adding analysis set column to orig CC data

README.md

@@ -118,6 +118,12 @@ configs:
       dtype: int64
       description: Count or score for composite binding events
       role: quantitative_measure
+    - name: analysis_set
+      dtype: bool
+      description: >-
+        TRUE if this record is to be used for analysis. FALSE otherwise.
+        This was determined in 2025. Replicates needed `>=`3k hops and
+        DTO `<=` 0.01 in either kemmeren or hackett
     - name: id
       dtype: string
       description: Unique identifier for the metadata record
@@ -209,6 +215,12 @@ configs:
     - name: batch
       dtype: string
       description: Experimental batch identifier for controlling batch effects
+    - name: analysis_set
+      dtype: bool
+      description: >-
+        For a TF with more than 1 passing replicate, a combined samples is created.
+        This is based on the QC done in 2025 for the modeling paper. See the
+        annotated_features_meta for more details
     - name: condition
       dtype: string
       description: Experimental condition for this sample

annotated_features_combined_meta.parquet

@@ -1,3 +1,3 @@
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-size 6401
+oid sha256:4a524b6e621e0b027bdca9c942ca639368f14618bb093ea4e27fb9dbeb9b29ad
+size 6642

annotated_features_meta.parquet

@@ -1,3 +1,3 @@
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-oid sha256:b0805253b44b0bea473a19bfbd6c38fe3ded123154f728a1c092f40ee97e878c
-size 24549
+oid sha256:a2f0c27cea8819144b7ec616a6e5165dee46b94d3f6ba0de2fbe0751bf753fe7
+size 24871

