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}}
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
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}}
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}}
>
> _______________________________________________
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--
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