Update README.md

README.md

@@ -27,6 +27,8 @@ configs:
 
 # ComputAge Bench Dataset
 
+=== For a more detailed User Guide, visit [our GitHub](https://github.com/ComputationalAgingLab/ComputAge). ===
+
 ## Introduction
 
 ![intro](fig1.png)
@@ -50,33 +52,42 @@ any conclusions upon it.**
 This dataset was collected by combining publicly available datasets of DNA methylation in human blood and saliva samples from the 
 [NCBI Gene Expression Omnibus](https://www.ncbi.nlm.nih.gov/geo/) (GEO) data repository according to the criteria described in [our publication](), 
 and it comprises samples from patients with aging-accelerating conditions and healthy controls. From all these public datasets, we selected only samples that had 
-annotated ages and conditions (some disease vs. healthy control).
+annotated ages and conditions (aging-accelerating condition vs. healthy control).
 
 All methylation profiling platforms in this dataset represent different generations of Illumina Infinium BeadChip human methylation arrays. 
 Detailed documentation for these platforms can be accessed at the [manufacturer’s website](https://support.illumina.com/?tab=microarrays) 
 (Methylation → Select One → Infinium Human Methylation 450K BeadChip or Infinium MethylationEPIC BeadChip).
 
-The dataset consists of two main parts: data and meta. Data is stored with parquet in the “\~/data” folder and contains methylation profiles across samples, 
-and meta contains additional information, such as subject’s age, gender, condition, etc., and is stored as “\~/computage_bench_meta.tsv”. 
-Both parts are described further below in more details.
+The dataset consists of two main parts: data and meta. Data is stored with parquet in the “\~/data” folder and contains methylation profiles across samples. 
+In it, the “\~/data/benchmark” folder contains datasets used for clock benchmarking, while matrices from the “\~/data/train” folder can be used to train novel clock models.
+Meta contains additional information, such as subject’s age, gender, condition, etc., and is stored as “\~/computage_bench_meta.tsv” and “\~/computage_train_meta.tsv” 
+for the benchmarking and training samples, respectively. 
+Both data and meta 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 [Usage Guidelines](https://huggingface.co/datasets/computage/computage_bench#usage-guidelines) section.
+In total, the benchmarking dataset comprises **10,404 samples** and **900,449 features** (DNA methylation sites) coming from 65 separate studies,
+while the training dataset consists of **7,419 samples** and **907,766 features** from 46 studies
+(some features are missing in some datasets). 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](https://huggingface.co/datasets/computage/computage_bench#usage-guidelines) section.
 
-Its main purpose is to be used in aging clock benchmarking (for more details on that, again, proceed to the 
+Its main purpose is to be used in aging clock benchmarking and training (for more details on that, again, proceed to the 
 [Usage Guidelines](https://huggingface.co/datasets/computage/computage_bench#usage-guidelines) and don’t hesitate to visit 
-[our paper]()). Nevertheless, you are free to use it for any other well-minded purpose you find suitable.
+[our pre-print](https://doi.org/10.1101/2024.06.06.597715)). Nevertheless, you are free to use it for any other well-minded purpose you find suitable.
 
 Repository structure:
 ```
 computage_bench
   |__ data # Folder with DNA methylation data
-    |__ computage_bench_data_<DatasetID_1>.parquet
-    |__ . . .
+    |__ benchmark # Benchmarking-related data with aging-accelerating conditions and healthy controls
+      |__ computage_bench_data_<DatasetID_1>.parquet
+      |__ . . .
+    |__ train # Data for clock training (healthy controls only)
+      |__ computage_train_data_<DatasetID_1>.parquet
+      |__ . . .
   |__ README.md # This file
-  |__ computage_bench_meta.tsv # Meta table with sample annotations (age, condition, etc.)
+  |__ computage_bench_meta.tsv # Meta table with sample annotations (age, condition, etc.) for the benchmarking samples
+  |__ computage_train_meta.tsv # Meta table (as above) for the training samples
 ```
 
