Table 7

Distribution-based Algorithm

Data: genes;

/* expression values */

Data: functions

/* for each function */

Result: significance, tailGenes;

   /* vector of zeros */

1 normGenes = normalize(genes);

2 hist = zeros(1, nPts);

3 foreach f ∈ function do

4    subset = findPoints(normGenes,f);

5    foreach x ∈ subset do

6       dens = NumberOfNeighbors(x);

7       hist(dens)++;

8    if NunmberOf(genes) greater than a threshold then

9       randHist = findTheoreticalHistogram(1, nPts, normGenes);

10    else

11       randHist = findRandomHistogram(1, nPts, normGenes);

12    significance(f) = chiSquaredGoodnessOfFit(hist, randHist);

13    tailGenes(f) = findTailGenes(hist, randHist);

14 return significance, tailGenes

Denton et al. BMC Bioinformatics 2008 9:294   doi:10.1186/1471-2105-9-294