Fix spelling and formatting issues in README

Updated spelling and formatting for consistency in README.

Author: saurabhinfocusp

README.md

@@ -14,20 +14,20 @@
 
 1. <b>Data Processing</b>: Large datasets undergo preprocessing and [normalization](https://colab.research.google.com/github/infocusp/scaLR/blob/main/tutorials/preprocessing/normalization.ipynb) (if the user opts to) and are segmented into training, testing, and validation sets.
 
-2. <b>Features Extraction</b>: A model is trained on feature subsets in a batch-wise process, so all features and samples are utilized in the feature selection process. Then, the top-k features are selected to train the final model, using a feature score based on the model's coefficients/weights or [SHAP analayis](https://colab.research.google.com/github/infocusp/scaLR/blob/main/tutorials/analysis/shap_analysis/shap_heatmap.ipynb). 
+2. <b>Features Extraction</b>: A model is trained on feature subsets in a batch-wise process, so all features and samples are utilised in the feature selection process. Then, the top-k features are selected to train the final model, using a feature score based on the model's coefficients/weights or [SHAP analayis](https://colab.research.google.com/github/infocusp/scaLR/blob/main/tutorials/analysis/shap_analysis/shap_heatmap.ipynb). 
 
 3. <b>Training</b>: A Deep Neural Network (DNN) is trained on the training dataset. The validation dataset is used to validate the model at each epoch, and early stopping is performed if applicable. Also, a [batch correction](https://colab.research.google.com/github/infocusp/scaLR/blob/main/tutorials/preprocessing/batch_correction.ipynb) method is available to correct batch effects during training in the pipeline.
 
-4. <b>Evaluation & Downstream Analysis</b>: The trained model is evaluated using the test dataset by calculating metrics such as precision, recall, f1-score, and accuracy. Various visualizations, such as ROC curve of class annotation, feature rank plots, heatmap of top genes per class, [DGE analysis](https://colab.research.google.com/github/infocusp/scaLR/blob/main/tutorials/analysis/differential_gene_expression/dge.ipynb), and [gene recall curves](https://colab.research.google.com/github/infocusp/scaLR/blob/main/tutorials/analysis/gene_recall_curve/gene_recall_curve.ipynb), are generated.
+4. <b>Evaluation & Downstream Analysis</b>: The trained model is evaluated using the test dataset by calculating metrics such as precision, recall, f1-score, and accuracy. Various visualisations, such as ROC curve of class annotation, feature rank plots, heatmap of top genes per class, [DGE analysis](https://colab.research.google.com/github/infocusp/scaLR/blob/main/tutorials/analysis/differential_gene_expression/dge.ipynb), and [gene recall curves](https://colab.research.google.com/github/infocusp/scaLR/blob/main/tutorials/analysis/gene_recall_curve/gene_recall_curve.ipynb), are generated.
 
-**The below flowchart also explains the major steps of the scaLR platform.**
+**The flowchart below also explains the major steps of the scaLR platform.**
 
 ![image.jpg](img/Schematic-of-scPipeline.jpg)
 
