I am summarising everything just so it is archived on the news group. This is the code I finally used: The summarised data is from ArrayExpress (accession number E-GEOD-22098). There is no bead-level data available. Each array is in a separate file, and the first 5 lines of the first file looks like this: Probe_ID Signal Detection ILMN_1809034 58.80201 0.003952569 ILMN_1660305 236.4589 0 ILMN_1792173 202.6858 0 ILMN_1762337 -4.230737 0.7285903 ILMN_2055271 7.409712 0.07641634 ... targets.txt looks like this: name GSM549324_4325540010_E_Raw.txt GSM549325_4325540026_A_Raw.txt GSM549326_4325540026_B_Raw.txt GSM549327_4335991057_D_Raw.txt GSM549328_4335991058_A_Raw.txt ... The code I used was: TB1 <- read.ilmn( files=as.character(targets$name)[1:5], probeid="Probe_ID", expr="Signal", sep="\t", other.columns="Detection" ) colnames(TB1$E) <- substr(targets$name[1:5],1,9) colnames(TB1$other$Detection) <- substr(targets$name[1:5],1,9) TB1$genes <- as.data.frame(TB1$genes) #read.ilmn reads in as vector. TB2 <- read.ilmn( files=as.character(targets$name)[6:21], probeid="Probe_ID", expr="Signal", sep="\t", other.columns="Detection" ) colnames(TB2$E) <- substr(targets$name[6:21],1,9) colnames(TB2$other$Detection) <- substr(targets$name[6:21],1,9) TB2$genes <- as.data.frame(TB2$genes) TB1.TB2 <- match(TB1$genes[[1]], TB2$genes[[1]]) TB <- cbind(TB1, TB2[TB1.TB2,]) On , Gavin Koh <gavin.koh@gmail.com> wrote: > Dear Wei, > I think that's worked! > Thank you! Gavin. > On 16 April 2011 13:25, Wei Shi shi@wehi.edu.au> wrote: > > Hi Gavin: > > > > I think the problem is that your TB1$genes (and TB2$genes) is a vector > rather than a data frame. This made cbind fail to combine them. I guess > the data you downloaded from the public repository is not the original > GenomeStudio/BeadStudio output. But you can fix this using the following > code: > > > > m > > TB1$genes > > TB2$genes > > TB > > > > I tried this code on my computer and it worked. Hope that will work for > you. > > > > Cheers, > > Wei > > > > On Apr 16, 2011, at 7:34 PM, Gavin Koh wrote: > > > >> Dear Wei, > >> > >> I am afraid it still doesn't work. I this is because TB1 is a list and > >> not a data frame and I cannot coerce it to become a dataframe. > >>> TB > >> Error in object$genes[i, , drop = FALSE] : incorrect number of > dimensions > >>> names(TB1) > >> [1] "source" "E" "genes" "targets" "other" > >>> class(TB1) > >> [1] "EListRaw" > >> attr(,"package") > >> [1] "limma" > >> > >> I checked EListRaw and it inherits directly from list and not from > data frame. > >> So sorry, > >> > >> Gavin. > >> > >> On 16 April 2011 08:38, Wei Shi shi@wehi.edu.au> wrote: > >>> Hi Gavin: > >>> > >>> Sorry, TB1[common.probes] should be changed to TB1[common.probes, ]. > >>> > >>> Hope it works now. > >>> > >>> Cheers, > >>> Wei > >>> > >>> > >>> On Apr 16, 2011, at 4:32 PM, Gavin Koh wrote: > >>> > >>>> Dear Wei, > >>>> > >>>> I am afraid this data is from a public repository, so I have no > >>>> control over what data is published or the format :-( > >>>> I am afraid cbind still does not appear to work with this > subscripting. > >>>> > >>>>> common.probes > >>>>> TB > >>>> Error: Two subscripts required > >>>> > >>>> Please help? > >>>> > >>>> Gavin 嘉威 敬上 > >>>> > >>>> On 16 April 2011 00:33, Wei Shi shi@wehi.edu.au> wrote: > >>>>> Dear Gavin: > >>>>> > >>>>> OK, so you did not input the control data. That is the reason why > my code did not work. You should really include the control data in your > analysis because they are very useful for the normalization. But you can > use the following code to merge the data you are having now: > >>>>> > >>>>> m > >>>>> merged > >>>>> > >>>>> This will remove the second ILMN_2038777 probe from TB1 and combine > probes from TB1 and TB2 in the right