I dealt with some Affymetrix data , a part of which is pasted below

ID_REF

VALUE

ABS_CALL

DETECTION P-VALUE

10071_s_at

 3473.6

 P

         0.000219

1053_at 

 643.2

 P

         0.000673

117_at

 564

 P

         0.000322

1255_g_at

 9.4

 A

         0.602006

1294_at

 845.6

 P

         0.000468

1320_at

 94.3

 A

         0.204022

1405_i_at

 6546.2

 M

         0.0631

14312_at

 54.1

 P

         0.003067

1438_at

 461.3

 P

         0.000562

Where i easily can decide the calls either Present, Absent or Marginal. 

I have some illumina bead array data also, shown below

ID
ILMN_1681101

Pvalue    Intenstiy
0.27403

6.966361247

ILMN_2094942

0.18961

7.00337736

ILMN_1703142

0

7.600470477

ILMN_2271336

0.37662

6.935459748

ILMN_2337789

0.08312

7.064877464

ILMN_1669592

0.00519

7.24596858

ILMN_1735038

0.05325

7.089582893

Can i use pvalue here to make Present, Absent or Marginal call same as Affymetrix data. Thanks

Yes. See the Illumina BeadChip case study in Section 17.3 of the edgeR User's Guide, which reads the detection p-values and uses them to filter probes.

You might also find this article interesting: http://www.ncbi.nlm.nih.gov/pubmed/20056656

 Dear hussainaaghaz,

yes it is sensible and useful to use the DetectionValue in Illumina to determine whether or not a probe is detected above a threshold level in each of the samples in your experiment. Thus, if you use the default detection p-value threshold( < 0.01), you can use the below naive functions to remove a probe from all the samples if is not detected on any of your total number of arrays. So a probe that is detected on at least one sample remains:

present_probes <- detectionCall(lumi_data) # lumi_data your raw data prior normalization

selected_probes <- exprs(norm_data)[present_probes >0, ]

and then you can see how many probes have remained and check your intensity distribution with some plots like histogram or densityPlot

Thanks dear Smyth for help and pointing to reference.