Fix spelling and formatting issues in README #30
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Updated spelling and formatting for consistency in README.
mayur-iipl
reviewed
Dec 1, 2025
| 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. | ||
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| 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. |
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visualizations is the correct spelling.
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Both are okay. I checked up on google
z -> american english
s - british english
So both works - we can decide to keep the same across all files.
| 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. | ||
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| **The below flowchart also explains the major steps of the scaLR platform.** | ||
| **The flowchart below also explains the major steps of the scaLR platform.** |
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It was corrected earlier. @anand-infocusp What do you think?
| 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`). |
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Same as previous comment.
| ## 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. |
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Full name was looking good. Why we are making too short?
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Same question? Is this some format requirement?
| 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. | ||
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| **The below flowchart also explains the major steps of the scaLR platform.** | ||
| **The flowchart below also explains the major steps of the scaLR platform.** |
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Suggested change
| **The flowchart below also explains the major steps of the scaLR platform.** | |
| **The flowchart attached below, explains the major steps of the scaLR platform.** |
Mayur dont remember exactly, but this suggested looks fine.
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anand-infocusp
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anand-infocusp
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Few comments, else looks fine.
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Updated spelling and formatting for consistency in README.