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run_humann_case_only.Rmd
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run_humann_case_only.Rmd
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---
title: "run_human_on_TEDDY_case_only"
author: "Sam Zimmerman"
date: "2023-08-13"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
setwd("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4")
abundance_files = paste("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/humann_analysis",c("TEDDY_pathabundance-cpm.tsv","TEDDY_species_abundances.tsv"),sep="/")
mapping_files = list.files(pattern = ".mapping.csv")
mapping_files = mapping_files[grep("_before_vs_far_from_condition_subject_level",mapping_files)]
mapping_files = mapping_files[grep("all_HLA",mapping_files)]
training_metadata_files_two_thirds_train = gsub(".mapping.csv","_proportion_train_0.66_train_metadata.csv",mapping_files)
training_metadata_files_fifty_train = gsub(".mapping.csv","_proportion_train_0.5_train_metadata.csv",mapping_files)
testing_metadata_files_two_third_train = gsub(".mapping.csv","_proportion_train_0.66_test_metadata.csv",mapping_files)
testing_metadata_files_fifty_train = gsub(".mapping.csv","_proportion_train_0.5_test_metadata.csv",mapping_files)
training_data_files_two_thirds_train = gsub(".mapping.csv","_proportion_train_0.66.train_subjects.txt",mapping_files)
training_data_files_fifty_train = gsub(".mapping.csv","_proportion_train_0.5.train_subjects.txt",mapping_files)
testing_data_files_two_third_train = gsub(".mapping.csv","_proportion_train_0.66.test_subjects.txt",mapping_files)
testing_data_files_fifty_train = gsub(".mapping.csv","_proportion_train_0.5.test_subjects.txt",mapping_files)
mapping_metadata_two_third_train = data.frame(training_metadata_files_two_thirds_train,testing_metadata_files_two_third_train,mapping_files,training_data_files_two_thirds_train,testing_data_files_two_third_train)
mapping_metadata_fifty_fity_train = data.frame(training_metadata_files_fifty_train,testing_metadata_files_fifty_train,mapping_files,training_data_files_fifty_train,testing_data_files_fifty_train)
mapping_metadata_species_level_two_third = cbind(abundance_files=rep(abundance_files[2],nrow(mapping_metadata_two_third_train)),mapping_metadata_two_third_train)
mapping_metadata_species_level_two_third$type = "species"
mapping_metadata_species_level_two_third$train_prop = "0.66"
mapping_metadata_species_level_fifty_fity = cbind(abundance_files=rep(abundance_files[2],nrow(mapping_metadata_fifty_fity_train)),mapping_metadata_fifty_fity_train)
mapping_metadata_species_level_fifty_fity$type = "species"
mapping_metadata_species_level_fifty_fity$train_prop = "0.5"
mapping_metadata_pathway_level_two_third = cbind(abundance_files=rep(abundance_files[1],nrow(mapping_metadata_two_third_train)),mapping_metadata_two_third_train)
mapping_metadata_pathway_level_two_third$type = "pathway"
mapping_metadata_pathway_level_two_third$train_prop = "0.66"
mapping_metadata_pathway_level_fifty_fity = cbind(abundance_files=rep(abundance_files[1],nrow(mapping_metadata_fifty_fity_train)),mapping_metadata_fifty_fity_train)
mapping_metadata_pathway_level_fifty_fity$type = "pathway"
mapping_metadata_pathway_level_fifty_fity$train_prop = "0.5"
write.table(mapping_metadata_species_level_two_third,file="species_level_metadata_two_thirds_before_vs_far_from_condition_subject_level.tsv",sep="\t",col.names=FALSE,row.names=FALSE,quote=FALSE)
write.table(mapping_metadata_species_level_fifty_fity,file="species_level_metadata_fifity_fifty_before_vs_far_from_condition_subject_level.tsv",sep="\t",col.names=FALSE,row.names=FALSE,quote=FALSE)
write.table(mapping_metadata_pathway_level_two_third,file="pathway_level_metadata_two_thirds_before_vs_far_from_condition_subject_level.tsv",sep="\t",col.names=FALSE,row.names=FALSE,quote=FALSE)
write.table(mapping_metadata_pathway_level_fifty_fity,file="pathway_level_metadata_fifty_fifty_before_vs_far_from_condition_subject_level.tsv",sep="\t",col.names=FALSE,row.names=FALSE,quote=FALSE)
```
#Now average subjects together, filter and normalize
```{bash}
cd /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4
sbatch -c 1 -t 0-01:00 -p short --mem=10G prep_abundance_for_voe_bulk_training_data_v2.bash species_level_metadata_two_thirds_before_vs_far_from_condition_subject_level.tsv
sbatch -c 1 -t 0-00:30 -p short --mem=5G prep_abundance_for_voe_bulk_training_data_v2.bash species_level_metadata_fifity_fifty_before_vs_far_from_condition_subject_level.tsv
sbatch -c 1 -t 0-01:00 -p short --mem=20G prep_abundance_for_voe_bulk_training_data_v2.bash pathway_level_metadata_two_thirds_before_vs_far_from_condition_subject_level.tsv
sbatch -c 1 -t 0-01:00 -p short --mem=20G prep_abundance_for_voe_bulk_training_data_v2.bash pathway_level_metadata_fifty_fifty_before_vs_far_from_condition_subject_level.tsv
cd /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis
ls /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/*_before_vs_far_from_condition_subject_level-all_HLA_*/*_test_TEDDY_pathabundance-cpm.tsv | while read line; do dirname $line; done > input_folder_list_before_vs_far_from_condition_subject_level.txt
sbatch -n 1 -c 1 --mem=60G -p short -t 0-02:00 scripts/filter_normalize_abundance_metadata_data_v3_pathways.bash input_folder_list_before_vs_far_from_condition_subject_level.txt 0.9 1
sbatch -n 1 -c 1 --mem=5G -p short -t 0-02:00 scripts/filter_normalize_abundance_metadata_data_v3_species.bash input_folder_list_before_vs_far_from_condition_subject_level.txt 0.3 1
sbatch -n 1 -c 1 --mem=10G -p short -t 0-01:00 scripts/filter_normalize_abundance_metadata_data_v3_pathways_cutoff_labeled.bash input_folder_list_before_vs_far_from_condition_subject_level.txt 0.3 1
```
#Create input for ttest
```{r}
all_dirs = read.table("input_folder_list_before_vs_far_from_condition_subject_level.txt",header=FALSE)[,1]
get_input_files = function(abundance_file_type) { # abundance_file_type is species or pathway
if(abundance_file_type == "pathway") {
transformed_abundance_file_list = lapply(all_dirs, function(x) {
files_temp = list.files(x,pattern="_pathabundance-cpm_filtered_transformed_abundance",full.names = TRUE)
files_temp = files_temp[!grepl("_names.csv",files_temp)]
})
} else if(abundance_file_type == "species") {
transformed_abundance_file_list = lapply(all_dirs, function(x) {
files_temp = list.files(x,pattern="_species_abundances_filtered_transformed_abundance_",full.names = TRUE)
files_temp = files_temp[!grepl("_names.csv",files_temp)]
})
}
names(transformed_abundance_file_list) = all_dirs
transformed_abundance_file_list = transformed_abundance_file_list[sapply(transformed_abundance_file_list, length)==2]
test_abundance_data = sapply(transformed_abundance_file_list, function(x) x[grep("transformed_abundance_test.csv",x)])
train_abundance_data = sapply(transformed_abundance_file_list, function(x) x[grep("transformed_abundance_train.csv",x)])
input_info = data.frame(test_abundance_data,train_abundance_data)
metadata_train = paste(rownames(input_info),"_train_metadata.csv",sep="")
metadata_test = paste(rownames(input_info),"_test_metadata.csv",sep="")
train_fold1 = paste(rownames(input_info),".train_subjects_1.txt",sep="")
train_fold2 = paste(rownames(input_info),".train_subjects_2.txt",sep="")
train_fold3 = paste(rownames(input_info),".train_subjects_3.txt",sep="")
test_subjects = paste(rownames(input_info),".test_subjects.txt",sep="")
input_info$metadata_train = metadata_train
input_info$metadata_test = metadata_test
input_info$train_fold1 = train_fold1
input_info$train_fold2 = train_fold2
input_info$train_fold3 = train_fold3
input_info$test_subjects = test_subjects
write.csv(input_info,file=paste("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level",abundance_file_type,".csv",sep=""),row.names = FALSE,quote=FALSE)
