• (zip and tar.gz, May 2018): Windows and Unix files for computing likelihood-based confidence intervals for causal interaction.
  • Sequgio (tar file, Nov 2013): Unix package files for pre-processing of RNAseq data. There is vignette in the manual subdirectory. Note also that you would need a highly parallel or cluster computing system in order to get results in reasonable time. Sequgio has been extensively tested to run on parallel system using the multicore package. See the pipeline here
  • RDR link to Rshiny page: web-based interface to run the program. And the zip ad tar.gz (Aug 2014): Windows and Unix files for computing re-discovery rate estimation in high-throughput studies.
  • splscox (zip ad tar.gz, May 2013): Windows and Unix files for performing sparse partial least squares for survival data using the Cox proportional hazard model.
  • Subtyping (zip and tar.gz, Sep 2013): Windows and Unix files for performing molecular subtyping of cancer. Needs rsmooth package below.
  • SPLS (zip and tar.gz, Aug 2014): Windows and Unix files for performing sparse partical least squares. Start with ?SPLS in R.
  • NEA (zip and tar.gz file, Dec 2012): package files for the network enrichment analysis. Start with ?nea in R. You might need these other files:
  • SCCA (zip and tar.gz file, June 2011): package files for sparse canonical covariance analysis. Built using R.12.0.
  • MPSS (zip and tar.gz file, Jan 2011): package files for multi-platform segmentation. Built using R2.12.0.
  • TDNenv (zip and tar.gz files, Nov 2010) package files for estimation of true discovery number (TDN) and its confidence bounds from genome-wide association studies.
  • SSPCA (zip and tar.gz files, Jan 2013) package files for the sparse PCA (Lee et al, BMC Bioinformatics 2010).
  • Cnvpack ( and cnvpack_0.4.10.tar.gz (Mar 2015) Windows binary and unix source for finding common cnv regions.
  • SLR: (17 Feb 2011) Windows binary for performing smoothed logistic regression for CGH data. Unix source slr_0.1.9.tar.gz.
  • MWT: (Apr 2015) Windows binary for Moderated Welch Test for microarray data (Demissie et al Bioinformatics 2008). Unix source: mwt_0.2.7.tar.gz.
  • FLUSH and LVS: (Sep 09) Windows-binary-installation R package to compute LVS normalization (Calza et al, BMC Bioinformatics 2008) and FLUSH filtering (Calza et al, Nucl Acid Research 2007). FLUSH has been revised to allow various background corrections. The data example in RData format: FLUSH.RData. The Unix source: FLUSH.LVS.bundle_1.3.1.tar.gz. To see the work flow: type vignette(‘FLUSH’) in R.
  • smoothseg: (Feb 2011) Windows-binary-installation R package to compute smooth-segmentation of array CGH data, including the estimation of FDR for comparative studies. The Unix version: smoothseg_0.0.4.tar.gz.
  • OCplus: This package is now maintained in Bioconductor. The details are given in Pawitan et al ‘FDR, sensitivity and sample size for microarray studies’ in Bioinformatics 2005.
  • rsmooth (zip and tar.gz): R package for robust smoothing
  • Prospect: ProSpect (Ver 0.3.6 – Dec 11) (Feb 2010): zip files of Windows R packages for processing of SELDI protein spectra. You need to install both in R for Windows; additionally you also need to install ‘quadprog’ package. After running library(ProSpect), type ?ProSpect.README for a short description and an example of a complete run. NOTE: names are case sensitive. Need rsmooth package above.
  • ProSpect_0.3.6.tar.gz (Dec 11): Unix version of ProSpect. See also the Windows note above. Needs rsmooth.
  • (version 1.1.0, Aug 2007) Windows-installed R package for gene filtering. Needs packages affy, affyPLM and quantreg. Run demo(FLUSH.tour) to start.
  • FLUSH_1.1.0.tar.gz (version 1.1.0, Aug 2007) gzipped version for Unix users
  • ELF: (June 2006): R codes and dataset to run the estimated latent FDR procedure.

Prof. Yudi Pawitan

Department of Medical Epidemiology and Biostatistics

Adress: PO Box 281 Karolinska Institutet, 171 77 Stockholm
Phone: 46-8-5248 3983
Fax : 46-8-314 975

Research interest

  • Statistical genetics, microarrays, family data
  • Biostatistics
  • Likelihood inference