This python package aims to find reliable features and compute the CCS values for chemical standards. It is assumed that prior to running this software you have:

• preprocessed the raw data files and each of the seven fields is split into a separate file (https://omics.pnl.gov/software/pnnl-preprocessor)
• obtained the correspondent features from Agilent's MP software (a CEF file from each field)

pip install -r requirements.txt

usage: ccs_auto_app.py [-h] [--target_list_file TARGET_LIST_FILE]
                       [--config_file CONFIG_FILE] [--data_folder DATA_FOLDER]
                       [--output OUTPUT]
 
optional arguments:
  -h, --help            show this help message and exit
  --target_list_file TARGET_LIST_FILE
                        Target list file (Tab-delimited text format)
  --config_file CONFIG_FILE
                        Configuration file
  --data_folder DATA_FOLDER
                        Data folder containing all the cef and meta data files
  --output OUTPUT       Output file to save a output table

You can test this package with the CEF files here included. This is a subset of the standards from the PNNL CCS DB found at https://panomics.pnnl.gov/metabolites/

python ccs_auto_app.py --target_list_file demo/TargetList.tsv --config_file demo/config.xml --data_folder demo/data

Please refer to the demo/results folder.

1. ccs_table.tsv

Running this package provides a summary of all about computing CCS values of compounds listed on a target list file (e.g., TargetList.tsv). Each row mean a CCS computation results of a single replicate. And each column means the following items.

2. <compound_id>.pdf (e.g., S0000001_neg_[M-H].pdf)

3. <compound_id>__intensity_dist.pdf (e.g., S0000001_neg_intensity_dist.pdf)

• x-axis: log-scale of original intensities

• y-axis: kde estimation (normalized distribution)

• dots: features within 15ppm (according to mz_tolerance setting in config.xml) of (e.g., [M-H] in the below)

• Vertical lines

  • Most left line: median of feature intensities
  • Center line: 10 * median
  • Most right line: 2 * std

4. <compound_id>__meta.pdf (e.g., S0000001_neg_meta.pdf)

In the figures, dots represent values in the frames of the selected ranges (frame_offset in config.xml) to compute CCS values.