scripts/cc_sra_submission_with_addtl.R

@@ -0,0 +1,247 @@
+library(tidyverse)
+library(here)
+
+# TODO: REMOVE HYPERGEOMETRIC PVALUE FROM PROCESSED DATA
+# TODO: when making processing scripts github, add the background file
+# and make sure the repo gets registered to zenodo. Put that link to the
+# background files in the SRA submission
+
+# for "data_usable" in sra submission, change to "perturbation_validated".
+# factor levels should be "true", "false", "unreviewed" (DTO only)
+# num_insertions for number of insertions
+#
+# cells grown on solid media at room temperature
+# put the definition of the "Description" values in the
+
+qc_django_data = read_csv("~/projects/parsing_yeast_database_data/data/qc_from_db/rr_20251222.csv")
+expr_django_data = read_csv("~/projects/parsing_yeast_database_data/data/qc_from_db/expr_20251222.csv")
+mcisaac_preferred_reps = expr_django_data %>%
+    filter(source_name == "mcisaac_oe") %>%
+    filter(preferred_replicate==TRUE) %>%
+    pull(id)
+
+qc_django_data_in_modeling = list(
+    kemmeren = qc_django_data %>%
+        filter(binding_source == "brent_nf_cc",
+               !is.na(single_binding)) %>%
+        filter(expression_source == "kemmeren_tfko"),
+    mcisaac = qc_django_data %>%
+        filter(binding_source == "brent_nf_cc",
+               !is.na(single_binding)) %>%
+        filter(expression_source == "mcisaac_oe"
+                & expression %in% mcisaac_preferred_reps))
+
+analysis_set_meta = read_csv("~/htcf_ref/data/yeast_database_modelling/pull_data_20250805/data/brent_nf_cc_meta_20250805.csv")
+composite_binding = read_csv("~/htcf_ref/data/yeast_database_modelling/pull_data_20250805/data/bindingconcat_meta_20250805.csv") %>%
+    filter(source_name == "brent_nf_cc")
+
+barcode_details_list = list.files("~/htcf_local/cc/yeast/data", "*_barcode_details.json",
+                                  recursive=TRUE)
+
+passing_fastq_from_bam_paths = list.files("~/htcf_local/cc/yeast/passing_fastq_from_bam")
+
+fastq_df = tibble(filename = passing_fastq_from_bam_paths) %>%
+    extract(
+        filename,
+        into = c("batch", "regulator_symbol_replicate"),
+        regex = "^(.+?)_([^_]+)_passing_tagged\\.fastq\\.gz$",
+        remove = FALSE) %>%
+    mutate(replicate = str_remove(str_extract(regulator_symbol_replicate, "x\\d"), "x")) %>%
+    mutate(regulator_symbol = str_remove(regulator_symbol_replicate, "x\\d")) %>%
+    replace_na(list(replicate = '1')) %>%
+    mutate(replicate = as.integer(replicate)) %>%
+    mutate(batch = ifelse(batch == "run_5690_correct", "run_5690", batch)) %>%
+    filter(!batch %in% c('dsir4')) %>%
+    filter(regulator_symbol != "undetermined") %>%
+    # this is empty -- no passing hops
+    filter(filename != "run_6739_MOT3_passing_tagged.fastq.gz")
+
+barcode_details_df = map(barcode_details_list, ~{
+    message(sprintf("working on %s", basename(.x)))
+    x = jsonlite::read_json(file.path("~/htcf_local/cc/yeast/data", .))
+    tibble(
+        seq = names(x$components$tf$map),
+        regulator_symbol = unlist(x$components$tf$map)) %>%
+        mutate(r1_index = substr(seq,1,5),
+               r2_index = substr(seq,6,nchar(seq))) %>%
+        mutate(batch = basename(dirname(.x)))}) %>%
+    bind_rows()
+
+composite_binding_unlisted = unlist(lapply(composite_binding$bindings, function(x) {
+        as.numeric(str_extract_all(x, "\\d+")[[1]])}))
+
+analysis_binding_ids = c(
+    pull(filter(analysis_set_meta, !is.na(single_binding)), single_binding),
+    composite_binding_unlisted
+    )
+
+
+binding_django = read_csv(here("data/binding_from_django_db_20260128.csv"))
+
+barcode_details_df_with_id = barcode_details_df %>%
+    mutate(replicate = str_remove(str_extract(regulator_symbol, "x\\d"), "x")) %>%
+    mutate(regulator_symbol = str_remove(regulator_symbol, "x\\d")) %>%
+    replace_na(list(replicate = '1')) %>%
+    mutate(replicate = as.integer(replicate)) %>%
+    filter(batch != "run_5690") %>%
+    mutate(batch = ifelse(batch == 'run_5690_correct', 'run_5690', batch)) %>%
+    left_join(
+        binding_django %>%
+            select(id, regulator_symbol, batch, replicate)) %>%
+    dplyr::rename(binding_id = id) %>%
+    mutate(binding_id = as.character(binding_id)) %>%
+    replace_na(list(binding_id = "NA"))
+
+brentlab_dirs = list.dirs("~/htcf_lts/sequence_data/yeast_cc/sequence", recursive=FALSE)
+mitra_dirs = list.dirs("~/htcf_lts/sequence_data/yeast_cc/sequence/mitra_data", recursive=FALSE)
+
+setdiff(basename(c(brentlab_dirs, mitra_dirs)), unique(binding_django$batch))
+
+gm_db = arrow::open_dataset("~/code/hf/callingcards/genome_map")
+
+genome_map_meta = arrow::read_parquet("~/code/hf/callingcards/genome_map_meta.parquet") %>%
+    # filter(condition == "standard") %>%
+    filter(!batch %in% c('dsir4'))
+
+fastq_df_with_id = fastq_df %>%
+    left_join(genome_map_meta %>%
+                  mutate(regulator_symbol = ifelse(str_detect(regulator_symbol, "unknown"),
+                                                   regulator_locus_tag,
+                                                   regulator_symbol))) %>%
+    filter(condition == 'standard')
+
+fastq_df_with_id %>%
+    filter(binding_id == "NA") %>%
+    filter(condition == "standard") %>%
+    filter(regulator_symbol != "OTU1") %>%
+    mutate(qbed_path = file.path(sprintf("/home/chase/htcf_local/cc/yeast/results/%s/hops/%s_%s.qbed", batch, batch, regulator_symbol_replicate))) %>%
+    select(qbed_path) %>%
+    write_tsv("~/tmp/unprocessed_in_db_qbeds_lookup.txt")
+
+
+# ACE2, ARG80, ARG81, LYS14, STB5, SWI5, SWI6 pass in at least one (didn't check which mcisaac cond pass, but all pass in kemmeren pass)
