How do you approach data integration from different single-cell omics modalities for comprehensive analysis?

Sample interview questions: How do you approach data integration from different single-cell omics modalities for comprehensive analysis?

Sample answer:

• Data Preprocessing and Quality Control:
  • Begin by ensuring data quality by performing basic preprocessing steps like removing outliers, normalizing data, and checking for batch effects.
  • Utilize specialized software like Seurat or Scanpy for single-cell RNA-sequencing (scRNA-seq) data preprocessing.
  • For ATAC-seq data, employ tools such as ArchR or scATAC-seq for preprocessing and quality control.
• Data Integration Strategies:
  • Concatenation: Combine datasets with similar cell types or experimental conditions by merging them into a single matrix.
  • Factor Analysis: Use techniques like principal component analysis (PCA) or singular value decomposition (SVD) to identify shared sources of variation across modalities.
  • Joint Dimensionality Reduction: Apply methods like canonical correlation analysis (CCA) or mutual information-based approaches to find common representations across modalities.
  • Network-Based Integration: Construct networks based on cellular interactions, gene co-expression, or other biological relationships and integrate data by propagating information across these networks.
• Multimodal Clustering and Annotation:
  • Perform clustering analysis using combined modalities to identify cell types or subclusters that share similar characteristics across modalities.
  • Utilize tools like SingleCellNet or scRNA-seq + ATAC-seq Integration Pipeline for multimodal clustering and annotation.
• Multimodal Trajectory Analysis:
  • Construct pseudoti… Read full answer

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