adding rnaseq reprocessed

README.md

@@ -496,6 +496,75 @@ configs:
       dtype: float64
       description: Log2 fold change (IAA/DMSO) for significantly affected genes (DESeq2, padj <0.1, FC >= 1.3)
 
+- config_name: rnaseq_reprocessed
+  description: Reprocessed nascent RNA-seq differential expression data using DESeq2 without thresholding, comparing IAA-induced TF degradation versus DMSO control
+  dataset_type: annotated_features
+  metadata_fields:
+    - regulator_locus_tag
+    - regulator_symbol
+    - sample_id
+    - env_condition
+    - timepoint
+  data_files:
+  - split: train
+    path: rnaseq_reprocessed.parquet
+  dataset_info:
+    features:
+    - name: sample_id
+      dtype: string
+      description: Composite identifier combining regulator, condition, timepoint, and treatment information from the merged IAA and DMSO sample IDs
+      role: sample_id
+    - name: regulator_locus_tag
+      dtype: string
+      description: Systematic gene identifier for the depleted transcription factor
+      role: regulator_identifier
+    - name: regulator_symbol
+      dtype: string
+      description: Standard gene symbol for the depleted transcription factor
+      role: regulator_identifier
+    - name: env_condition
+      dtype:
+        class_label:
+          names: ["standard_30C", "SM", "galactose", "raffinose", "heat_shock_37C"]
+      description: Environmental growth condition for this experiment
+      role: experimental_condition
+    - name: timepoint
+      dtype: float64
+      description: Time point in minutes (standard is 30 minutes post-treatment. very few other timepoints)
+      role: experimental_condition
+    - name: target_locus_tag
+      dtype: string
+      description: Systematic gene identifier for the differentially expressed target gene
+      role: target_identifier
+    - name: target_symbol
+      dtype: string
+      description: Standard gene symbol for the differentially expressed target gene
+      role: target_identifier
+    - name: baseMean
+      dtype: float64
+      description: Mean of normalized counts across all samples (DESeq2 output)
+      role: quantitative_measure
+    - name: log2FoldChange
+      dtype: float64
+      description: Log2 fold change IAA versus DMSO (DESeq2 output, no thresholding applied)
+      role: quantitative_measure
+    - name: lfcSE
+      dtype: float64
+      description: Standard error of the log2 fold change estimate (DESeq2 output)
+      role: quantitative_measure
+    - name: stat
+      dtype: float64
+      description: Wald test statistic (DESeq2 output)
+      role: quantitative_measure
+    - name: pvalue
+      dtype: float64
+      description: Wald test p-value (DESeq2 output)
+      role: quantitative_measure
+    - name: padj
+      dtype: float64
+      description: Benjamini-Hochberg adjusted p-value (DESeq2 output)
+      role: quantitative_measure
+
 - config_name: degron_counts_meta
   description: Sample-level metadata for auxin-inducible degron perturbation experiments with HTSeq count statistics
   dataset_type: metadata

rnaseq_reprocessed.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66a53db51bfadb326f663f90a9da4a64d8e4724da679ad17851a530541ea3fb4
+size 43776387

