[BioC] Deciding on a cut off after QC
Ankit Pal
pal_ankit2000 at yahoo.com
Mon May 16 13:49:38 CEST 2005
Dear Dr Smyth,
I'm sorry for not having specified which result file.
It is the final result summary we get after we give
the command
Resultfile <- topTable(fit,n=200, adjust="fdr")
A sample result file has been attached.
The code I used for my analysis is
> targets <- readTargets("target.txt")
#The QC filter
> myfun <- function(x,threshold=55){
+ okred <- abs(x[,"% > B635+2SD"]) > threshold
+ okgreen <- abs(x[,"% > B532+2SD"]) > threshold
+ okflag <- abs(x[,"Flags"]) > 0
+ okRGN <- abs(x[,"Rgn R²"]) > 0.6
+ as.numeric(okgreen || okred || okflag || okRGN)
+ }
#end of QC filter
> RG_7 <- read.maimages(targets$FileName,
source="genepix",wt.fun=myfun)
> RG_7$genes <- readGAL()
> RG_7$printer <- getLayout(RG_7$genes)
> MA_7 <- normalizeWithinArrays(RG_7,method="loess")
> MA_7 <- normalizeBetweenArrays(MA_7)
> fit_7 <- lmFit(MA_7, design=c(1,-1,1,-1))
> fit_7 <- eBayes(fit_7)
> options(digits=3)
> Resultfile_7 <- topTable(fit_7, n=39000,
adjust="fdr")
> Resdat_7 <-data.frame(Resultfile_7)
> write.table(Resdat_,file='Result.csv',quote = FALSE,
sep = "\t")
I understand that the spots that do not qualify the QC
filter are given a weight of "0" by limma and are not
considered for normalization and will not affect the
analysis.
The result file I get contains all the spots (38000)
in my case.
Didn't the spots that were bad get removed from the
final result?
If not what is the cut off value (B, p etc) that I
need to use to get a set of reliable spots(I cant use
all the 38000) from my result file for my analysis.
Is there a fixed formula to derive the same as the
values vary with the analysis.
Waiting for your reply,
Thank you,
-Ankit
--- Gordon K Smyth <smyth at wehi.EDU.AU> wrote:
> > Date: Sun, 15 May 2005 21:34:18 -0700 (PDT)
> > From: Ankit Pal <pal_ankit2000 at yahoo.com>
> > Subject: [BioC] Deciding on a cut off after QC
> > To: bioconductor at stat.math.ethz.ch
> >
> > Dear All,
> > I'm using LIMMA to analyse a set of GPR files.
> > I used the weight fuction to apply QC parameter
> > threshold values recommended by Genepix.
> > The code for the same is
> >
> >>myfun <- function(x,threshold=55){
> > + okred <- abs(x[,"% > B635+2SD"]) < threshold
> > + okgreen <- abs(x[,"% > B532+2SD"]) < threshold
> > + as.numeric(okgreen & okred)
> > }
> >
> > On completion of the analysis, all the spots
> showed
> > up in the results file inspite of being flagged
> off. I
> > understand that on being flagged off by limma (wt
> =
> > 0), the spots are not considered for further
> analysis.
> > Is there any way they can be excluded from the
> final
> > result file.
>
> What result file?
>
> > Also, if I get an output of all the spots (38000
> in my
> > case) how do I decide on a cut off. Do I use the
> rank
> > or something else?
>
> What output?
