Quantum-enabled multi-omics analysis

Abstract

Single-cell and -omic analyses has provided profound insights on heterogeneity of complex tissues measuring multiple cells together, including a wide array of multi-omics data such as genomics, proteomics, transcriptomics, etc. The single cell analysis is often plagued by many uncertainties such as missingness, developing robust machine learning algorithms for discovering complex features across, finding patterns in spatial structure of single cell transcriptomics or proteomics, and most importantly integrating multi-omics data to create meaningful embeddings for the cells. Machine Learning (ML) techniques have been extensively used in analyzing, predicting, and understanding multi-omics data. For the purposes of this tutorial, we will use the term classical ML to refer to these the potential to overcome a lot of the above limitations of ML in single-cell analysis. This tutorial will be structured into five sessions as follows: In the first session we will introduce quantum computing fundamentals such as notations, operations, quantum states, entanglement, quantum gates, and circuits. In the second session, we will set up Qiskit, an open-source quantum computing toolkit based on Python and run a demo algorithm. In the third session, we will process and analyze single-cell multi-omics data from the Single Cell atlas or TCGA, etc. using classical ML algorithms to create baseline. In the fourth session, we will set up the data in Qiskit and run a QML algorithm to classify disease sub types. In the fifth and concluding session, we will summarize the tutorial and do an interactive Q&A session with the attendees.