hits=-2.6 tests=BAYES_00 X-USF-Spam-Flag: NO Dear List, I am trying to use CGHcall function from CGHcall package, trying to use my own normalized and segmented dataframes (Agilent oligo Human 44A, data normalized with MANOR and segmented with GLAD), buy I get this error: EM algorithm started ... Error en regionsdat[k, 1]:regionsdat[k, 2] : Argumento NA/NaN Which I don't get when I use Wilting data from the vignette example, so it could be a problem with the format of my data (although I can't find any...). I wondered if maybe the dataframes were too large, but I also get (another) error if I only run CGHcall with 1 chromosome: EM algorithm done ... Error en (posteriorfin2[profile == k, ])[, -1] : n?mero incorreto de dimensiones # Incorrect dimension number I would appreciate any suggestion. Best, Daniel Details: > load("norm3.RData") > load("seg3.RData") > library(CGHcall) Loading required package: impute Loading required package: DNAcopy > ls() [1] "norm3" "seg3" > head(norm3) BAC.clone Chromosome bp.position X13713 X13819 X13820 X13821 X13822 X13859 1 1:604268 1 604268 0.05 0.10 0.40 -0.05 0.24 0.27 2 1:801796 1 801796 0.17 -0.15 0.03 -0.12 -0.05 0.05 3 1:827354 1 827354 0.13 0.15 0.11 0.17 0.01 -0.17 4 1:1059676 1 1059676 0.03 -0.18 0.00 -0.11 -0.10 -0.29 5 1:1089934 1 1089934 -0.23 -0.02 0.47 0.07 0.14 0.13 6 1:1139597 1 1139597 0.11 -0.05 0.03 0.03 0.08 0.06 X13860 X13862 X15740 X16421 X16422 X16578 X16579 X17264 X17274 X17278 X17279 1 -0.07 0.64 0.31 0.39 0.10 0.39 0.47 -0.08 -0.08 0.07 0.10 2 0.19 -0.23 0.12 0.09 -0.17 0.01 -0.09 -0.07 -0.07 0.07 0.24 3 -0.05 -0.17 0.32 0.03 -0.16 0.02 0.02 0.00 0.00 0.25 0.04 4 0.01 -0.33 -0.19 -0.10 -0.02 -0.17 -0.13 -0.30 -0.30 -0.03 -0.13 5 -0.02 0.18 -0.08 -0.92 -0.94 -0.02 0.12 0.16 0.16 -0.02 0.09 6 0.07 0.19 0.09 0.18 0.11 -0.03 0.04 0.16 0.16 0.01 0.01 X17385 X17386 X17388 X17446 X17447 X17448 1 0.52 -0.63 -0.50 0.24 0.05 0.60 2 -0.19 -0.26 0.08 -0.01 0.17 0.05 3 -0.12 -0.01 0.20 -0.15 -0.04 0.00 4 -0.08 -0.12 -0.05 -0.01 -0.01 0.07 5 -0.09 0.17 0.17 0.25 0.33 0.42 6 0.17 0.48 0.19 0.24 0.39 0.36 > head(seg3) BAC.clone Chromosome bp.position X13713 X13819 X13820 X13821 X13822 X13859 1 1:604268 1 604268 0.02 0 0.01 0.01 0.01 -0.01 2 1:801796 1 801796 0.02 0 0.01 0.01 0.01 -0.01 3 1:827354 1 827354 0.02 0 0.01 0.01 0.01 -0.01 4 1:1059676 1 1059676 0.02 0 0.01 0.01 0.01 -0.01 5 1:1089934 1 1089934 0.02 0 0.01 0.01 0.01 -0.01 6 1:1139597 1 1139597 0.02 0 0.01 0.01 0.01 -0.01 X13860 X13862 X15740 X16421 X16422 X16578 X16579 X17264 X17274 X17278 X17279 1 0 0.01 -0.01 0 -0.01 0.01 0.02 0.03 0.03 0.03 -0.01 2 0 0.01 -0.01 0 -0.01 0.01 0.02 0.03 0.03 0.03 -0.01 3 0 0.01 -0.01 0 -0.01 0.01 0.02 0.03 0.03 0.03 -0.01 4 0 0.01 -0.01 0 -0.01 0.01 0.02 0.03 0.03 0.03 -0.01 5 0 0.01 -0.01 0 -0.01 0.01 0.02 0.03 