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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        About MultiQC

        This report was generated using MultiQC, version 1.33

        Video: Using MultiQC Reports MultiQC homepage MultiQC documentation Source code Issue tracker

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        MultiQC is developed by Seqera.

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        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2025-12-25, 13:03 UTC based on data in: /home/runner/work/pmultiqc/pmultiqc/data_temp
        Because this report contains a lot of samples, you may need to click 'Show plot' to see some graphs.

        pmultiqc

        pmultiqc is a MultiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms, MaxQuant, and DIA-NN.https://github.com/bigbio/pmultiqc

        Precursor Ion

        Distribution of Precursor Charges

        This bar chart shows the distribution of precursor ion charges.
        [result_performance.csv] The precursor ion charge extracted from the 'precursor ion' column.
        Created with MultiQC

        Intensity

        Distribution of Intensity (for Conditions)

        Distribution of 'log_Intensity_mean' under Condition A and B.
        [result_performance.csv] This plot visualizes the distribution of mean intensity values after log2 transformation for both Condition A and Condition B.
        Created with MultiQC

        Missing Values (for Conditions)

        Number of missing (NA) values in 'log_Intensity_mean' for Condition A and B.
        [result_performance.csv] This plot shows the number of missing (NA) values in the mean log2-transformed intensities for Condition A and Condition B.
        Created with MultiQC

        Distribution of Intensity (for Runs)

        Distribution of intensity for each run.
        [result_performance.csv] Distribution of intensity for each run.
        Created with MultiQC

        Missing Values (for Runs)

        Number of missing (NA) values for each run.
        [result_performance.csv] Number of missing (NA) values for each run.
        Created with MultiQC

        Number of Detected Features per Run

        Number of detected (Non-NA) and missing (NA) values per sample file.
        [result_performance.csv] This plot shows the number of missing (NA) values in the mean log2-transformed intensities for Condition A and Condition B.
        Created with MultiQC

        Standard Deviations of Intensity

        Standard Deviation of Intensity

        This plot shows the distribution of 'log_Intensity_std' values for Condition A and Condition B.
        [result_performance.csv] This plot shows the distribution of standard deviations calculated from log2-transformed intensity values for Condition A and Condition B.
        Created with MultiQC

        Coefficient of Variation

        Distribution of CV

        This plot shows the distribution of coefficient of variation (CV) values for Condition A and Condition B.
        [result_performance.csv] This plot shows the distribution of coefficient of variation (CV) values for Condition A and Condition B.
        Created with MultiQC

        Missing Values

        This plot shows the number of missing values (NAs) in the coefficient of variation (CV) for condition A and B.
        [result_performance.csv] This plot shows the number of missing values (NAs) in the coefficient of variation (CV) for condition A and B.
        Created with MultiQC

        Log2 Fold Change

        Log2 Fold Change (A vs B)

        This plot shows the distribution of 'log2_A_vs_B' values for Condition A and Condition B.
        [result_performance.csv] Distribution of log₂ fold changes (log2_A_vs_B) between Condition A and B based on mean log2-transformed intensities.
        Created with MultiQC

        log2FC vs logIntensityMean

        Distribution of mean intensity across all runs and log2 fold change. Legend: ECOLI (blue), HUMAN (green), YEAST (red).
        [result_performance.csv] Distribution of mean intensity across all runs and log2 fold change (log2FC). Legend: ECOLI (blue), HUMAN (green), YEAST (red).

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

        Distribution of Epsilon

        Distribution of 'epsilon' values (difference between observed and expected log2 fold changes).
        [result_performance.csv] 'Epsilon' measures the deviation between observed and expected log2 fold changes, indicating agreement between data and expectations.
        Created with MultiQC

        MultiQC v1.33 - Written by Phil Ewels, available on GitHub.

        This report uses Plotly, jQuery, jQuery UI, Bootstrap and FileSaver.js.