>
> Gordon
>
> > Waiting eagerly for a reply,
> > thank you,
> > -Ankit
>
>
>
Stay connected, organized, and protected. Take the tour:
-------------- next part --------------
Block Row Column ID Name M A t P.Value B
4004 5 29 12 NC_001552 9627219_516_rc | Sendai virus -2.33 2.57 -5.07 0.679 -2.72
4013 5 29 21 - MJ-2000-88_501 -2.72 4.05 -4.95 0.679 -2.76
34570 43 22 25 NM_019694 scl0056384.1_230 2.24 5.51 4.87 0.679 -2.78
3502 5 10 23 - scl13898.1.1_259 -2.60 4.37 -4.86 0.679 -2.78
32222 40 25 23 NM_153458 scl0001924.1_45 2.09 5.64 4.82 0.679 -2.79
31332 39 22 23 NM_025574 scl0066459.2_45 2.04 4.39 4.81 0.679 -2.80
7238 9 29 10 NC_001552 9627219_435_rc | Sendai virus -2.17 2.72 -4.71 0.679 -2.83
27862 35 14 5 - scl21854.2_284 2.05 3.97 4.70 0.679 -2.83
32256 40 27 3 NM_146139 scl0001929.1_72 2.00 5.97 4.66 0.679 -2.84
26620 33 28 3 NM_029357 scl0002178.1_304 2.45 6.12 4.60 0.679 -2.86
3275 5 2 12 XM_135092 scl36514.13.4_62 -3.29 3.07 -4.55 0.679 -2.88
34632 43 25 6 - EMPTY 2.20 7.70 4.55 0.679 -2.88
3490 5 10 11 - scl30794.6.1_93 -2.68 5.65 -4.54 0.679 -2.88
167 1 7 5 - scl37855.3.1_1 -2.11 3.18 -4.52 0.679 -2.89
31373 39 24 10 NM_007692 scl0012651.2_322 2.25 6.42 4.52 0.679 -2.89
31919 40 14 17 XM_130346 scl19143.19.1_31 2.29 4.18 4.51 0.679 -2.89
31455 39 27 11 NM_139236 scl0002764.1_60 2.00 5.00 4.51 0.679 -2.89
21694 27 25 12 NM_010325 scl000715.1_17 2.26 3.15 4.50 0.679 -2.89
4003 5 29 11 NC_001503 9626965_214_rc | Mouse mammary tumor virus -2.71 2.81 -4.48 0.679 -2.90
22370 28 20 14 NM_008645 scl017836.18_18 2.70 6.33 4.43 0.679 -2.92
244 1 10 1 - scl45289.2.1_35 -2.32 3.48 -4.42 0.679 -2.92
33082 41 27 20 BC042842 scl0003611.1_1554 2.04 5.38 4.41 0.679 -2.93
3278 5 2 15 XM_135940 scl54814.1.2_130 -2.62 3.57 -4.40 0.679 -2.93
32204 40 25 5 NM_008183 scl0014863.1_0 2.02 5.65 4.37 0.679 -2.94
7216 9 28 15 NM_008697 scl0002340.1_98 -2.42 3.53 -4.36 0.679 -2.94
29998 38 3 11 NM_011361 scl39091.7.1_0 2.40 3.78 4.35 0.679 -2.95
31475 39 28 4 M33467 scl0003417.1_10 1.83 4.12 4.30 0.679 -2.97
274 1 11 4 NM_178646 scl47812.1_441 -2.12 3.56 -4.28 0.679 -2.97
29838 37 27 12 NM_009372 scl0001623.1_60 2.52 5.75 4.28 0.679 -2.97
30621 38 26 13 NM_029620 scl0003537.1_10 2.17 5.43 4.27 0.679 -2.98
3926 5 26 15 NM_018865 scl0002564.1_1395 -2.16 3.13 -4.25 0.679 -2.98
3299 5 3 9 NM_173735 scl42940.8_203 -2.42 2.78 -4.24 0.679 -2.99
10018 13 12 13 NM_153786 scl38936.3.1_27 -2.73 2.57 -4.22 0.679 -3.00
