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This code demonstrates the use of machine learning to model the multimodal nature of a single cell. Using machine learning to predict RNA from DNA, that is, using chromatin accessibility data to predict the RNA gene expression and to predict surface protein from RNA, that is, using RNA sequence data to predict surface protein levels in a cell

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Predictive Modelling of Multimodal Single Cell Genomic Data with Machine Learning Algorithms

  • Following code is for analyzing the Multimodal nature of a Single Cell
  • Data provided by https://openproblems.bio/
  • Compress the Multiome data to sparse matrix first and unzip all the data in a folder named "Data" before running rest of the files
  • Libraries to install : numpy, pandas, matplotlib, seaborn, scikit-learn, keras, XGBoost, LightGBM, CatBoost, tables

Multiome Problem Pipeline

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Citeseq Problem Pipeline

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This code demonstrates the use of machine learning to model the multimodal nature of a single cell. Using machine learning to predict RNA from DNA, that is, using chromatin accessibility data to predict the RNA gene expression and to predict surface protein from RNA, that is, using RNA sequence data to predict surface protein levels in a cell

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numpy scikit-learn pandas seaborn xgboost scipy gpu-acceleration matplotlib sparse-matrix keras-tensorflow mlp-regressor catboost ensemble-machine-learning lightgbm-regressor

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