return(input_info)
}
species_info = get_input_files(abundance_file_type="species")
pathway_info = get_input_files(abundance_file_type="pathway")
get_input_files_with_cutoff = function(abundance_file_type,cutoff) { # abundance_file_type is species or pathway
if(abundance_file_type == "pathway") {
transformed_abundance_file_list = lapply(all_dirs, function(x) {
files_temp = list.files(x,pattern=paste("_pathabundance-cpm",cutoff,"_filtered_transformed_abundance",sep=""),full.names = TRUE)
files_temp = files_temp[!grepl("_names.csv",files_temp)]
})
} else if(abundance_file_type == "species") {
transformed_abundance_file_list = lapply(all_dirs, function(x) {
files_temp = list.files(x,pattern="_species_abundances_filtered_transformed_abundance_",full.names = TRUE)
files_temp = files_temp[!grepl("_names.csv",files_temp)]
})
}
names(transformed_abundance_file_list) = all_dirs
transformed_abundance_file_list = transformed_abundance_file_list[sapply(transformed_abundance_file_list, length)==2]
test_abundance_data = sapply(transformed_abundance_file_list, function(x) x[grep("transformed_abundance_test.csv",x)])
train_abundance_data = sapply(transformed_abundance_file_list, function(x) x[grep("transformed_abundance_train.csv",x)])
input_info = data.frame(test_abundance_data,train_abundance_data)
metadata_train = paste(rownames(input_info),"_train_metadata.csv",sep="")
metadata_test = paste(rownames(input_info),"_test_metadata.csv",sep="")
train_fold1 = paste(rownames(input_info),".train_subjects_1.txt",sep="")
train_fold2 = paste(rownames(input_info),".train_subjects_2.txt",sep="")
train_fold3 = paste(rownames(input_info),".train_subjects_3.txt",sep="")
test_subjects = paste(rownames(input_info),".test_subjects.txt",sep="")
input_info$metadata_train = metadata_train
input_info$metadata_test = metadata_test
input_info$train_fold1 = train_fold1
input_info$train_fold2 = train_fold2
input_info$train_fold3 = train_fold3
input_info$test_subjects = test_subjects
write.csv(input_info,file=paste("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level",abundance_file_type,cutoff,".csv",sep=""),row.names = FALSE,quote=FALSE)
return(input_info)
}
pathway_info = get_input_files_with_cutoff(abundance_file_type="pathway",cutoff=0.3)
```
#Now run initial ttests
```{bash}
tail -n +2 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelpathway.csv > /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelpathway_noheader.csv
tail -n +2 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelspecies.csv > /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelspecies_noheader.csv
tail -n +2 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelpathway0.3.csv > /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelpathway_noheader0.3.csv
head -n 1 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelpathway_noheader0.3.csv > test.csv
sbatch -n 1 -c 1 --mem=5G -p short -t 0-01:00 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/scripts/run_paired_univariate_feature_selection_batch.bash models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelpathway_noheader.csv 1 train
sbatch -n 1 -c 1 --mem=5G -p short -t 0-01:00 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/scripts/run_paired_univariate_feature_selection_batch.bash models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelspecies_noheader.csv 1 train
sbatch -n 1 -c 1 --mem=5G -p short -t 0-01:00 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/scripts/run_paired_univariate_feature_selection_batch_cutoff.bash models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelpathway_noheader0.3.csv 1 0.3 train
sbatch -n 1 -c 1 --mem=5G -p short -t 0-01:00 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/scripts/run_paired_univariate_feature_selection_batch_cutoff.bash models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelpathway_noheader.csv 1 0.9 test
sbatch -n 1 -c 1 --mem=5G -p short -t 0-01:00 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/scripts/run_paired_univariate_feature_selection_batch_cutoff.bash models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelspecies_noheader.csv 1 0.3 test
sbatch -n 1 -c 1 --mem=5G -p short -t 0-01:00 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/scripts/run_paired_univariate_feature_selection_batch_cutoff.bash models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelpathway_noheader0.3.csv 1 0.3 test
```
#Get significant genes
```{r}
pway_results = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern="_pathwayoutput_ttest_results.rds",full.names = TRUE,recursive = TRUE)
pway_results = pway_results[grep("before_vs_far_from_condition_subject_level",pway_results)]
species_results = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern="speciesoutput_ttest_results.rds",full.names = TRUE,recursive = TRUE)
species_results = species_results[grep("before_vs_far_from_condition_subject_level",species_results)]
pway_results_test_0.9 = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern="_pathway0.9testoutput_ttest_results.rds",full.names = TRUE,recursive = TRUE)
pway_results_test_0.9 = pway_results_test_0.9[grep("before_vs_far_from_condition_subject_level",pway_results_test_0.9)]
pway_results_test_0.3 = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern="_pathway0.3testoutput_ttest_results.rds",full.names = TRUE,recursive = TRUE)
pway_results_test_0.3 = pway_results_test_0.3[grep("before_vs_far_from_condition_subject_level",pway_results_test_0.3)]
species_results_test_0.3 = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern="_species0.3testoutput_ttest_results.rds",full.names = TRUE,recursive = TRUE)
species_results_test_0.3 = species_results_test_0.3[grep("before_vs_far_from_condition_subject_level",species_results_test_0.3)]
get_sig_genes = function(file_list,input_csv_param,output_file) {
results_list = lapply(file_list,function(x) readRDS(x))
results_list_BY = lapply(results_list, function(x) x[[2]])
names(results_list_BY) = file_list
mean(sapply(results_list_BY,length))
my_genes_before_vs_far_list_BY_gt0 = results_list_BY[sapply(results_list_BY,length)>0]
input_csv = read.table(input_csv_param,sep=",",header=FALSE)
dirs_to_get = basename(dirname(names(my_genes_before_vs_far_list_BY_gt0)))
input_csv_for_lmer = input_csv[sapply(dirs_to_get, function(x) grep(x,input_csv[,1])),]
write.table(input_csv_for_lmer,file=output_file,sep=",",col.names=FALSE,row.names=FALSE,quote=FALSE)
}
get_sig_genes(pway_results,"/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelpathway_noheader.csv","models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway.csv")
get_sig_genes(species_results,"/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelspecies_noheader.csv","models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_species.csv")
get_sig_genes(pway_results_cutoff,"/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelpathway_noheader0.3.csv","/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway0.3.csv")
get_sig_genes(pway_results_test_0.9,"/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelpathway_noheader.csv","/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway_test_0.9.csv")
get_sig_genes(pway_results_test_0.3,"/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelpathway_noheader0.3.csv","/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway_test_0.3.csv")
get_sig_genes(species_results_test_0.3,"/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_levelspecies_noheader.csv","/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_species_test_0.3.csv")
```
#Now its time for mixed effect model
#First for each metadata file get the number of samples per subject so I can normalize by that in my model.
