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325 mixed model in limma #339

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KamilMaliszArdigen
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@KamilMaliszArdigen KamilMaliszArdigen commented Nov 5, 2024 •
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This is a limma module update to provide new logic related to mixed models

PR checklist

  • This comment contains a description of changes (with reason).
  • If you've fixed a bug or added code that should be tested, add tests!
  • If you've added a new tool - have you followed the pipeline conventions in the contribution docs
  • If necessary, also make a PR on the nf-core/differentialabundance branch on the nf-core/test-datasets repository.
  • Make sure your code lints (nf-core lint).
  • Ensure the test suite passes (nf-test test main.nf.test -profile test,docker).
  • Check for unexpected warnings in debug mode (nextflow run . -profile debug,test,docker --outdir <OUTDIR>).
  • Usage Documentation in docs/usage.md is updated.
  • Output Documentation in docs/output.md is updated.
  • CHANGELOG.md is updated.
  • README.md is updated (including new tool citations and authors/contributors).

@KamilMaliszArdigen KamilMaliszArdigen linked an issue Nov 5, 2024 that may be closed by this pull request
Mixed-model in Limma #325
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@KamilMaliszArdigen KamilMaliszArdigen changed the base branch from master to dev November 5, 2024 15:04
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github-actions bot commented Nov 5, 2024 •
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nf-core pipelines lint overall result: Passed ✅ ⚠️

Posted for pipeline commit dc88a9b

+| ✅ 300 tests passed       |+
#| ❔   6 tests were ignored |#
!| ❗   4 tests had warnings |!

❗ Test warnings:

  • pipeline_todos - TODO string in main.nf: Optionally add in-text citation tools to this list.
  • pipeline_todos - TODO string in main.nf: Optionally add bibliographic entries to this list.
  • pipeline_todos - TODO string in main.nf: Only uncomment below if logic in toolCitationText/toolBibliographyText has been filled!
  • pipeline_todos - TODO string in base.config: Check the defaults for all processes

❔ Tests ignored:

✅ Tests passed:

Run details

  • nf-core/tools version 3.0.2
  • Run at 2024-11-26 09:26:23

@KamilMaliszArdigen
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Hi @pinin4fjords, This PR is updating limma module with it's latest version. I will be more than happy to provide any additional information if needed.

@KamilMaliszArdigen
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@pinin4fjords I would really appreciate your input in this topic. Thank you in advance.

pinin4fjords
pinin4fjords reviewed Nov 12, 2024
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@DSchreyer
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DSchreyer commented Nov 15, 2024 •
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I am getting this error when running a simple comparison. It seems that in FILTER_DIFFTABLE it expects a log2FoldChange column which does not exist.

This is the command:

        nextflow run output/differentialabundance/main.nf \
          --input ${ diffabundance_samplesheet } \
          --matrix ${ diffabundance_counts } \
          --contrasts ${ diffabundance_contrasts } \
          --outdir output \
          --observations_id_col sample \
          --features_id_col geneID \
          -profile rnaseq \
          --differential_use_limma 

Workflow execution completed unsuccessfully
The exit status of the task that caused the workflow execution to fail was: 1

Error executing process > 'NFCORE_DIFFERENTIALABUNDANCE:DIFFERENTIALABUNDANCE:FILTER_DIFFTABLE (1)'

Caused by:
  Process `NFCORE_DIFFERENTIALABUNDANCE:DIFFERENTIALABUNDANCE:FILTER_DIFFTABLE (1)` terminated with an error exit status (1)


Command executed:

  #!/usr/bin/env python
  
  from math import log2
  from os import path
  import pandas as pd
  import platform
  from sys import exit
  
  # 1. Check that the current logFC/padj is not NA
  # 2. Check that the current logFC is >= threshold (abs does not work, so use a workaround)
  # 3. Check that the current padj is <= threshold
  # If this is true, the row is written to the new file, otherwise not
  if not any("VISIT_V05_vs_V02.limma.results.tsv".endswith(ext) for ext in [".csv", ".tsv", ".txt"]):
      exit("Please provide a .csv, .tsv or .txt file!")
  
  table = pd.read_csv("VISIT_V05_vs_V02.limma.results.tsv", sep=("," if "VISIT_V05_vs_V02.limma.results.tsv".endswith(".csv") else "	"), header=0)
  logFC_threshold = log2(float("2.0"))
  table = table[~table["log2FoldChange"].isna() &
              ~table["padj"].isna() &
              (pd.to_numeric(table["log2FoldChange"], errors='coerce').abs() >= float(logFC_threshold)) &
              (pd.to_numeric(table["padj"], errors='coerce') <= float("0.05"))]
  
  table.to_csv(path.splitext(path.basename("VISIT_V05_vs_V02.limma.results.tsv"))[0]+"_filtered.tsv", sep="	", index=False)
  
  with open('versions.yml', 'a') as version_file:
      version_file.write('"NFCORE_DIFFERENTIALABUNDANCE:DIFFERENTIALABUNDANCE:FILTER_DIFFTABLE":' + "\n")
      version_file.write("    pandas: " + str(pd.__version__) + "\n")

Command exit status:
  1

Command output:
  (empty)

