Table 3 An example for computing fitting confidence. A randomly chosen spectrum is used to demonstrate the computation of the fitting confidence in detail. In each of the N = 28 numerical rows, the first entry is the score, the second entry records the LDpdf and the third entry corresponds to the LMpdf. Using the LDpdf as the x-coordinate and the Mpdf as the y-coordinate, we perform least square linear regression and find: an intercept value a = -0.00421 and a slope b = 0.9992. Eq. (18) is then used to compute t1 (t1 = 0.0421) and the goodness number, 1 - A(t1|N -2), is found to be 0.96674 through (19). To test the strength of correlation between the second column and the third column, we use (20) to compute r and through (21) we find the t2 value to be 0.99567. Given r = 0.99567 and = 25, through (22) we find the P M value to be 2.58 × 10-27.
From: RAId_DbS: Peptide Identification using Database Searches with Realistic Statistics
S | ln [Dpdf(S)] | ln [Mpdf(S)] |
|---|---|---|
0.0284661 | 0.479518 | 0.438266 |
0.0691319 | 0.431753 | 0.407608 |
0.109798 | 0.369235 | 0.351511 |
0.150463 | 0.2708 | 0.270076 |
0.191129 | 0.163419 | 0.163403 |
0.231795 | 0.014358 | 0.031592 |
0.272461 | -0.156812 | -0.125259 |
0.313127 | -0.340242 | -0.307054 |
0.353792 | -0.551264 | -0.513698 |
0.394458 | -0.79275 | -0.745095 |
0.435124 | -1.04746 | -1.00115 |
0.47579 | -1.34063 | -1.28178 |
0.516456 | -1.63587 | -1.58688 |
0.557121 | -1.96251 | -1.91636 |
0.597787 | -2.2322 | -2.27015 |
0.638453 | -2.72001 | -2.64814 |
0.679119 | -3.00809 | -3.05025 |
0.719785 | -3.52319 | -3.4764 |
0.76045 | -3.94211 | -3.92649 |
0.801116 | -4.31754 | -4.40045 |
0.841782 | -4.72005 | -4.89819 |
0.882448 | -5.27305 | -5.41962 |
0.923114 | -5.73387 | -5.96467 |
0.963779 | -7.04955 | -6.53326 |
1.00445 | -6.55707 | -7.1253 |
1.04511 | -7.368 | -7.74071 |
1.08578 | -9.44744 | -8.37942 |
1.12644 | -8.75429 | -9.04134 |