```{r}
get_samples_per_subject = function(input_file,output_file,test_or_train) {
input_file = read.csv(input_file,header=FALSE)
if(test_or_train == "train") {
metadata_train = input_file[,3]
} else {
metadata_train = input_file[,4]
}
new_metadata_file_names = sapply(metadata_train, function(metadatafile) {
metadata_temp = read.csv(metadatafile)
mapping_file = paste(strsplit(metadatafile,split="_proportion_train_")[[1]][1],".mapping.csv",sep="")
mapping_df = read.csv(mapping_file)
samples_per_subject = sapply(metadata_temp$SubjectID, function(subjectID) {
sample_num_per_window = sum(mapping_df[,1]==subjectID)
return(sample_num_per_window)
})
metadata_temp$samples_per_subject = samples_per_subject
metadatafile_new = gsub("_metadata.csv","_metadata_for_lmer.csv",metadatafile)
write.csv(metadata_temp,file=metadatafile_new)
return(metadatafile_new)
})
if(test_or_train == "train") {
input_file[,3] = new_metadata_file_names
} else {
input_file[,4] = new_metadata_file_names
}
write.table(input_file,file=output_file,sep=",",col.names=FALSE,row.names=FALSE,quote=FALSE)
}
get_samples_per_subject("models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway.csv","models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway_2.csv","train")
get_samples_per_subject("models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_species.csv","models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_species_2.csv","train")
get_samples_per_subject("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway0.3.csv","/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway0.3_2.csv","train")
get_samples_per_subject("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway_test_0.9.csv","/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway_test_0.9_2.csv","test")
get_samples_per_subject("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway_test_0.3.csv","/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway_test_0.3_2.csv","test")
get_samples_per_subject("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_species_test_0.3.csv","/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_species_test_0.3_2.csv","test")
```
#Now run lmer
```{bash}
head -n 1 models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway_2.csv > test.csv
sbatch -n 1 -c 1 --mem=5G -p short -t 0-01:00 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/scripts/run_paired_univariate_feature_selection_v3_bulk.bash models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway_2.csv 1
sbatch -n 1 -c 1 --mem=5G -p short -t 0-01:00 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/scripts/run_paired_univariate_feature_selection_v3_bulk.bash models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_species_2.csv 1
sbatch -n 1 -c 1 --mem=10G -p short -t 0-10:00 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/scripts/run_paired_univariate_feature_selection_v3_bulk_cutoff.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway0.3_2.csv 1 0.3 train
# do for test
sbatch -n 1 -c 1 --mem=10G -p short -t 0-10:00 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/scripts/run_paired_univariate_feature_selection_v3_bulk_cutoff.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway_test_0.9_2.csv 1 0.9 test
sbatch -n 1 -c 1 --mem=10G -p short -t 0-10:00 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/scripts/run_paired_univariate_feature_selection_v3_bulk_cutoff.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_pathway_test_0.3_2.csv 1 0.3 test
sbatch -n 1 -c 1 --mem=10G -p short -t 0-10:00 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/scripts/run_paired_univariate_feature_selection_v3_bulk_cutoff.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/models_input_files_parsed_v4_before_vs_far_from_condition_subject_level_no_header_for_lmer_species_test_0.3_2.csv 1 0.3 test
```
#Now get input for round 1 of permutation tests
```{r}
#rm /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/*/*_speciesoutput_lmer_feature_selection_results_window_size_adjustedbootstrap_one_sig_genes.txt if I need to redo
library(data.table)
get_permutation_input = function(genetype,output) {
if(genetype=="species") {
my_genes_files = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern="_speciesoutput_lmer_feature_selection_results_window_size_adjusted.rds",full.names = TRUE,recursive = TRUE)
} else if(genetype == "pathway") {
my_genes_files = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern="_pathwayoutput_lmer_feature_selection_results_window_size_adjusted.rds",full.names = TRUE,recursive = TRUE)
}
my_genes_before_vs_far = my_genes_files[grep("before_vs_far_from_condition_subject_level",my_genes_files)]
my_genes_before_vs_far = my_genes_before_vs_far[grep("all_HLA",my_genes_before_vs_far)]
my_genes_before_vs_far_list = lapply(my_genes_before_vs_far,function(x) readRDS(x))
my_genes_before_vs_far_list_sig = lapply(1:length(my_genes_before_vs_far_list), function(ind) {
mydf = my_genes_before_vs_far_list[[ind]]
mydf = mydf[mydf$singular == 0,]
mydf = mydf[!is.na(mydf$pvalue),]
mydf$BY = p.adjust(mydf$pvalue,method="BY")
mydf = mydf[mydf$BY < 0.05,]
mydf = mydf[order(mydf$pvalue),]
outputname = gsub(".rds","bootstrap_one_sig_genes.txt",my_genes_before_vs_far[ind])
if(length(rownames(mydf))>0) {
write.table(rownames(mydf),file=outputname,col.names=FALSE,row.names=FALSE,quote=FALSE)
}
mapping_files = gsub("_proportion_train_0.66","",basename(dirname(my_genes_before_vs_far[[ind]])))
mapping_files = gsub("_proportion_train_0.5","",mapping_files)
mapping_files = paste(mapping_files,".mapping.csv",sep="")
train_metadata = paste(basename(dirname(my_genes_before_vs_far[[ind]])),"_train_metadata.csv",sep="")
train_subjects = paste(basename(dirname(my_genes_before_vs_far[[ind]])),".train_subjects.txt",sep="")
return(c(mapping_files,train_metadata,train_subjects,outputname,nrow(mydf)))
})
my_genes_before_vs_far_list_sig = do.call("rbind",my_genes_before_vs_far_list_sig)
my_genes_before_vs_far_list_sig = as.data.frame(my_genes_before_vs_far_list_sig)
my_genes_before_vs_far_list_sig[,5] = as.numeric(my_genes_before_vs_far_list_sig[,5])
my_genes_before_vs_far_list_sig = my_genes_before_vs_far_list_sig[my_genes_before_vs_far_list_sig[,5]>0,]
my_genes_before_vs_far_list_sig = my_genes_before_vs_far_list_sig[,-5]
write.table(my_genes_before_vs_far_list_sig,file=output,sep="\t",col.names=FALSE,row.names=FALSE,quote=FALSE)
return(my_genes_before_vs_far_list_sig)
}
species_blah = get_permutation_input(genetype="species",output="before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_species.txt")
pathway_blah = get_permutation_input(genetype="pathway",output="before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway.txt")
get_permutation_input_with_cutoff = function(genetype,output,cutoff) {
if(genetype=="species") {
my_genes_files = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern=paste("_species",cutoff,"output_lmer_feature_selection_results_window_size_adjusted.rds",sep=""),full.names = TRUE,recursive = TRUE)
} else if(genetype == "pathway") {
my_genes_files = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern=paste("_pathway",cutoff,"output_lmer_feature_selection_results_window_size_adjusted.rds",sep=""),full.names = TRUE,recursive = TRUE)
}
my_genes_before_vs_far = my_genes_files[grep("before_vs_far_from_condition_subject_level",my_genes_files)]
my_genes_before_vs_far = my_genes_before_vs_far[grep("all_HLA",my_genes_before_vs_far)]
