MIAME object and abstract
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Daniel Brewer ★ 1.9k
@daniel-brewer-1791
Last seen 9.7 years ago
I am creating a MIAME object to include in an ExpressionSet as experiment data. My question is regards the abstract field. When I try to enter an abstract it cuts it short. I assume this is because a string can only be so long in R or something with my readline implementation. Has anyone else this problem? How do you over come it? Thanks -- ************************************************************** Daniel Brewer, Ph.D. Institute of Cancer Research Molecular Carcinogenesis Email: daniel.brewer at icr.ac.uk ************************************************************** The Institute of Cancer Research: Royal Cancer Hospital, a charitable Company Limited by Guarantee, Registered in England under Company No. 534147 with its Registered Office at 123 Old Brompton Road, London SW7 3RP. This e-mail message is confidential and for use by the a...{{dropped:2}}
Cancer Cancer • 920 views
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rgentleman ★ 5.5k
@rgentleman-7725
Last seen 9.0 years ago
United States
Hi Daniel, The posting guide does ask for reproducible examples, and for the rather important reason that it would help someone try to help you. Could you please include things like sessionInfo and small self-contained examples? In the present case, I doubt that the abstract is truncated, but rather that the printed version is, but without a lot more detail from you, I wouldn't know. best wishes Robert Daniel Brewer wrote: > I am creating a MIAME object to include in an ExpressionSet as > experiment data. My question is regards the abstract field. When I try > to enter an abstract it cuts it short. I assume this is because a > string can only be so long in R or something with my readline > implementation. > > Has anyone else this problem? How do you over come it? > > Thanks > -- Robert Gentleman, PhD Program in Computational Biology Division of Public Health Sciences Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N, M2-B876 PO Box 19024 Seattle, Washington 98109-1024 206-667-7700 rgentlem at fhcrc.org
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Sorry about that. Here is an example: > exptData <- new("MIAME",title="Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy.",pubMedIds="15948174",name="Andrew J. Stephenson",abstract="BACKGROUND: Gene expression profiling of prostate carcinoma offers an alternative means to distinguish aggressive tumor biology and may improve the accuracy of outcome prediction for patients with prostate carcinoma treated by radical prostatectomy. METHODS: Gene expression differences between 37 recurrent and 42 nonrecurrent primary prostate tumor specimens were analyzed by oligonucleotide microarrays. Two logistic regression modeling approaches were used to predict prostate carcinoma recurrence after radical prostatectomy. One approach was based exclusively on gene expression differences between the two classes. The second approach integrated prognostic gene variables with a validated postoperative predictive model based on standard variables (nomogram). The predictive accuracy of these modeling approaches was evaluated by leave-one-out cross-validation (LOOCV) and compared with the nomogram. RESULTS: The modeling approach using gene variables alone accurately classified 59 (75%) tissue samples in LOOCV, a classification rate substantially higher than expected by chance. However, this predictive accuracy was inferior to the nomogram (concordance index, 0.75 vs. 0.84, P = 0.01). Models combining clinical and gene variables accurately classified 70 (89%) tissue samples and the predictive accuracy using this approach (concordance index, 0.89) was superior to the nomogram (P = 0.009) and models based on gene variables alone (P < 0.001). Importantly, the combined approach provided a marked improvement for patients whose nomogram-predicted likelihood of disease recurrence was in the indeterminate range (7-year disease progression-free probability, 30-70%; concordance index, 0.83 vs. 0.59, P = 0.01). CONCLUSIONS: Integration of gene expression signatures and clinical variables produced predictive models for prostate carcinoma recurrence that perform significantly better than those based on either clinical variables or gene expression information alone.") +") > abstract(exptData) [1] "BACKGROUND: