Thanks Sean for your quick response! The other part I am stuck on is the gene information.I discussed this with you earlier so I have the code which should run but not getting the expected results. The issue comes when I am looping over my gplllist object and when there are more than 10 gpls,and I dont get the information I need.If the gpllist object has around 3 to 4 gpl objects, it works!! annotgpl = dbGetquery(con,"select distinct GPL from gds") gpllist <- sapply(annotgpl[1:10,1],getGEO,AnnotGPL=TRUE) genes_new<- unlist(sapply(gpllist[1:10], function(a) {Table(a)[,'Gene ID']})) This is the result of first 3 gene id's in first gpl object(GPL13) using the command above. "1""GPL13" "13""GPL13" "26""GPL13" However, I am getting altogether different gene ids's while doing it for all 3 gpl objects together as seen above.If I am doing it separately, then only i am getting the gene id's which are the correct ones, that I notice in gpllist object. The count of genes in each case (doing together vs separate )remain same  ii.) genes_new<- unlist(sapply(gpllist[1], function(a) {Table(a)[,'Gene ID']}))  "818888" "GPL13" "821523" "GPL13" "824405" "GPL13" Is this an issue to huge amount of data? Thanks, Rohan On Tuesday, 3 December 2013 4:31 PM, Sean Davis <sdavis2@mail.nih.gov> wrote: On Tue, Dec 3, 2013 at 4:20 PM, rohan bareja <rohan_1925@yahoo.co.in> wrote: Hi, > >I would like to get information on which samples are cases and which are controls. >I am using the following commands : > >#creating GDS list >annotgds = dbGetQuery(con,"select GDS from gds") > >#getting data for first 20 GDS >gdslist <- sapply(annotgds[1:20,1],getGEO) > >#using column method get the info >gds_col=sapply(gdslist[1:20],function(a) {Columns(a)} > >Below is the result for first two GDS: > >$GDS10 >   sample tissue        strain      disease.state description >1  GSM582 spleen           NOD           diabetic           Value for GSM582: NOD_S1; src: Spleen >2  GSM589 spleen           NOD           diabetic           Value for GSM589: NOD_S2; src: Spleen >3  GSM583 spleen          Idd3 diabetic-resistant          Value for GSM583: Idd3_S1; src: Spleen >4  GSM590 spleen          Idd3 diabetic-resistant          Value for GSM590: Idd3_S2; src: Spleen >5  GSM584 spleen          Idd5 diabetic-resistant          Value for GSM584: Idd5_S1; src: Spleen >6  GSM591 spleen          Idd5 diabetic-resistant          Value for GSM591: Idd5_S2; src: Spleen >7  GSM585 spleen     Idd3+Idd5 diabetic-resistant        Value for GSM585: Idd3+5_S1; src: Spleen >8  GSM592 spleen     Idd3+Idd5 diabetic-resistant        Value for GSM592: Idd3+5_S2; src: Spleen >9  GSM586 spleen          Idd9 diabetic-resistant          Value for GSM586: Idd9_S1; src: Spleen >10 GSM593 spleen          Idd9 diabetic-resistant          Value for GSM593: Idd9_S2; src: Spleen >11 GSM587 spleen      B10.H2g7        nondiabetic      Value for GSM587: B10.H2g7_S1; src: Spleen >12 GSM594 spleen      B10.H2g7        nondiabetic      Value for GSM594: B10.H2g7_S2; src: Spleen >13 GSM588 spleen B10.H2g7 Idd3        nondiabetic Value for GSM588: B10.H2g7 Idd3_S1; src: Spleen >14 GSM595 spleen B10.H2g7 Idd3        nondiabetic Value for GSM595: B10.H2g7 Idd3_S2; src: Spleen >15 GSM596 thymus           NOD           diabetic           Value for GSM596: NOD_T1; src: Thymus >16 GSM603 thymus           NOD           diabetic           Value for GSM603: NOD_T2; src: Thymus >17 GSM597 thymus          Idd3 diabetic-resistant          Value for GSM597: Idd3_T1; src: Thymus >18 GSM604 thymus          Idd3 diabetic-resistant          Value for GSM604: Idd3_T2; src: Thymus >19 GSM598 thymus          Idd5 diabetic-resistant          Value for GSM598: Idd5_T1; src: Thymus >20 GSM605 thymus          Idd5 diabetic-resistant          Value for GSM605: Idd5_T2; src: Thymus >21 GSM599 thymus     Idd3+Idd5 diabetic-resistant        Value for GSM599: Idd3+5_T1; src: Thymus >22 GSM606 thymus     Idd3+Idd5 diabetic-resistant        Value for GSM606: Idd3+5_T2; src: Thymus >23 GSM600 thymus          Idd9 diabetic-resistant          Value for GSM600: Idd9_T1; src: Thymus >24 GSM607 thymus          Idd9 diabetic-resistant          Value for GSM607: Idd9_T2; src: Thymus >25 GSM601 thymus      B10.H2g7        nondiabetic      Value for GSM601: B10.H2g7_T1; src: Thymus >26 GSM608 thymus      B10.H2g7        nondiabetic      Value for GSM608: B10.H2g7_T2; src: Thymus >27 GSM602 thymus B10.H2g7 Idd3        nondiabetic Value for GSM602: B10.H2g7 Idd3_T1; src: Thymus >28 GSM609 thymus B10.H2g7 Idd3        