1. R software for High-Dimensional Two-Sample Testing [R Code]

  1. R software for Multivariate Geneset Testing based on Graphical Models [R Code]

  1. R software for Differential Network Analysis and Network-Based Clustering [R Code]

  1. Matlab software for State Transitions using Aggregates Markov Models (STAMM) [Matlab Code]


  1. Two-Sample Testing in High-Dimensional Models [arXiv]
    N. Städler and S. Mukherjee.


  1. Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines.
    Casale FP, Giurato G, Nassa G, Armond J, Oates CJ, Cora D, Gamba A, Mukherjee S, Weisz A & Nicodemi M (2014)

  PLoS One, in press.

    1. Joint Structure Learning of Multiple Non-Exchangeable Networks.
      C.J. Oates & S. Mukherjee (2014)

      Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS), to appear.

      1. A stochastic model dissects cellular states and heterogeneity in transition processes.
        Armond J, Saha K, Rana AA, Oates CJ, Jaenisch R, Nicodemi M & Mukherjee S (2014)

        Nature Scientific Reports, in press.

        1. Penalized estimation in high-dimensional hidden Markov models with state-specific graphical models

        N. Städler and S. Mukherjee (2013)

          Annals of Applied Statistics, to appear

        1. Bayesian Inference of Signaling Network Topology in a Cancer Cell Line
          S. M. Hill, Y. Lu, J. Molina, L. M. Heiser, P. T. Spellman, T. P. Speed, J. W. Gray, G. B. Mills and S. Mukherjee (2012)

          Bioinformatics, 28(21):2804-2810. [code] [data]

        1. Network Inference Using Steady-State Data and Goldbeter-Koshland Kinetics
          C. J. Oates, B. T. Hennessy, Y. Lu, G. B. Mills and S. Mukherjee (2012)

          Bioinformatics 28(18):2342-8 [code]

        1. Statistical Analysis of Varieties of English
          C. Nam, S. Mukherjee, M. Schilk & J. Mukherjee

          Journal of the Royal Statistical Society, Series A, to appear.

        1. Network inference and biological dynamics
          C. J. Oates & S. Mukherjee (2012)
          Annals of Applied Statistics 6(3):1209-1235 [pdf]

        1. Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology
          S. M. Hill, R. M. Neve, N. Bayani, W-L. Kuo, S. Ziyad, P. T. Spellman, J. W. Gray  & S. Mukherjee (2012)

          BMC Bioinformatics 13:94 (Highly accessed paper) [code]

        1. Exploratory network analysis of large social science questionnaires
          R. J. B. Goudie, S. Mukherjee & F. Griffiths (2011)
          In Proceedings of Bayesian Modelling Applications Workshop (BMAW-11)

        1. Transcriptomic Technologies and Statistical Data Analysis
          E. Purdom & S. Mukherjee (2011)
          In Handbook of Statistical Systems Biology (eds. D. Balding, M. Girolami & M. Stumpf), Wiley.

        1. Network clustering: probing biological heterogeneity by sparse graphical models [code]
          S. Mukherjee & S. M. Hill (2011)
          Bioinformatics 27(7):994-1000

        1. MC4: a tempering algorithm for large-sample network inference [pre-print]
          D. Barker, S. M. Hill and S. Mukherjee (2010)
          In Proceedings of the 5th IAPR international Conference on Pattern Recognition in Bioinformatics, V. Kadirkamanathan, G. Sanguinetti, M. Girolami, M. Niranjan, and J. Noirel (eds.), Lecture Notes in Bioinformatics 6282, Springer.

        1. Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana
          S. J. Kiddle, O. P. F. Windram, S. McHattie, A. Mead, J. Beynon, V. Buchanan-Wollaston, K. J. Denby & S. Mukherjee (2010)
          Bioinformatics 26(3):355-362. [code]

        1. Sparse combinatorial inference with an application in cancer biology
          S. Mukherjee, S. Pelech, R. M. Neve, W-L. Kuo, S. Ziyad, P. T. Spellman, J. W. Gray and T. P. Speed (2009)
          Bioinformatics 25(2): 265-271. [code]

        1. Network inference using informative priors
          S. Mukherjee and T. P. Speed (2008)
          Proceedings of the National Academy of Sciences 105(38): 14313–14318

        1. Next station in microarray data analysis: GEPAS
          D. Montaner, J. Tarraga, J. Huerta-Cepas, J. Burguet, J. Vaquerizas, L. Conde, P. Minguez, J. Vera, S. Mukherjee, J. Valls, M. Pujana, E. Alloza, J. Herrero, F. Al-Shahrour and J. Dopazo (2006)
          Nucleic Acids Research 34, W486-W491

        1. Data-adaptive test statistics for microarray data
          S. Mukherjee, S. J. Roberts and M. J. van der Laan (2005)
          Bioinformatics 2(ii), ii108-ii114