0.03 0.03 -0.01 6 0 0.01 -0.01 0 -0.01 0.01 0.02 0.03 0.03 0.03 -0.01 X17385 X17386 X17388 X17446 X17447 X17448 1 -0.08 -0.43 0.02 0.01 0.01 -0.01 2 -0.08 -0.43 0.02 0.01 0.01 -0.01 3 -0.08 0.02 0.02 0.01 0.01 -0.01 4 -0.08 0.02 0.02 0.01 0.01 -0.01 5 -0.08 0.02 0.02 0.01 0.01 -0.01 6 -0.08 0.02 0.02 0.01 0.01 -0.01 > dim(norm3) [1] 37203 26 > dim(seg3) [1] 37203 26 > args(CGHcall) function (inputNormalized, inputSegmented, typeNormalized = "dataframe", typeSegmented = "dataframe", prior = "auto", nclass = 3, organism = "human") NULL > Result <- CGHcall(norm3, seg3, organism="human") Dividing chromosomes into arms: New chromosome: 1 Arm: 1 Centromere found: 122356957 Arm: 2 New chromosome: 2 Arm: 3 Centromere found: 93189898 Arm: 4 New chromosome: 3 Arm: 5 Centromere found: 92037544 Arm: 6 New chromosome: 4 Arm: 7 Centromere found: 50854874 Arm: 8 New chromosome: 5 Arm: 9 Centromere found: 47941398 Arm: 10 New chromosome: 6 Arm: 11 Centromere found: 60438125 Arm: 12 New chromosome: 7 Arm: 13 Centromere found: 59558273 Arm: 14 New chromosome: 8 Arm: 15 Centromere found: 45458052 Arm: 16 New chromosome: 9 Arm: 17 Centromere found: 48607499 Arm: 18 New chromosome: 10 Arm: 19 Centromere found: 40434941 Arm: 20 New chromosome: 11 Arm: 21 Centromere found: 52950781 Arm: 22 New chromosome: 12 Arm: 23 Centromere found: 35445461 Arm: 24 New chromosome: 13 Arm: 25 Centromere found: 16934000 Arm: 26 New chromosome: 14 Arm: 27 Centromere found: 16570000 Arm: 28 New chromosome: 15 Arm: 29 Centromere found: 16760000 Arm: 30 New chromosome: 16 Arm: 31 Centromere found: 36043302 Arm: 32 New chromosome: 17 Arm: 33 Centromere found: 22237133 Arm: 34 New chromosome: 18 Arm: 35 Centromere found: 16082897 Arm: 36 New chromosome: 19 Arm: 37 Centromere found: 28423622 Arm: 38 New chromosome: 20 Arm: 39 Centromere found: 27150400 Arm: 40 New chromosome: 21 Arm: 41 Centromere found: 11760000 Arm: 42 New chromosome: 22 Arm: 43 Centromere found: 12830000 Arm: 44 EM algorithm started ... Error en regionsdat[k, 1]:regionsdat[k, 2] : Argumento NA/NaN # I also tried with just one chromosome, but: > Result <- CGHcall(norm3[norm3$Chromosome=="1",], seg3[norm3$Chromosome=="1",], organism="human") Dividing chromosomes into arms: New chromosome: 1 Arm: 1 Centromere found: 122356957 Arm: 2 EM algorithm started ... Calling iteration 1 : [1] 2.300000e+01 -4.372806e+04 -1.367577e+00 -4.412366e-01 -2.586618e-03 [6] 4.344142e-01 1.170159e+00 3.031326e-01 1.226376e-01 3.581388e-02 [11] 2.452344e-01 -2.338149e-03 Calling iteration 2 : [1] 2.300000e+01 -4.372728e+04 -1.433100e+00 -4.457681e-01 -2.129149e-03 [6] 4.289389e-01 1.159208e+00 2.682406e-01 1.340851e-01 3.530604e-02 [11] 2.503167e-01 -5.316184e-04 EM algorithm done ... Error en (posteriorfin2[profile == k, ])[, -1] : n?mero incorreto