order. > >>>>> > >>>>> Cheers, > >>>>> Wei > >>>>> > >>>>> On Apr 16, 2011, at 1:58 AM, Gavin Koh wrote: > >>>>> > >>>>>> Dear Wei > >>>>>> > >>>>>> I am very sorry, but this still does not work. > >>>>>> > >>>>>> ILMN_2038777 is not missing in TB1, but duplicated. The batches > with > >>>>>> 48804 probes contain two copies of ILMN_2038777. The batches with > >>>>>> 48803 probes contain only one copy of ILMN_2038777. The order of > >>>>>> probes also seems to be different from batch to batch. > >>>>>> > >>>>>> TB1 was generated using: > >>>>>> > >>>>>> TB1 > >>>>>> files=as.character(targets$name)[1:5], > >>>>>> probeid="Probe_ID", > >>>>>> expr="Signal", sep="\t", > >>>>>> other.columns="Detection" > >>>>>> ) > >>>>>> > >>>>>> The reason for this being that the summarized data for each array > is > >>>>>> in a separate file. There is no bead level data available. There > is no > >>>>>> xxx_profile.txt file. > >>>>>> > >>>>>> I tried removing ILMN_2038777, but I cannot. Am I right in saying > that > >>>>>> this method of subsetting is only applicable to data frames? > >>>>>>> TB1 > >>>>>> Error in object$genes[i, , drop = FALSE] : incorrect number of > dimensions > >>>>>>> TB1 > >>>>>> Error in object$genes[i, , drop = FALSE] : incorrect number of > dimensions > >>>>>> > >>>>>> Just so you can see the structure of the file that read.ilmn() has > produced: > >>>>>> > >>>>>> --begin screen dump-- > >>>>>> > >>>>>>> TB1 > >>>>>> An object of class "EListRaw" > >>>>>> $source > >>>>>> [1] "illumina" > >>>>>> > >>>>>> $E > >>>>>> [,1] [,2] [,3] [,4] [,5] > >>>>>> ILMN_1809034 58.802010 24.907950 13.905010 10.07729 7.044668 > >>>>>> ILMN_1660305 236.458900 113.218000 193.581800 282.36350 127.023400 > >>>>>> ILMN_1792173 202.685800 120.449500 208.370600 242.63090 130.447200 > >>>>>> ILMN_1762337 -4.230737 -3.899888 -3.654122 -3.30873 -5.115820 > >>>>>> ILMN_2055271 7.409712 8.776000 9.394149 12.66054 1.250353 > >>>>>> 48799 more rows ... > >>>>>> > >>>>>> $genes > >>>>>> > [1] "ILMN_1809034" "ILMN_1660305" "ILMN_1792173" "ILMN_1762337" "ILMN_2055271" > >>>>>> 48799 more elements ... > >>>>>> > >>>>>> $targets > >>>>>> [1] SampleNames > >>>>>> (or 0-length row.names) > >>>>>> > >>>>>> $other > >>>>>> $Detection > >>>>>> [,1] [,2] [,3] [,4] [,5] > >>>>>> ILMN_1809034 0.003952569 0.01844532 0.03952569 0.08432148 > 0.111989500 > >>>>>> ILMN_1660305 0.000000000 0.00000000 0.00000000 0.00000000 > 0.001317523 > >>>>>> ILMN_1792173 0.000000000 0.00000000 0.00000000 0.00000000 > 0.001317523 > >>>>>> ILMN_1762337 0.728590300 0.75230570 0.68247690 0.57444010 > 0.708827400 > >>>>>> ILMN_2055271 0.076416340 0.05138340 0.05665349 0.06719368 > 0.283267500 > >>>>>> 48799 more rows ... > >>>>>> > >>>>>> --end screen dump-- > >>>>>> > >>>>>> Gavin > >>>>>> > >>>>>> On 15 April 2011 12:24, Wei Shi shi@wehi.edu.au> wrote: > >>>>>>> Dear Gavin: > >>>>>>> > >>>>>>> Thanks for the further information. The probe "ILMN_2038777" is > not only a gene probe but also a positive control probe (control type: > housekeeping). You can find more information about this probe in the HT12 > manifest file. But I do not know why it was absent in your TB2 dataset. > Anyway, it will be quite safe to remove the housekeeping "ILMN_2038777" > from your TB1 dataset. Then you can combine these two datasets together. > Below is the code to do this: > >>>>>>> > >>>>>>> x1 > >>>>>>> x2 > >>>>>>> x1 > >>>>>>> m > >>>>>>> x.merged > >>>>>>> > >>>>>>> This will combine TB1 with TB2. For the other four datasets, you > can merge them to x.merged using the same procedure (removing > housekeeping "ILMN_2038777" from the dataset first if it has, then using > match and cbind commands to merge them). > >>>>>>> > >>>>>>> Hope this will work for you. But let you know it doesn't. > >>>>>>> > >>>>>>> Cheers, > >>>>>>> Wei > >>>>>>> > >>>>>>> > >>>>>>> On Apr 15, 2011, at 9:16 PM, Gavin Koh wrote: > >>>>>>> > >>>>>>>> Dear Wei, > >>>>>>>> > >>>>>>>> Thank you for replying so quickly. There appear to be 6 batches > in > >>>>>>>> this dataset (TB1 to 6) > >>>>>>>> > >>>>>>>>> TB1$genes[1:10] > >>>>>>>> [1] "ILMN_1809034" "ILMN_1660305" "ILMN_1792173" "ILMN_1762337" > >>>>>>>> "ILMN_2055271" "ILMN_1736007" "ILMN_1814316" > >>>>>>>> [8] "ILMN_2359168" "ILMN_1731507" "ILMN_1787689" > >>>>>>>>> TB2$genes[1:10] > >>>>>>>> [1] "ILMN_1762337" "ILMN_2055271" "ILMN_1736007" "ILMN_2383229" > >>>>>>>> "ILMN_1806310" "ILMN_1779670" "ILMN_2321282" > >>>>>>>> [8] "ILMN_1671474" "ILMN_1772582" "ILMN_1735698" > >>>>>>>>> TB3$genes[1:10] > >>>>>>>> [1] "ILMN_1809034" "ILMN_1660305" "ILMN_1792173" "ILMN_1762337" > >>>>>>>> "ILMN_2055271" "ILMN_1736007" "ILMN_1814316" > >>>>>>>> [8] "ILMN_2359168" "ILMN_1731507" "ILMN_1787689" > >>>>>>>>> TB4$genes[1:10] > >>>>>>>> [1] "ILMN_1762337" "ILMN_2055271" "ILMN_1736007" "ILMN_2383229" > >>>>>>>> "ILMN_1806310" "ILMN_1779670" "ILMN_2321282" > >>>>>>>> [8] "ILMN_1671474" "ILMN_1772582" "ILMN_1735698" > >>>>>>>>> TB5$genes[1:10] > >>>>>>>> [1] "ILMN_1809034" "ILMN_1660305" "ILMN_1792173" "ILMN_1762337" > >>>>>>>> "ILMN_2055271" "ILMN_1736007" "ILMN_1814316" > >>>>>>>> [8] "ILMN_2359168" "ILMN_1731507" "ILMN_1787689" > >>>>>>>>> TB6$genes[1:10] > >>>>>>>> [1] "ILMN_1762337" "ILMN_2055271" "ILMN_1736007" "ILMN_2383229" > >>>>>>>> "ILMN_1806310" "ILMN_1779670" "ILMN_2321282" > >>>>>>>> [8] "ILMN_1671474" "ILMN_1772582" "ILMN_1735698" > >>>>>>>> > >>>>>>>> 多謝謝您的幫助！ > >>>>>>>> > >>>>>>>> Gavin > >>>>>>>> > >>>>>>>> On 15 April 2011 11:45, Wei Shi shi@wehi.edu.au> wrote: > >>>>>>>>> Hi Gavin: > >>>>>>>>> > >>>>>>>>> It would be best if you can match the two batches using the > probe identifiers because they are much less likely to have duplicates. > Would it possible to show the first several probes in each dataset so > that I can write some code to help you do this? > >>>>>>>>> > >>>>>>>>> Cheers, > >>>>>>>>> Wei > >>>>>>>>> > >>>>>>>>> > >>>>>>>>> On Apr 15, 2011, at 7:54 PM, Gavin Koh wrote: > >>>>>>>>> > >>>>>>>>>> Dear Wei, > >>>>>>>>>> > >>>>>>>>>> A little more information: the difference seems to be a single > duplicated probe. > >>>>>>>>>> Just comparing two batches (TB1 and TB2) with different probe > numbers: > >>>>>>>>>>> length(TB1$genes) > >>>>>>>>>> [1] 48804 > >>>>>>>>>>> length(TB2$genes) > >>>>>>>>>> [1] 48803 > >>>>>>>>>>> length(unique(TB2$genes)) > >>>>>>>>>> [1] 48803 > >>>>>>>>>>> length(unique(TB1$genes)) > >>>>>>>>>> [1] 48803 > >>>>>>>>>>> setdiff(TB1$genes,TB2$genes) > >>>>>>>>>> character(0) > >>>>>>>>>>> setequal(TB1$genes,TB2$genes) > >>>>>>>>>> [1] TRUE > >>>>>>>>>> > >>>>>>>>>> That still leaves me the problem that I don't know how to > identify the > >>>>>>>>>> repeated probe or how to cbind TB1 and TB2... :-( > >>>>>>>>>> > >>>>>>>>>> Gavin > >>>>>>>>>> > >>>>>>>>>> On 15 April 2011 02:38, Wei Shi shi@wehi.edu.au> wrote: > >>>>>>>>>>> Hi Gavin: > >>>>>>>>>>> > >>>>>>>>>>> The number of probes which were present in one batch but not > in others should be very small. So you can use the probes which are > common in all batches for your analysis. > >>>>>>>>>>> > >>>>>>>>>>> Hope this helps. > >>>>>>>>>>> > >>>>>>>>>>> Cheers, > >>>>>>>>>>> Wei > >>>>>>>>>>> > >>>>>>>>>>> On Apr 15, 2011, at 1:20 AM, Gavin Koh wrote: > >>>>>>>>>>> > >>>>>>>>>>>> I am trying to analyse data from ArrayExpress E-GEOD-22098 > (published > >>>>>>>>>>>> Dec last year). > >>>>>>>>>>>> According to the study methods, the data are Illumina > HumanHT-12 v3 > >>>>>>>>>>>> Expression BeadChips, but the hybridisation seems to have > been done in > >>>>>>>>>>>> several batches, with different numbers of probes in each > batch, > >>>>>>>>>>>> alternating between 48803 and 48804. Can anyone tell me how > to combine > >>>>>>>>>>>> these different batches into the same file, please? I am > trying to > >>>>>>>>>>>> read the probe data using the read.ilmn() function in limma, > but > >>>>>>>>>>>> failing, because cbind complains the matrices are not the > same length > >>>>>>>>>>>> (precise error is "Error in cbind(out$E, objects[[i]]$E) : > number of > >>>>>>>>>>>> rows of matrices must match (see arg 2)"). > >>>>>>>>>>>> > >>>>>>>>>>>> Thank you in advance, > >>>>>>>>>>>> > >>>>>>>>>>>> Gavin Koh > >>>>>>>>>>>> > >>>>>>>>>>>> _______________________________________________ > >>>>>>>>>>>> Bioconductor mailing list > >>>>>>>>>>>> Bioconductor@r-project.org > >>>>>>>>>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor > >>>>>>>>>>>> Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > >>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>> > ______________________________________________________________________ > >>>>>>>>>>> The information in this email is confidential and intended > solely for the addressee. > >>>>>>>>>>> You must not disclose, forward, print or use it without the > permission of the sender. > >>>>>>>>>>> > ______________________________________________________________________ > >>>>>>>>>>> > >>>>>>>>>> > >>>>>>>>>> > >>>>>>>>>> > >>>>>>>>>> -- > >>>>>>>>>> Hofstadter's Law: It always takes longer than you expect, even > when > >>>>>>>>>> you take into account Hofstadter's Law. > >>>>>>>>>> —Douglas Hofstadter (in Gödel, Escher, Bach, 1979) > >>>>>>>>> > >>>>>>>>> > >>>>>>>>> > ______________________________________________________________________ > >>>>>>>>> The information in this email is confidential and intended > solely for the addressee. > >>>>>>>>> You must not disclose, forward, print or use it without the > permission of the sender. > >>>>>>>>> > ______________________________________________________________________ > >>>>>>>>> > >>>>>>>> > >>>>>>>> > >>>>>>>> > >>>>>>>> -- > >>>>>>>> Hofstadter's Law: It always takes longer than you expect, even > when > >>>>>>>> you take into account Hofstadter's Law. > >>>>>>>> —Douglas Hofstadter (in Gödel, Escher, Bach, 1979) > >>>>>>> > >>>>>>> > >>>>>>> > ______________________________________________________________________ > >>>>>>> The information in this email is confidential and intended solely > for the addressee. > >>>>>>> You must not disclose, forward, print or use it without the > permission of the sender. > >>>>>>> > ______________________________________________________________________ > >% [[alternative HTML version deleted]]

Thanks for the summarization, Gavin. It is good to see things finally worked. Just a comment on the data normalization if you are going to do it next. Although control probes are not available in your ArrayExpress download, you can still perform neqc normalization using the derived negative controls, which are inferred from the gene probe intensities and their detection p values. To do this, you just need to provide the Detection value matrix of your TB object to detection.p parameter of neqc function. Cheers, Wei On Apr 17, 2011, at 10:39 PM, gavin.koh at gmail.com wrote: > I am summarising everything just so it is archived on the news group. This is the code I finally used: > > The summarised data is from ArrayExpress (accession number E-GEOD-22098). > There is no bead-level data available. > Each array is in a separate file, and the first 5 lines of the first file looks like this: > Probe_ID Signal Detection > ILMN_1809034 58.80201 0.003952569 > ILMN_1660305 236.4589 0 > ILMN_1792173 202.6858 0 > ILMN_1762337 -4.230737 0.7285903 > ILMN_2055271 7.409712 0.07641634 > ... > > targets.txt looks like this: > name > GSM549324_4325540010_E_Raw.txt > GSM549325_4325540026_A_Raw.txt > GSM549326_4325540026_B_Raw.txt > GSM549327_4335991057_D_Raw.txt > GSM549328_4335991058_A_Raw.txt > ... > > The code I used was: > > TB1 <- read.ilmn( > files=as.character(targets$name)[1:5], > probeid="Probe_ID", > expr="Signal", sep="\t", > other.columns="Detection" > ) > colnames(TB1$E) <- substr(targets$name[1:5],1,9) > colnames(TB1$other$Detection) <- substr(targets$name[1:5],1,9) > TB1$genes <- as.data.frame(TB1$genes) #read.ilmn reads in as vector. > TB2 <- read.ilmn( > files=as.character(targets$name)[6:21], > probeid="Probe_ID", > expr="Signal", sep="\t", > other.columns="Detection" > ) > colnames(TB2$E) <- substr(targets$name[6:21],1,9) > colnames(TB2$other$Detection) <- substr(targets$name[6:21],1,9) > TB2$genes <- as.data.frame(TB2$genes) > TB1.TB2 <- match(TB1$genes[[1]], TB2$genes[[1]]) > TB <- cbind(TB1, TB2[TB1.TB2,]) > > > On , Gavin Koh <gavin.koh at="" gmail.com=""> wrote: > > Dear Wei, > > > > I think that's worked! > > > > Thank you! Gavin. > > > > > > > > On 16 April 2011 13:25, Wei Shi shi at wehi.edu.au> wrote: > > > > > Hi Gavin: > > > > > > > > > > I think the problem is that your TB1$genes (and TB2$genes) is a vector rather than a data frame. This made cbind fail to combine them. I guess the data you downloaded from the public repository is not the original GenomeStudio/BeadStudio output. But you can fix this using the following code: > > > > > > > > > > m > > > TB1$genes > > > TB2$genes > > > TB > > > > > > > > I tried this code on my computer and it worked. Hope that will work for you. > > > > > > > > > > Cheers, > > > > > Wei > > > > > > > > > > On Apr 16, 2011, at 7:34 PM, Gavin Koh wrote: > > > > > > > > > >> Dear Wei, > > > > >> > > > > >> I am afraid it still doesn't work. I this is because TB1 is a list and > > > > >> not a data frame and I cannot coerce it to become a dataframe. > > > > >>> TB > > >> Error in object$genes[i, , drop = FALSE] : incorrect number of dimensions > > > > >>> names(TB1) > > > > >> [1] "source" "E" "genes" "targets" "other" > > > > >>> class(TB1) > > > > >> [1] "EListRaw" > > > > >> attr(,"package") > > > > >> [1] "limma" > > > > >> > > > > >> I checked EListRaw and it inherits directly from list and not from data frame. > > > > >> So sorry, > > > > >> > > > > >> Gavin. > > > > >> > > > > >> On 16 April 2011 08:38, Wei Shi shi at wehi.edu.au> wrote: > > > > >>> Hi Gavin: > > > > >>> > > > > >>> Sorry, TB1[common.probes] should be changed to TB1[common.probes, ]. > > > > >>> > > > > >>> Hope it works now. > > > > >>> > > > > >>> Cheers, > > > > >>> Wei > > > > >>> > > > > >>> > > > > >>> On Apr 16, 2011, at 4:32 PM, Gavin Koh wrote: > > > > >>> > > > > >>>> Dear Wei, > > > > >>>> > > > > >>>> I am afraid this data is from a public repository, so I have no > > > > >>>> control over what data is published or the format :-( > > > > >>>> I am afraid cbind still does not appear to work with this subscripting. > > > > >>>> > > > > >>>>> common.probes > > >>>>> TB > > >>>> Error: Two subscripts required > > > > >>>> > > > > >>>> Please help? > > > > >>>> > > > > >>>> Gavin ?? ?? > > > > >>>> > > > > >>>> On 16 April 2011 00:33, Wei Shi shi at wehi.edu.au> wrote: > > > > >>>>> Dear Gavin: > > > > >>>>> > > > > >>>>> OK, so you did not input the control data. That is the reason why my code did not work. You should really include the control data in your analysis because they are very useful for the normalization. But you can use the following code to merge the data you are having now: > > > > >>>>> > > > > >>>>> m > > >>>>> merged > > >>>>> > > > > >>>>> This will remove the second ILMN_2038777 probe from TB1 and combine probes from TB1 and TB2 in the right order. > > > > >>>>> > > > > >>>>> Cheers, > > > > >>>>> Wei > > > > >>>>> > > > > >>>>> On Apr 