Accompanying code to Sakarika, Kerckhof et al. 2023 - "The nutritional composition and cell size of microbial biomass for food applications are defined by the growth conditions" (Microb Cell Fact. 2023 Dec 11;22(1):254. doi: 10.1186/s12934-023-02265-1).
Unless otherwise stated, most code is provided in the R language and most
coherent scripts are grouped into R markdown
files (*.Rmd), in order to allow for reproducible data science.
| Script | Language | Purpose |
|---|---|---|
| Proteomics/GO_mapping_for_visualization.Rmd | R (markdown) | simplification of GO results into data frame for simplified visualization using the QuickGO REST API |
| Flow_cytometry/Gating_and_preprocessing_FCM | R (markdown) | data extraction and gating script from raw flow cytometry standard (FCS) files |
| Proteomics/Retrieve-GO-terms.ipynb | Python (iPython notebook) | Retrieve and organize GO id's from protein info |
The mass spectrometry data and flow cytometry data have been published in dedicated repositories:
- Proteomics data can be found at: ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD041491.
- Flow cytometry data can be found at the FlowRepository with identifier FR-FCM-Z6Y6.
-
tidyverse: Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686.
-
ggcyto: Van P, Jiang W, Gottardo R, Finak G (2018). “ggcyto: Next-generation open-source visualization software for cytometry.” Bioinformatics. https://doi.org/10.1093/bioinformatics/bty441.
-
Phenoflow: Props R, Monsieurs P, Mysara M, Clement L, Boon N (2016). Measuring the biodiversity of microbial communities by flow cytometry. Methods in Ecology and Evolution 7: 1376-1385. < https://doi.org/10.1111/2041-210X.12607>.
-
flowAI: Monaco G, Chen H, Poidinger M, Chen J, de Magalhaes J, Larbi A (2016). “flowAI: automatic and interactive anomaly discerning tools for flow cytometry data.” Bioinformatics, 32(16). 10.1093/bioinformatics/btw191.