my_genes_before_vs_far_list = lapply(my_genes_before_vs_far,function(x) readRDS(x))
my_genes_before_vs_far_list_sig = lapply(1:length(my_genes_before_vs_far_list), function(ind) {
mydf = my_genes_before_vs_far_list[[ind]]
mydf = mydf[mydf$singular == 0,]
mydf = mydf[!is.na(mydf$pvalue),]
mydf$BY = p.adjust(mydf$pvalue,method="BY")
mydf = mydf[mydf$BY < 0.05,]
mydf = mydf[order(mydf$pvalue),]
outputname = gsub(".rds","bootstrap_one_sig_genes.txt",my_genes_before_vs_far[ind])
if(length(rownames(mydf))>0) {
write.table(rownames(mydf),file=outputname,col.names=FALSE,row.names=FALSE,quote=FALSE)
}
mapping_files = gsub("_proportion_train_0.66","",basename(dirname(my_genes_before_vs_far[[ind]])))
mapping_files = gsub("_proportion_train_0.5","",mapping_files)
mapping_files = paste(mapping_files,".mapping.csv",sep="")
train_metadata = paste(basename(dirname(my_genes_before_vs_far[[ind]])),"_train_metadata.csv",sep="")
train_subjects = paste(basename(dirname(my_genes_before_vs_far[[ind]])),".train_subjects.txt",sep="")
return(c(mapping_files,train_metadata,train_subjects,outputname,nrow(mydf)))
})
my_genes_before_vs_far_list_sig = do.call("rbind",my_genes_before_vs_far_list_sig)
my_genes_before_vs_far_list_sig = as.data.frame(my_genes_before_vs_far_list_sig)
my_genes_before_vs_far_list_sig[,5] = as.numeric(my_genes_before_vs_far_list_sig[,5])
my_genes_before_vs_far_list_sig = my_genes_before_vs_far_list_sig[my_genes_before_vs_far_list_sig[,5]>0,]
my_genes_before_vs_far_list_sig = my_genes_before_vs_far_list_sig[,-5]
write.table(my_genes_before_vs_far_list_sig,file=output,sep="\t",col.names=FALSE,row.names=FALSE,quote=FALSE)
return(my_genes_before_vs_far_list_sig)
}
pathway_blah = get_permutation_input_with_cutoff(genetype="pathway",output="before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_cutoff_0.3.txt",0.3)
get_permutation_input_with_cutoff_test_or_train = function(genetype,output,cutoff,test_or_train) {
if(genetype=="species") {
my_genes_files = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern=paste("_species",cutoff,test_or_train,"output_lmer_feature_selection_results_window_size_adjusted.rds",sep=""),full.names = TRUE,recursive = TRUE)
} else if(genetype == "pathway") {
my_genes_files = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern=paste("_pathway",cutoff,test_or_train,"output_lmer_feature_selection_results_window_size_adjusted.rds",sep=""),full.names = TRUE,recursive = TRUE)
}
my_genes_before_vs_far = my_genes_files[grep("before_vs_far_from_condition_subject_level",my_genes_files)]
my_genes_before_vs_far = my_genes_before_vs_far[grep("all_HLA",my_genes_before_vs_far)]
my_genes_before_vs_far_list = lapply(my_genes_before_vs_far,function(x) readRDS(x))
my_genes_before_vs_far_list_sig = lapply(1:length(my_genes_before_vs_far_list), function(ind) {
mydf = my_genes_before_vs_far_list[[ind]]
mydf = mydf[mydf$singular == 0,]
mydf = mydf[!is.na(mydf$pvalue),]
mydf$BY = p.adjust(mydf$pvalue,method="BY")
mydf = mydf[mydf$BY < 0.05,]
mydf = mydf[order(mydf$pvalue),]
outputname = gsub(".rds","bootstrap_one_sig_genes.txt",my_genes_before_vs_far[ind])
if(length(rownames(mydf))>0) {
write.table(rownames(mydf),file=outputname,col.names=FALSE,row.names=FALSE,quote=FALSE)
}
mapping_files = gsub("_proportion_train_0.66","",basename(dirname(my_genes_before_vs_far[[ind]])))
mapping_files = gsub("_proportion_train_0.5","",mapping_files)
mapping_files = paste(mapping_files,".mapping.csv",sep="")
train_metadata = paste(basename(dirname(my_genes_before_vs_far[[ind]])),"_test_metadata.csv",sep="")
train_subjects = paste(basename(dirname(my_genes_before_vs_far[[ind]])),".test_subjects.txt",sep="")
return(c(mapping_files,train_metadata,train_subjects,outputname,nrow(mydf)))
})
my_genes_before_vs_far_list_sig = do.call("rbind",my_genes_before_vs_far_list_sig)
my_genes_before_vs_far_list_sig = as.data.frame(my_genes_before_vs_far_list_sig)
my_genes_before_vs_far_list_sig[,5] = as.numeric(my_genes_before_vs_far_list_sig[,5])
my_genes_before_vs_far_list_sig = my_genes_before_vs_far_list_sig[my_genes_before_vs_far_list_sig[,5]>0,]
my_genes_before_vs_far_list_sig = my_genes_before_vs_far_list_sig[,-5]
write.table(my_genes_before_vs_far_list_sig,file=output,sep="\t",col.names=FALSE,row.names=FALSE,quote=FALSE)
return(my_genes_before_vs_far_list_sig)
}
pathway_blah_test_0.9 = get_permutation_input_with_cutoff_test_or_train(genetype="pathway",output="before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_cutoff_0.9.test.txt",0.9,"test")
pathway_blah_test_0.3 = get_permutation_input_with_cutoff_test_or_train(genetype="pathway",output="before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_cutoff_0.3.test.txt",0.3,"test")
species_blah_test = get_permutation_input_with_cutoff_test_or_train(genetype="species",output="before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_species_cutoff_0.3.test.txt",0.3,"test")
```
```{bash}
cd /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4
#rm */*species_bootstrap_ttest_results_round1.rds if need to redo
head -n 4 /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway.txt | tail -n 1 > test.txt
sbatch -c 1 -t 0-02:00 -p short --mem=10G prep_abundance_for_voe_bulk_training_data_v3_create_boostraps_species_pathway.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway.txt 1 pathway
sbatch -c 1 -t 0-02:00 -p short --mem=10G prep_abundance_for_voe_bulk_training_data_v3_create_boostraps_species_pathway.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_species.txt 1 species
sbatch -c 1 -t 0-04:00 -p short --mem=10G prep_abundance_for_voe_bulk_training_data_v3_create_boostraps_species_pathway_cutoff.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_cutoff_0.3.txt 1 pathway 0.3
#do on test data
sbatch -c 1 -t 0-04:00 -p short --mem=10G prep_abundance_for_voe_bulk_training_data_v3_create_boostraps_species_pathway_cutoff_train_or_test.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_cutoff_0.9.test.txt 1 pathway 0.9 test
sbatch -c 1 -t 0-04:00 -p short --mem=10G prep_abundance_for_voe_bulk_training_data_v3_create_boostraps_species_pathway_cutoff_train_or_test.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_cutoff_0.3.test.txt 1 pathway 0.3 test
sbatch -c 1 -t 0-04:00 -p short --mem=10G prep_abundance_for_voe_bulk_training_data_v3_create_boostraps_species_pathway_cutoff_train_or_test.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_species_cutoff_0.3.test.txt 1 species 0.3 test
```
#Now get input for second round of permutation test where I look at window size
```{r}
# rm */*species_bootstrap_ttest_results_round1_sig_names.txt if I want to redo something
library(data.table)
get_permutation_input = function(genetype,output) {
if(genetype=="species") {
my_genes_files = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern="species_bootstrap_ttest_results_round1.rds",full.names = TRUE,recursive = TRUE)
my_genes_files = my_genes_files[-grep("testspecies",my_genes_files)]
} else if(genetype == "pathway") {
my_genes_files = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern="pathway_bootstrap_ttest_results_round1.rds",full.names = TRUE,recursive = TRUE)
my_genes_files = my_genes_files[-grep("testpathway",my_genes_files)]
my_genes_files = my_genes_files[-grep("0.3",my_genes_files)]
}
still_sig_gene_names = lapply(my_genes_files, function(myoutfile) {
print(myoutfile)
myrds = readRDS(myoutfile)
myrds_pvals = do.call("cbind",lapply(myrds, function(mydf) mydf[,1]))
rownames(myrds_pvals) = rownames(myrds[[1]])