16, 2011, at 1:58 AM, Gavin Koh wrote: > > > > >>>>> > > > > >>>>>> Dear Wei > > > > >>>>>> > > > > >>>>>> I am very sorry, but this still does not work. > > > > >>>>>> > > > > >>>>>> ILMN_2038777 is not missing in TB1, but duplicated. The batches with > > > > >>>>>> 48804 probes contain two copies of ILMN_2038777. The batches with > > > > >>>>>> 48803 probes contain only one copy of ILMN_2038777. The order of > > > > >>>>>> probes also seems to be different from batch to batch. > > > > >>>>>> > > > > >>>>>> TB1 was generated using: > > > > >>>>>> > > > > >>>>>> TB1 > > >>>>>> files=as.character(targets$name)[1:5], > > > > >>>>>> probeid="Probe_ID", > > > > >>>>>> expr="Signal", sep="\t", > > > > >>>>>> other.columns="Detection" > > > > >>>>>> ) > > > > >>>>>> > > > > >>>>>> The reason for this being that the summarized data for each array is > > > > >>>>>> in a separate file. There is no bead level data available. There is no > > > > >>>>>> xxx_profile.txt file. > > > > >>>>>> > > > > >>>>>> I tried removing ILMN_2038777, but I cannot. Am I right in saying that > > > > >>>>>> this method of subsetting is only applicable to data frames? > > > > >>>>>>> TB1 > > >>>>>> Error in object$genes[i, , drop = FALSE] : incorrect number of dimensions > > > > >>>>>>> TB1 > > >>>>>> Error in object$genes[i, , drop = FALSE] : incorrect number of dimensions > > > > >>>>>> > > > > >>>>>> Just so you can see the structure of the file that read.ilmn() has produced: > > > > >>>>>> > > > > >>>>>> --begin screen dump-- > > > > >>>>>> > > > > >>>>>>> TB1 > > > > >>>>>> An object of class "EListRaw" > > > > >>>>>> $source > > > > >>>>>> [1] "illumina" > > > > >>>>>> > > > > >>>>>> $E > > > > >>>>>> [,1] [,2] [,3] [,4] [,5] > > > > >>>>>> ILMN_1809034 58.802010 24.907950 13.905010 10.07729 7.044668 > > > > >>>>>> ILMN_1660305 236.458900 113.218000 193.581800 282.36350 127.023400 > > > > >>>>>> ILMN_1792173 202.685800 120.449500 208.370600 242.63090 130.447200 > > > > >>>>>> ILMN_1762337 -4.230737 -3.899888 -3.654122 -3.30873 -5.115820 > > > > >>>>>> ILMN_2055271 7.409712 8.776000 9.394149 12.66054 1.250353 > > > > >>>>>> 48799 more rows ... > > > > >>>>>> > > > > >>>>>> $genes > > > > >>>>>> [1] "ILMN_1809034" "ILMN_1660305" "ILMN_1792173" "ILMN_1762337" "ILMN_2055271" > > > > >>>>>> 48799 more elements ... > > > > >>>>>> > > > > >>>>>> $targets > > > > >>>>>> [1] SampleNames > > > > >>>>>> (or 0-length row.names) > > > > >>>>>> > > > > >>>>>> $other > > > > >>>>>> $Detection > > > > >>>>>> [,1] [,2] [,3] [,4] [,5] > > > > >>>>>> ILMN_1809034 0.003952569 0.01844532 0.03952569 0.08432148 0.111989500 > > > > >>>>>> ILMN_1660305 0.000000000 0.00000000 0.00000000 0.00000000 0.001317523 > > > > >>>>>> ILMN_1792173 0.000000000 0.00000000 0.00000000 0.00000000 0.001317523 > > > > >>>>>> ILMN_1762337 0.728590300 0.75230570 0.68247690 0.57444010 0.708827400 > > > > >>>>>> ILMN_2055271 0.076416340 0.05138340 0.05665349 0.06719368 0.283267500 > > > > >>>>>> 48799 more rows ... > > > > >>>>>> > > > > >>>>>> --end screen dump-- > > > > >>>>>> > > > > >>>>>> Gavin > > > > >>>>>> > > > > >>>>>> On 15 April 2011 12:24, Wei Shi shi at wehi.edu.au> wrote: > > > > >>>>>>> Dear Gavin: > > > > >>>>>>> > > > > >>>>>>> Thanks for the further information. The probe "ILMN_2038777" is not only a gene probe but also a positive control probe (control type: housekeeping). You can find more information about this probe in the HT12 manifest file. But I do not know why it was absent in your TB2 dataset. Anyway, it will be quite safe to remove the housekeeping "ILMN_2038777" from your TB1 dataset. Then you can combine these two datasets together. Below is the code to do this: > > > > >>>>>>> > > > > >>>>>>> x1 > > >>>>>>> x2 > > >>>>>>> x1 > > >>>>>>> m > > >>>>>>> x.merged > > >>>>>>> > > > > >>>>>>> This will combine TB1 with TB2. For the other four datasets, you can merge them to x.merged using the same procedure (removing housekeeping "ILMN_2038777" from the dataset first if it has, then using match and cbind commands to merge them). > > > > >>>>>>> > > > > >>>>>>> Hope this will work for you. But let you know it doesn't. > > > > >>>>>>> > > > > >>>>>>> Cheers, > > > > >>>>>>> Wei > > > > >>>>>>> > > > > >>>>>>> > > > > >>>>>>> On Apr 15, 2011, at 9:16 PM, Gavin Koh wrote: > > > > >>>>>>> > > > > >>>>>>>> Dear Wei, > > > > >>>>>>>> > > > > >>>>>>>> Thank you for replying so quickly. There appear to be 6 batches in > > > > >>>>>>>> this dataset (TB1 to 6) > > > > >>>>>>>> > > > > >>>>>>>>> TB1$genes[1:10] > > > > >>>>>>>> [1] "ILMN_1809034" "ILMN_1660305" "ILMN_1792173" "ILMN_1762337" > > > > >>>>>>>> "ILMN_2055271" "ILMN_1736007" "ILMN_1814316" > > > > >>>>>>>> [8] "ILMN_2359168" "ILMN_1731507" "ILMN_1787689" > > > > >>>>>>>>> TB2$genes[1:10] > > > > >>>>>>>> [1] "ILMN_1762337" "ILMN_2055271" "ILMN_1736007" "ILMN_2383229" > > > > >>>>>>>> "ILMN_1806310" "ILMN_1779670" "ILMN_2321282" > > > > >>>>>>>> [8] "ILMN_1671474" "ILMN_1772582" "ILMN_1735698" > > > > >>>>>>>>> TB3$genes[1:10] > > > > >>>>>>>> [1] "ILMN_1809034" "ILMN_1660305" "ILMN_1792173" "ILMN_1762337" > > > > >>>>>>>> "ILMN_2055271" "ILMN_1736007" "ILMN_1814316" > > > > >>>>>>>> [8] "ILMN_2359168" "ILMN_1731507" "ILMN_1787689" > > > > >>>>>>>>> TB4$genes[1:10] > > > > >>>>>>>> [1] "ILMN_1762337" "ILMN_2055271" "ILMN_1736007" "ILMN_2383229" > > > > >>>>>>>> "ILMN_1806310" "ILMN_1779670" "ILMN_2321282" > > > > >>>>>>>> [8] "ILMN_1671474" "ILMN_1772582" "ILMN_1735698" > > > > >>>>>>>>> TB5$genes[1:10] > > > > >>>>>>>> [1] "ILMN_1809034" "ILMN_1660305" "ILMN_1792173" "ILMN_1762337" > > > > >>>>>>>> "ILMN_2055271" "ILMN_1736007" "ILMN_1814316" > > > > >>>>>>>> [8] "ILMN_2359168" "ILMN_1731507" "ILMN_1787689" > > > > >>>>>>>>> TB6$genes[1:10] > > > > >>>>>>>> [1] "ILMN_1762337" "ILMN_2055271" "ILMN_1736007" "ILMN_2383229" > > > > >>>>>>>> "ILMN_1806310" "ILMN_1779670" "ILMN_2321282" > > > > >>>>>>>> [8] "ILMN_1671474" "ILMN_1772582" "ILMN_1735698" > > > > >>>>>>>> > > > > >>>>>>>> ???????? > > > > >>>>>>>> > > > > >>>>>>>> Gavin > > > > >>>>>>>> > > > > >>>>>>>> On 15 April 2011 11:45, Wei Shi shi at wehi.edu.au> wrote: > > > > >>>>>>>>> Hi Gavin: > > > > >>>>>>>>> > > > > >>>>>>>>> It would be best if you can match the two batches using the probe identifiers because they are much less likely to have duplicates. Would it possible to show the first several probes in each dataset so that I can write some code to help you do this? > > > > >>>>>>>>> > > > > >>>>>>>>> Cheers, > > > > >>>>>>>>> Wei > > > > >>>>>>>>> > > > > >>>>>>>>> > > > > >>>>>>>>> On Apr 15, 2011, at 7:54 PM, Gavin Koh wrote: > > > > >>>>>>>>> > > > > >>>>>>>>>> Dear Wei, > > > > >>>>>>>>>> > > > > >>>>>>>>>> A little more information: the difference seems to be a single duplicated probe. > > > > >>>>>>>>>> Just comparing two batches (TB1 and TB2) with different probe numbers: > > > > >>>>>>>>>>> length(TB1$genes) > > > > >>>>>>>>>> [1] 48804 > > > > >>>>>>>>>>> length(TB2$genes) > > > > >>>>>>>>>> [1] 48803 > > > > >>>>>>>>>>> length(unique(TB2$genes)) > > > > >>>>>>>>>> [1] 48803 > > > > >>>>>>>>>>> length(unique(TB1$genes)) > > > > >>>>>>>>>> [1] 48803 > > > > >>>>>>>>>>> setdiff(TB1$genes,TB2$genes) > > > > >>>>>>>>>> character(0) > > > > >>>>>>>>>>> setequal(TB1$genes,TB2$genes) > > > > >>>>>>>>>> [1] TRUE > > > > >>>>>>>>>> > > > > >>>>>>>>>> That still leaves me the problem that I don't know how to identify the > > > > >>>>>>>>>> repeated probe or how to cbind TB1 and TB2... :-( > > > > >>>>>>>>>> > > > > >>>>>>>>>> Gavin > > > > >>>>>>>>>> > > > > >>>>>>>>>> On 15 April 2011 02:38, Wei