Gene expression profiling of prostate carcinoma offers an alternative means to distinguish aggressive tumor biology and may improve the accuracy of outcome prediction for patients with prostate carcinoma treated by radical prostatectomy. METHODS: Gene expression differences between 37 recurrent and 42 nonrecurrent primary prostate tumor specimens were analyzed by oligonucleotide microarrays. Two logistic regression modeling approaches were used to predict prostate carcinoma recurrence after radical prostatectomy. One approach was based exclusively on gene expression differences between the two classes. The second approach integrated prognostic gene variables with a validated postoperative predictive model based on standard variables (nomogram). The predictive accuracy of these)\n" Not sure why you have to add the extra "). > sessionInfo() R version 2.5.1 (2007-06-27) x86_64-pc-linux-gnu locale: LC_CTYPE=en_GB.UTF-8;LC_NUMERIC=C;LC_TIME=en_GB.UTF-8;LC_COLLATE=en_GB .UTF-8;LC_MONETARY=en_GB.UTF-8;LC_MESSAGES=en_GB.UTF-8;LC_PAPER=en_GB. UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_GB.UTF-8 ;LC_IDENTIFICATION=C attached base packages: [1] "tools" "stats" "graphics" "grDevices" "utils" "datasets" [7] "methods" "base" other attached packages: Biobase "1.14.1" Thanks Dan Robert Gentleman wrote: > Hi Daniel, > The posting guide does ask for reproducible examples, and for the > rather important reason that it would help someone try to help you. > Could you please include things like sessionInfo and small > self-contained examples? > > In the present case, I doubt that the abstract is truncated, but rather > that the printed version is, but without a lot more detail from you, I > wouldn't know. > > best wishes > Robert > > > Daniel Brewer wrote: >> I am creating a MIAME object to include in an ExpressionSet as >> experiment data. My question is regards the abstract field. When I try >> to enter an abstract it cuts it short. I assume this is because a >> string can only be so long in R or something with my readline >> implementation. >> >> Has anyone else this problem? How do you over come it? >> >> Thanks >> > -- ************************************************************** Daniel Brewer, Ph.D. Institute of Cancer Research Molecular Carcinogenesis Email: daniel.brewer at icr.ac.uk ************************************************************** The Institute of Cancer Research: Royal Cancer Hospital, a charitable Company Limited by Guarantee, Registered in England under Company No. 534147 with its Registered Office at 123 Old Brompton Road, London SW7 3RP. This e-mail message is confidential and for use by the a...{{dropped:2}}
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@martin-morgan-1513
Last seen 26 days ago
United States
Hi Daniel -- I think there was a parser limit in your version of R, and that it is no longer there. From the NEWS file with R-2.7.0 o The parser limit on string size has been removed. At least cut and paste into my R-2.7.0 does not have these problems. For what it's worth, > info <- getPMInfo(pubmed("15948174")) will retrieve a list (pubmed takes a vector argument) with useful entries > sapply(info, names) 15948174 [1,] "JrnlInfo" [2,] "title" [3,] "abstract" [4,] "authors" [5,] "MedlineTA" Might save some typing / copy/pasting. Martin Daniel Brewer <daniel.brewer at="" icr.ac.uk=""> writes: > Sorry about that. Here is an example: > >> exptData <- new("MIAME",title="Integration of gene expression > profiling and clinical variables to predict prostate carcinoma > recurrence after radical > prostatectomy.",pubMedIds="15948174",name="Andrew J. > Stephenson",abstract="BACKGROUND: Gene expression profiling of prostate > carcinoma offers an alternative means to distinguish aggressive tumor > biology and may improve the accuracy of outcome prediction for patients > with prostate carcinoma treated by radical prostatectomy. METHODS: Gene > expression differences between 37 recurrent and 42 nonrecurrent primary > prostate tumor specimens were analyzed by oligonucleotide microarrays. > Two logistic regression modeling approaches were used to predict > prostate carcinoma recurrence after radical prostatectomy. One approach > was based exclusively on gene expression differences between the two > classes. The second approach integrated prognostic gene variables with a > validated postoperative predictive model based on standard variables > (nomogram). The predictive accuracy of these modeling approaches was > evaluated by leave-one-out cross-validation (LOOCV) and compared with > the nomogram. RESULTS: The modeling approach using gene variables alone > accurately classified 59 (75%) tissue samples in LOOCV, a classification > rate substantially higher than expected by chance. However, this > predictive accuracy was inferior to the nomogram (concordance index, > 0.75 vs. 0.84, P = 0.01). Models combining clinical and gene variables > accurately classified 70 (89%) tissue samples and the predictive > accuracy using this approach (concordance index, 0.89) was superior to > the nomogram (P = 0.009) and models based on gene variables alone (P < > 0.001). Importantly, the combined approach provided a marked improvement > for patients whose nomogram-predicted likelihood of disease recurrence > was in the indeterminate range (7-year disease progression-free > probability, 30-70%; concordance index, 0.83 vs. 0.59, P = 0.01). > CONCLUSIONS: Integration of gene expression signatures and clinical > variables produced predictive models for prostate carcinoma recurrence > that perform significantly better than those based on either clinical > variables or gene expression information alone.") > +") > >> abstract(exptData) > [1] "BACKGROUND: Gene expression profiling of prostate carcinoma offers > an alternative means to distinguish aggressive tumor biology and may > improve the accuracy of outcome prediction for patients with prostate > carcinoma treated by radical prostatectomy. METHODS: Gene expression > differences between 37 recurrent and 42 nonrecurrent primary prostate > tumor specimens were analyzed by oligonucleotide microarrays. Two > logistic regression modeling approaches were used to predict prostate > carcinoma recurrence after radical prostatectomy. One approach was based > exclusively on gene expression differences between the two classes. The > second approach integrated prognostic gene variables with a validated > postoperative predictive model based on standard variables (nomogram). > The predictive accuracy of these)\n" > > Not sure why you have to add the extra "). > >> sessionInfo() > R version 2.5.1 (2007-06-27) > x86_64-pc-linux-gnu > > locale: > LC_CTYPE=en_GB.UTF-8;LC_NUMERIC=C;LC_TIME=en_GB.UTF-8;LC_COLLATE=en_ GB.UTF-8;LC_MONETARY=en_GB.UTF-8;LC_MESSAGES=en_GB.UTF-8;LC_PAPER=en_G B.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_GB.UTF -8;LC_IDENTIFICATION=C > > attached base packages: > [1] "tools" "stats" "graphics" "grDevices" "utils" "datasets" > [7] "methods" "base" > > other attached packages: > Biobase > "1.14.1" > > > Thanks > > Dan > > Robert Gentleman wrote: >> Hi Daniel, >> The posting guide does ask for reproducible examples, and for the >> rather important reason that it would help someone try to help you. >> Could you please include things like sessionInfo and small >> self-contained examples? >> >> In the present case, I doubt that the abstract is truncated, but rather >> that the printed version is, but without a lot more detail from you, I >> wouldn't know. >> >> best wishes >> Robert >> >> >> Daniel Brewer wrote: >>> I am creating a MIAME object to include in an ExpressionSet as >>> experiment data. My question is regards the abstract field. When I try >>> to enter an abstract it cuts it short. I assume this is because a >>> string can only be so long in R or something with my readline >>> implementation. >>> >>> Has anyone else this problem? How do you over come it? >>> >>> Thanks >>> >> > > -- > ************************************************************** > Daniel Brewer, Ph.D. > > Institute of Cancer Research > Molecular Carcinogenesis > Email: daniel.brewer at icr.ac.uk > ************************************************************** > > The Institute of Cancer Research: Royal Cancer Hospital, a charitable Company Limited by Guarantee, Registered in England under Company No. 534147 with its Registered Office at 123 Old Brompton Road, London SW7 3RP. > > This e-mail message is confidential and for use by the a...{{dropped:2}} > > _______________________________________________ > 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 -- Martin Morgan Computational Biology / Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 Location: Arnold Building M2 B169 Phone: (206) 667-2793
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