proportion_sig = rowSums(myrds_pvals<0.05,na.rm = TRUE)/ncol(myrds_pvals)
proportion_still_sig = proportion_sig[proportion_sig>0.95]
outputname = gsub("_bootstrap_ttest_results_round1.rds","_bootstrap_ttest_results_round1_sig_names.txt",myoutfile)
if(length(names(proportion_still_sig))>0) {
write.table(names(proportion_still_sig),file=outputname,col.names=FALSE,row.names=FALSE,quote=FALSE)
}
mapping_files = gsub("_proportion_train_0.66","",basename(dirname(myoutfile)))
mapping_files = gsub("_proportion_train_0.5","",mapping_files)
mapping_files = paste(mapping_files,".mapping.csv",sep="")
train_metadata = paste(basename(dirname(myoutfile)),"_train_metadata.csv",sep="")
train_subjects = paste(basename(dirname(myoutfile)),".train_subjects.txt",sep="")
return(c(mapping_files,train_metadata,train_subjects,outputname,length(proportion_still_sig)))
})
my_genes_before_vs_far_list_sig = do.call("rbind",still_sig_gene_names)
my_genes_before_vs_far_list_sig = as.data.frame(my_genes_before_vs_far_list_sig)
my_genes_before_vs_far_list_sig[,5] = as.numeric(my_genes_before_vs_far_list_sig[,5])
my_genes_before_vs_far_list_sig = my_genes_before_vs_far_list_sig[my_genes_before_vs_far_list_sig[,5]>0,]
my_genes_before_vs_far_list_sig = my_genes_before_vs_far_list_sig[,-5]
write.table(my_genes_before_vs_far_list_sig,file=output,sep="\t",col.names=FALSE,row.names=FALSE,quote=FALSE)
return(my_genes_before_vs_far_list_sig)
}
species_blah = get_permutation_input(genetype="species",output="before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_species_2.txt")
pathway_blah = get_permutation_input(genetype="pathway",output="before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_2.txt")
get_permutation_input_cutoff = function(genetype,output,cutoff) {
if(genetype=="species") {
my_genes_files = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern=paste(cutoff,"species_bootstrap_ttest_results_round1.rds",sep=""),full.names = TRUE,recursive = TRUE)
} else if(genetype == "pathway") {
my_genes_files = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern=paste(cutoff,"pathway_bootstrap_ttest_results_round1.rds",sep=""),full.names = TRUE,recursive = TRUE)
}
still_sig_gene_names = lapply(my_genes_files, function(myoutfile) {
myrds = readRDS(myoutfile)
myrds_pvals = do.call("cbind",lapply(myrds, function(mydf) mydf[,1]))
rownames(myrds_pvals) = rownames(myrds[[1]])
proportion_sig = rowSums(myrds_pvals<0.05,na.rm=TRUE)/ncol(myrds_pvals)
proportion_still_sig = proportion_sig[proportion_sig>0.95]
outputname = gsub("_bootstrap_ttest_results_round1.rds","_bootstrap_ttest_results_round1_sig_names.txt",myoutfile)
if(length(names(proportion_still_sig))>0) {
write.table(names(proportion_still_sig),file=outputname,col.names=FALSE,row.names=FALSE,quote=FALSE)
}
mapping_files = gsub("_proportion_train_0.66","",basename(dirname(myoutfile)))
mapping_files = gsub("_proportion_train_0.5","",mapping_files)
mapping_files = paste(mapping_files,".mapping.csv",sep="")
train_metadata = paste(basename(dirname(myoutfile)),"_train_metadata.csv",sep="")
train_subjects = paste(basename(dirname(myoutfile)),".train_subjects.txt",sep="")
return(c(mapping_files,train_metadata,train_subjects,outputname,length(proportion_still_sig)))
})
my_genes_before_vs_far_list_sig = do.call("rbind",still_sig_gene_names)
my_genes_before_vs_far_list_sig = as.data.frame(my_genes_before_vs_far_list_sig)
my_genes_before_vs_far_list_sig[,5] = as.numeric(my_genes_before_vs_far_list_sig[,5])
my_genes_before_vs_far_list_sig = my_genes_before_vs_far_list_sig[my_genes_before_vs_far_list_sig[,5]>0,]
my_genes_before_vs_far_list_sig = my_genes_before_vs_far_list_sig[,-5]
write.table(my_genes_before_vs_far_list_sig,file=output,sep="\t",col.names=FALSE,row.names=FALSE,quote=FALSE)
return(my_genes_before_vs_far_list_sig)
}
pathway_blah = get_permutation_input_cutoff(genetype="pathway",output="before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_2_with_cutoff.txt",cutoff=0.3)
get_permutation_input_cutoff_train_or_test = function(genetype,output,cutoff,train_or_test) {
if(genetype=="species") {
my_genes_files = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern=paste(cutoff,train_or_test,"species_bootstrap_ttest_results_round1.rds",sep=""),full.names = TRUE,recursive = TRUE)
} else if(genetype == "pathway") {
my_genes_files = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/",pattern=paste(cutoff,train_or_test,"pathway_bootstrap_ttest_results_round1.rds",sep=""),full.names = TRUE,recursive = TRUE)
}
still_sig_gene_names = lapply(my_genes_files, function(myoutfile) {
myrds = readRDS(myoutfile)
myrds_pvals = do.call("cbind",lapply(myrds, function(mydf) mydf[,1]))
rownames(myrds_pvals) = rownames(myrds[[1]])
proportion_sig = rowSums(myrds_pvals<0.05,na.rm=TRUE)/ncol(myrds_pvals)
proportion_still_sig = proportion_sig[proportion_sig>0.95]
outputname = gsub("_bootstrap_ttest_results_round1.rds","_bootstrap_ttest_results_round1_sig_names.txt",myoutfile)
if(length(names(proportion_still_sig))>0) {
write.table(names(proportion_still_sig),file=outputname,col.names=FALSE,row.names=FALSE,quote=FALSE)
}
mapping_files = gsub("_proportion_train_0.66","",basename(dirname(myoutfile)))
mapping_files = gsub("_proportion_train_0.5","",mapping_files)
mapping_files = paste(mapping_files,".mapping.csv",sep="")
if(train_or_test == "train") {
train_metadata = paste(basename(dirname(myoutfile)),"_train_metadata.csv",sep="")
train_subjects = paste(basename(dirname(myoutfile)),".train_subjects.txt",sep="")
} else {
train_metadata = paste(basename(dirname(myoutfile)),"_test_metadata.csv",sep="")
train_subjects = paste(basename(dirname(myoutfile)),".test_subjects.txt",sep="")
}
return(c(mapping_files,train_metadata,train_subjects,outputname,length(proportion_still_sig)))
})
my_genes_before_vs_far_list_sig = do.call("rbind",still_sig_gene_names)
my_genes_before_vs_far_list_sig = as.data.frame(my_genes_before_vs_far_list_sig)
my_genes_before_vs_far_list_sig[,5] = as.numeric(my_genes_before_vs_far_list_sig[,5])
my_genes_before_vs_far_list_sig = my_genes_before_vs_far_list_sig[my_genes_before_vs_far_list_sig[,5]>0,]
my_genes_before_vs_far_list_sig = my_genes_before_vs_far_list_sig[,-5]
write.table(my_genes_before_vs_far_list_sig,file=output,sep="\t",col.names=FALSE,row.names=FALSE,quote=FALSE)
return(my_genes_before_vs_far_list_sig)
}
pathway_0.3_test_blah = get_permutation_input_cutoff_train_or_test(genetype="pathway",output="before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_0.3_test_2.txt",cutoff=0.3,train_or_test="test")
pathway_0.9_test_blah = get_permutation_input_cutoff_train_or_test(genetype="pathway",output="before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_0.9_test_2.txt",cutoff=0.9,train_or_test="test")
species_0.9_test_blah = get_permutation_input_cutoff_train_or_test(genetype="species",output="before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_species_0.3_test_2.txt",cutoff=0.3,train_or_test="test")
```
```{bash}
cd /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4
# if I want to redo rm */*species_bootstrap_ttest_results_round2.rds
sbatch -c 1 -t 0-02:00 -p short --mem=10G prep_abundance_for_voe_bulk_training_data_v3_create_boostraps_window_size_species_pathway.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_2.txt 1 pathway
sbatch -c 1 -t 0-02:00 -p short --mem=10G prep_abundance_for_voe_bulk_training_data_v3_create_boostraps_window_size_species_pathway.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_species_2.txt 1 species