 ## Pre-requisites and installation scaLR
 
 
-- ScaLR can be installed using git or pip. It is tested in Python 3.10 and it is recommended to use that environment.
+- ScaLR can be installed using git or pip. It is tested in Python 3.10, and it is recommended to use that environment.
 
 ```
 conda create -n scaLR_env python=3.10
@@ -46,7 +46,7 @@ pip install -r requirements.txt
 ```
 pip install pyscaLR
 ```
-**Note:** If the user wants to run the entire pipeline via installing pip pyscalr, they should clone/download these files(`pipeline.py` and `config.yaml`) from the git repository.
+**Note:** If the user wants to run the entire pipeline by installing pip pyscalr, they should clone/download these files(`pipeline.py` and `config.yaml`) from the git repository.
 
 ## Input data format
 - Currently the pipeline expects all datasets in [anndata](https://anndata.readthedocs.io/en/latest/tutorials/notebooks/getting-started.html) formats (`.h5ad` files only).
@@ -60,7 +60,7 @@ pip install pyscaLR
 
 1. It is necessary that the user modify the configuration file, and each stage of the pipeline is available inside the config folder [config.yml] as per your requirements. Simply omit/comment out stages of the pipeline you do not wish to run.
 2. Refer **config.yml** & **it's detailed config** [README](https://github.com/infocusp/scaLR/blob/main/config/README.md) file on how to use different parameters and files.
-3. Then use the `pipeline.py` file to run the entire pipeline according to your configurations. This file takes as argument the path to config (`-c | --config`), along with optional flags to log all parts of the pipelines (`-l | --log`) and to analyze memory usage (`-m | --memoryprofiler`).
+3. Then use the `pipeline.py` file to run the entire pipeline according to your configurations. This file takes as argument the path to config (`-c | --config`), along with optional flags to log all parts of the pipelines (`-l | --log`) and to analyse memory usage (`-m | --memoryprofiler`).
 5. `python pipeline.py --config /path/to/config.yaml -l -m` to run the scaLR.
 
 ## Example configs
@@ -315,13 +315,13 @@ Detailed tutorials have been made on how to use some pipeline functionalities as
 - **Normalization** [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/infocusp/scaLR/blob/main/tutorials/preprocessing/normalization.ipynb)
 - **Batch correction** [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/infocusp/scaLR/blob/main/tutorials/preprocessing/batch_correction.ipynb)
 
-- **An example of jupyter notebook to [run scaLR in local machine](https://github.com/infocusp/scaLR/blob/main/tutorials/pipeline/scalr_pipeline_local_run.ipynb)**.
+- **An example of Jupyter notebook to [run scaLR in local machine](https://github.com/infocusp/scaLR/blob/main/tutorials/pipeline/scalr_pipeline_local_run.ipynb)**.
 
 ## Experiment output structure
 - **pipeline.py**:
 The main script that performs an end-to-end run.
     - `exp_dir`: root experiment directory for the storage of all step outputs of the platform specified in the config.
-    - `config.yml`: copy of config file to reproduce the user-defined experiment.
+    - `config.yml`: copy of the config file to reproduce the user-defined experiment.
 
 - **data_ingestion**:
 Reads the data and splits it into Train/Validation/Test sets for the pipeline. Then, it performs sample-wise normalization on the data.
@@ -346,25 +346,25 @@ Trains a final model based on `train_datapath` and `val_datapath` in config.
         - `model`
             - `logs`: directory containing Tensorboard Logs for the training of the model.
             - `checkpoints`: directory containing model weights checkpointed at every interval specified in config.
-            - `best_model`: the best model checkpoint contains information to use model for inference/resume training.
+            - `best_model`: the best model checkpoint contains information to use the model for inference/resume training.
                 - `model_config.yaml`: config file containing model parameters.
-                - `mappings.json`: contains mapping of class_names to class_ids used by model during training.
+                - `mappings.json`: contains the mapping of class_names to class_ids used by the model during training.
                 - `model.pt`: contains model weights.
 
 - **eval_and_analysis**:
-Performs evaluation of best model trained on user-defined metrics on the test set. Also performs various downstream tasks.
+Performs evaluation of the best model trained on user-defined metrics on the test set. Also performs various downstream tasks.
    - `exp_dir`
         - `analysis`
-            - `classification_report.csv`: contains classification report showing Precision, Recall, F1, and accuracy metrics for each class on the test set.
+            - `classification_report.csv`: contains a classification report showing Precision, Recall, F1, and accuracy metrics for each class on the test set.
             - `gene_analysis`
                 - `score_matrix.csv`: score of the final model, for each feature and class. shape: n_classes X n_features.
                 - `top_features.json`: a file containing a list of selected top features/biomarkers.
             - `test_samples/full_samples`
                 -  `heatmaps`
-                    - `class_name.svg`: heatmap for top genes of a particular class w.r.t those genes association in other classes. E.g., B.svg, C.svg, etc.
+                    - `class_name.svg`: heatmap for top genes of a particular class w.r.t. those genes' association in other classes. E.g., B.svg, C.svg, etc.
                 - `roc_auc.svg`: contains ROC-AUC plot for all classes.
                 - `gene_recall_curve.svg`: contains gene recall curve plots.
-                - `gene_recall_curve_info.json`: contains reference genes list which are present in top_K ranked genes per class for each model.
+                - `gene_recall_curve_info.json`: contains the reference genes list, which are present in the top_K ranked genes per class for each model.
                 - `pseudobulk_dge_result`
                     - `pbkDGE_celltype_factor_categories_0_vs_factor_categories_1.csv`: contains Pseudobulk DGE results between selected factor categories for a celltype.
                     - `pbkDGE_celltype_factor_categories_0_vs_factor_categories_1.svg`: volcano plot of Log2Foldchange vs -log10(p-value) of genes.
@@ -374,5 +374,5 @@ Performs evaluation of best model trained on user-defined metrics on the test se
   
 ## Citation
 
-Jogani Saiyam, Anand Santosh Pol, Mayur Prajapati, Amit Samal, Kriti Bhatia, Jayendra Parmar, Urvik Patel, Falak Shah, Nisarg Vyas, and Saurabh Gupta. "scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery." bioRxiv (2024): 2024-09.
+Jogani, S., Pol, A. S., Prajapati, M., Samal, A., Bhatia, K., Parmar, J., ... & Gupta, S. (2025). scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery. Briefings in Bioinformatics, 26(3), bbaf243.