OCIAD1 KO Analysis 🧬

Abstract

This repository contains the code of the statisticial and data analysis of the data from the article "Integrated proteome and lipidome analyses place OCIAD1 at mitochondria-peroxisome intersection balancing lipid metabolism" (https://doi.org/10.1242/jcs.263729).

Repository content 🌳 📁

│   README.md
│
├───lipidomics
│       Data_extraction.R
│       Fatty_acids_boxplot.R
│       Fatty_acid_analysis_MITOS.R
│       Fatty_acid_analysis_TOTALS.R
│       fxn.R
│       Lipid_class_boxplot.R
│       Preprocessing_MITOS.R
│       Preprocessing_TOTALS.R
│       Supplementary_excel_file.R
│       Volcano_plot.R
│
└───proteomics
        EigenMS.zip
        fold_change_calculation_MITOS.R
        fold_change_calculation_TOTALS.R
        fxn.R
        GO_enrichment.R
        imputation_MITOS.R
        imputation_TOTALS.R
        normalization_MITOS.R
        normalization_TOTALS.R
        organelles_comparison.R
        peroxisomeDB.R
        pipeline.R
        plotting.R
        split_violin_and_statistical_analysis.R
        supplementary_excel_file.R

To see the result you should clone this repository to your own local machine, extract the EigenMS.zip folder, create some folders like data and inside of it a cleaned and a GO_enrichment folder, set the working directory to OCIAD1-Analysis and then run the files.

Proteomics analysis

We can break down proteomics analysis into the following steps:

1. Data preparation and calculation of all necessary measures.
2. Visualisation of the results.

1st step (Preparing Data)

1. Run the imputation_MITOS.R and imputation_TOTALS.R files first. The imputation has been done using the Ludovic method from protti library. The input for both code files is the OCIAD1_prot_raw_data sheet from the supplementary file.
2. Next is the normalization, so run the files normalization_MITOS.R and normalization_TOTALS.R. The normalization was performed using the EigenMS method.
3. The last process is the fold change calculation. For this you have to run the files fold_change_calculation_MITOS.R and fold_change_calculation_TOTALS.R.
4. Now you can also analyse the data in terms of gene ontology (GO term analysis). To do this, run the GO_enrichment.R file.

2nd step (Visualizing the results)

1. The data is ready for plotting using the plotting.R file.
2. To plot the split violin and also perform statistical analysis, run the split_violin_and_statistical_analysis.R file.
3. To create a supplementary file with all the data, run the supplementary_excel_file.R file. Note: Before you running this file, you need to create a file with organelles comparison. To do this run organelles_comparison.R.

Another way to run the analysis is to run the pipeline.R file and most of the analysis will be done except plotting files.

Good luck! 😉

Lipidomics analysis

Similarly, the lipidomics analysis can be broken up into into the following steps:

1. Data preparation and calculation of all necessary measures.
2. Visualisation of the results.

1st step (Preparing Data)

1. The Data_extraction.R file takes in the raw data and extracts the information we need and separates it for MITOS and TOTALS.
2. The Preprocessing_MITOS.R and Preprocessing_TOTALS.R files perform the normalisation, fold change calculation, and statistical testing of the data.
3. The Fatty_acids_MITOS.R and Fatty_acids_TOTALS.R files extract useful information about fatty acid chains, such as the chain lengths and number of double bonds, from the lipid identifiers.

2nd step (Visualizing the results)

1. The data is ready for plotting using the Volcano_plot.R, Lipid_class_boxplot.R, and Fatty_acids_boxplot.R files.
2. The Lipid_class_boxplot.R creates a boxplot and scatter plot combination of fold changes for each lipid class.
3. The Fatty_acids_boxplot.R creates a boxplot and scatter plot combination of fold changes for each combination of fatty acid chain characteristics, even/odd length of the chains and whether they are ether lipids.
4. A supplementary file with all the data can be generated using the Supplementary_excel_file.R file.

Enjoy!

Authors of code: Vanessa Linke, Mateusz Chodkowski, Kacper Kaszuba

References

1. PMID: 19602524. "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition". Karpievitch YV, Taverner T, Adkins JN, Callister SJ, Anderson GA, Smith RD, Dabney AR. Bioinformatics 2009

2. "Metabolomics data normalization with EigenMS" Karpievitch YV, Nikolic SB, Wilson R, Sharman JE, Edwards LM. PLoS One 2014

3. Jan-Philipp Quast, Dina Schuster, Paola Picotti. protti: an R package for comprehensive data analysis of peptide- and protein-centric bottom-up proteomics data. Bioinformatics Advances, Volume 2, Issue 1, 2022, vbab041, https://doi.org/10.1093/bioadv/vbab041

4. Rath, S.,* Sharma, R., Gupta, R., ..., Calvo, S.E., Mootha, V.K.. MitoCarta3.0: an updated inventory of the mitochondrial proteome, now with sub-organelle localization and pathway annotations (2020). Nucleic Acids Research Pubmed: 33174596

5. Schluter, A., Real-Chicharro, A., Gabaldon, T., Sanchez-Jimenez, F. and Pujol, A. (2010) PeroxisomeDB 2.0: An integrative view of the global peroxisomal metabolome. Nucleic Acids Res, 38, D800-5. doi: https://doi.org/10.1093/nar/gkp935