version	2.15.5
-------------

  o updated korg to included over 80 newly added species, such as
    sheep, apple, mandarin orange etc. Pathview can work with 3050
    species now.

version	2.14.3
-------------

  o revised the internal function gs.heatmap as to allow margin sizes
    ajustment for gene set heatmaps directly or through sigGeneSet.

version	2.14.1
-------------

  o revised "RNA-Seq Data Pathway and Gene-set Analysis Workflows" to
    reflect summarizeOverlaps() migration to GenomicAlignments package
    for Bioc 2.14.

version	2.13.5
-------------

  o add function go.gsets, which generates up-to-date GO gene sets for
    19 common species annotated in Bioconductor and more others by the
    users.

version	2.13.3 (2.12.3)
-------------

  o updated korg to included over 600 newly added species. kegg.gsets
    can work with 2970 species now.

  o fixed typos in joint workflows with Cufflinks (page 13):
    range(exp.fc) to range(cuff.fc)

version	2.12.2
-------------

  o fixed typos in joint workflows with DESeq2 (page 11): cnts.kegg.p
    should be fc.kegg.p

version 2.12.1

  o remove pathview from the imports list for easier installation and
    loading.

version 2.11.3

  o add secondary vignette, "RNA-Seq Data Pathway and Gene-set
    Analysis Workflows".

  o add function kegg.gsets, which generates up-to-date KEGG pathway
    gene sets for any specified KEGG species.

version 2.9.4

  o add secondary vignette, "Gene set and data preparation", on data
    preparation.

version 2.9.2

  o suggests and connected to pathview package for results visualization.

version 2.9.1

  o removed dependency on multtest package for p-value FDR adjustment,
    use p.adjust function of the stat package instead.

  o change "depends" to "imports" for graph package as we only need to
    import graphNEL class and connComp method.

  o subset kegg.gs. From now, kegg.gs only include the subset of canonical
    signaling and metabolic pathways from KEGG pathway database, and
    kegg.gs.dise is the subset of disease pathways. And it is
    recommended to do KEGG pathway analysis with either kegg.gs or
    kegg.gs.dise seperately (rather than combined altogether) for
    better defined results. Note that kegg.gs and subsets are be
    defined slightly different in gageData package.

  o In gage vignette, add citation section and an example of including all
    genes (rather than those selected using essGene function) in top
    gene set result check using geneData function.


version 2.2.0

  o More robust p-value summarization using Stouffer's method through
    argument use.stouffer=TRUE. The original p-value summarization,
    i.e. negative log sum following a Gamma distribution as the Null
    hypothesis, may produce less stable global p-values for large or
    heterogenous datasets. In other words, the global p-value could be
    heavily affected by a small subset of extremely small individual
    p-values from pair-wise comparisons. Such sensitive global p-value
    leads to the "dual signficance" phenomenon. Dual-signficant means
    a gene set is called significant simultaneously in both
    1-direction tests (up- and down-regulated). "Dual signficance"
    could be informative in revealing the sub-types or sub-classes in
    big clinical or disease studies, but may not be desirable in other
    cases.


  o Output of gage function now includes the gene set test statistics
    from pair-wise comparisons for all proper gene sets. The output is
    always a named list now, with either 3 elements ("greater",
    "less", "stats") for one-directional test or 2 elements
    ("greater", "stats") for two-directional test.

  o The individual p-value (and test statistics)from dependent
    pair-wise comparisions, i.e. comparisions between the same
    experiment vs different controls, are now summarized into a single
    value. In other words, the column number of individual p-values or
    statistics is always the same as the sample number in the
    experiment (or disease) group. This change made the argument value
    compare="1ongroup" and argument full.table less useful. It also
    became easier to check the perturbations at gene-set level for
    individual samples.

  o Whole gene-set level changes (either p-values or statistics) can
    now be visualized using heatmaps due to the third change above.
    Correspondingly, functions \code{sigGeneSet} and \code{gagePipe}
    have been revised to plot heatmaps for whole gene sets.

  o Fixed a bug in gs.zTest function: mod <- (length(ix)/s)^(1/2), it
    is mod <- length(ix)^(1/2)/s before. Thanks to Nhan Thi HO from
    Michigan State University.