem=30G ./score_genes.sh OAC esophagus Esophagus T /home/mourikisa/data/OAC/129_OAC/ML /home/mourikisa/data/OAC/config.R F
Use this command to predict in a new cohort
qsub -N OAC_score -l h_vmem=30G ./predict_new_samples.sh OAC esophagus Esophagus T /mnt/lustre/users/k1469280/mourikisa/data/OAC/validation_cohort_OAC_TCGA /mnt/lustre/users/k1469280/mourikisa/data/OAC/config.R /mnt/lustre/users/k1469280/mourikisa/data/OAC/validation_cohort_OAC_TCGA/prediction_set.Rdata /mnt/lustre/users/k1469280/mourikisa/data/OAC/validation_cohort_OAC_TCGA/prediction_set_noScale.Rdata /mnt/lustre/users/k1469280/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/best_models.tsv /mnt/lustre/users/k1469280/mourikisa/data/OAC/validation_cohort_OAC_TCGA/sysSVM/OAC/config.txt
---------------------------------------
Get the ranks in each sample
---------------------------------------
Add refined CGCs, exclude "Not expressed" genes and define patient ranks
Then I use the getSysCans (source it from config.R) to get a data frame with the ranks. Command as follows:
syscan = getSysCans(path = "~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/", cancer_type = "OAC", tissue = "Esophagus")
And then save it
save(syscan, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/syscan.Rdata")
---------------------------------------
gene set enrichment analysis
---------------------------------------
gsea.noAmp.top10.plusCGC = pathwayEnrichmentTopGenes() ## With default parameters save(gsea.noAmp.top10.plusCGC, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.noAmp.top10.plusCGC.Rdata") gsea.withAmp.top10.plusCGC = pathwayEnrichmentTopGenes(remove.amplifications=F) save(gsea.withAmp.top10.plusCGC, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.top10.plusCGC.Rdata")
-------------------------------------------------------------
Sys-candidates + Refined CGCs to enriched pathways
-------------------------------------------------------------
This function returns the gsea list above with an extra element which is the genes mapped to pathways (pathways also annotated as Level 1/2 and also with the number of samples/genes they cover)
gsea.noAmp.top10.plusCGC.pathways = mapPathways2Samples(gsea=gsea.noAmp.top10.plusCGC) save(gsea.noAmp.top10.plusCGC.pathways, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.noAmp.top10.plusCGC.pathways.Rdata") gsea.withAmp.top10.plusCGC.pathways = mapPathways2Samples(gsea=gsea.withAmp.top10.plusCGC) save(gsea.withAmp.top10.plusCGC.pathways, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.top10.plusCGC.pathways.Rdata")
---------------------------------------
Gene sets to containers
---------------------------------------
gsea.noAmp.top10.plusCGC.containers = getContainers(gsea=gsea.noAmp.top10.plusCGC.pathways[["genes2paths"]]) save(gsea.noAmp.top10.plusCGC.containers, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.noAmp.top10.plusCGC.containers.Rdata") gsea.withAmp.top10.plusCGC.containers = getContainers(gsea=gsea.withAmp.top10.plusCGC.pathways[["genes2paths"]]) save(gsea.withAmp.top10.plusCGC.containers, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.top10.plusCGC.containers.Rdata")
--------------------------------------------------------
Gene patient groups using shared containers
--------------------------------------------------------
common_processes_top5_plusCGC = getPatientCommonProcesses(genes2paths=gsea.withAmp.onlyTop5.containers[["60"]][["genes2paths"]]) sample_combns = common_processes_top5_plusCGC save(sample_combns, file="~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/common_processes_top5_plusCGC.Rdata")
-------------------------------------------------------------------
GSEA/processes/patient groups on additional datasets
-------------------------------------------------------------------
Only CGCs
gsea.withAmp.onlyCGC = pathwayEnrichmentTopGenes(remove.amplifications = F, genes.per.patient = NULL) save(gsea.withAmp.onlyCGC, file="~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.onlyCGC.Rdata") gsea.withAmp.onlyCGC.pathways = mapPathways2Samples(gsea=gsea.withAmp.onlyCGC) save(gsea.withAmp.onlyCGC.pathways, file="~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.onlyCGC.pathways.Rdata") gsea.withAmp.onlyCGC.containers = getContainers(gsea=gsea.withAmp.onlyCGC.pathways[["genes2paths"]]) save(gsea.withAmp.onlyCGC.containers, file = "~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.onlyCGC.containers.Rdata")
Only Top 10 syscans
gsea.withAmp.onlyTop10 = pathwayEnrichmentTopGenes(remove.amplifications = F, genes.per.patient = 10, CGC.plus = F, remove.cgc = T) save(gsea.withAmp.onlyTop10, file="~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.onlyTop10.Rdata") gsea.withAmp.onlyTop10.pathways = mapPathways2Samples(gsea=gsea.withAmp.onlyTop10) save(gsea.withAmp.onlyTop10.pathways, file="~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.onlyTop10.pathways.Rdata") gsea.withAmp.onlyTop10.containers = getContainers(gsea=gsea.withAmp.onlyTop10.pathways[["genes2paths"]]) save(gsea.withAmp.onlyTop10.containers, file = "~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.onlyTop10.containers.Rdata") common_processes_onlyTop10 = getPatientCommonProcesses(genes2paths=gsea.withAmp.onlyTop10.containers[["60"]][["genes2paths"]]) save(common_processes_onlyTop10, file = "~/rosalind_lustre/mourikisa/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/common_processes_onlyTop10.Rdata")
All predictions
gsea.withAmp.allPredictionSet = pathwayEnrichmentTopGenes(remove.amplifications = F, genes.per.patient = NULL, CGC.plus = NULL, given_tb = validation_ns) save(gsea.withAmp.allPredictionSet, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.allPredictionSet.Rdata") gsea.withAmp.allPredictionSet.pathways = mapPathways2Samples(gsea=gsea.withAmp.allPredictionSet) save(gsea.withAmp.allPredictionSet.pathways, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.allPredictionSet.pathways.Rdata") gsea.withAmp.allPredictionSet.containers = getContainers(gsea=gsea.withAmp.allPredictionSet.pathways[["genes2paths"]]) save(gsea.withAmp.allPredictionSet.containers, file = "~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.allPredictionSet.containers.Rdata")
All predictions + CGCs (validation+training sets essentially)
cohort = rbind(training_ns%>%select(-type), validation_ns) gsea.withAmp.allPredictionSet.plusCGC = pathwayEnrichmentTopGenes(remove.amplifications = F, genes.per.patient = NULL, CGC.plus = NULL, given_tb = cohort) save(gsea.withAmp.allPredictionSet.plusCGC, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.allPredictionSet.plusCGC.Rdata") gsea.withAmp.allPredictionSet.plusCGC.pathways = mapPathways2Samples(gsea=gsea.withAmp.allPredictionSet.plusCGC) save(gsea.withAmp.allPredictionSet.plusCGC.pathways, file="~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.allPredictionSet.plusCGC.pathways.Rdata") gsea.withAmp.allPredictionSet.plusCGC.containers = getContainers(gsea=gsea.withAmp.allPredictionSet.plusCGC.pathways[["genes2paths"]]) save(gsea.withAmp.allPredictionSet.plusCGC.containers, file = "~/athena/data/OAC/Combined_ICGC_cohort_129_66_71/sysSVM/OAC/gsea.withAmp.allPredictionSet.plusCGC.containers.Rdata")
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