 ## Data description
@@ -112,7 +123,7 @@ GEO sample IDs, as in data columns
 
 **PlatformID**: GEO ID of a platform that was used to obtain a respective sample. 
 Platform IDs can be mapped to the common platform names (that represent an approximate number of profiled methylation sites) as follows:
-GPL8490 = 27K, GPL13534 = 450K, GPL16304 = 450K, GPL21145 = 850K/EPIC, GPL23976 = 850K/EPIC, GPL29753 = 850K/EPIC.
+GPL8490 = 27K, GPL13534 = 450K, GPL16304 = 450K, GPL21145 = 850K/EPIC, GPL23976 = 850K/EPIC, GPL29753 = 850K/EPIC (EPICv2).
 
 **Tissue**: sample source tissue. “*Blood*” stands for peripheral blood samples, “*Saliva*” stands for saliva samples, and “*Buccal*” stands for buccal swab samples.
 
@@ -131,10 +142,12 @@ it can be either rounded by the researchers to full years, or converted from mon
 
 ## Usage Guidelines
 
+Once again, for a more detailed User Guide, visit [our GitHub](https://github.com/ComputationalAgingLab/ComputAge).
+
 ### Benchmark a new aging clock
 
 First, install our nice [library](https://github.com/ComputationalAgingLab/ComputAge) for convenient interaction with datasets and other tools built to design 
-aging clocks:
+aging clocks (creating a new Python environment in Conda beforehand is highly recommended):
 
 `pip install computage`
 
@@ -156,19 +169,20 @@ models_config = {
         'HorvathV1':{'imputation':imputation},
         'Hannum':{'imputation':imputation},
         'PhenoAgeV2':{'imputation':imputation},
-				},
-	# here we can define a name of our new model, as well as path
-    # to the pickle file (.pkl) that contains it
+        },
+	# here we can define a name of our new model, 
+    # as well as path to the pickle file (.pkl) that contains it
     'new_models':{
-        #'my_new_model_name': {'path':/path/to/model.pkl}
+        # 'my_new_model_name': {'path':/path/to/model.pkl}
         }
 }
 
 # run the benchmark
-bench = run_benchmark(models_config, 
-        experiment_prefix='my_model_test',
-        output_folder='./benchmark'
-        )
+bench = run_benchmark(
+    models_config, 
+    experiment_prefix='my_model_test',
+    output_folder='./benchmark'
+)
 
 # upon completion, the results will be saved in the folder you have specified for output
 ```
@@ -182,14 +196,17 @@ from huggingface_hub import snapshot_download
 snapshot_download(
     repo_id='computage/computage_bench', 
     repo_type="dataset",
-    local_dir='.')
+    local_dir='.'
+)
 ```
+
 Once downloaded, the dataset can be open with `pandas` (or any other `parquet` reader).
 ```python
 import pandas as pd
 
 # let's choose a study id, for example `GSE100264`
-df = pd.read_parquet('data/computage_bench_data_GSE100264.parquet').T 
+df = pd.read_parquet('data/benchmark/computage_bench_data_GSE100264.parquet').T
+
 # note that we transpose data for a more convenient perception of samples and features
 # don't forget to explore metadata (which is common for all datasets):
 meta = pd.read_csv('computage_bench_meta.tsv', sep='\t', index_col=0)
@@ -207,3 +224,15 @@ Please, cite us as
 
 ComputAgeBench: Epigenetic Aging Clocks Benchmark 
 https://doi.org/10.1101/2024.06.06.597715
+
+or as
+```
+@article{kriukov2024computagebench,
+  title={ComputAgeBench: Epigenetic Aging Clocks Benchmark},
+  author={Kriukov, Dmitrii and Efimov, Evgeniy and Kuzmina, Ekaterina A and Khrameeva, Ekaterina E and Dylov, Dmitry V},
+  journal={bioRxiv},
+  pages={2024--06},
+  year={2024},
+  publisher={Cold Spring Harbor Laboratory}
+}
+```