Command error:
  Traceback (most recent call last):
    File "/usr/local/lib/python3.11/site-packages/pandas/core/indexes/base.py", line 3803, in get_loc
      return self._engine.get_loc(casted_key)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "pandas/_libs/index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc
    File "pandas/_libs/index.pyx", line 165, in pandas._libs.index.IndexEngine.get_loc
    File "pandas/_libs/hashtable_class_helper.pxi", line 5745, in pandas._libs.hashtable.PyObjectHashTable.get_item
    File "pandas/_libs/hashtable_class_helper.pxi", line 5753, in pandas._libs.hashtable.PyObjectHashTable.get_item
  KeyError: 'log2FoldChange'
  
  The above exception was the direct cause of the following exception:
  
  Traceback (most recent call last):
    File ".command.sh", line 18, in <module>
      table = table[~table["log2FoldChange"].isna() &
                     ~~~~~^^^^^^^^^^^^^^^^^^
    File "/usr/local/lib/python3.11/site-packages/pandas/core/frame.py", line 3805, in __getitem__
      indexer = self.columns.get_loc(key)
                ^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/local/lib/python3.11/site-packages/pandas/core/indexes/base.py", line 3805, in get_loc
      raise KeyError(key) from err
  KeyError: 'log2FoldChange'

@KamilMaliszArdigen
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KamilMaliszArdigen commented Nov 15, 2024 •
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@DSchreyer There are differences in column names in outputs of deseq2 and limma so we created dedicated profile rnaseq_limma where this is adjusted for next steps. You can see in details what options are set here:

differentialabundance/conf/rnaseq_limma.config

Line 26 in 81cc521

// Differential options

Please try to launch pipeline with following command:

        nextflow run output/differentialabundance/main.nf \
          --input ${ diffabundance_samplesheet } \
          --matrix ${ diffabundance_counts } \
          --contrasts ${ diffabundance_contrasts } \
          --outdir output \
          --observations_id_col sample \
          --features_id_col geneID \
          -profile rnaseq_limma

@DSchreyer
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@DSchreyer There are differences in column names in outputs of deseq2 and limma so we created dedicated profile rnaseq_limma where this is adjusted for next steps. You can see in details what options are set here:

differentialabundance/conf/rnaseq_limma.config

Line 26 in 81cc521

// Differential options

Please try to launch pipeline with following command:

        nextflow run output/differentialabundance/main.nf \
          --input ${ diffabundance_samplesheet } \
          --matrix ${ diffabundance_counts } \
          --contrasts ${ diffabundance_contrasts } \
          --outdir output \
          --observations_id_col sample \
          --features_id_col geneID \
          -profile rnaseq_limma

@KamilMaliszArdigen Thanks, yes this helped to solve some of the issues. However, I am still getting the following error in the PLOT_EXPLORATORY process with my own dataset.

  In cond_log2_transform_matrix(matrix_data = assay_data[[index]],  :
    NaNs produced
  Fontconfig error: No writable cache directories
  Error in density.default(cond_log2_transform_matrix(plotmatrices[[pm]][,  : 
    'x' contains missing values
  Calls: ggplot_densityplot ... lapply -> FUN -> density -> density -> density.default
  Execution halted

could this come from the issue that we can have negative values in the normalised count matrix ? I also tested it with the test_rnaseq_limma profile which works as expected. However, i checked the normalised count tables from the test data and there are no negative values. Could this be an issue ?

@KamilMaliszArdigen
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@DSchreyer So main difference is that in rnaseq profile we have exploratory_log2_assays = 'raw,normalised' and in rnaseq_limma this is left empty. I'm not sure what is happening in your data please take a look at the work directory and the LIMMA count table - I expect that there might be negative values present. If this is the case it will be needed to keep exploratory_log2_assays empty at least this is my understanding of the plotting logic.

@KamilMaliszArdigen
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@nf-core-bot fix linting

@DSchreyer
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@DSchreyer So main difference is that in rnaseq profile we have exploratory_log2_assays = 'raw,normalised' and in rnaseq_limma this is left empty. I'm not sure what is happening in your data please take a look at the work directory and the LIMMA count table - I expect that there might be negative values present. If this is the case it will be needed to keep exploratory_log2_assays empty at least this is my understanding of the plotting logic.

Hi @KamilMaliszArdigen yes, i checked and there are negative values. When using --limma_use_voom false it skips the plotting which resolves the issue. Do you think that is a good idea or could interfere with the previous steps ?

@KamilMaliszArdigen
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@DSchreyer So main difference is that in rnaseq profile we have exploratory_log2_assays = 'raw,normalised' and in rnaseq_limma this is left empty. I'm not sure what is happening in your data please take a look at the work directory and the LIMMA count table - I expect that there might be negative values present. If this is the case it will be needed to keep exploratory_log2_assays empty at least this is my understanding of the plotting logic.

Hi @KamilMaliszArdigen yes, i checked and there are negative values. When using --limma_use_voom false it skips the plotting which resolves the issue. Do you think that is a good idea or could interfere with the previous steps ?

Well this will not work as expected voom is responsible for normalisation of rna_seq data. So this will result in not normalised data processing. I recommend to set the --exploratory_log2_assays = '' and than simply during plot generation log2 will not be generated.

pinin4fjords
pinin4fjords requested changes Nov 22, 2024
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Could this go into the usage docs please?

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Mixed-model in Limma
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@KamilMaliszArdigen @DSchreyer @pinin4fjords @nf-core-bot