update upload assemble and upload scripts

src/02.2_assemble_K50_dG_dataset.R

@@ -11,38 +11,10 @@ ThermoMPNN_splits <- arrow::read_parquet("intermediate/ThermoMPNN_splits.parquet
 # Dataset1 consists of all cDNA proteolysis measurements of stability
 dataset1 <- readr::read_csv(
     file = "data/Processed_K50_dG_datasets/Tsuboyama2023_Dataset1_20230416.csv",
-    col_types = readr::cols(
-        name = readr::col_character(),
-        dna_seq = readr::col_character(),
-        log10_K50_t = readr::col_double(),
-        log10_K50_t_95CI_high = readr::col_double(),
-        log10_K50_t_95CI_low = readr::col_double(),
-        log10_K50_t_95CI = readr::col_double(),
-        fitting_error_t = readr::col_double(),
-        log10_K50unfolded_t = readr::col_double(),
-        deltaG_t = readr::col_double(),
-        deltaG_t_95CI_high = readr::col_double(),
-        deltaG_t_95CI_low = readr::col_double(),
-        deltaG_t_95CI = readr::col_double(),
-        log10_K50_c = readr::col_double(),
-        log10_K50_c_95CI_high = readr::col_double(),
-        log10_K50_c_95CI_low = readr::col_double(),
-        log10_K50_c_95CI = readr::col_double(),
-        fitting_error_c = readr::col_double(),
-        log10_K50unfolded_c = readr::col_double(),
-        deltaG_c = readr::col_double(),
-        deltaG_c_95CI_high = readr::col_double(),
-        deltaG_c_95CI_low = readr::col_double(),
-        deltaG_c_95CI = readr::col_double(),
-        deltaG = readr::col_double(),
-        deltaG_95CI_high = readr::col_double(),
-        deltaG_95CI_low = readr::col_double(),
-        deltaG_95CI = readr::col_double(),
-        log10_K50_trypsin_ML = readr::col_double(),
-        log10_K50_chymotrypsin_ML = readr::col_double()))
+    show_col_types = FALSE)
 
 # note that some of the log10_K50_trypsin_ML and log10_K50_chmotrypsin_ML values are "-" and ">2.5".
-# These are parsed as NA values"
+# to maintain these non-standard values, we keep them as strings for the full dataset
 
 dataset1 |>
     arrow::write_parquet(
@@ -59,15 +31,21 @@ dataset1 |>
 
 dataset2 <- readr::read_csv(
     file = "data/Processed_K50_dG_datasets/Tsuboyama2023_Dataset2_Dataset3_20230416.csv",
-    show_col_types = FALSE)
+    show_col_types = FALSE) |>
+    dplyr::mutate(
+        log10_K50_trypsin_ML = as.numeric(log10_K50_trypsin_ML),
+        log10_K50_chymotrypsin_ML = as.numeric(log10_K50_chymotrypsin_ML),
+        dG_ML = as.numeric(dG_ML),
+        ddG_ML = as.numeric(ddG_ML))
 # 776,298 rows
 
 dataset2 |>
     arrow::write_parquet(
         "intermediate/dataset2.parquet")
+    
 
 dataset3 <- dataset2 |>
-    dplyr::filter(ddG_ML != "-")
+    dplyr::filter(!is.na(ddG_ML))
 