+# CUP9 and DAL80 pass in mcisaac at 15 minutes
+#
+# WTM1, MIG2, DIG1 ACA1 fails due to non-passing in kemmeren and/or mcisaac. NOTE: the only timepoint MIG2 fails is 15 minutes.
+# UME6 has no 15 minute condition in mcisaac and is not in kemmeren
+
+af_django_meta = arrow::read_parquet("~/code/hf/callingcards/annotated_features_meta.parquet") %>%
+    filter(condition == "standard") %>%
+    filter(genome_map_id %in% genome_map_meta$id) %>%
+    replace_na(list(binding_id = "NA")) %>%
+    mutate(data_usable = case_when(
+        batch %in% c('run_7477', 'run_7487')
+        & regulator_symbol %in% c('ACE2', 'ARG80', 'ARG81', 'LYS14',
+                                  'STB5', 'SWI5', 'SWI6', 'CUP9', 'DAL80') ~ "pass",
+        batch %in% c('run_7477', 'run_7487')
+        & regulator_symbol %in% c('WTM1', 'MIG2', 'DIG1', 'ACA1') ~ "fail",
+        .default = data_usable)) %>%
+    mutate(in_modeling_analysis = binding_id %in% analysis_binding_ids) %>%
+    mutate(kemmeren_dto = binding_id %in%
+               (qc_django_data_in_modeling$kemmeren %>%
+               filter(dto_empirical_pvalue <= 0.01) %>%
+               pull(single_binding))) %>%
+    mutate(mcisaac_dto = binding_id %in%
+               (qc_django_data_in_modeling$mcisaac %>%
+               filter(dto_empirical_pvalue <= 0.01) %>%
+               pull(single_binding))) %>%
+    left_join(qc_django_data %>%
+                  filter(!is.na(single_binding), binding_source == "brent_nf_cc") %>%
+                  select(single_binding, genomic_inserts) %>%
+                  distinct() %>%
+                  mutate(single_binding = as.character(single_binding)) %>%
+                  dplyr::rename(binding_id = single_binding)) %>%
+    left_join(select(barcode_details_df_with_id, regulator_symbol, batch, binding_id, r1_index, r2_index)) %>%
+    dplyr::select(id, genome_map_id, batch,
+                  r1_index, r2_index,
+                  regulator_locus_tag, regulator_symbol,
+                  data_usable, kemmeren_dto, mcisaac_dto, genomic_inserts,
+                  in_modeling_analysis) %>%
+    mutate(notes = case_when(
+        genome_map_id %in% c(690, 685) ~ "manually exluded from analysis in favor of library 242",
+        genome_map_id %in% c(26, 612, 300, 119) ~ "less than 3k insertions",
+        .default = "none")) %>%
+    arrange(genome_map_id)
+
+af_db = arrow::open_dataset("~/code/hf/callingcards/annotated_features")
+
+setdiff(basename(c(brentlab_dirs, mitra_dirs)), unique(genome_map_meta$batch))
+setdiff(unique(genome_map_meta$batch),basename(c(brentlab_dirs, mitra_dirs)))
+
+
+# db2506 = read_tsv("~/Downloads/2506.qbed.gz")
+# hf2506 = gm_db %>%
+#     filter(id == 1) %>%
+#     collect()
+
+library(arrow)
+library(dplyr)
+library(readr)
+
+# Specify output directory
+# output_dir <- here("~/htcf_local/cc/yeast/callingcards_geo_submission/processed")
+# dir.create(output_dir, showWarnings = FALSE, recursive = TRUE)
+
+# Get unique combinations of id and batch
+id_batch_combos <- af_django_meta %>%
+    select(id, genome_map_id, batch) %>%
+    distinct()
+
+# for (i in 1:nrow(id_batch_combos)) {
+#     current_id <- id_batch_combos$id[i]
+#     current_batch <- id_batch_combos$batch[i]
+#     gm_id = id_batch_combos$genome_map_id[i]
+#
+#     # Filter and format as af file
+#     af_data <- af_db %>%
+#         filter(id == current_id,
+#                batch == current_batch) %>%
+#         collect() %>%
+#         mutate(genome_map_id = gm_id) %>%
+#         select(-id) %>%
+#         dplyr::rename(library_name = genome_map_id) %>%
+#         mutate(target_symbol = ifelse(str_detect(target_symbol, "unknown"),
+#                                       target_locus_tag, target_symbol)) %>%
+#         dplyr::relocate(library_name, batch)
+#
+#     # Create filename
+#     filename <- file.path(output_dir, paste0(gm_id, ".csv.gz"))
+#
+#     # Write gzipped csv file
+#     write_csv(af_data, filename)
+#
+#     if (i %% 10 == 0) cat("Wrote", i, "of", nrow(id_batch_combos), "files\n")
+# }
+#
+# cat("Done! Wrote", nrow(id_batch_combos), "CSV files to", output_dir, "\n")
+
+submission_df = af_django_meta %>%
+    select(-id) %>%
+    arrange(regulator_locus_tag) %>%
+    mutate(regulator_symbol = ifelse(
+        str_detect(regulator_symbol, "unknown"),
+        regulator_locus_tag,
+        regulator_symbol)) %>%
+    mutate(`library name` = paste0(regulator_locus_tag, "_", regulator_symbol, "_", genome_map_id)) %>%
+    mutate(title = paste0(regulator_locus_tag, " (", regulator_symbol, ") calling cards"),
+           `library strategy` = "CallingCards",
+           organism = 'Saccharomyces cerevisiae',
+           strain = '',
+           molecule = 'genomic DNA',
+           `single or paired-end` = 'single-end',
+           `instrument model` = 'illumina MiSeq i100',
+           description = paste0(regulator_locus_tag, " tagged callingcards experiment. ",
+                                "kemmeren_dto: ", kemmeren_dto, "; mcisaac_dto: ", mcisaac_dto,
+                                "; genomic_inserts: ", genomic_inserts,
+                                "; in_modeling_analysis: ", in_modeling_analysis,
+                                "; notes: ", notes),
+           `processed data file` =  paste0(`library name`, ".csv.gz"),
+           `raw file` = paste0(`library name`, ".fastq.gz")) %>%
+    dplyr::select(
+        `library name`, title, `library strategy`, organism,
+        strain, molecule, `single or paired-end`, `instrument model`,
+        description,
+        `processed data file`, `raw file`
+    )
+
+# write_csv(submission_df, here("data/cc_submission_df.csv"))