inst/ProstarApp/md/links.md

In Prostar: Provides a GUI for DAPAR

User manuals and tutorials

1. Prostar user manual
2. Prostar tutorial (not up-to-date)
3. cp4p tutorial
4. Prostar protein-level protocol (tba)

Contact

If you need any help, but also if you wish to make comments or suggestions, please contact Samuel Wieczorek or Thomas Burger ([e-mail]).

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. Coute 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.
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. Coute, 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. Q. Giai Gianetto, C. Lazar, S. Wieczorek, C. Bruley, Y. Coute and T. Burger. Multiple imputation strategy for mass spectrometry-based proteomic data. (in preparation).

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., Sültmann, 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.

Prostar documentation built on April 11, 2021, 6 p.m.