Support & Resources

User manuals and tutorials

  1. Prostar user manual (corresponds to Prostar release 1.14)
  2. cp4p tutorial
  3. Prostar protein-level protocol (corresponds to Prostar release 1.14)
  4. A five step tutorial for differential analysis (Wieczorek et al. 2019, Journal of Proteomics) as well as its Supplemental Material.
  5. Prostar peptide-level and peptidomics protocols (corresponds to Prostar release 1.24).

Contact

If you need any help, but also if you wish to make comments or suggestions, please contact Samuel Wieczorek or Thomas Burger (firstname.lastname@cea.fr).

Reference manuals

  1. Prostar reference manual
  2. DAPAR reference manual
  3. MSnbase package webpage
  4. CP4P reference manual
  5. IMP4P reference manual

Bibliographical references

Our referenced works

  1. Q. Giai Gianetto, F. Combes, C. Ramus, C. Bruley, Y. Couté and T. Burger. Calibration Plot for Proteomics (cp4p): A graphical tool to visually check the assumptions underlying FDR control in quantitative experiments. Proteomics, 16(1):29-32, 2016. (Supplemental Material).
  2. C. Lazar, L. Gatto, M. Ferro, C. Bruley, T. Burger. Accounting for the multiple natures of missing values in label-free quantitative proteomics datasets to compare imputation strategies. Journal of Proteome Research, 15(4):1116-1125, 2016.
  3. Q. Giai Gianetto, Y. Coute, C. Bruley and T. Burger. Uses and misuses of the fudge factor in quantitative discovery proteomics. Proteomics, 16(14):1955-60, 2016.
  4. S. Wieczorek, F. Combes, C. Lazar, Q. Giai-Gianetto, L. Gatto, A. Dorffer, A.-M. Hesse, Y. Couté, M. Ferro, C. Bruley and T. Burger. DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics, Bioinformatics, 33(1):135-136, 2017
  5. T. Burger. Gentle introduction to the statistical foundations of false discovery rate in quantitative proteomics. Journal of Proteome Research, 17(1):12-22, 2017.
  6. L. Jacob, F. Combes and T. Burger. PEPA test : fast and powerful differential analysis from relative quantitative proteomics data using shared peptides. Biostatistics, kxy021, 2018.
  7. S. Wieczorek, F. Combes, H. Borges, T. Burger. Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR. Proteomics for Biomarker Discovery, V. Brun, Y. Coute (Eds.), Methods in Molecular Biology Series, Vol. 1959, pp. 225-246, 2019.
  8. H. Borges(+), R. Guibert(+), O. Permiakova(+), T. Burger. Distinguishing between spectral clustering and cluster analysis of mass spectra. Journal of Proteome Research, 18 (1), pp 571-573, 2019.
  9. S. Wieczorek, Q. Giai Gianetto, T. Burger. Five simple yet essential steps to correctly estimate the rate of false differentially abundant proteins in mass spectrometry analyses. Journal of Proteomics, vol 207, p. 103441, 2019.
  10. Y. Couté, C. Bruley, T. Burger. Beyond target-decoy competition: stable validation of peptide and protein identifications in mass spectrometry-based discovery proteomics. Analytical Chemistry, 92(22), pp 14898–14906, 2020.
  11. Q. Giai Gianetto, S. Wieczorek, Y. Coute and T. Burger. Multiple peptide-level imputation strategy for mass spectrometry-based proteomic data. (in preparation).
  12. T. Burger. Can Omics Biology Go Subjective because of Artificial Intelligence? A Comment on “Challenges and Opportunities for Bayesian Statistics in Proteomics” by Crook et al. Journal of Proteome Research, 21(7), pp. 1783–1786, 2022.

Other references

  1. Bolstad BM (2017). preprocessCore: A collection of pre-processing functions. R package version 1.38.1
  2. Hastie T, Tibshirani R, Narasimhan B and Chu G (2017). impute: Imputation for microarray data. R package version 1.50.1
  3. Gatto L and Lilley K (2012). MSnbase - an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. Bioinformatics, 28, pp. 288-289.
  4. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK (2015). Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), pp. e47.
  5. Cleveland, W. S., & Devlin, S. J. (1988). Locally weighted regression: an approach to regression analysis by local fitting. Journal of the American statistical association, 83(403), 596-610.
  6. Huber, W., Von Heydebreck, A., Sultmann, H., Poustka, A., & Vingron, M. (2002). Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics, 18(suppl_1), S96-S104.

Forum

Our community forum is hosted by the Bioconductor: https://support.bioconductor.org/t/prostar/