//1 define the type of normalization to use for each heatmap
analysis_specs = [ 
	[ "QI", allQI, StraightDifference ], 
	[ "QESV", allQESV, StraightDifference ], 
	[ "Phi2", allPhi2, LogFoldChange ] 
]

//2 create and empty array to contain results, and begin looping though our analysis_specs
final_results = []
for( var i = 0 ; i < analysis_specs.length ; i++ ){
	paramName = analysis_specs[i][0]
	normalizationType = analysis_specs[i][2]
	phi2_raw = analysis_specs[i][1]
	
    //3 for each iteration of the loop, apply the normalization process
    
	//this is how you would load raw data from harddrive
	//rawDataPath = rawDataFolder + "\\multi-all" + paramName + "_FILTERED_completed_LJS.txt"
	//phi2_raw = GLOBAL.loadHeatMapFromPath(rawDataPath)

	//normalize the experimetn groups against the control groups
    refTable = new ReferenceTable( phi2_raw, "Flat" )
	phi2_lfc = new HeatmapNormalizer( phi2_raw ).buildNormalizedMap( normalizationType, refTable )

	//replace infinite values with blanks
	phi2_lfc.replaceInfiniteValues( NaN )

	//separate control and experiment groups into different heatmaps
	sel_wt = phi2_lfc.searchRowLabels( "col-0" )
	phi2_lfc_wt = phi2_lfc.getSubMap( sel_wt )
	phi2_lfc_mt = phi2_lfc.getSubMap( phi2_lfc.invert( sel_wt ) )

	//create control and experiemnt heatmaps averaged by plantName/Flat
	phi2_lfc_mt_avg = new HeatmapAverager( phi2_lfc_mt ).getAveragedMap( "PlantName", "flat" )
	phi2_lfc_wt_avg = new HeatmapAverager( phi2_lfc_wt ).getAveragedMap( "PlantName", "flat" )

	//create another heatmap averaged by AtNumber
	phi2_lfc_mt_avg_atg = new HeatmapAverager( phi2_lfc_mt_avg ).getAveragedMap( "AtNumber" )

	//reaplcea blanks with zeroes
	phi2_lfc_mt_avg.replaceValues( function(x){ return x=NaN; }, 0 )
	phi2_lfc_wt_avg.replaceValues( function(x){ return x=NaN; }, 0 )
	phi2_lfc_mt_avg_atg.replaceValues( function(x){ return x=NaN; }, 0 )

	//delete non-relevant rows
	phi2_lfc_mt_avg.deleteRows( phi2_lfc_mt_avg.searchRowLabels( "null" ) )
	phi2_lfc_wt_avg.deleteRows( phi2_lfc_wt_avg.searchRowLabels( "null" ) )
	phi2_lfc_mt_avg_atg.deleteRows( phi2_lfc_mt_avg_atg.searchRowLabels( "null" ) )

	//display results in UI
	phi2_lfc_mt_avg.setTitle( normalizationType + " " + paramName + " - mutants averaged by PlantName/Flat" )
	phi2_lfc_wt_avg.setTitle( normalizationType + " " + paramName + " - wildtypes averaged by PlantName/Flat" )
	phi2_lfc_mt_avg_atg.setTitle( normalizationType + "  " + paramName + " - mutants averaged by AtNumber" )
	GLOBAL.display( phi2_lfc_mt_avg, phi2_lfc_wt_avg, phi2_lfc_mt_avg_atg )

	//save the atnumber result for the significance analysis (outside of this loop)
	final_results[i] = phi2_lfc_mt_avg_atg
}

//4 create and display a combined figure using significance analysis
sigAnalysis = new SignificanceAnalysis( final_results )
saResults = sigAnalysis.getAnalysisResult_PercentileThreshold( Absolute, Entire_Map, Distinct_Colors, false, true, 5.0, 1)
GLOBAL.display( saResults )