assess_linearization

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library(dplyr)
library(glue)
library(tidyverse)

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path_to_predictdb_summaries <- "/beagle3/haky/users/saideep/projects/aracena_modeling/linearization/predixcan/models/summary"

s_files <- list.files(path_to_predictdb_summaries)

s_dfs <- list()
for (s_file in s_files) {
    s_dfs[[s_file]] <- read_delim(glue("{path_to_predictdb_summaries}/{s_file}"), delim = "\t")
}

full_df <- bind_rows(s_dfs)

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hist(full_df$rho_avg, breaks = 100)

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charles_linearize_sum_path <- "/beagle3/haky/users/charles/project/singleXcanDL/PredicDB/Corrected_T2D_Geuvadis/work_dir/results/database/Model_summary.txt"

charles_df <- read_delim(charles_linearize_sum_path, delim = "\t")

hist(charles_df$rho_avg, breaks = 100)

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full_df_reorder <- full_df %>% 
    arrange(gene_id)

charles_df_reorder <- charles_df %>% 
    arrange(gene)

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full_df_reorder

# A tibble: 18,714 × 24
   gene_id         gene_name gene_type    alpha n_snps_in_window n_snps_in_model
   <chr>           <chr>     <chr>        <dbl>            <dbl>           <dbl>
 1 ENSG00000000419 DPM1      protein_cod…   0.5             2915              88
 2 ENSG00000000457 SCYL3     protein_cod…   0.5             2760             209
 3 ENSG00000000460 C1orf112  protein_cod…   0.5             2731             192
 4 ENSG00000000938 FGR       protein_cod…   0.5             1139              97
 5 ENSG00000000971 CFH       protein_cod…   0.5             1598              37
 6 ENSG00000001036 FUCA2     protein_cod…   0.5             2255             169
 7 ENSG00000001084 GCLC      protein_cod…   0.5             2861             152
 8 ENSG00000001167 NFYA      protein_cod…   0.5             3285             171
 9 ENSG00000001460 STPG1     protein_cod…   0.5             2324             182
10 ENSG00000001461 NIPAL3    protein_cod…   0.5             2293              99
# ℹ 18,704 more rows
# ℹ 18 more variables: lambda_min_mse <dbl>, test_R2_avg <dbl>,
#   test_R2_sd <dbl>, cv_R2_avg <dbl>, cv_R2_sd <dbl>, in_sample_R2 <dbl>,
#   nested_cv_fisher_pval <dbl>, rho_avg <dbl>, rho_se <dbl>, rho_zscore <dbl>,
#   rho_avg_squared <dbl>, zscore_pval <dbl>, cv_rho_avg <dbl>,
#   cv_rho_se <dbl>, cv_rho_avg_squared <dbl>, cv_zscore_est <dbl>,
#   cv_zscore_pval <dbl>, cv_pval_est <dbl>

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charles_df_reorder

# A tibble: 18,714 × 24
   gene            gene_name gene_type    alpha n_snps_in_window n_snps_in_model
   <chr>           <chr>     <chr>        <dbl>            <dbl>           <dbl>
 1 ENSG00000000419 DPM1      protein_cod…   0.5             2915              96
 2 ENSG00000000457 SCYL3     protein_cod…   0.5             2760             292
 3 ENSG00000000460 C1orf112  protein_cod…   0.5             2731             159
 4 ENSG00000000938 FGR       protein_cod…   0.5             1139             131
 5 ENSG00000000971 CFH       protein_cod…   0.5             1598              52
 6 ENSG00000001036 FUCA2     protein_cod…   0.5             2255             155
 7 ENSG00000001084 GCLC      protein_cod…   0.5             2860             139
 8 ENSG00000001167 NFYA      protein_cod…   0.5             3285             199
 9 ENSG00000001460 STPG1     protein_cod…   0.5             2324             141
10 ENSG00000001461 NIPAL3    protein_cod…   0.5             2293             264
# ℹ 18,704 more rows
# ℹ 18 more variables: lambda_min_mse <dbl>, test_R2_avg <dbl>,
#   test_R2_sd <dbl>, cv_R2_avg <dbl>, cv_R2_sd <dbl>, in_sample_R2 <dbl>,
#   nested_cv_fisher_pval <dbl>, rho_avg <dbl>, rho_se <dbl>, rho_zscore <dbl>,
#   rho_avg_squared <dbl>, zscore_pval <dbl>, cv_rho_avg <dbl>,
#   cv_rho_se <dbl>, cv_rho_avg_squared <dbl>, cv_zscore_est <dbl>,
#   cv_zscore_pval <dbl>, cv_pval_est <dbl>

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plot(full_df_reorder$rho_avg, charles_df_reorder$rho_avg)

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rho_df <- data.frame(sai=full_df_reorder$rho_avg, charles=charles_df_reorder$rho_avg)

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library(ggplot2)

ggplot(rho_df) + geom_density_2d_filled(aes(x=sai, y=charles))