 dataset3 |>
     arrow::write_parquet(

src/02.2_check_assembled_datasets.R

@@ -1,7 +1,38 @@
 
 
 # consistency between models and function predictions
-source("product/MPI/src/summarize_map.R")
+source("src/summarize_map.R")
+
+
+dataset1_name <- arrow::read_parquet(
+    "intermediate/dataset1.parquet",
+    col_select = "name") |>
+    dplyr::mutate(
+        WT_name = name |> stringr::str_replace("pdb_[A-Z][0-9]+[A-Z]", "pdb")) |>
+    dplyr::filter(
+        !(WT_name |> stringr::str_detect("[0-9][A-Z0-9a-z]{3}([.]pdb)?")),
+        !(WT_name |> stringr::str_detect("ruler")))
+
+names_joined |> dplyr::filter(is.na(name_models)) |> dplyr::select(-name_models) |> dplyr::filter(!(WT_name |> stringr::str_detect("ruler")), !(WT_name |> stringr::str_detect("set")), !(WT_name |> stringr::str_detect("_del")), !(WT_name |> stringr::str_detect("_ins")), !(WT_name |> stringr::str_detect("_wt[a-z]")), !(WT_name |> stringr::str_detect("scramble")), !(WT_name |> stringr::str_detect("(PP5|ZF5)[.]3")), !(WT_name |> stringr::str_detect("(UAH|SAH)-p53-8R"))) |> dplyr::filter(WT_name |> stringr::str_detect("pdb"))
+
+models_name <- arrow::read_parquet(
+    "intermediate/AlphaFold_model_PDBs.parquet",
+    col_select = "name") |>
+    dplyr::mutate(
+        name = name |> stringr::str_replace(":", "[|]"))
+
+names_joined <- dplyr::full_join(
+    dataset1_name |> dplyr::mutate(name_dataset1 = WT_name),
+    models_name |> dplyr::mutate(name_models = name),
+    by = c("WT_name" = "name"))
+
+
+names_joined_summary <- names_joined |>
+    summarize_map(
+        x_cols = name_dataset1,
+        y_cols = name_models,
+        verbose = TRUE)
+
 
 
 
@@ -13,7 +44,7 @@ check_id_consistency <- function(
     if (verbose) {
         cat("Loading model ids...\n")
     }
-    ids <- arrow::read_parquet(
+    dataset1 <- arrow::read_parquet(
         paste0("intermediate/", dataset_tag, "_", split, ".parquet"),
         col_select = "id")
 

src/02.3_assemble_structure_datasets.R

@@ -0,0 +1,49 @@
+#' Assemble PDBs
+#'
+#' @param data_path character directory .pdb.gz files are located
+#' @param output_path character output .parquet path
+#'
+#' Write output_path .parquet file with columns
+#'   <id> <pdb>
+assemble_models <- function(
+    data_path,
+    output_path) {
+
+    cat(
+        "data path: ", data_path, "\n",
+        "output path: ", output_path, "\n",
+        sep = "")
+
+    file_index <- 1
+    models <- list.files(
+        path = data_path,
+        full.names = TRUE,
+        pattern = "*.pdb",
+        recursive = TRUE) |>
+        purrr::map_dfr(.f = function(path) {
+            file_handle <- path |>
+                file(open = "rb") |>
+                gzcon()
+
+            if( file_index %% 20 == 0) {
+                cat("Reading  '", path, "' ", file_index, "\n", sep = "")
+            }
+            file_index <<- file_index + 1
+
+            lines <- file_handle |> readLines()
+            file_handle |> close()
+
+            data.frame(
+                name = path |>
+                    basename() |>
+                    stringr::str_replace("[:]", "|"),
+                pdb = lines |> paste0(collapse = "\n"))
+        })
+    models |> arrow::write_parquet(output_path)
+    models
+}
+
+
+assemble_models(
+    data_path = "data/AlphaFold_model_PDBs",
+    output_path = "intermediate/AlphaFold_model_PDBs.parquet")

src/03.1_upload_data.py

@@ -20,7 +20,7 @@ import datasets
 # dataset2
 # dataset3
 # dataset3_single
-# dataset3_single_CV
+# dataset3_single_cv
 
 
 
@@ -94,32 +94,32 @@ dataset.push_to_hub(
     commit_message = "Upload dataset3_single")
 
