runningJetta: graphical interface to MADS/jetta R library.

In oneChannelGUI: A graphical interface designed to facilitate analysis of microarrays and miRNA/RNA-seq data on laptops

Description

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.

Usage

runningJetta()

Details

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

Author(s)

[e-mail]

oneChannelGUI documentation built on Nov. 17, 2017, 11:02 a.m.