de dimensiones #Incorrect dimen When I used the Wilting data following the vignette: > result <- CGHcall(norm.cghdata, seg.cghdata) EM algorithm started ... Calling iteration 1 : [1] 2.000000e+00 -4.244272e+03 -5.832847e-01 -2.831586e-01 5.078766e-03 [6] 3.289769e-01 1.157954e+00 -4.264512e-04 1.257185e-01 6.996470e-02 [11] 4.429449e-02 1.000000e-04 Calling iteration 2 : [1] 2.000000e+00 -4.243597e+03 -5.762129e-01 -2.760868e-01 7.852040e-03 [6] 3.283777e-01 1.156755e+00 -2.940006e-04 1.215480e-01 6.854895e-02 [11] 3.598413e-02 1.000000e-04 EM algorithm done ... FINISHED! Total time: 1 minutes > head(norm.cghdata) BAC.clone Chromosome bp.position AdCA10 SCC27 1 RP11-465B22 1 1082138 -0.1804618 0.5999086 3 RP4-785P20 1 3318085 -0.1137811 0.7727828 4 RP1-37J18 1 4552927 0.4363701 0.6400294 6 RP4-706A17 1 6371642 0.5338766 0.1358740 7 RP3-438L4 1 7134999 0.4395028 0.6378606 8 RP11-338N10 1 7754212 0.2839457 0.5351469 > head(seg.cghdata) BAC.clone Chromosome bp.position AdCA10 SCC27 1 RP11-465B22 1 1082138 0.3214 0.5804 3 RP4-785P20 1 3318085 0.3214 0.5804 4 RP1-37J18 1 4552927 0.3214 0.5804 6 RP4-706A17 1 6371642 0.3214 0.5804 7 RP3-438L4 1 7134999 0.3214 0.5804 8 RP11-338N10 1 7754212 0.3214 0.5804 > sessionInfo() R version 2.6.0 (2007-10-03) x86_64-unknown-linux-gnu locale: LC_CTYPE=es_ES at euro;LC_NUMERIC=C;LC_TIME=es_ES at euro;LC_COLLATE=es_ES at euro;LC_MONETARY=es_ES at euro;LC_MESSAGES=es_ES at euro;LC_PAPER=es_ES at euro;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=es_ES at euro;LC_IDENTIFICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] CGHcall_1.0.0 DNAcopy_1.12.0 impute_1.10.0 loaded via a namespace (and not attached): [1] rcompgen_0.1-15 -- ******************************************** Daniel Rico Rodriguez, PhD. Structural Computational Biology Group Spanish National Cancer Research Center, CNIO Melchor Fernandez Almagro, 3. 28029 Madrid, Spain. Phone: +34 91 224 69 00 #2256 drico at cnio.es http://www.cnio.es ******************************************** **NOTA DE CONFIDENCIALIDAD** Este correo electr?nico, y ...{{dropped:3}}

On Feb 5, 2008 3:44 AM, Daniel Rico <drico at="" cnio.es=""> wrote: > hits=-2.6 tests=BAYES_00 > X-USF-Spam-Flag: NO > > Dear List, > > I am trying to use CGHcall function from CGHcall package, trying to use > my own normalized and segmented dataframes (Agilent oligo Human 44A, > data normalized with MANOR and segmented with GLAD), buy I get this error: > > EM algorithm started ... > Error en regionsdat[k, 1]:regionsdat[k, 2] : Argumento NA/NaN > > Which I don't get when I use Wilting data from the vignette example, so > it could be a problem with the format of my data (although I can't find > any...). I wondered if maybe the dataframes were too large, but I also > get (another) error if I only