Fixing CPM Counts Error In RNA-Seq Pipeline

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Fixing CPM Counts Error in RNA-Seq Pipeline

Hey guys! 👋 I've been troubleshooting an issue with the epi2me-labs/wf-transcriptomes pipeline, and I wanted to share the details and solution in case you run into it too. The problem centers around a mismatch in column names within the differential expression analysis, specifically with the cpm_gene_counts_colnames file. Let's dive into the details, the cause, and how to fix it.

The Problem: Column Name Mismatch 😥

The core of the issue lies in how the read.csv() function in R handles column names. When the pipeline runs the differential expression analysis, it automatically applies make.names() to the column names in the counts file. This function is designed to ensure that column names are valid in R, which means it might change them (e.g., by adding a prefix or replacing spaces). However, the sample IDs in the sample sheet aren't modified in the same way. This leads to a mismatch between the column names in the counts data and the expected sample IDs, causing the error: "Column names in cpm_gene_counts_colnames do not match expected aliases."

Basically, the pipeline expects the column names in the count data to exactly match the sample IDs listed in the sample sheet. If make.names() alters these names in one place but not the other, the analysis falls apart. I know, it's frustrating! But don't worry, we can fix it.

Detailed Breakdown

Let's break down the error messages and the files involved to get a clearer picture:

  • Error Message: "Column names in cpm_gene_counts_colnames do not match expected aliases." This is the direct symptom of the problem. It tells us that the column names in the cpm_gene_counts_colnames file don't align with what the pipeline anticipates.
  • Sample Sheet (sample_sheet.csv): This file contains your sample information, including the sample IDs. The pipeline uses these IDs to link the count data to the experimental conditions (e.g., control vs. treated).
  • cpm_gene_counts_colnames: This file (or, more precisely, its column names) is where the mismatch occurs. It's derived from the count data file and contains the column names representing your samples.
  • expected_colnames: This file is created by the pipeline from the sample sheet. The pipeline compares the cpm_gene_counts_colnames to expected_colnames to check for consistency.

When these don't match, the error is triggered. The crucial part here is the transformation that R does on the column names when reading the counts data using read.csv().

Identifying the Root Cause

The primary culprit is the read.csv() function's behavior. In R, read.csv() by default will convert column names to valid R names using make.names(). This function ensures names are syntactically valid by adding prefixes (like X. if a name starts with a number) or replacing invalid characters (like spaces) with periods (.).

Here's an example to illustrate this:

If your sample ID in the sample sheet is ALL_14, and the count data file also has a column named ALL_14, read.csv() might transform that to X.ALL_14. This would cause a mismatch with what is in the sample sheet. Thus, even if the data seems correct, the program won't be able to map things correctly.

The Solution: Aligning the Column Names ✨

To resolve this, we need to ensure that the column names in the count data exactly match the sample IDs in the sample sheet. Here are a couple of approaches:

1. Modify the Sample Sheet

This is often the simplest solution. You can manually adjust the sample IDs in your sample sheet to match the transformed column names in the counts file. For instance, if read.csv() adds an X. prefix, add it to your sample sheet entries. Remember to double-check that this change doesn't cause confusion or errors elsewhere in your analysis. Be careful not to make unintentional typos!

2. Modify the R Script

If you prefer to avoid changing your sample sheet, you can modify the R script used in the deAnalysis step to disable or counteract the make.names() transformation. This might involve specifying check.names = FALSE in the read.csv() call, or using a function to remove or revert the transformations applied by make.names().

Keep in mind that changing the R script will require a bit more understanding of the pipeline's code and how the sample sheet is processed. Make sure to back up your original script before making changes and testing thoroughly.

Example (Illustrative, Modify as Needed)

Let's say your sample sheet looks like this:

sample,condition,fastq_1,fastq_2
ALL_14,control,sample_14_R1.fastq.gz,sample_14_R2.fastq.gz
ALL_22,control,sample_22_R1.fastq.gz,sample_22_R2.fastq.gz

And after the read.csv() in R, the column names in your counts data become X.ALL_14, X.ALL_22, etc.

Solution 1 (Modify Sample Sheet): Change the sample sheet to:

sample,condition,fastq_1,fastq_2
X.ALL_14,control,sample_14_R1.fastq.gz,sample_14_R2.fastq.gz
X.ALL_22,control,sample_22_R1.fastq.gz,sample_22_R2.fastq.gz

Solution 2 (Modify R Script): This requires identifying where read.csv() is called (in the R script used by the pipeline) and adding the check.names = FALSE argument.

Step-by-Step Troubleshooting Guide

Here’s a practical approach to troubleshoot this issue:

  1. Examine the cpm_gene_counts_colnames file: Look at the output of cat cpm_gene_counts_colnames. This will show you the exact column names the pipeline is using.
  2. Compare with the Sample Sheet: Open your sample_sheet.csv and compare the sample IDs to the column names from step 1. Do they match exactly?
  3. Check the all_counts.tsv file: This file contains the actual count data. See if the column names match the format of the sample sheet.
  4. If there's a mismatch: Apply one of the solutions outlined above to align the column names.
  5. Re-run the pipeline: After making the changes, re-run the pipeline to confirm that the error is resolved.

Important Considerations

  • Pipeline Version: Ensure you're using the latest version of the epi2me-labs/wf-transcriptomes pipeline. Updates often include bug fixes and improvements.
  • Data Integrity: Always back up your original data and files before making changes. It's always better to be safe than sorry!
  • Documentation: Review the pipeline's documentation and any relevant forum posts or issues. This can provide additional context and insights.
  • Testing: After applying a fix, test it thoroughly with a small subset of your data before running it on the entire dataset.

Conclusion

This "Column names in cpm_gene_counts_colnames do not match expected aliases" error is a common problem in RNA-Seq pipelines, and it can be caused by the inconsistent handling of column names. By carefully comparing the column names in your counts data with the sample IDs in your sample sheet, and then either modifying the sample sheet or the R script, you can easily solve this issue. I hope this helps you guys! If you have any questions or run into trouble, feel free to ask. Happy analyzing! 🚀