[BioC] Deciding on a cut off after QC
Ankit Pal
pal_ankit2000 at yahoo.com
Tue May 17 06:58:16 CEST 2005
Dear Dr Smyth,
I would prefer using the p-value with a threshold
value of 0.05.
In the case of an experiment(I sent you the code) I
did, the reult file contains p-values (after fdr) in
range of 0.96 - 0.97.
How did I get such values and where do I place a cut
off in this case?
Am I doing something wrong?
In the user guide it says
"If none of the raw p-value are less than 1/G,
where G is the number of genes included in the fit,
then all of the adjusted p-values will the
equal to 1.Since 1/G is about the expected size of the
smallest p-values given purely random variation and
uniform p-values, this means that there
is no overall evidence of differential expression."
I am not too sure I understand what it means.
Should there be atleast one p-value < 1/G?
I am attaching a sample data output file to this mail.
What should be done in my case?
Thank you,
sincerely,
-Ankit
--- Gordon Smyth <smyth at wehi.edu.au> wrote:
> At 01:48 PM 17/05/2005, Ankit Pal wrote:
> >Dear Adai,
> >Thank you for the detailed response.
> >Correct me if I'm wrong.
> >What you are saying is that after applying the
> "fdr"
> >I give a cut off of 0.05 for the p-value and write
> all
> >those spots into an object (positives).
> >That means, I do not consider the B values.
>
> This is one way to proceed. Have you read the
> section in the User's Guide
> on "Statistics for differential expression" which
> discusses which statistic
> to use?
>
> >In essence, it is just a one sample t test afetr
> >adjusting the p-value and taking into consideration
> >all those spots that have a p-value < 0.05?
> >
> > From my code in a previous mail to Gordon Smyth, I
> >have done a quality control (QC) using parameters
> and
> >threshold values prescribed by genepix.
> >I know from experience, a large number of spots do
> not
> >get through the filter.
> >As I understand from LIMMA, a weight of "0" is
> given
> >to any spot that does not get through the QC
> filter.
> >How do I exclude these spots from the final result
> >summary file?
>
> These spots are already excluded. You seem to be
> confused by the fact that
> excluding a spot does not exclude the corresponding
> probe, since there are
> four spots for each probe. You will get a valid
> t-statistic and p-value for
> a probe if any one of the spots for that probe get a
> positive weight. (Note
> you can get a Bayesian t-statistic even for a probe
> with no residual df.)
>
> >I need to get a set of significantly differentially
> >expressed genes that have got through the QC
> filter.
> >How do I do it using LIMMA?
>
> This is what you have been getting all along.
>
> Gordon
>
> >Thank you,
> >-Ankit
>
>
__________________________________
-------------- next part --------------
Block Row Column ID Name M A t P.Value B
15254 19 26 17 NM_033613 scl0001747.1_14 1.4052 5.986 5.654 0.9627 -4.360
29822 37 26 23 AB117944 scl0003290.1_30 1.5988 5.571 5.557 0.9627 -4.362
18221 23 16 18 NM_033578 scl093703.1_214 1.3846 7.291 5.498 0.9627 -4.363
33734 42 21 25 NM_175677 scl00319236.1_170 1.2535 7.807 5.123 0.9627 -4.370
30787 39 2 18 - scl11230.1.1_125 1.5640 10.169 4.974 0.9627 -4.374
21676 27 24 21 NM_175657 scl00319161.1_8 1.3489 6.576 4.839 0.9627 -4.377
10983 14 18 7 NM_007999 scl014156.1_86 1.2005 4.078 4.821 0.9627 -4.377
26138 33 10 7 NM_028209 scl24036.11_204 1.4833 10.811 4.772 0.9627 -4.378
25635 32 21 16 NM_207298 scl0099151.1_201 1.2067 7.145 4.751 0.9627 -4.379
33449 42 11 10 - scl42410.1.3_0 -1.1950 6.997 -4.685 0.9627 -4.381
11683 15 14 6 - scl35668.26.1_1 1.3209 2.752 4.499 0.9627 -4.385
13694 17 28 21 - scl000055.1_1_REVCOMP 1.1755 6.008 4.496 0.9627 -4.386
14944 19 15 4 - scl27546.1.1069_39 -1.0644 6.182 -4.489 0.9627 -4.386
29762 37 24 17 NM_025284 scl0019240.2_329 1.2948 9.347 4.479 0.9627 -4.386
11789 15 18 4 XM_148353 scl067609.1_307 1.6845 3.191 4.433 0.9627 -4.387
2764 4 13 13 NM_178934 scl39088.5.1_30 1.1694 6.481 4.367 0.9627 -4.389
29678 37 21 14 NM_146075 scl00224640.2_275 1.2598 6.786 4.359 0.9627 -4.389
33435 42 10 23 - scl11006.1.1_298 -1.5154 4.933 -4.258 0.9627 -4.393
12481 16 13 22 NM_173731 scl36691.12_254 1.3582 2.918 4.248 0.9627 -4.393
19597 25 7 19 NM_147027 scl46618.1.187_149 2.2705 9.797 4.190 0.9627 -4.395
32290 40 28 10 NM_153798 scl0000113.1_1 1.0130 4.491 4.157 0.9627 -4.396
11762 15 17 4 - scl073362.2_30 1.4282 2.531 4.128 0.9627 -4.397
15866 20 19 9 - scl077449.2_20 1.3121 2.870 4.127 0.9627 -4.397
14762 19 8 11 NM_025536 scl47782.2_279 1.0271 5.729 4.109 0.9627 -4.397
27464 34 29 11 NC_001819 9629514_329 | Rauscher murine leukemia virus 1.2519 9.932 4.048 0.9627 -4.399
32482 41 5 14 NM_016875 scl41354.10.1_7 1.3216 10.076 4.030 0.9627 -4.400
9365 12 18 7 - scl069350.1_47 1.2922 4.009 4.003 0.9627 -4.401
15841 20 18 11 NM_027268 scl069938.2_27 1.7431 2.031 4.000 0.9627 -4.401
7426 10 6 10 - scl28349.3_240 1.1551 7.934 3.981 0.9627 -4.402
33257 42 4 7 NM_028243 scl32376.11_593 0.9688 6.692 3.977 0.9627 -4.402
15479 20 4 27 NM_146551 scl5601.1.1_211 0.9451 4.314 3.962 0.9627 -4.402
5896 8 9 17 NM_028108 scl49079.8_50 -1.2436 6.015 -3.962 0.9627 -4.402
21375 27 13 17 NM_007830 scl16375.7_22 0.9112 6.972 3.909 0.9627 -4.404
21793 27 29 3 K00604 IGHV1S16|K00604|Ig_heavy_variable_1S16_234 0.9863 6.598 3.904 0.9627 -4.404
29994 38 3 7 NM_018827 scl33727.4.1_6 1.0797 7.621 3.891 0.9627 -4.405
13393 17 17 17 NM_019737 scl056386.2_4 1.2250 3.139 3.871 0.9627 -4.406
15345 19 29 27 - AFFX-BioB-M-at_62 0.9412 6.040 3.864 0.9627 -4.406
29758 37 24 13 NM_013655 scl0020315.1_134 0.9538 5.124 3.861 0.9627 -4.406
15716 20 13 21 NM_175528 scl29095.10_195 1.3016 2.642 3.847 0.9627 -4.406
10670 14 6 18 XM_134476 scl34486.9.1_200 1.7255 9.395 3.839 0.9627 -4.407
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