I am running DESeq2, and am running into a problem where when I plot the results as a volcano plot, I am experiencing this "missing band" of data. The missing band goes from the range (4.0, 5.0] on the -log10(padj) y axis.

As far as I can tell, the values that should be there all have a baseMean of 0, and then 0 or undefined p-values/padj. I understand that baseMean 0 arises when a gene has zero expression across all samples, however why is it all concentrated in this range? I have tried disabling cook's cutoff and independent filtering to see if they were "erasing" these p-values, but to no success. Some other thoughts have been a precision error, but we are clearly seeing points both above and below this range. Any idea what may be causing this?

Here is the relevant code:

Preprocessing

 expression_data <- loadedData$expression_data
  cohort_A <- loadedData$cohort_A
  cohort_B <- loadedData$cohort_B

  start_time <- Sys.time()

  cat("Preprocessing data for DESeq2...\n")

  # Print number of genes
  cat("Number of genes:", nrow(expression_data), "\n")

  # Combine cohort data with appropriate condition labels
  cat("Combining cohort data\n")
  cohort_data <- rbind(cohort_A, cohort_B)
  cohort_data$Condition <- ifelse(cohort_data$cohortName == "A", "A", "B")
  cohort_data <- cohort_data[, c("sids", "Condition")]
  rownames(cohort_data) <- cohort_data$sids

  # Create counts_matrix
  cat("Creating counts matrix\n")
  counts_matrix <- as.data.frame(expression_data$counts)
  colnames(counts_matrix) <- expression_data$geneID

  cat("Counts matrix size:", dim(counts_matrix), "\n")

  # Filter out samples not in cohort A or B
  cat("Filtering out samples that are not in cohort A or B\n")
  samples_to_keep <- rownames(counts_matrix) %in% rownames(cohort_data)
  counts_matrix <- counts_matrix[samples_to_keep, ]

  cat("Counts matrix size:", dim(counts_matrix), "\n")

  # Filter out cohort members not in counts matrix
  cat("Filtering out cohort members that are not in counts matrix\n")
  samples_to_keep <- rownames(cohort_data) %in% rownames(counts_matrix)
  cohort_data <- cohort_data[samples_to_keep, , drop = FALSE]

  # rename cohort data columns
  colnames(cohort_data) <- c("Sample", "Condition")

  cat("Counts matrix size:", dim(counts_matrix), "\n")
  cat("Cohort data size:", dim(cohort_data), "\n")

  # Sort indices
  cat("Sorting indices\n")

  counts_matrix <- counts_matrix[match(rownames(cohort_data), rownames(counts_matrix)), ]

  # Transpose counts_matrix
  cat("Transposing counts matrix\n")
  counts_matrix <- t(counts_matrix)


  cat("\nFinal preprocessed data dimensions:\n")
  cat("Counts matrix size:", dim(counts_matrix), "\n")
  cat("cohort_data size:", dim(cohort_data), "\n")

  cat("PREPROCESSING FINISHED. Runtime (s):", as.numeric(Sys.time() - start_time), "\n")

And then DESeq2

  counts_matrix <- preprocessed$counts_matrix
  cohort_data <- preprocessed$cohort_data

  # Run DESeq
  dds <- DESeqDataSetFromMatrix(countData = counts_matrix, colData = cohort_data, design = ~Condition)
  print(dds)

  # Run DESeq2 analysis
  dds <- DESeq(dds)

  # Extract results
  res <- results(dds, contrast=c("Condition", "A", "B"), alpha=0.05)

Can you plot hist(res$pvalue), hist(-log10(res$pvalue)), and hist(-log10(res$padj))