anndata is a Python package for handling annotated data matrices in memory and on disk, positioned between pandas and xarray. anndata offers a broad range of computationally efficient features including, among others, sparse data support, lazy operations, and a PyTorch interface.
ScRNA can be analyzed using machine learning to improve the performance of quantitative scRNA in future cancer treatment., it can discover new insights about your data and find hidden patterns that you didn't see before. it can identify outliers, detect anomalies, and uncover connections between various components of your data.
On this notebook : Single_cell_RNA_seq (t).ipynb
This repository contains a Scikit-Learn based machine learning project that utilizes the QLattice Feyn Model to improve the performance of quantitative scRNA analysis for cancer treatment. The project aims to discover new insights and hidden patterns in the data that were not visible before, identify outliers, detect anomalies, and uncover connections between different components of the data.
The project includes both classification and regression models that are designed to analyze single-cell RNA sequencing data. These models are implemented using the Scikit-Learn library, which is a popular open-source machine learning library in Python.
The QLattice Feyn Model, an innovative machine learning tool, is used to improve the performance of the models. It is designed to automatically discover the underlying structure and relationships in the data, and create accurate and interpretable models.
The project's code is organized into three main parts:
Data preprocessing: In this part, the data is prepared for analysis by cleaning and normalizing it, as well as preparing it for use in the Scikit-Learn models.
Model training: In this part, the Scikit-Learn models are trained on the preprocessed data, and the QLattice Feyn Model is used to improve the performance of the models.
Results analysis: In this part, the performance of the models is evaluated using various metrics and visualizations. The results are analyzed to gain insights into the data and identify potential areas for improvement.
To run this project, you will need to have the following software installed:
Python 3.x Scikit-Learn QLattice
To get started, clone the repository and follow the instructions in the README.md file. You can also use the Jupyter notebook included in the repository to run the code step by step and analyze the results.
If you have any questions or feedback, please don't hesitate to get in touch. We hope that this project will be useful for those interested in using machine learning for ScRNA analysis in cancer treatment.
ON this notebook : feyn.ipynb
fast automatic report analysis it's an awesome tool to have a great idea about the data and the correlation and work more on those in different tools
Single-test.ipynb can see the report on : report.html