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# System prepended metadata

title: Dataflow diagram

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# Dataflow diagram

1. **Rat proteomics**
    1. [Rodent stroke proteomics from paper supplement](https://pubs.acs.org/doi/suppl/10.1021/acs.jproteome.9b00220/suppl_file/pr9b00220_si_002.xlsx).
    
    `data/rat.proteomics/ratbr-ais.xlsx`
    
    2. Table for converting UNIPROT accession to gene names is downloaded from UNIPROT interface
    
    `annotations/uniprot-rat-entries-to-gns.txt`
    
    4. The quantities normalized to sham
    
    `data/rat.proteomics/rat-proteomics-norm-by-sham.tsv` is created from 1.2., 1.1 by `notebooks/rat-AIS-proteomics.ipynb`
    
    5. WGCNA `meig` (module eigengene) and `g2m` (gene-to-module) files
    
    `data/rat.proteomics/wgcna-pr-rats-meigs.csv`, `data/rat.proteomics/wgcna-pr-rats-g2m.csv` - these files are generated from 1.4 by `annotations/wgcna-proteomics-rats.ipynb`
    
    6. GOEA analysis produces xlsx reports from gene lists of every WGCNA module (`data/rat.proteomics/wgcna-pr-rats-g2m.csv`)
    
    form 1.5 reports are produced by `notebooks/wgcna-proteomics-rats.ipynb`

    7. *.. find better representation ..*

2. **Mouse proteomics**
    1. Mouse stroke proteomics from paper [(Gu, 2021)](https://10.1021/acs.jproteome.1c00259) deposit.
    
    `data/mouse.proteomics/protein-?.tsv`
    `data/mouse.proteomics/proteins_combined.tsv`
    
    2. The quantities normalized to sham
    
    2.1 are processed into `data/mouse.proteomics/mice-Gu-28days-normalized.tsv` by the notebook `notebooks/mouse-proteomics-preprocess.ipynb`