Setup CI/CD Pipeline for MSStats

[anshuman-raina]

User description

Motivation and Context

Part of Dataset Benchmarking Task

Changes

Please provide a detailed bullet point list of your changes.

• Added slurm file for job set up
• Added github actions
• Added benchmark script

Testing

Please describe any unit tests you added or modified to verify your changes.

Checklist Before Requesting a Review

• I have read the MSstats contributing guidelines
• My changes generate no new warnings
• Any dependent changes have been merged and published in downstream modules
• I have run the devtools::document() command after my changes and committed the added files

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PR Type

enhancement, tests

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Description

• Added a GitHub Actions workflow to automate running R scripts on HPC using Slurm, including SSH setup, file transfer, job submission, and output retrieval.
• Developed a comprehensive R benchmarking script leveraging MSstats and MSstatsConvert for data processing and analysis, with parallel execution for improved performance.
• Created a Slurm configuration script to define job parameters and execute the benchmarking script on HPC.

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Changes walkthrough 📝

	Relevant files
Enhancement	slurm_poc.yml Add GitHub Actions workflow for HPC integration using Slurm .github/workflows/slurm_poc.yml Added a GitHub Actions workflow to run R scripts on HPC via Slurm. Configured SSH access and file transfer to HPC. Implemented job submission and monitoring using Slurm. Set up artifact upload for job output. +48/-0
Tests	benchmark.R Add benchmarking script with data processing and analysis benchmark/benchmark.R Introduced a benchmarking script using MSstats and MSstatsConvert. Implemented data processing and result calculation functions. Utilized parallel processing for efficiency. Calculated and displayed performance metrics. +178/-0
Configuration changes	config.slurm Create Slurm configuration script for benchmarking job      benchmark/config.slurm Created a Slurm configuration script for job submission. Defined job parameters such as name, output, and resources. Configured the execution of the R benchmarking script. +13/-0

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[github-actions]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 4 🔵🔵🔵🔵⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Hardcoded Values The workflow contains hardcoded server addresses and user names which could lead to security risks and lack of flexibility. Consider using environment variables or GitHub secrets to manage these values dynamically. Error Handling The script lacks error handling mechanisms throughout the data processing and analysis phases. Adding try-catch blocks or condition checks can help in managing exceptions and ensuring the robustness of the script. Performance Concern The script uses multiple instances of 'paste' and 'str_split' within loops which can be optimized. Consider pre-processing these operations outside of loops or using vectorized operations to enhance performance.

[github-actions]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Score
Security	Improve security by setting SSH key permissions before writing the key Ensure that the SSH key permissions are set before writing the key to prevent potential race conditions where the key file could be accessed with incorrect permissions. .github/workflows/slurm_poc.yml [18-20] mkdir -p ~/.ssh +touch ~/.ssh/id_rsa +chmod 600 ~/.ssh/id_rsa echo "${{ secrets.SSH_PRIVATE_KEY }}" > ~/.ssh/id_rsa -chmod 600 ~/.ssh/id_rsa Suggestion importance[1-10]: 9 Why: This suggestion addresses a potential security issue by ensuring that the SSH key file has the correct permissions before writing the key, reducing the risk of unauthorized access. It is a critical improvement for maintaining security in the workflow.	9
Possible bug	Add error handling to SSH and SCP commands to ensure workflow reliability Add error handling for SSH and SCP commands to ensure that the workflow fails gracefully if these commands do not execute successfully. .github/workflows/slurm_poc.yml [21-29] -ssh-keyscan -H login-00.discovery.neu.edu >> ~/.ssh/known_hosts -scp benchmark.R config.slurm raina.ans@login-00.discovery.neu.edu:/home/raina.ans/R -ssh raina.ans@login-00.discovery.neu.edu "cd R && sbatch config.slurm" +ssh-keyscan -H login-00.discovery.neu.edu >> ~/.ssh/known_hosts || exit 1 +scp benchmark.R config.slurm raina.ans@login-00.discovery.neu.edu:/home/raina.ans/R || exit 1 +ssh raina.ans@login-00.discovery.neu.edu "cd R && sbatch config.slurm" || exit 1 Suggestion importance[1-10]: 8 Why: Adding error handling to SSH and SCP commands enhances the robustness of the workflow by ensuring that failures in these commands are detected and handled gracefully, preventing potential issues in subsequent steps.	8
Enhancement	Update the function used for group comparison to align with the latest MSstats package Replace the deprecated groupComparison function with the updated groupComparisonTMT or groupComparisonLabelFree depending on the experimental design to ensure compatibility with the latest MSstats package. benchmark/benchmark.R [10] -model = groupComparison("pairwise", summarized) +model = groupComparisonLabelFree("pairwise", summarized) Suggestion importance[1-10]: 7 Why: Updating the function to align with the latest MSstats package ensures compatibility and future-proofing of the code. This enhancement is beneficial for maintaining the code's functionality with updated libraries.	7
Maintainability	Use descriptive variable names for clarity and maintainability Consider using more descriptive variable names than TP, FP, TN, FN for better code readability and maintainability. benchmark/benchmark.R [75-84] -TP <- comparisonResult %>% filter(grepl(paste(proteins, collapse = "|"), Protein) & adj.pvalue < 0.05) %>% nrow() -FP <- comparisonResult %>% filter(!grepl(paste(proteins, collapse = "|"), Protein) & adj.pvalue < 0.05) %>% nrow() -TN <- comparisonResult %>% filter(!grepl(paste(proteins, collapse = "|"), Protein) & adj.pvalue >= 0.05) %>% nrow() -FN <- comparisonResult %>% filter(grepl(paste(proteins, collapse = "|"), Protein) & adj.pvalue >= 0.05) %>% nrow() +true_positives <- comparisonResult %>% filter(grepl(paste(proteins, collapse = "|"), Protein) & adj.pvalue < 0.05) %>% nrow() +false_positives <- comparisonResult %>% filter(!grepl(paste(proteins, collapse = "|"), Protein) & adj.pvalue < 0.05) %>% nrow() +true_negatives <- comparisonResult %>% filter(!grepl(paste(proteins, collapse = "|"), Protein) & adj.pvalue >= 0.05) %>% nrow() +false_negatives <- comparisonResult %>% filter(grepl(paste(proteins, collapse = "|"), Protein) & adj.pvalue >= 0.05) %>% nrow() Suggestion importance[1-10]: 5 Why: While using more descriptive variable names can improve code readability and maintainability, the current names (TP, FP, TN, FN) are standard in statistical contexts. The suggestion is valid but offers only a minor improvement.	5