sbatch -c 1 -t 0-06:00 -p short --mem=10G prep_abundance_for_voe_bulk_training_data_v3_create_boostraps_window_size_species_pathway_cutoff.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_2_with_cutoff.txt 1 pathway 0.3
# do test
sbatch -c 1 -t 0-06:00 -p short --mem=10G prep_abundance_for_voe_bulk_training_data_v3_create_boostraps_window_size_species_pathway_cutoff_train_test.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_0.3_test_2.txt 1 pathway 0.3 test
sbatch -c 1 -t 0-06:00 -p short --mem=10G prep_abundance_for_voe_bulk_training_data_v3_create_boostraps_window_size_species_pathway_cutoff_train_test.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_0.9_test_2.txt 1 pathway 0.9 test
sbatch -c 1 -t 0-06:00 -p short --mem=10G prep_abundance_for_voe_bulk_training_data_v3_create_boostraps_window_size_species_pathway_cutoff_train_test.bash /n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_species_0.3_test_2.txt 1 species 0.3 test
```
#Get all sig genes so far
#Do one last bootstrapping where I take random control subjects and compare them against each other as a negative control
```{r}
library(data.table)
library(RNOmni)
# first get list of sig gene
get_sig_genes = function(original_input,abundance_type,cutoff,train_or_test) {
input_orig = read.table(original_input,header=FALSE,sep="\t")
input_final_list = apply(input_orig, 1, function(myrow) {
my_dirname = dirname(myrow[4])
print(my_dirname)
if(abundance_type == "species") {
myfiles = list.files(my_dirname,pattern="species_bootstrap_ttest_results_round2.rds",full.names = TRUE)
if(cutoff == 0.3) {
if(train_or_test == "train") {
myfiles = myfiles[match(paste(my_dirname,"/",basename(my_dirname),"species_bootstrap_ttest_results_round2.rds",sep=""),myfiles)]
} else {
myfiles = myfiles[match(paste(my_dirname,"/",basename(my_dirname),"0.3testspecies_bootstrap_ttest_results_round2.rds",sep=""),myfiles)]
}
}
} else if(abundance_type == "pathway") {
myfiles = list.files(my_dirname,pattern="pathway_bootstrap_ttest_results_round2.rds",full.names = TRUE)
if(cutoff == 0.3) {
if(train_or_test == "train") {
myfiles = myfiles[match(paste(my_dirname,"/",basename(my_dirname),"0.3pathway_bootstrap_ttest_results_round2.rds",sep=""),myfiles)]
} else {
myfiles = myfiles[match(paste(my_dirname,"/",basename(my_dirname),"0.3testpathway_bootstrap_ttest_results_round2.rds",sep=""),myfiles)]
}
} else if(cutoff == 0.9) {
if(train_or_test == "train") {
myfiles = myfiles[match(paste(my_dirname,"/",basename(my_dirname),"pathway_bootstrap_ttest_results_round2.rds",sep=""),myfiles)]
} else {
myfiles = myfiles[match(paste(my_dirname,"/",basename(my_dirname),"0.9testpathway_bootstrap_ttest_results_round2.rds",sep=""),myfiles)]
}
}
}
myrds = readRDS(myfiles)
myrds_pvals = do.call("cbind",lapply(myrds, function(mydf) mydf[,1]))
rownames(myrds_pvals) = rownames(myrds[[1]])
proportion_sig = rowSums(myrds_pvals<0.05)/ncol(myrds_pvals)
proportion_still_sig = proportion_sig[proportion_sig>0.95]
if(length(proportion_still_sig)>0) {
return(data.frame(geneName=names(proportion_still_sig),model=basename(my_dirname)))
} else {
return(NULL)
}
})
input_final_list = do.call("rbind",input_final_list)
return(input_final_list)
}
pathway_0.9_train = get_sig_genes(original_input="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_2.txt",abundance_type="pathway",cutoff=0.9,train_or_test="train")
species_0.3_train = get_sig_genes(original_input="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_species_2.txt",abundance_type="species",cutoff=0.3,train_or_test="train")
pathway_0.3_train = get_sig_genes(original_input="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_2_with_cutoff.txt",abundance_type="pathway",cutoff=0.3,train_or_test="train")
pathway_0.3_test = get_sig_genes(original_input="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_0.3_test_2.txt",abundance_type="pathway",cutoff=0.3,train_or_test="test")
pathway_0.9_test = get_sig_genes(original_input="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_0.9_test_2.txt",abundance_type="pathway",cutoff=0.9,train_or_test="test")
species_0.3.test = get_sig_genes(original_input="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_species_0.3_test_2.txt",abundance_type="species",cutoff=0.3,train_or_test="test")
# now find genes that are sig in the train and test
pathway_0.9_train_test_sig_genes = data.table(rbind(pathway_0.9_train,pathway_0.9_test))[,.N,by=.(model,geneName)]
pathway_0.9_train_test_sig_genes = pathway_0.9_train_test_sig_genes[N==2]
pathway_0.3_train_test_sig_genes = data.table(rbind(pathway_0.3_train,pathway_0.3_test))[,.N,by=.(model,geneName)]
pathway_0.3_train_test_sig_genes = pathway_0.3_train_test_sig_genes[N==2]
species_0.3_train_test_sig_genes = data.table(rbind(species_0.3_train,species_0.3.test))[,.N,by=.(model,geneName)]
species_0.3_train_test_sig_genes = species_0.3_train_test_sig_genes[N==2]
get_negative_genes = function(abundance_type,gene_model) {
gene_model$condition = sapply(strsplit(gene_model$model,split="-"), function(my_temp_list) {
beggin_remove = min(grep("month",my_temp_list))
return(paste(my_temp_list[-seq(beggin_remove,length(my_temp_list))],collapse="-"))
})
gene_model$age = sapply(strsplit(gene_model$model,split="-"), function(my_temp_list) {
beggin_remove = grep("month",my_temp_list)
month_parts = my_temp_list[beggin_remove]
month_parts = gsub("_before_vs_far_from_condition_subject_level","",month_parts)
return(paste(month_parts,collapse="-"))
})
gene_model$proportion_train = sapply(strsplit(gene_model$model,split="_proportion_train_"), function(my_temp_list) {
train_prop = strsplit(my_temp_list[2],split="_")[[1]][1]
return(train_prop)
})
if(length(-grep("serconverters_or_T1D",gene_model$condition))>0) {
gene_model = gene_model[-grep("serconverters_or_T1D",gene_model$condition),]
}
geneNames = gene_model$geneName
metadata = read.csv('/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/teddy_metadata_20190821_with_GRS2.csv')
# don't include metadata we don't have GRS scores for
metadata = metadata[!is.na(metadata$GRS_score),]
# also only include samples we have abundance data for
abundance_samples = read.table("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/sample_we_have_abundance_data_for.txt",header=FALSE)
abundance_samples = abundance_samples[,1]
abundance_metadata_samples = intersect(metadata$Run,abundance_samples)
metadata = metadata[match(abundance_metadata_samples,metadata$Run),]
metadata_healthy = metadata[is.na(metadata$age_first_MIAA) & is.na(metadata$age_first_GAD) & is.na(metadata$age_first_IA2A) & is.na(metadata$age_t1d),]
if(abundance_type == "species") {
abundance_data = fread("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/humann_analysis/TEDDY_species_abundances.tsv",sep="\t",header = TRUE,data.table = FALSE)
rownames(abundance_data) = abundance_data[,1]
abundance_data = abundance_data[,-1]
myfiles = list.files("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4",pattern="species_bootstrap_ttest_results_round2.rds",full.names = TRUE,recursive = TRUE)
} else if(abundance_type == "pathway") {
abundance_data = fread("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/humann_analysis/TEDDY_pathabundance-cpm.tsv",sep="\t",header=TRUE,data.table = FALSE)
rownames(abundance_data) = abundance_data[,1]
abundance_data = abundance_data[,-1]
colnames(abundance_data) = gsub("_human_free_Abundance","",colnames(abundance_data))
}
rownames(abundance_data) = make.names(rownames(abundance_data))
