Update README.md

README.md

@@ -129,40 +129,48 @@ it can be either rounded by the researchers to full years, or converted from mon
 
 ### Benchmark a new aging clock
 
-First install our nice [library](https://github.com/ComputationalAgingLab/ComputAge) for convenient operation with datasets and other tools for aging clock design:
+First, install our nice [library](https://github.com/ComputationalAgingLab/ComputAge) for convenient interaction with datasets and other tools built to design 
+aging clocks:
 
 `pip install computage`
 
-Now, suppose you trained a brand-new epigenetic aging clock model using classic `scikit-learn` library. You saved your model as `pickle` file. Then, the following block of code can be used for benchmarking your model. We also added several other published aging clocks for comparison with yours.
+Now, suppose you have trained your brand-new epigenetic aging clock model using the classic `scikit-learn` library. You saved your model as `pickle` file. 
+Then, the following block of code can be used to benchmark your model. We also added several published aging clock models for comparison.
+
 ```python
 from computage import run_benchmark
 
-#first define NaN imputation method for in_library models
-#for simlicity here we recommend to use imputation with zeros
-imputation = 'none'
+# first, define a method to impute NaNs for the in_library models
+# we recommend using imputation with gold standard values from the [SeSAMe package](https://github.com/zwdzwd/sesame)
+imputation = 'sesame_450k'
+
+# for example, take these three clock models for benchmarking
 models_config = {
-    "in_library":{
+    'in_library':{
         'HorvathV1':{'imputation':imputation},
         'Hannum':{'imputation':imputation},
         'PhenoAgeV2':{'imputation':imputation},
 				},
-	#here we should define a name of our new model as well as path
-    #to the pickle file (.pkl) of the model
-    "new_models":{
+	# 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}
         }
 }
-#now run the benchmark
+
+# run the 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 specified
+
+# upon completion, the results will be saved in the folder you have specified for output
 ```
 
 ### Explore the dataset
 
-In case you want just to explore our dataset locally, use the following commands for downloading.
+In case you want just to explore our dataset locally, use the following commands for downloading:
+
 ```python
 from huggingface_hub import snapshot_download
 snapshot_download(
@@ -170,13 +178,16 @@ snapshot_download(
     repo_type="dataset",
     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`
+
+# let's choose a study id, for example `GSE100264`
 df = pd.read_parquet('data/computage_bench_data_GSE100264.parquet').T 
-#note we transpose data for more convenient perception
-#Don't forget to explore metadata (which is common for all datasets):
+# 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)
 ```