Comments (2)
The plan is to parse the tags from the tag:value "characteristic" pairs to create column names. Then, parse the data into the correct columns, filling with NA where a sample did not include that characteristic.
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@DarioS, this took me way too long, but it is now in GEOquery. The example above parses to:
data_row_count accession age_at_dx:ch2 debulking:ch2 grade:ch2
GSM1304246 41000 V2 59 Optimal 4
GSM1304247 41000 V3 59 Optimal 4
GSM1304248 41000 V4 81 Sub-optimal 3
GSM1304249 41000 V5 61 Optimal 3
GSM1304250 41000 V6 56 Optimal 4
GSM1304251 41000 V7 73 Sub-optimal 3
morphology:ch2 stage_#:ch2 Stage:ch2 substage:ch2
GSM1304246 Serous 2 II B
GSM1304247 Serous 3 III C
GSM1304248 Serous 4 IV <NA>
GSM1304249 Serous 3 III C
GSM1304250 Serous 3 III C
GSM1304251 Serous 4 IV <NA>
tcga_sampleid:ch2 time_fu_months:ch2 tissue:ch1 vital_status:ch2
GSM1304246 <NA> 95.86 ovarian Dead
GSM1304247 TCGA-25-1870-01A-01 14.15 ovarian Dead
GSM1304248 <NA> 1.12 ovarian Dead
GSM1304249 <NA> 130.40 ovarian Dead
GSM1304250 <NA> 85.20 ovarian Dead
GSM1304251 <NA> 47.20 ovarian Dead
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