37140 46 28 6 NM_009759 scl0002909.1_40 1.89 5.01 4.22 0.679 -3.00
3516 5 11 10 NM_173423 scl51436.1.1_135 -2.04 3.03 -4.19 0.679 -3.01
3461 5 9 9 - scl24020.1.1_3 -2.67 3.99 -4.19 0.679 -3.01
30367 38 17 2 NM_027853 scl071664.1_311 2.06 3.69 4.18 0.679 -3.01
7265 9 30 10 - MJ-250-11_13 -2.57 2.83 -4.16 0.679 -3.02
791 1 30 8 - MJ-1000-72_435 -2.39 3.26 -4.15 0.679 -3.02
28154 35 24 27 NM_010325 scl0014719.1_330 1.80 5.64 4.14 0.679 -3.02
31503 39 29 5 AF304551 IGHV1S131|AF304551|Ig_heavy_variable_1S131_48 2.18 6.61 4.14 0.679 -3.02
25577 32 19 12 NM_010561 scl016201.19_34 2.64 6.13 4.14 0.679 -3.02
33092 41 28 3 NM_028990 scl0001077.1_623 2.32 6.10 4.13 0.679 -3.03
26519 33 24 10 NM_018823 scl0054446.1_138 2.42 6.60 4.12 0.679 -3.03
254 1 10 11 NM_146446 scl28511.1.1_86 -2.08 3.04 -4.12 0.679 -3.03
3973 5 28 8 NM_023655 scl0003501.1_700 -1.97 2.70 -4.11 0.679 -3.03
7240 9 29 12 NC_001846 9629812_535 | Murine hepatitis virus -1.75 2.58 -4.11 0.679 -3.03
169 1 7 7 NM_023608 scl54748.8.1_13 -2.08 3.79 -4.10 0.679 -3.04
36918 46 19 27 NM_011949 scl026413.2_16 2.18 8.92 4.09 0.679 -3.04
31417 39 25 27 NM_184053 scl0001200.1_31 1.78 4.72 4.08 0.679 -3.05
26621 33 28 4 NM_183336 scl0002922.1_48 2.11 3.59 4.08 0.679 -3.05
3835 5 23 5 NM_013560 scl0015507.1_320 -2.16 2.86 -4.08 0.679 -3.05
3497 5 10 18 - scl43880.2_317 -1.97 3.93 -4.06 0.679 -3.05
37929 47 27 13 BC019769 scl0002315.1_12 1.89 4.69 4.06 0.679 -3.06
28828 36 19 27 XM_358370 scl017364.14_266 2.25 5.99 4.05 0.679 -3.06
38757 48 28 5 NM_177089 scl000578.1_190 2.03 4.57 4.05 0.679 -3.06
23424 29 29 16 - MJ-3000-114_1506 2.56 5.87 4.04 0.679 -3.06
29003 36 26 13 BC066093 scl0002019.1_208 2.77 8.09 4.04 0.679 -3.06
13281 17 13 13 NM_025872 scl1412.1.1_281 -1.77 3.17 -4.01 0.679 -3.08
25725 32 24 25 NM_019927 scl0023806.2_110 1.99 3.80 3.99 0.679 -3.08
35186 44 15 21 NM_175347 scl0106393.1_61 1.76 3.80 3.99 0.679 -3.08
32853 41 19 7 XM_488538 scl0319934.1_56 3.07 6.50 3.97 0.679 -3.09
171 1 7 9 - scl41474.1.1_55 -3.29 2.83 -3.97 0.679 -3.09
38779 48 28 27 AE000663 TRBV5|AE000663|T_cell_receptor_beta_variable_5_143 2.13 4.85 3.97 0.679 -3.09
37127 46 27 20 AK031926 scl0003274.1_34 2.08 6.41 3.96 0.679 -3.09
3761 5 20 12 NM_008852 scl018742.1_85 -1.79 2.58 -3.96 0.679 -3.09
308 1 12 11 XM_488860 scl14835.1.1_101 -2.82 2.90 -3.95 0.679 -3.10
97 1 4 16 NM_013633 scl50775.4.1_25 -2.34 3.36 -3.94 0.679 -3.10
33223 42 2 27 NM_019420 scl50036.1.4_192 3.04 8.01 3.94 0.679 -3.10