run CGHcall with 1 chromosome: > > EM algorithm done ... > Error en (posteriorfin2[profile == k, ])[, -1] : > n?mero incorreto de dimensiones # Incorrect dimension number Without looking at the code itself, that looks like a classical mistake. When writing posteriorfin2[profile == k, ] without an explicit 'drop=FALSE', the developer assumes that 'profile == k' will match two or more rows in the 'posteriorfin2' matrix/data.frame. I suspect that in your case 'profile == k' is only TRUE in one case, which makes 'posteriorfin2[profile == k, ]' return a vector and not a matrix/data.frame. This will cause the next subsetting '[,-1]' to fail, because there are no columns in a plain vector ("Incorrect dimension number"). If the code would have said posteriorfin2[profile == k,,drop=FALSE] the particular error would not show up. However, in the end of the day, the real question might be why you end up with only a single case for which 'profile == k' is TRUE. That's my $0.02 Henrik > > I would appreciate any suggestion. > Best, > Daniel > > Details: > > > load("norm3.RData") > > load("seg3.RData") > > library(CGHcall) > Loading required package: impute > Loading required package: DNAcopy > > ls() > [1] "norm3" "seg3" > > head(norm3) > BAC.clone Chromosome bp.position X13713 X13819 X13820 X13821 X13822 X13859 > 1 1:604268 1 604268 0.05 0.10 0.40 -0.05 0.24 0.27 > 2 1:801796 1 801796 0.17 -0.15 0.03 -0.12 -0.05 0.05 > 3 1:827354 1 827354 0.13 0.15 0.11 0.17 0.01 -0.17 > 4 1:1059676 1 1059676 0.03 -0.18 0.00 -0.11 -0.10 -0.29 > 5 1:1089934 1 1089934 -0.23 -0.02 0.47 0.07 0.14 0.13 > 6 1:1139597 1 1139597 0.11 -0.05 0.03 0.03 0.08 0.06 > X13860 X13862 X15740 X16421 X16422 X16578 X16579 X17264 X17274 X17278 > X17279 > 1 -0.07 0.64 0.31 0.39 0.10 0.39 0.47 -0.08 -0.08 > 0.07 0.10 > 2 0.19 -0.23 0.12 0.09 -0.17 0.01 -0.09 -0.07 -0.07 > 0.07 0.24 > 3 -0.05 -0.17 0.32 0.03 -0.16 0.02 0.02 0.00 0.00 > 0.25 0.04 > 4 0.01 -0.33 -0.19 -0.10 -0.02 -0.17 -0.13 -0.30 -0.30 -0.03 > -0.13 > 5 -0.02 0.18 -0.08 -0.92 -0.94 -0.02 0.12 0.16 0.16 > -0.02 0.09 > 6 0.07 0.19 0.09 0.18 0.11 -0.03 0.04 0.16 0.16 > 0.01 0.01 > X17385 X17386 X17388 X17446 X17447 X17448 > 1 0.52 -0.63 -0.50 0.24 0.05 0.60 > 2 -0.19 -0.26 0.08 -0.01 0.17 0.05 > 3 -0.12 -0.01 0.20 -0.15 -0.04 0.00 > 4 -0.08 -0.12 -0.05 -0.01 -0.01 0.07 > 5 -0.09 0.17 0.17 0.25 0.33 0.42 > 6 0.17 0.48 0.19 0.24 0.39 0.36 > > head(seg3) > BAC.clone Chromosome bp.position X13713 X13819 X13820 X13821 X13822 X13859 > 1 1:604268 1 604268 0.02 0 0.01 0.01 0.01 -0.01 > 2 1:801796 1 801796 0.02 0 0.01 0.01 0.01 -0.01 > 3 1:827354 1 827354 0.02 0 0.01 0.01 0.01 -0.01 > 4 1:1059676 1 1059676 0.02 0 0.01 0.01 0.01 -0.01 > 5 1:1089934 1 1089934 0.02 0 0.01 0.01 0.01 -0.01 > 6 1:1139597 1 1139597 0.02 0 0.01 0.01 0.01 -0.01 > X13860 X13862 