code editing convertSampleDataIntoCoIntensityNetwork function 11-06-2014d1616

2014-11-07

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11-07-2014d1327

I think using all 45,000 genes from some of the gene expression datasets will produce networks that are too big to analyze with a normal analysis.  I think I will just randomly select 500 genes.

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11-07-2014d1011

I'll try to precalculate parts for lists.

formula for pearsons correlation coefficient here
-http://www.socscistatistics.com/tests/pearson/

Is Sum((xi-xbar)*(yi-ybar)) the same as Sum(xi-xbar)*Sum(yi-ybar)?

I'll do a little test with a few lists.
-"C:\Sync\pDR\2014\2014\11-07-2014d1014\Calculation test 11-07-2014d1014.xlsx"
--This spreadsheet also has a spreadsheet calculating later verifcations of the correlation calculation.

No they are not the same thing.  Sum(xi-xbar)*Sum(yi-ybar) will always be 0.

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code before trying to split matrix into blocks
{
	public void convertSampleDataIntoCoIntensityNetwork(double[][] values, double threshold, String output_directory)
	{
		PearsonsCorrelation pc = new PearsonsCorrelation();
		
		String[] sarray = new String[values.length];
		useful_tools.createTextFile(output_directory, "row1.txt", Arrays.toString(values[0]));
		//fill up the similarity score with absolute value correlation coefficient values, but just write each row to a separate text file
		
		String edges = "";
		for(int i=0; i<values.length; i++)
		{
			System.out.println(i);
			String current_row = "";
			for(int j=i+1; j<values.length; j++)
			{
				
				double calculated_similarity_score = Math.abs(pc.correlation(values[i], values[j]));
				int link=0;
				if(calculated_similarity_score>threshold)
				{
					link = 1;
				}
				if(i==(values.length-1) && link==1)
				{
					edges+=i+"\t"+j;
				}
				else if(link==1)
				{
					edges+=i+"\t"+j+"\r\n";
				}
			}
		}
		
		useful_tools.createTextFile(output_directory, "network.txt", edges);
	}
}

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