Shi shi at wehi.edu.au> wrote: > > > > >>>>>>>>>>> Hi Gavin: > > > > >>>>>>>>>>> > > > > >>>>>>>>>>> The number of probes which were present in one batch but not in others should be very small. So you can use the probes which are common in all batches for your analysis. > > > > >>>>>>>>>>> > > > > >>>>>>>>>>> Hope this helps. > > > > >>>>>>>>>>> > > > > >>>>>>>>>>> Cheers, > > > > >>>>>>>>>>> Wei > > > > >>>>>>>>>>> > > > > >>>>>>>>>>> On Apr 15, 2011, at 1:20 AM, Gavin Koh wrote: > > > > >>>>>>>>>>> > > > > >>>>>>>>>>>> I am trying to analyse data from ArrayExpress E-GEOD-22098 (published > > > > >>>>>>>>>>>> Dec last year). > > > > >>>>>>>>>>>> According to the study methods, the data are Illumina HumanHT-12 v3 > > > > >>>>>>>>>>>> Expression BeadChips, but the hybridisation seems to have been done in > > > > >>>>>>>>>>>> several batches, with different numbers of probes in each batch, > > > > >>>>>>>>>>>> alternating between 48803 and 48804. Can anyone tell me how to combine > > > > >>>>>>>>>>>> these different batches into the same file, please? I am trying to > > > > >>>>>>>>>>>> read the probe data using the read.ilmn() function in limma, but > > > > >>>>>>>>>>>> failing, because cbind complains the matrices are not the same length > > > > >>>>>>>>>>>> (precise error is "Error in cbind(out$E, objects[[i]]$E) : number of > > > > >>>>>>>>>>>> rows of matrices must match (see arg 2)"). > > > > >>>>>>>>>>>> > > > > >>>>>>>>>>>> Thank you in advance, > > > > >>>>>>>>>>>> > > > > >>>>>>>>>>>> Gavin Koh > > > > >>>>>>>>>>>> > > > > >>>>>>>>>>>> _______________________________________________ > > > > >>>>>>>>>>>> Bioconductor mailing list > > > > >>>>>>>>>>>> Bioconductor at r-project.org > > > > >>>>>>>>>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor > > > > >>>>>>>>>>>> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > >>>>>>>>>>> > > > > >>>>>>>>>>> > > > > >>>>>>>>>>> ______________________________________________________________________ > > > > >>>>>>>>>>> The information in this email is confidential and intended solely for the addressee. > > > > >>>>>>>>>>> You must not disclose, forward, print or use it without the permission of the sender. > > > > >>>>>>>>>>> ______________________________________________________________________ > > > > >>>>>>>>>>> > > > > >>>>>>>>>> > > > > >>>>>>>>>> > > > > >>>>>>>>>> > > > > >>>>>>>>>> -- > > > > >>>>>>>>>> Hofstadter's Law: It always takes longer than you expect, even when > > > > >>>>>>>>>> you take into account Hofstadter's Law. > > > > >>>>>>>>>> ?Douglas Hofstadter (in G?del, Escher, Bach, 1979) > > > > >>>>>>>>> > > > > >>>>>>>>> > > > > >>>>>>>>> ______________________________________________________________________ > > > > >>>>>>>>> The information in this email is confidential and intended solely for the addressee. > > > > >>>>>>>>> You must not disclose, forward, print or use it without the permission of the sender. > > > > >>>>>>>>> ______________________________________________________________________ > > > > >>>>>>>>> > > > > >>>>>>>> > > > > >>>>>>>> > > > > >>>>>>>> > > > > >>>>>>>> -- > > > > >>>>>>>> Hofstadter's Law: It always takes longer than you expect, even when > > > > >>>>>>>> you take into account Hofstadter's Law. > > > > >>>>>>>> ?Douglas Hofstadter (in G?del, Escher, Bach, 1979) > > > > >>>>>>> > > > > >>>>>>> > > > > >>>>>>> ______________________________________________________________________ > > > > >>>>>>> The information in this email is confidential and intended solely for the addressee. > > > > >>>>>>> You must not disclose, forward, print or use it without the permission of the sender. > > > > >>>>>>> ______________________________________________________________________ > > > > >% ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:6}}