[BioC] MIAME object and abstract

[Martin Morgan]

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_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.
>
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-- 
Martin Morgan
Computational Biology / Fred Hutchinson Cancer Research Center
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