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QC Report

From Quantified Expression Profiles to QC Report


You can find the source code on chinese-quartet/quartet-protqc-report

I. Prepare data & metadata files

Data File

The data file provides the quantified proteins that are mapped to gene symbols and quantified peptide sequences, and the missing values are allowed. The required file format has the columns named Type and Feature.

Please ensure that there are no duplicated column names in the data file.

If the type of features is Gene Symbol only, then the metric Relative Correlation with Reference Datasets (RC) will not be calculated.

Please see the example of the required data file as follows.

Metadata File

The metadata file has the information of each sample ID in the data file. With columns named "library", "sample" (D5, D6, F7 and M8 for Quartet samples).

Remember that the column "library" and column names of the data file table must be in one-to-one correspondence.

If the sample type "D6" is missing, then the metric Relative Correlation with Reference Datasets (RC) will not be calculated.

Please see the example of the required data file as follows.

II. Step by Step Guide

To analyze your data on Quartet Data Portal

See details on Step by Step Guide

To analyze your data on your own server

  1. Pull docker image

    More versions on Docker Registry

    docker pull
  2. Run quartet-protqc-report with docker image

    Assuming that your data file is named data.csv and metadata file is named metadata.csv and all files are placed in /your-dir directory.

    docker run -v /your-dir:/data -it -d /data/data.csv -m /data/metadata.csv -o /data
  3. Find your QC report in /your-dir/multiqc_report.html

III. QC metrics

The package protqc output Quality Control(QC) results of proteomics data for Quartet Project. The QC pipeline starts from the expression profiles at peptide/protein levels, and enables to calculate 6 metrics. A Total score is the geometric mean of the linearly normalized values of these metrics.

  1. Number of features: We expect as many proteins (mapped to gene symbols) as possible for downstreaming analyses. Identified proteins were filtered by the rule of 5% FDR. However, if you set stricter rules (e.g., 1% FDR), less number but high confidence of proteins will be retained (then your data may rank relatively low in terms of Number of features).

  2. Missing percentage (%): Too many missing values interfere with comparability. This metric is calculated globally.

  3. Coefficient of variantion (CV, %): A CV value is calculated to indicate the dispersion within replicates feature by feature.

  4. Absolute Correlation: Pearson correlation reflects overall reproducibility within replicates. We calculate correlation coefficients between each two replicates within each biological sample (D5, D6, F7, M8), and take the median as the final value for absolute correlation.

  5. Signal-to-Noise Ratio (SNR): SNR is established to characterize the ability of a platform or lab or batch, which is able to distinguish intrinsic differences among distinct biological sample groups (“signal”) from variations in technical replicates of the same sample group ("noise").

  6. Relative Correlation with Reference Datasets (RC): RC is used for assessment of quantitative consistency with the reference dataset at relative levels. For shotgun proteomics, quantitation at peptide levels is theoretically more reliable. Therefore, the reference dataset is established by benchmarking the relative expression values (log2FCs), for each peptide sequence of each sample pair (D5/D6, F7/D6, M8/D6), in historical datasets at peptide levels. We calculate relatively qualified (satisfied with thresholds of p < 0.05) log2FCs of the queried data, for overlapped peptides with the reference dataset, as the input for the assessment of quantitative consistency. Then RC value is Pearson correlation coefficient between the test dataset and the reference dataset.

Last update: 2023-06-20
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