scripts/rnaseq_differential_expression.R

@@ -0,0 +1,120 @@
+library(tidyverse)
+library(DESeq2)
+library(arrow)
+library(here)
+
+
+run_degron_deseq2 <- function(regulator_sym, env_cond, timepoint_val,
+                              degron_data = degron,
+                              baseline_data = baseline_control) {
+
+    # Filter degron data for the specified regulator, condition, and timepoint
+    degron_meta_filtered <- degron_data$meta %>%
+        filter(
+            regulator_symbol == regulator_sym,
+            env_condition == env_cond,
+            timepoint == timepoint_val) %>%
+        mutate(degron_treatment = factor(degron_treatment,
+                                         levels = c("DMSO", "IAA")))
+
+
+
+    if (nrow(degron_meta_filtered) != 6) {
+        stop(sprintf("We expect exactly 3 replicates in each conditition for a total of 6 samples. These parameters result in %s", nrow(degron_meta_filtered)))
+    }
+
+    # Get the IAA and DMSO samples
+    iaa_samples <- degron_meta_filtered %>%
+        filter(degron_treatment == "IAA") %>%
+        pull(sra_accession)
+
+    dmso_samples <- degron_meta_filtered %>%
+        filter(degron_treatment == "DMSO") %>%
+        pull(sra_accession)
+
+    if (length(iaa_samples) != 3 || length(dmso_samples) != 3) {
+        stop("Missing IAA or DMSO samples for the specified parameters")
+    }
+
+    # Get counts for these samples
+    counts_filtered <- degron_data$counts_long %>%
+        filter(sra_accession %in% c(iaa_samples, dmso_samples))
+
+    # Convert to wide format (genes x samples)
+    counts_wide <- counts_filtered %>%
+        select(target_locus_tag, sra_accession, count) %>%
+        pivot_wider(
+            names_from = sra_accession,
+            values_from = count,
+            values_fill = 0) %>%
+        column_to_rownames("target_locus_tag")
+
+    # Create sample metadata
+    sample_meta <- degron_meta_filtered %>%
+        select(sra_accession, degron_treatment) %>%
+        column_to_rownames("sra_accession")
+
+    # Ensure counts columns match metadata rows
+    counts_wide <- counts_wide[, rownames(sample_meta)]
+
+    # Create DESeq2 dataset
+    dds <- DESeqDataSetFromMatrix(
+        countData = counts_wide,
+        colData = sample_meta,
+        design = ~ degron_treatment
+    )
+
+    # Run DESeq2
+    dds <- DESeq(dds)
+
+    # Get results
+    res <- results(dds)
+
+    # Return both the DESeq object and results
+    return(list(
+        dds = dds,
+        results = res %>%
+            as_tibble(rownames="target_locus_tag") %>%
+            mutate(sample_id = paste(unique(degron_meta_filtered$sample_id), collapse="_")) %>%
+            left_join(distinct(degron$counts_long %>% select(target_locus_tag, target_symbol))) %>%
+            mutate(regulator_locus_tag = unique(degron_meta_filtered$regulator_locus_tag),
+                   regulator_symbol = unique(degron_meta_filtered$regulator_symbol),
+                   env_condition = unique(degron_meta_filtered$env_condition),
+                   timepoint = unique(degron_meta_filtered$timepoint)) %>%
+            dplyr::relocate(sample_id, regulator_locus_tag, regulator_symbol,
+                            env_condition, timepoint),
+        metadata = degron_meta_filtered
+    ))
+}
+
+
+baseline_control = list(
+    counts_long = arrow::read_parquet(
+        "~/code/hf/mahendrawada_2025/wt_baseline_counts.parquet"),
+    meta = arrow::read_parquet(
+        "~/code/hf/mahendrawada_2025/wt_baseline_counts_meta.parquet")
+)
+
+degron = list(
+    counts_long = arrow::read_parquet("~/code/hf/mahendrawada_2025/degron_counts.parquet"),
+    meta = arrow::read_parquet("~/code/hf/mahendrawada_2025/degron_counts_meta.parquet")
+)
+
+x = map(unique(degron$meta$regulator_symbol),
+        ~run_degron_deseq2(., 'standard_30C', 30, degron, baseline_control))
+
+results_df <- map_dfr(x, ~.$results)
+
+# results_df %>%
+#   write_parquet("~/code/hf/mahendrawada_2025/rnaseq_reprocessed.parquet",
+#                 compression = "zstd",
+#                 write_statistics = TRUE,
+#                 chunk_size = 6708,
+#                 use_dictionary = c(
+#                   sample_id = TRUE,
+#                   regulator_locus_tag = TRUE,
+#                   regulator_symbol = TRUE,
+#                   target_locus_tag = TRUE,
+#                   target_symbol = TRUE
+#                 )
+#   )