example

Diffacto: Examples

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To run these examples, clone this git repository and decend to the example directory. To install dependencies run pip install pyteomics numpy networkx scikit-learn scipy

Print usage information

python ../run_diffacto.py -h

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Example-1:

python ../run_diffacto.py -i iPRG.novo.pep.csv -samples iPRG.samples.lst -out iPRG.denovo.protein.txt -mc_out iPRG.denovo.protein.FDR -min_samples 4 -impute_threshold 0.9 -use_unique True -log2 False

• input-1, peptide abundances: iPRG.novo.pep.csv
• input-2, sample list: iPRG.samples.lst
• output-1, protein quantification: iPRG.denovo.protein.txt
• output-2, FDR estimation by MC tests: iPRG.denovo.protein.FDR
• other parameters:
  -min_samples 4 (peptide quantified in at least four runs)
  -impute_threshold 0.9 (threshold for missing value imputation 90%)
  -use_unique True (only use unique peptides for quantification)
  -log2 False (input abundances are not in log scale)

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Example-2:

python ../run_diffacto.py -i HBY20Mix.peptides.csv -samples HBY20Mix.samples.lst -db UP000002311_559292.fasta -out HBY20Mix.protein.txt -min_samples 30 -impute_threshold 0.7 -log2 False -reference REF

• input-1, peptide abundances: HBY20Mix.peptides.csv
• input-2, sample list: HBY20Mix.samples.lst
• input-3, protein database: UP000002311_559292.fasta
• output-1, protein quantification: HBY20Mix.protein.txt
• other parameters:
  -min_samples 30 (peptide quantified in at least 30 runs)
  -impute_threshold 0.7 (threshold for missing value imputation 70%)
  -log2 False (input abundances are not in log scale)
  -reference REF (use the runs labeled 'REF' as the internal reference)