X15740 X16421 X16422 X16578 X16579 X17264 X17274 X17278 > X17279 > 1 0 0.01 -0.01 0 -0.01 0.01 0.02 0.03 0.03 0.03 > -0.01 > 2 0 0.01 -0.01 0 -0.01 0.01 0.02 0.03 0.03 0.03 > -0.01 > 3 0 0.01 -0.01 0 -0.01 0.01 0.02 0.03 0.03 0.03 > -0.01 > 4 0 0.01 -0.01 0 -0.01 0.01 0.02 0.03 0.03 0.03 > -0.01 > 5 0 0.01 -0.01 0 -0.01 0.01 0.02 0.03 0.03 0.03 > -0.01 > 6 0 0.01 -0.01 0 -0.01 0.01 0.02 0.03 0.03 0.03 > -0.01 > X17385 X17386 X17388 X17446 X17447 X17448 > 1 -0.08 -0.43 0.02 0.01 0.01 -0.01 > 2 -0.08 -0.43 0.02 0.01 0.01 -0.01 > 3 -0.08 0.02 0.02 0.01 0.01 -0.01 > 4 -0.08 0.02 0.02 0.01 0.01 -0.01 > 5 -0.08 0.02 0.02 0.01 0.01 -0.01 > 6 -0.08 0.02 0.02 0.01 0.01 -0.01 > > dim(norm3) > [1] 37203 26 > > dim(seg3) > [1] 37203 26 > > args(CGHcall) > function (inputNormalized, inputSegmented, typeNormalized = "dataframe", > typeSegmented = "dataframe", prior = "auto", nclass = 3, > organism = "human") > NULL > > Result <- CGHcall(norm3, seg3, organism="human") > Dividing chromosomes into arms: > > New chromosome: 1 Arm: 1 > Centromere found: 122356957 Arm: 2 > New chromosome: 2 Arm: 3 > Centromere found: 93189898 Arm: 4 > New chromosome: 3 Arm: 5 > Centromere found: 92037544 Arm: 6 > New chromosome: 4 Arm: 7 > Centromere found: 50854874 Arm: 8 > New chromosome: 5 Arm: 9 > Centromere found: 47941398 Arm: 10 > New chromosome: 6 Arm: 11 > Centromere found: 60438125 Arm: 12 > New chromosome: 7 Arm: 13 > Centromere found: 59558273 Arm: 14 > New chromosome: 8 Arm: 15 > Centromere found: 45458052 Arm: 16 > New chromosome: 9 Arm: 17 > Centromere found: 48607499 Arm: 18 > New chromosome: 10 Arm: 19 > Centromere found: 40434941 Arm: 20 > New chromosome: 11 Arm: 21 > Centromere found: 52950781 Arm: 22 > New chromosome: 12 Arm: 23 > Centromere found: 35445461 Arm: 24 > New chromosome: 13 Arm: 25 > Centromere found: 16934000 Arm: 26 > New chromosome: 14 Arm: 27 > Centromere found: 16570000 Arm: 28 > New chromosome: 15 Arm: 29 > Centromere found: 16760000 Arm: 30 > New chromosome: 16 Arm: 31 > Centromere found: 36043302 Arm: 32 > New chromosome: 17 Arm: 33 > Centromere found: 22237133 Arm: 34 > New chromosome: 18 Arm: 35 > Centromere found: 16082897 Arm: 36 > New chromosome: 19 Arm: 37 > Centromere found: 28423622 Arm: 38 > New chromosome: 20 Arm: 39 > Centromere found: 27150400 Arm: 40 > New chromosome: 21 Arm: 41 > Centromere found: 11760000 Arm: 42 > New chromosome: 22 Arm: 43 > Centromere found: 12830000 Arm: 44 > EM algorithm started ... > Error en regionsdat[k, 1]:regionsdat[k, 2] : Argumento NA/NaN > > # I also tried with just one chromosome, but: > > > Result <- CGHcall(norm3[norm3$Chromosome=="1",], > seg3[norm3$Chromosome=="1",], organism="human") > Dividing chromosomes into arms: > > New chromosome: 1 Arm: 1 > Centromere found: 122356957 Arm: 2 > EM