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. 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? Waiting eagerly for a reply, thank you, -Ankit Stay connected, organized, and protected. Take the tour:

> Date: Sun, 15 May 2005 21:34:18 -0700 (PDT) > From: Ankit Pal <pal_ankit2000@yahoo.com> > Subject: [BioC] Deciding on a cut off after QC > To: bioconductor@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

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 R˛"]) > 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@wehi.edu.au> wrote: > > Date: Sun, 15 May 2005 21:34:18 -0700 (PDT) > > From: Ankit Pal <pal_ankit2000@yahoo.com> > > Subject: [BioC] Deciding on a cut off after QC > > To: bioconductor@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

Dear Dr Smyth, I guess there has been a mistake in communication. There are 39000 spots on the array but the number of probes are lesser as there are replicates(not a fixed number) on the array. The output from toptable() gives me a total of 39000 rows, one for each spot. I need to give an output of the spots most significant in this list. What is the cut off value(p or B or any other) I need to use above which I can consider a set of differentially expressed genes significant enough for further use (eg :function based clustering of all significantly overexpressed genes). I cannot use all the genes in the topTable() output. Where do I put the cut off? Is there a way I can calculate it? sincerely, -Ankit --- Gordon K Smyth <smyth@wehi.edu.au> wrote: > On Mon, May 16, 2005 9:49 pm, Ankit Pal said: > > 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 R˛"]) > 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() > > As I said last week, this command is not needed with > GenePix data. You should omit it. > > >> 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) > > Resultfile_7 is already a data.frame. > > >> 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. > > Mmm, this doesn't make sense. You have 4 arrays > with 39000 spots on each array. Hence you have > 4*39000 spots, not 39000. The output from > topTable() gives a summarized log-ratio for each > probe, > not a result for each spot. > > Gordon > > > 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 > > >

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@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