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seandavi avatar seandavi commented on July 4, 2024

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.

from geoquery.

seandavi avatar seandavi commented on July 4, 2024

@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

from geoquery.

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