algorithm started ... > Calling iteration 1 : > [1] 2.300000e+01 -4.372806e+04 -1.367577e+00 -4.412366e-01 -2.586618e-03 > [6] 4.344142e-01 1.170159e+00 3.031326e-01 1.226376e-01 3.581388e-02 > [11] 2.452344e-01 -2.338149e-03 > Calling iteration 2 : > [1] 2.300000e+01 -4.372728e+04 -1.433100e+00 -4.457681e-01 -2.129149e-03 > [6] 4.289389e-01 1.159208e+00 2.682406e-01 1.340851e-01 3.530604e-02 > [11] 2.503167e-01 -5.316184e-04 > EM algorithm done ... > Error en (posteriorfin2[profile == k, ])[, -1] : > n?mero incorreto de dimensiones #Incorrect dimen > > When I used the Wilting data following the vignette: > > > result <- CGHcall(norm.cghdata, seg.cghdata) > EM algorithm started ... > Calling iteration 1 : > [1] 2.000000e+00 -4.244272e+03 -5.832847e-01 -2.831586e-01 5.078766e-03 > [6] 3.289769e-01 1.157954e+00 -4.264512e-04 1.257185e-01 6.996470e-02 > [11] 4.429449e-02 1.000000e-04 > Calling iteration 2 : > [1] 2.000000e+00 -4.243597e+03 -5.762129e-01 -2.760868e-01 7.852040e-03 > [6] 3.283777e-01 1.156755e+00 -2.940006e-04 1.215480e-01 6.854895e-02 > [11] 3.598413e-02 1.000000e-04 > EM algorithm done ... > FINISHED! > Total time: 1 minutes > > head(norm.cghdata) > BAC.clone Chromosome bp.position AdCA10 SCC27 > 1 RP11-465B22 1 1082138 -0.1804618 0.5999086 > 3 RP4-785P20 1 3318085 -0.1137811 0.7727828 > 4 RP1-37J18 1 4552927 0.4363701 0.6400294 > 6 RP4-706A17 1 6371642 0.5338766 0.1358740 > 7 RP3-438L4 1 7134999 0.4395028 0.6378606 > 8 RP11-338N10 1 7754212 0.2839457 0.5351469 > > head(seg.cghdata) > BAC.clone Chromosome bp.position AdCA10 SCC27 > 1 RP11-465B22 1 1082138 0.3214 0.5804 > 3 RP4-785P20 1 3318085 0.3214 0.5804 > 4 RP1-37J18 1 4552927 0.3214 0.5804 > 6 RP4-706A17 1 6371642 0.3214 0.5804 > 7 RP3-438L4 1 7134999 0.3214 0.5804 > 8 RP11-338N10 1 7754212 0.3214 0.5804 > > > > sessionInfo() > R version 2.6.0 (2007-10-03) > x86_64-unknown-linux-gnu > > locale: > LC_CTYPE=es_ES at euro;LC_NUMERIC=C;LC_TIME=es_ES at euro;LC_COLLATE=es_ES at euro;LC_MONETARY=es_ES at euro;LC_MESSAGES=es_ES at euro;LC_PAPER=es_ES at euro;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=es_ES at euro;LC_IDENTIFICATION=C > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] CGHcall_1.0.0 DNAcopy_1.12.0 impute_1.10.0 > > loaded via a namespace (and not attached): > [1] rcompgen_0.1-15 > > > > -- > ******************************************** > > Daniel Rico Rodriguez, PhD. > Structural Computational Biology Group > Spanish National Cancer Research Center, CNIO > Melchor Fernandez Almagro, 3. > 28029 Madrid, Spain. > Phone: +34 91 224 69 00 #2256 > drico at cnio.es > http://www.cnio.es > > ******************************************** > > > **NOTA DE CONFIDENCIALIDAD** Este correo electr?nico, y ...{{dropped:3}} > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >