Description Format Details Source
ExpressionSet for the MAQC2 Dataset
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | experimentData(eset):
Experiment data
Experimenter name:
Laboratory:
Contact information: http://www.ncbi.nlm.nih.gov/pubmed/?term=20064235
Title:
URL: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20194
PMIDs: 20064235
No abstract available.
notes:
summary:
It is possible to build multi-gene classifiers of clinical outcome. Predic
tion accuracy depends on training sample size and classification difficult
y.
mapping.method:
maxRowVariance
mapping.group:
EntrezGene.ID
preprocessing:
As published by original author.
featureData(eset):
An object of class 'AnnotatedDataFrame'
featureNames: 1007_s_at 1053_at ... AFFX-HUMISGF3A/M97935_MB_at
(20967 total)
varLabels: probeset gene EntrezGene.ID best_probe
varMetadata: labelDescription
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | assayData: 20967 features, 230 samples
Platform type:
---------------------------
Available sample meta-data:
---------------------------
sample_name:
Length Class Mode
230 character character
alt_sample_name:
Length Class Mode
230 character character
sample_type:
tumor
230
er:
negative positive
89 141
pgr:
negative positive
126 104
her2:
negative positive
190 40
N:
0 1
66 164
age_at_initial_pathologic_diagnosis:
Min. 1st Qu. Median Mean 3rd Qu. Max.
26.00 45.00 51.00 52.02 59.00 79.00
grade:
1 2 3
13 94 123
treatment:
chemotherapy
230
batch:
MAQC2
230
uncurated_author_metadata:
Length Class Mode
230 character character
|
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20194
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