geneNames = make.names(geneNames)
# get all significant genes so far in any model
abundance_data = abundance_data[geneNames,]
samples_to_keep = intersect(colnames(abundance_data),metadata_healthy$Run)
abundance_data = abundance_data[,match(samples_to_keep,colnames(abundance_data))]
metadata_healthy = metadata_healthy[match(samples_to_keep,metadata_healthy$Run),]
#
boot_function = function(window_size,myseed,mygene) {
mygene = make.names(mygene)
# choose a random age in the metadata
set.seed(myseed)
# lets make sure that we sample something large enough so we have a sample less than, to compare to
min_age_to_sample = min(metadata_healthy$age_at_collection) + window_size
sampled_age = sample(metadata_healthy$age_at_collection[metadata_healthy$age_at_collection>min_age_to_sample],size=1)
min_age = sampled_age-(window_size/2)
max_age = sampled_age+(window_size/2)
indexes_in_window = metadata_healthy$age_at_collection >= min_age & metadata_healthy$age_at_collection <= max_age
metadata_in_window = metadata_healthy[which(indexes_in_window),,drop=FALSE]
metadata_out_window = metadata_healthy[metadata_healthy$age_at_collection <min_age,,drop=FALSE]
#metadata_out_window = metadata_healthy[which(!indexes_in_window),]
abundance_in_window = abundance_data[mygene,metadata_in_window$Run]
abundance_out_window = abundance_data[mygene,metadata_out_window$Run]
# set colnames of abundance to subject IDs
colnames(abundance_in_window) = metadata_in_window$maskid
colnames(abundance_out_window) = metadata_out_window$maskid
abundance_in_window$name = rownames(abundance_in_window)
abundance_out_window$name = rownames(abundance_out_window)
abundance_in_window_dt = as.data.table(abundance_in_window)
abundance_out_window_dt = as.data.table(abundance_out_window)
abundance_in_window_dt_melt = data.table::melt(abundance_in_window_dt,id.vars=c("name"))
abundance_out_window_dt_melt = data.table::melt(abundance_out_window_dt,id.vars=c("name"))
abundance_in_window_avg = abundance_in_window_dt_melt[,mean(value),by=.(name,variable)]
abundance_out_window_avg = abundance_out_window_dt_melt[,mean(value),by=.(name,variable)]
abundance_in_window_avg_long = dcast(abundance_in_window_avg,variable ~ name, value.var="V1")
abundance_out_window_avg_long = dcast(abundance_out_window_avg,variable ~ name, value.var="V1")
abundance_in_window_avg_long = as.data.frame(abundance_in_window_avg_long)
abundance_out_window_avg_long = as.data.frame(abundance_out_window_avg_long)
rownames(abundance_in_window_avg_long) = abundance_in_window_avg_long[,1]
rownames(abundance_out_window_avg_long) = abundance_out_window_avg_long[,1]
abundance_in_window_avg_long = abundance_in_window_avg_long[,-1,drop=FALSE]
abundance_out_window_avg_long = abundance_out_window_avg_long[,-1,drop=FALSE]
# find the subjects that are in both
subjects_in_out_window = intersect(rownames(abundance_in_window_avg_long),rownames(abundance_out_window_avg_long))
abundance_in_window_avg_long = abundance_in_window_avg_long[subjects_in_out_window,,drop=FALSE]
abundance_out_window_avg_long = abundance_out_window_avg_long[subjects_in_out_window,,drop=FALSE]
rownames(abundance_out_window_avg_long) = paste(rownames(abundance_out_window_avg_long),"_0",sep="")
rownames(abundance_in_window_avg_long) = paste(rownames(abundance_in_window_avg_long),"_1",sep="")
abundance_in_out_window_avg_long = rbind(abundance_in_window_avg_long,abundance_out_window_avg_long)
abundance_in_out_window_avg_long = apply(abundance_in_out_window_avg_long,2, function(mycol) {
return(RankNorm(mycol))
})
abundance_out_window_avg_long2 = abundance_in_out_window_avg_long[grep("_0",rownames(abundance_in_out_window_avg_long)),,drop=FALSE]
abundance_in_window_avg_long2 = abundance_in_out_window_avg_long[grep("_1",rownames(abundance_in_out_window_avg_long)),,drop=FALSE]
ttest_results = sapply(1:ncol(abundance_out_window_avg_long2), function(i) {
pval = t.test(abundance_in_window_avg_long2[,i],abundance_out_window_avg_long2[,i],paired=TRUE,alternative="two.sided")$p.value
return(pval)
})
return(ttest_results)
}
all_pvalues = sapply(1:nrow(gene_model), function(myindex) {
myrow = unlist(gene_model[myindex,])
mygene = myrow["geneName"]
if(myrow["age"] == "2month") {
window_size = 60
} else if(myrow["age"] == "3month") {
window_size = 90
} else {
window_size = 30
}
boot_res_one_gene = sapply(1:100,function(x) {
boot_res = boot_function(window_size=window_size,myseed=x,mygene = mygene)
return(boot_res)
})
proportion_0.05 = sum(boot_res_one_gene < 0.05)/100
return(c(myrow,proportion_sig=proportion_0.05))
})
all_pvalues = t(all_pvalues)
return(all_pvalues)
}
species_0.3_train_test_sig_genes_negatives = get_negative_genes(abundance_type="species",gene_model=species_0.3_train_test_sig_genes)
species_0.3_train_test_sig_genes_negatives = as.data.frame(species_0.3_train_test_sig_genes_negatives)
species_0.3_train_test_sig_genes_negatives[,7] = as.numeric(species_0.3_train_test_sig_genes_negatives[,7])
species_0.3_train_test_sig_genes_negatives_to_keep = species_0.3_train_test_sig_genes_negatives[species_0.3_train_test_sig_genes_negatives$proportion_sig<0.05,]
pathway_0.3_train_test_sig_genes_negatives = get_negative_genes(abundance_type="pathway",gene_model=pathway_0.3_train_test_sig_genes)
pathway_0.3_train_test_sig_genes_negatives = as.data.frame(pathway_0.3_train_test_sig_genes_negatives)
pathway_0.3_train_test_sig_genes_negatives[,7] = as.numeric(pathway_0.3_train_test_sig_genes_negatives[,7])
pathway_0.3_train_test_sig_genes_negatives_to_keep = pathway_0.3_train_test_sig_genes_negatives[pathway_0.3_train_test_sig_genes_negatives$proportion_sig<0.05,]
pathway_0.9_train_test_sig_genes_negatives = get_negative_genes(abundance_type="pathway",gene_model=pathway_0.9_train_test_sig_genes)
pathway_0.9_train_test_sig_genes_negatives = as.data.frame(pathway_0.9_train_test_sig_genes_negatives)
pathway_0.9_train_test_sig_genes_negatives[,7] = as.numeric(pathway_0.9_train_test_sig_genes_negatives[,7])
pathway_0.9_train_test_sig_genes_negatives_to_keep = pathway_0.9_train_test_sig_genes_negatives[pathway_0.9_train_test_sig_genes_negatives$proportion_sig<0.05,]
length(species_0.3_train_test_sig_genes_negatives) # 0
length(pathway_0.3_train_test_sig_genes_negatives) # 0
length(pathway_0.9_train_test_sig_genes_negatives) # 0
if(length(species_0.3_train_test_sig_genes_negatives)>0) {
species_0.3_train_test_sig_genes = species_0.3_train_test_sig_genes[-match(species_0.3_train_test_sig_genes_negatives,species_0.3_train_test_sig_genes$geneName),]
}
if(length(pathway_0.3_train_test_sig_genes_negatives)>0) {
pathway_0.3_train_test_sig_genes = pathway_0.3_train_test_sig_genes[-match(pathway_0.3_train_test_sig_genes_negatives,pathway_0.3_train_test_sig_genes$geneName),]
}
if(length(pathway_0.9_train_test_sig_genes_negatives)>0) {
pathway_0.9_train_test_sig_genes = pathway_0.9_train_test_sig_genes[-match(pathway_0.9_train_test_sig_genes_negatives,pathway_0.9_train_test_sig_genes$geneName),]
}
write.csv(pathway_0.9_train_test_sig_genes,file="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/pathway_0.9_train_test_sig_genes.csv",row.names=FALSE)
write.csv(pathway_0.3_train_test_sig_genes,file="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/pathway_0.3_train_test_sig_genes.csv",row.names=FALSE)
write.csv(species_0.3_train_test_sig_genes,file="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/species_0.3_train_test_sig_genes.csv",row.names=FALSE)
```
#Try stratifying by age. So compare different individuals who get phenotype and don't get phenotype in a window