34200 43 9 6 - scl25225.3.1_30 1.79 5.43 3.93 0.679 -3.11
32104 40 21 13 - scl0077969.1_1 2.09 5.59 3.93 0.679 -3.11
35566 44 29 23 - MJ-1000-76_241 1.95 7.52 3.90 0.679 -3.12
59 1 3 5 NM_025696 scl52928.27_344 -1.93 3.95 -3.89 0.679 -3.12
31204 39 18 3 NM_207550 scl0404310.1_281 3.68 9.19 3.89 0.679 -3.12
30594 38 25 13 AF124385 scl0001994.1_3 2.97 6.47 3.89 0.679 -3.12
27142 34 17 13 - scl0320791.1_13 2.38 6.91 3.89 0.679 -3.12
7021 9 21 9 NM_172824 scl00239839.2_254 -2.23 4.00 -3.89 0.679 -3.12
25810 32 28 2 NM_023061 scl0003554.1_4 1.85 6.25 3.88 0.679 -3.13
25635 32 21 16 NM_207298 scl0099151.1_201 2.40 6.52 3.87 0.679 -3.13
31258 39 20 3 NM_009686 scl011787.1_19 1.79 4.94 3.87 0.679 -3.13
6700 9 9 12 NM_027614 scl43167.2.1_10 -2.01 3.32 -3.87 0.679 -3.13
15674 20 12 6 NM_173421 scl47999.3_178 1.84 5.89 3.86 0.679 -3.13
231 1 9 15 - scl53117.1.4_247 -2.24 3.82 -3.86 0.679 -3.14
3282 5 2 19 NM_018871 scl25917.6_37 -2.95 3.95 -3.85 0.679 -3.14
217 1 9 1 NM_029681 scl25936.3.1_82 -1.72 3.71 -3.85 0.679 -3.14
30669 38 28 7 BC044883 scl0004073.1_36 2.61 7.36 3.84 0.679 -3.14
32274 40 27 21 NM_029945 scl0001884.1_59 1.72 4.92 3.84 0.679 -3.14
37809 47 23 1 XM_129809 scl0070155.1_99 2.29 8.15 3.83 0.679 -3.15
35928 45 13 8 NM_170758 scl40740.8_132 -2.30 3.37 -3.83 0.679 -3.15
32641 41 11 11 XM_484710 scl52179.17.1_47 2.15 6.46 3.83 0.679 -3.15
21676 27 24 21 NM_175657 scl00319161.1_8 2.12 5.11 3.82 0.679 -3.15
29680 37 21 16 - scl0066491.1_300 1.66 5.04 3.81 0.679 -3.15
8796 11 27 4 NM_133798 scl0002392.1_125 3.35 5.83 3.80 0.679 -3.16
87 1 4 6 XM_355937 scl32097.7.1_17 -2.36 4.67 -3.79 0.679 -3.16
28118 35 23 18 NM_009567 scl0022755.1_133 1.73 4.77 3.78 0.679 -3.17
4107 6 3 8 NM_013465 scl49315.8.1_6 -2.93 4.70 -3.78 0.679 -3.17
37052 46 24 26 NM_026293 scl0067652.2_149 1.86 4.99 3.77 0.679 -3.17
33140 41 29 24 - MJ-3000-103_2290 1.66 5.11 3.77 0.679 -3.17
32208 40 25 9 - EMPTY 1.85 6.74 3.77 0.679 -3.17
14776 19 8 25 - scl3899.5.1_15 2.48 6.50 3.77 0.679 -3.17
33200 42 2 4 NM_011118 scl0018812.1_77 -2.16 3.47 -3.76 0.679 -3.18
36336 45 28 11 BC054076 scl0003827.1_21 1.67 5.36 3.75 0.679 -3.18
30701 38 29 12 NC_002512 20198505_5605 | Rat cytomegalovirus 1.65 4.92 3.73 0.679 -3.19
38722 48 26 24 NM_025578 scl0001137.1_14 2.12 7.26 3.73 0.679 -3.19
30244 38 12 14 NM_011079 scl25995.10_72 1.75 5.06 3.71 0.679 -3.20
746 1 28 17 NM_134046 scl000016.1_3 -1.73 3.41 -3.71 0.679 -3.20
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