 
-##### dataset3_single_CV #######
+##### dataset3_single_cv #######
 dataset = datasets.load_dataset(
     "parquet",
-    name = "dataset3_single_CV",
+    name = "dataset3_single_cv",
     data_dir = "./intermediate",
     data_files = {
-        "train_0" : "dataset3_single_CV_train_0.parquet",
-        "train_1" : "dataset3_single_CV_train_1.parquet",        
-        "train_2" : "dataset3_single_CV_train_2.parquet",
-        "train_3" : "dataset3_single_CV_train_3.parquet",
-        "train_4" : "dataset3_single_CV_train_4.parquet",        
-        "val_0" : "dataset3_single_CV_val_0.parquet",
-        "val_1" : "dataset3_single_CV_val_1.parquet",        
-        "val_2" : "dataset3_single_CV_val_2.parquet",
-        "val_3" : "dataset3_single_CV_val_3.parquet",
-        "val_4" : "dataset3_single_CV_val_4.parquet",        
-        "test_0" : "dataset3_single_CV_test_0.parquet",
-        "test_1" : "dataset3_single_CV_test_1.parquet",        
-        "test_2" : "dataset3_single_CV_test_2.parquet",
-        "test_3" : "dataset3_single_CV_test_3.parquet",
-        "test_4" : "dataset3_single_CV_test_4.parquet"},
+        "train_0" : "dataset3_single_cv_train_0.parquet",
+        "train_1" : "dataset3_single_cv_train_1.parquet",        
+        "train_2" : "dataset3_single_cv_train_2.parquet",
+        "train_3" : "dataset3_single_cv_train_3.parquet",
+        "train_4" : "dataset3_single_cv_train_4.parquet",        
+        "val_0" : "dataset3_single_cv_val_0.parquet",
+        "val_1" : "dataset3_single_cv_val_1.parquet",        
+        "val_2" : "dataset3_single_cv_val_2.parquet",
+        "val_3" : "dataset3_single_cv_val_3.parquet",
+        "val_4" : "dataset3_single_cv_val_4.parquet",        
+        "test_0" : "dataset3_single_cv_test_0.parquet",
+        "test_1" : "dataset3_single_cv_test_1.parquet",        
+        "test_2" : "dataset3_single_cv_test_2.parquet",
+        "test_3" : "dataset3_single_cv_test_3.parquet",
+        "test_4" : "dataset3_single_cv_test_4.parquet"},
     cache_dir = "/scratch/maom_root/maom0/maom",
     keep_in_memory = True)
 
 dataset.push_to_hub(
-    repo_id = "MaomLab/MegaScale",
-    config_name = "dataset3_single_CV",
-    data_dir = "datase3_single_CV/data",
-    commit_message = "Upload dataset3_single_CV")
+    repo_id = "maom/MegaScale",
+    config_name = "dataset3_single_cv",
+    data_dir = "datase3_single_cv/data",
+    commit_message = "Upload dataset3_single_cv")

src/03.2_check_uploaded_data.py

@@ -27,20 +27,20 @@ test_local_hf_match("dataset3_single", "train")
 test_local_hf_match("dataset3_single", "val")
 test_local_hf_match("dataset3_single", "test")
 
-test_local_hf_match("dataset3_single_CV", "train_0")
-test_local_hf_match("dataset3_single_CV", "train_1")
-test_local_hf_match("dataset3_single_CV", "train_2")
-test_local_hf_match("dataset3_single_CV", "train_3")
-test_local_hf_match("dataset3_single_CV", "train_4")
-
-test_local_hf_match("dataset3_single_CV", "val_0")
-test_local_hf_match("dataset3_single_CV", "val_1")
-test_local_hf_match("dataset3_single_CV", "val_2")
-test_local_hf_match("dataset3_single_CV", "val_3")
-test_local_hf_match("dataset3_single_CV", "val_4")
-
-test_local_hf_match("dataset3_single_CV", "test_0")
-test_local_hf_match("dataset3_single_CV", "test_1")
-test_local_hf_match("dataset3_single_CV", "test_2")
-test_local_hf_match("dataset3_single_CV", "test_3")
-test_local_hf_match("dataset3_single_CV", "test_4")
+test_local_hf_match("dataset3_single_cv", "train_0")
+test_local_hf_match("dataset3_single_cv", "train_1")
+test_local_hf_match("dataset3_single_cv", "train_2")
+test_local_hf_match("dataset3_single_cv", "train_3")
+test_local_hf_match("dataset3_single_cv", "train_4")
+
+test_local_hf_match("dataset3_single_cv", "val_0")
+test_local_hf_match("dataset3_single_cv", "val_1")
+test_local_hf_match("dataset3_single_cv", "val_2")
+test_local_hf_match("dataset3_single_cv", "val_3")
+test_local_hf_match("dataset3_single_cv", "val_4")
+
+test_local_hf_match("dataset3_single_cv", "test_0")
+test_local_hf_match("dataset3_single_cv", "test_1")
+test_local_hf_match("dataset3_single_cv", "test_2")
+test_local_hf_match("dataset3_single_cv", "test_3")
+test_local_hf_match("dataset3_single_cv", "test_4")