runningJetta          package:oneChannelGUI          R Documentation

_g_r_a_p_h_i_c_a_l _i_n_t_e_r_f_a_c_e _t_o _M_A_D_S/_j_e_t_t_a _R _l_i_b_r_a_r_y.

_D_e_s_c_r_i_p_t_i_o_n:

     MADS, which stands for Microarray Analysis of Differential
     Splicing, is a tool to  identify differential alternative splicing
     by exon array. The principle of MADS is  to increase the precision
     of exon-level and gene-level expression estimates by  correcting,
     as much as possible, noise in observed probe intensities due to 
     background and cross-hybridization.  MADS incorporates a series of
     novel algorithms motivated by the probe-rich design  of
     exon-tiling arrays, such as background correction, iterative probe
     selection and  removal of sequence-specific cross-hybridization to
     off-target transcripts.  MADS was published in RNA,2008,14(8):
     1470-1479. Junction and Exon array Toolkit for Transcriptome
     Analysis (JETTA) is compacted version of MADS.

_U_s_a_g_e:

     runningJetta()

_D_e_t_a_i_l_s:

     Expression indexes are calculated as the order of Background
     Correction,  Normalization and Summarization.  In the
     Summarization step, background corrected and normalized probe
     intensities  of a meta probeset are summarized to expression of
     the meta probeset.  Meta probesets can be defined as
     gene/transcript clust/exon level.

     Background Correction JETTA estimates background signal using
     background probes and subtracts it from the probe intensity.  If
     the probe intensity is less than the estimated background signal,
     the background  subtracted signal is truncated to 1.  Estimation
     of background signal is based on several models: Median GC: median
     of background probe signal of the same GC counts MAT: linear model
     of probe sequence with 80 parameters. see Kapur et al, 2007 

     Normalization Normalization of JETTA is done for core probes
     defined in probeset annotation file.  If the PSA file is not
     specified, it considers all probes in the MPS files as core
     probes. Median scaling: scaling each array so that its median is
     100 Quantile: all probes of the same signal quantile have the same
     signal 

     Summarization LiWong model: multiplication model of expression and
     probe effect, see Li and Wong, 2001 Probe selection: select probes
     based on cross-array correlation of signal. see Xing et al, 2006
     Median-polish 

     Alternative Splicing Detection Detecting alternatively expressed
     PSR/Exon between two sample groups based on  background corrected
     and normalized probe intensities. It has several criteria to 
     filter out transcript clusts and probes from the analysis. TC
     expression level: excluding low-expressed transcript clusts TC
     expression fold change: excluding transcript clusts which have big
     fold change between two groups Extreme probe signal: excluding
     probes of which signal is extremely high Cross-hybridized probes:
     excluding cross hybridized probes, currently pre-calculated
     results are needed

_A_u_t_h_o_r(_s):

     jseok@stanford.edu

