Single-Cell Multi-Omics Integration

"Single-cell multi-omics integration" refers to the process of integrating and analyzing multiple types of data from different molecular levels (such as genomics, transcriptomics, proteomics, etc.) at the single-cell level. This integration method allows researchers to gain a deeper understanding of the complex interactions at different molecular levels within cells and how these interactions collectively determine cellular function and state.

To achieve single-cell multi-omics integration, the following steps are usually required:

1. Data Acquisition:

Using high-throughput technologies such as single-cell sequencing (scRNA-seq), single-cell ATAC-seq (for detecting chromatin accessibility), single-cell proteomics, etc., to collect different types of data from individual cells.

2. Data Preprocessing:

Perform quality control, normalization, noise reduction, and other preprocessing steps on the collected data to prepare for analysis.

3. Data Integration:

Use statistical methods and computational models to integrate different types of data for joint analysis. This may involve data alignment, matching the same cells across different datasets, and integrating multiple data types.

4. Biological Interpretation:

Analyze the integrated data, including clustering, differential expression analysis, identifying key regulatory networks, etc., to reveal cell states, cell types, and biological processes.

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