https://github.com/bioconductor-source/xhybcasneuf

https://github.com/bioconductor-source/xhybcasneuf

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#################
##    DATA     ##  in data/ subdirectory
#################

1] 2 data.frames containing all probe set pairs with Q75 >= 55 for both the 
    Affymetrix and the custom-made CDF.
   Both contain a 'peCC' column which holds the pairs Pearson correlation 
    coefficient calculated on the Tissue dataset (orig from AtgenExpress data).
   The 'alSum' column contains the Q75 value of the alignment scores of X's 
    reporters to the transcript of Y.
   The 'meant2hit' column contains the gene locus the probe set was designed
    to target according to Affymetrix (extracted from ATH1-121501_annot.csv).

    AffysTissue.rda
    ===============
    data.frame with 42826 rows.
    > colnames(AffysTissue)
    [1] "pr"         "target"     "alSum"      "meant2hit"  "PSofTarget"
    [6] "peCC"      
    
    
    CustomsTissue.rda
    =================
    data.frame with 40094 rows.
    > colnames(CustomsTissue)
    [1] "pr"     "target" "alSum"  "peCC"  


2] Same as above but gene pairs that align to each other with BLAST in at 
    least one direction with an E-value smaller than 10^{-10} are omitted 

    AffysTissue.noBl.rda
    ====================
    data.frame with 11361 rows.
    
    CustomsTissue.noBl.rda
    ======================
    data.frame with 10702 rows.
    

3] Same as 1] but a column containing the metacorrelation coefficients added.
   Only those probe set pairs are included for which is this value =! NA (which
    is the case when the alignment scores of X's reporters to Y's transcript are 
    all identical -> no correlation coefficient can be calculated).
       
    AffysTissueMC.rda
    =================
    data.frame with 42815 rows.
    > colnames(AffysTissueMC)
    [1] "pr"         "target"     "alSum"      "meant2hit"  "PSofTarget"
    [6] "peCC"       "Mcor"      

    
    CustomsTissueMC.rda
    ===================
    data.frame with 39424 rows.
    > colnames(CustomsTissueMC)
    [1] "pr"     "target" "alSum"  "peCC"   "Mcor"  


4] Cross-hybridisation examples.   
   All three are S4 objects. To look at the data, must set class first:

   setClass("XhybExamples", representation(X = "character", Y = "character", 
   IVX = "numeric", IVY = "numeric", IVXi = "matrix", ai = "numeric"))

   The slots of these objects contain:
    X    : the name of probe set X
    Y    : the name of probe set Y    
    IVX  : the expression intensities of X in the Tissue dataset  
    IVY  : the expression intensities of Y in the Tissue dataset  
    IVXi : the expression intensities of X's reporters in the Tissue dataset   
    ai   : the alignment scores of X's reporters to Y's transcript sequence  

    ex1.rda
    =======
    X: "245875_at"
    Y: "AT3G28550"
   
    ex2.rda
    =======
    X: "250857_at"
    Y: "AT1G75180"

    ex3.rda
    =======
    X: "258508_at"
    Y: "AT3G06650"



####################
##    SCRIPTS     ##    in R/ subdirectory
####################

1]  An S4 method to plot S4 objects of 'XhybExamples' objects.
    This generates a window with 3 plots: 
      - The left one plots the summarised expression patterns of X and Y.
      - The middle plot shows the background corrected normalised intensity
        values of X's reporters. The colours used correspond to the alignment
        strength (ai) of the reporter to Y's transcript sequence.
      - The plot on the right plots the Pearson correlation coefficient of the
        reporters' expression patterns to Y's expression pattern in function
        of their alignment score, ai.
      

    plotExample.R
    =============
    
2] 'runSimulation' is a function that will run the simulation we ran for our study.
   By modifying the parameters, users can play with it themselves.

    runSimulation.R 
    ===============

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