```{r}
library(data.table)
library(RNOmni)
# first get list of sig gene
get_sig_genes = function(original_input,abundance_type,cutoff,train_or_test) {
input_orig = read.table(original_input,header=FALSE,sep="\t")
input_final_list = apply(input_orig, 1, function(myrow) {
my_dirname = dirname(myrow[4])
if(abundance_type == "species") {
myfiles = list.files(my_dirname,pattern="species_bootstrap_ttest_results_round2.rds",full.names = TRUE)
if(cutoff == 0.3) {
if(train_or_test == "train") {
myfiles = myfiles[match(paste(my_dirname,"/",basename(my_dirname),"species_bootstrap_ttest_results_round2.rds",sep=""),myfiles)]
} else {
myfiles = myfiles[match(paste(my_dirname,"/",basename(my_dirname),"0.3testspecies_bootstrap_ttest_results_round2.rds",sep=""),myfiles)]
}
}
} else if(abundance_type == "pathway") {
myfiles = list.files(my_dirname,pattern="pathway_bootstrap_ttest_results_round2.rds",full.names = TRUE)
if(cutoff == 0.3) {
if(train_or_test == "train") {
myfiles = myfiles[match(paste(my_dirname,"/",basename(my_dirname),"0.3pathway_bootstrap_ttest_results_round2.rds",sep=""),myfiles)]
} else {
myfiles = myfiles[match(paste(my_dirname,"/",basename(my_dirname),"0.3testpathway_bootstrap_ttest_results_round2.rds",sep=""),myfiles)]
}
} else if(cutoff == 0.9) {
if(train_or_test == "train") {
myfiles = myfiles[match(paste(my_dirname,"/",basename(my_dirname),"pathway_bootstrap_ttest_results_round2.rds",sep=""),myfiles)]
} else {
myfiles = myfiles[match(paste(my_dirname,"/",basename(my_dirname),"0.9testpathway_bootstrap_ttest_results_round2.rds",sep=""),myfiles)]
}
}
}
myrds = readRDS(myfiles)
myrds_pvals = do.call("cbind",lapply(myrds, function(mydf) mydf[,1]))
rownames(myrds_pvals) = rownames(myrds[[1]])
proportion_sig = rowSums(myrds_pvals<0.05)/ncol(myrds_pvals)
proportion_still_sig = proportion_sig[proportion_sig>0.95]
if(length(proportion_still_sig)>0) {
return(data.frame(geneName=names(proportion_still_sig),model=basename(my_dirname)))
} else {
return(NULL)
}
})
input_final_list = do.call("rbind",input_final_list)
return(input_final_list)
}
pathway_0.9_train = get_sig_genes(original_input="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_2.txt",abundance_type="pathway",cutoff=0.9,train_or_test="train")
species_0.3_train = get_sig_genes(original_input="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_species_2.txt",abundance_type="species",cutoff=0.3,train_or_test="train")
pathway_0.3_train = get_sig_genes(original_input="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_2_with_cutoff.txt",abundance_type="pathway",cutoff=0.3,train_or_test="train")
pathway_0.3_test = get_sig_genes(original_input="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_0.3_test_2.txt",abundance_type="pathway",cutoff=0.3,train_or_test="test")
pathway_0.9_test = get_sig_genes(original_input="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_pathway_0.9_test_2.txt",abundance_type="pathway",cutoff=0.9,train_or_test="test")
species_0.3.test = get_sig_genes(original_input="/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_alignments/parsed_data_4/before_vs_after_subject_level_bootstrapping_input_files_3_window_size_boot_species_0.3_test_2.txt",abundance_type="species",cutoff=0.3,train_or_test="test")
# now find genes that are sig in the train and test
pathway_0.9_train_test_sig_genes = data.table(rbind(pathway_0.9_train,pathway_0.9_test))[,.N,by=.(model,geneName)]
pathway_0.9_train_test_sig_genes = pathway_0.9_train_test_sig_genes[N==2]
pathway_0.3_train_test_sig_genes = data.table(rbind(pathway_0.3_train,pathway_0.3_test))[,.N,by=.(model,geneName)]
pathway_0.3_train_test_sig_genes = pathway_0.3_train_test_sig_genes[N==2]
species_0.3_train_test_sig_genes = data.table(rbind(species_0.3_train,species_0.3.test))[,.N,by=.(model,geneName)]
species_0.3_train_test_sig_genes = species_0.3_train_test_sig_genes[N==2]
get_sig_genes_age_stratified = function(abundance_type,gene_model) {
metadata = read.csv('/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/teddy_metadata_20190821_with_GRS2.csv')
# don't include metadata we don't have GRS scores for
metadata = metadata[!is.na(metadata$GRS_score),]
# also only include samples we have abundance data for
abundance_samples = read.table("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/sample_we_have_abundance_data_for.txt",header=FALSE)
abundance_samples = abundance_samples[,1]
abundance_metadata_samples = intersect(metadata$Run,abundance_samples)
metadata = metadata[match(abundance_metadata_samples,metadata$Run),]
metadata$num_autoantibody_ever = apply(metadata[,c("age_first_MIAA","age_first_GAD","age_first_IA2A")], 1, function(x) sum(!is.na(x)))
metadata$sero2 = metadata$num_autoantibody_ever>=2
metadata_temp_healthy = metadata[metadata$t1d_sero_control == "control",]
gene_model$condition = sapply(strsplit(gene_model$model,split="-"), function(my_temp_list) {
beggin_remove = min(grep("month",my_temp_list))
return(paste(my_temp_list[-seq(beggin_remove,length(my_temp_list))],collapse="-"))
})
gene_model$age = sapply(strsplit(gene_model$model,split="-"), function(my_temp_list) {
beggin_remove = grep("month",my_temp_list)
month_parts = my_temp_list[beggin_remove]
month_parts = gsub("_before_vs_far_from_condition_subject_level","",month_parts)
return(paste(month_parts,collapse="-"))
})
gene_model$proportion_train = sapply(strsplit(gene_model$model,split="_proportion_train_"), function(my_temp_list) {
train_prop = strsplit(my_temp_list[2],split="_")[[1]][1]
return(train_prop)
})
if(length(-grep("serconverters_or_T1D",gene_model$condition))>0) {
gene_model = gene_model[-grep("serconverters_or_T1D",gene_model$condition),]
}
if(abundance_type == "species") {
abundance_data = fread("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/humann_analysis/TEDDY_species_abundances.tsv",sep="\t",header = TRUE,data.table = FALSE)
rownames(abundance_data) = abundance_data[,1]
abundance_data = abundance_data[,-1]
} else if(abundance_type == "pathway") {
abundance_data = fread("/n/data1/joslin/icrb/kostic/szimmerman/TEDDY_analysis/humann_analysis/TEDDY_pathabundance-cpm.tsv",sep="\t",header=TRUE,data.table = FALSE)
rownames(abundance_data) = abundance_data[,1]
abundance_data = abundance_data[,-1]
colnames(abundance_data) = gsub("_human_free_Abundance","",colnames(abundance_data))
}
rownames(abundance_data) = make.names(rownames(abundance_data))
gene_matrix_list = lapply(1:nrow(gene_model), function(myrow_index) {
myrow = unlist(gene_model[myrow_index,])
print(myrow)
geneName = myrow[2]
model_name = myrow[1]
condtion = myrow["condition"]
window = myrow["age"]
if(condtion == "healthy_pre-GAD") {
metadata_temp = metadata[!is.na(metadata$age_first_GAD),]
metadata_temp$time_to_condition = metadata_temp$age_first_GAD - metadata_temp$age_at_collection
} else if(condtion == "healthy_pre-sero") {
metadata_temp = metadata[metadata$sero2 ==TRUE,]
metadata_temp$time_to_condition = metadata_temp$age_mult_persist - metadata_temp$age_at_collection
} else if(condtion == "healthy_pre-IA2A") {
metadata_temp = metadata[!is.na(metadata$age_first_IA2A),]
metadata_temp$time_to_condition = metadata_temp$age_first_IA2A - metadata_temp$age_at_collection
} else if(condtion == "healthy_pre-t1d") {
metadata_temp = metadata[!is.na(metadata$age_t1d),]
metadata_temp$time_to_condition = metadata_temp$age_t1d - metadata_temp$age_at_collection
} else if(condtion == "healthy_pre-MIAA") {
metadata_temp = metadata[!is.na(metadata$age_first_MIAA),]