Datasets:

computage
/

computage_bench

@@ -13,6 +13,7 @@ size_categories:
 - 10K<n<100K
 language:
 - en
 ---
 # ComputAge Bench Dataset
@@ -51,9 +52,9 @@ Both parts are described further below in more details.
 In total, the dataset comprises **10,404 samples** and **900,449 features** (DNA methylation sites) coming from 65 separate studies
 (some features are missing in some files). It is common for biological (omics) datasets to have N << P, so we had to put samples as columns and features as rows
 in order to save the dataset in the parquet format. We recommend transposing data upon loading in order to match with meta rows,
-as mentioned further below in the Guidelines section.
-Its main purpose is to be used in aging clock benchmarking (for more details on that, again, proceed to the Guidelines section and don’t hesitate to visit
 [our paper]()). Nevertheless, you are free to use it for any other well-minded purpose you find suitable.
 ## Data description
@@ -67,7 +68,7 @@ In the original datasets deposited on GEO, therefore, DNA methylation values wer
 beta percentages (ranging from 0 to 100), or M-values (can be both negative and positive, equals 0 when beta equals 0.5). We converted all data
 to the beta-value fractions ranging from 0 to 1. The values outside this range were treated as missing values (NaNs), as they are not biological.
-In each dataset, only samples that appeared in the cleaned meta table were retained.
 ### Row names
@@ -106,6 +107,9 @@ it can be either rounded by the researchers to full years, or converted from mon
 **Class**:  class of the respective sample condition. Healthy control samples (HC) are included in a separate healthy control class with the same abbreviation (HC).
 ## Additional Information

 - 10K<n<100K
 language:
 - en
+pretty_name: ComputAge Bench
 ---
 # ComputAge Bench Dataset
 In total, the dataset comprises **10,404 samples** and **900,449 features** (DNA methylation sites) coming from 65 separate studies
 (some features are missing in some files). It is common for biological (omics) datasets to have N << P, so we had to put samples as columns and features as rows
 in order to save the dataset in the parquet format. We recommend transposing data upon loading in order to match with meta rows,
+as mentioned further below in the Usage Guidelines section.
+Its main purpose is to be used in aging clock benchmarking (for more details on that, again, proceed to the Usage Guidelines section and don’t hesitate to visit
 [our paper]()). Nevertheless, you are free to use it for any other well-minded purpose you find suitable.
 ## Data description
 beta percentages (ranging from 0 to 100), or M-values (can be both negative and positive, equals 0 when beta equals 0.5). We converted all data
 to the beta-value fractions ranging from 0 to 1. The values outside this range were treated as missing values (NaNs), as they are not biological.
+In each dataset, only samples that appeared in the cleaned meta table (that is, were relevant for benchmarking) were retained.
 ### Row names
 **Class**:  class of the respective sample condition. Healthy control samples (HC) are included in a separate healthy control class with the same abbreviation (HC).
+## Usage Guidelines
+<...>
 ## Additional Information