Prepare data for analysis

First, create a new folder in data folder for all data, for example, 2020_11_12.

internal_standard for internal standard for retention time confirmation

Create a internal_standard folder in 2020_11_12 folder. And then create a mzXML folder in this folder. The mzXML data of QC samples should be placed in POS and NEG folder, respectively.

The second file, the table of internal standard (xlsx format) should also be placed in this folder. The table should be like this:

IS_quantification for Quantification for internal standard

Create a IS_quantification folder in 2020_11_12 folder and then put all the mzXML of samples in the POS and NEG folder, respectivey.

lipid_search_quantification for quantification for all the lipids

Create a lipid_search_quantification folder in 2020_11_12 folder

The peak table (csv) from lipidSearch should be placed in one folder named as lipid_search, and then put all the lipid table from lipid search into POS and NEG, respectively.

Data analysis

step0_parameter_setting.R: RT confirmation for internal standards

Open the code folder R/20201111. Open the step0_parameter_setting.R, and then set the parameters for this analysis.

  1. file_path: the folder that contains all the data, for example “2020_11_12”.

  2. is_table_name: the name of internal standard table.

  3. lipid_data_pos_name: the name of lipid table (positive mode) from lipid search and this table should be in 2020-11-12/lipid_search_quantification/POS.

  4. lipid_data_neg_name: the name of lipid table (negative mode) from lipid search and this table should be in 2020-11-12/lipid_search_quantification/NEG.

step1_internal_standard_RT_confirmation.R: Extract peaks of internal standards in QC samples

Open the code folder R/20201111. Open the step1_internal_standard_RT_confirmation.R, and then run code row by row.

After all analysis is done, all the plots of internal standards with different adducts are generated in the positive_plot and negative_plot folders.

Then please open the internal standard table and then refill the RT NEG (min), Adduct NEG, RT POS (second), RT NEG (second) according to the peak shapes plot.

step2_internal_standard_quantification.R: Relative quantification for internal standards

Copy the internal standard table you have filled rt and adduct to IS_quantification/POS and IS_quantification/NEG.

Open the step2_internal_standard_quantification.R and then run code row by row. For positive and negative mode, the quantification tables are generated to POS and NEG folders with name quantification_data_final.xlsx.

step4_absolute_quantification.R: Absolute quantification

Open the step4_absolute_quantification.R and then run code row by row.

step5_combine_pos_neg.R: Combine positive and negative bbsolute quantification

Open the step5_combine_pos_neg.R and then run code row by row. Then all the results are generated in absolute_quantification/Result folder.

  1. lipid_data_um.csv: Absolute quantification for lipid in um.

  1. lipid_data_ug_ml.csv: Absolute quantification for lipid in ug/ml.

  2. lipid_data_class_um.csv: Absolute quantification for lipid class in um.

  3. lipid_data_class_ug_ml.csv: Absolute quantification for lipid class in ug/ml.

  4. lipid_data_um_per.csv: Absolute quantification for lipid class in um percentage.

  5. lipid_data_class_um_per.csv: Absolute quantification for lipid class in ug.ml percentage.

step6_tidy_peaks_plot.R: Reorganize the peak plot and integrate plot.

Open the step6_tidy_peaks_plot.R and then run code row by row. Then all the results are generated in absolute_quantification/Result folder.