nondiabetic Value for GSM609: B10.H2g7 Idd3_T2; src: Thymus > >$GDS100 >  sample       protocol      time >1 GSM549 not irradiated  0 minute >2 GSM542 not irradiated 20 minute >3 GSM543 not irradiated 60 minute >4 GSM547     irradiated  5 minute >5 GSM544     irradiated 10 minute >6 GSM545     irradiated 20 minute >7 GSM546     irradiated 40 minute >8 GSM548     irradiated 60 minute > > >The columns are different in each GDS,so I am not able to get that information and combine it. > > > >ii) The other technique I used is the expression set.The commands for this are: > >eset_list <- sapply(gdslist[1:3],GDS2eSet,do.log2=TRUE) > >eset_col=sapply(eset_list[1:3],function(a) {pData(a)} > >$GDS10 >       sample tissue        strain      disease.state description >GSM582 GSM582 spleen           NOD           diabetic           Value for GSM582: NOD_S1; src: Spleen >GSM589 GSM589 spleen           NOD           diabetic           Value for GSM589: NOD_S2; src: Spleen >GSM583 GSM583 spleen          Idd3 diabetic-resistant          Value for GSM583: Idd3_S1; src: Spleen >GSM590 GSM590 spleen          Idd3 diabetic-resistant          Value for GSM590: Idd3_S2; src: Spleen >GSM584 GSM584 spleen          Idd5 diabetic-resistant          Value for GSM584: Idd5_S1; src: Spleen >GSM591 GSM591 spleen          Idd5 diabetic-resistant          Value for GSM591: Idd5_S2; src: Spleen >GSM585 GSM585 spleen     Idd3+Idd5 diabetic-resistant        Value for GSM585: Idd3+5_S1; src: Spleen >GSM592 GSM592 spleen     Idd3+Idd5 diabetic-resistant        Value for GSM592: Idd3+5_S2; src: Spleen >GSM586 GSM586 spleen          Idd9 diabetic-resistant          Value for GSM586: Idd9_S1; src: Spleen >GSM593 GSM593 spleen          Idd9 diabetic-resistant          Value for GSM593: Idd9_S2; src: Spleen >GSM587 GSM587 spleen      B10.H2g7        nondiabetic      Value for GSM587: B10.H2g7_S1; src: Spleen >GSM594 GSM594 spleen      B10.H2g7        nondiabetic      Value for GSM594: B10.H2g7_S2; src: Spleen >GSM588 GSM588 spleen B10.H2g7 Idd3        nondiabetic Value for GSM588: B10.H2g7 Idd3_S1; src: Spleen >GSM595 GSM595 spleen B10.H2g7 Idd3        nondiabetic Value for GSM595: B10.H2g7 Idd3_S2; src: Spleen >GSM596 GSM596 thymus           NOD           diabetic           Value for GSM596: NOD_T1; src: Thymus >GSM603 GSM603 thymus           NOD           diabetic           Value for GSM603: NOD_T2; src: Thymus >GSM597 GSM597 thymus          Idd3 diabetic-resistant          Value for GSM597: Idd3_T1; src: Thymus >GSM604 GSM604 thymus          Idd3 diabetic-resistant          Value for GSM604: Idd3_T2; src: Thymus >GSM598 GSM598 thymus          Idd5 diabetic-resistant          Value for GSM598: Idd5_T1; src: Thymus >GSM605 GSM605 thymus          Idd5 diabetic-resistant          Value for GSM605: Idd5_T2; src: Thymus >GSM599 GSM599 thymus     Idd3+Idd5 diabetic-resistant        Value for GSM599: Idd3+5_T1; src: Thymus >GSM606 GSM606 thymus     Idd3+Idd5 diabetic-resistant        Value for GSM606: Idd3+5_T2; src: Thymus >GSM600 GSM600 thymus          Idd9 diabetic-resistant          Value for GSM600: Idd9_T1; src: Thymus >GSM607 GSM607 thymus          Idd9 diabetic-resistant          Value for GSM607: Idd9_T2; src: Thymus >GSM601 GSM601 thymus      B10.H2g7        nondiabetic      Value for GSM601: B10.H2g7_T1; src: Thymus >GSM608 GSM608 thymus      B10.H2g7        nondiabetic      Value for GSM608: B10.H2g7_T2; src: Thymus >GSM602 GSM602 thymus B10.H2g7 Idd3        nondiabetic Value for GSM602: B10.H2g7 Idd3_T1; src: Thymus >GSM609 GSM609 thymus B10.H2g7 Idd3        nondiabetic Value for GSM609: B10.H2g7 Idd3_T2; src: Thymus > >$GDS100 >       sample       protocol      time >GSM549 GSM549 not irradiated  0 minute >GSM542 GSM542 not irradiated 20 minute >GSM543 GSM543 not irradiated 60 minute >GSM547 GSM547     irradiated  5 minute >GSM544 GSM544     irradiated 10 minute >GSM545 GSM545     irradiated 20 minute >GSM546 GSM546     irradiated 40 minute >GSM548 GSM548     irradiated 60 minute > > >here, also the results are same and I am not able to extract the information I need due to difference in column names. >Can anyone help on this? > Hi, Rohan. The limitation is with the GEO submission process which does not really result in a set of computable columns for annotation. Unfortunately, you'll likely need to do some work by hand to determine what annotation columns are important for each GDS.  As you have figured out, there is no concept of a generic case/control in GEO annotations. Sean [[alternative HTML version deleted]]