DBTMEE
Database of Transcriptome in Mouse Early Embryos


LLM (Log-Linear Model)

: inferring probabilistic conditional independency from combinatorial regulation of transcription factors


Transcription factors (TFs) regulate gene expressions by intricate combinatorial interactions. A computational modeling of the interactions is essential to understand the nature of transcriptional regulation. LLM (log-linear modeling) deals with spurious TF interactions that may lead deceptive inferences. Given discrete (0 or 1) input data of TF-DNA binding instances, this method uses a log-linear modeling that deals with probabilistic conditional independency for detecting spuriousness.

NOTE:
  • Input data is a n x m matrix, where n row is the number of promoters (genes) and m column is the number of TFs.
  • The matirx size is restrited to 50≤ n ≤2000 and 2≤ m ≤10
  • Input file has to include "#ITEM: TF1 TF2 TF2..."
  • Each line has to be TAB deliminated; 'name[tab]0[tab]1[tab]1[tab]0...'
  • Two columns for one item (TF): '0[tab]1' means absence, '1[tab]0' means presence
  • [see an example input data]

  • Two thresholds are required,
    1. p-value cutoff for the test of deviance of a current RM (reduced model) from the FM (full model)
    2. p-value cutoff for the test of deviance of a current RM (reduced model) from the previous RM


  • Upload input:

    1. p_value cutoff for cur_RM vs. FM :  
    2. p_value cutoff for cur_RM vs. prev_RM :  



    Reference
    1. Lauritzen, S.L., "Graphical Models", Oxford University Press, 1996
    2. Christensen, R., "Log-Linear Models and Logistic Regression", Springer-Verlag, 1997
    3. Park SJ, Ichinose N, Yada T, "Probabilistic Graphical Modeling for Large-scale Combinatorial Regulation of Transcription Factors", Proc. the workshop on Knowledge, Language, and Learning in Bioinformatics (KLLBI), 72-86, 2008
    4. Park SJ, Umemoto T, Saito-Adachi M, Shiratsuchi Y, Yamato M, Nakai K, "Computational Promoter Modeling Identifies the Modes of Transcriptional Regulation in Hematopoietic Stem Cells", PLoS ONE 9(4): e93853, 2014