rna-seq-expression-alignment-across-cells

rna-seq-expression-alignment-across-cells

Summary

Integrate scRNA-seq gene expression data with scATAC-seq chromatin accessibility data in ArchR by aligning cells across modalities and creating a unified reduced-dimension embedding. This skill enables joint analysis of paired multiome datasets where the same cells have been profiled for both gene expression and chromatin accessibility.

When to use

You have paired scATAC-seq and scRNA-seq data from the same cells (multiome experiment) and want to perform integrated analysis that leverages both chromatin accessibility and gene expression signals. This is appropriate when you need to correlate regulatory DNA accessibility with transcriptional output in a unified coordinate space.

When NOT to use

• Data are unpaired — scATAC-seq and scRNA-seq were generated from different cell populations or tissues.
• Cell identifiers do not match across modalities or alignment is ambiguous.
• You only have scATAC-seq data without accompanying scRNA-seq gene expression.

Inputs

• scATAC-seq peak matrix with metadata
• scRNA-seq gene expression matrix
• Aligned cell identifiers across modalities
• ArchR project object

Outputs

• ArchR project with integrated gene expression data
• Joint reduced-dimension embedding (combined dims)
• Unified single-cell coordinate space for both modalities

How to apply

Load the scATAC-seq peak matrix into an ArchR project object using importFeatureMatrix. Next, load the scRNA-seq gene expression matrix and append it to the same project using addGeneExpressionMatrix, which aligns cells across both modalities based on cell identifiers. Execute addIterativeLSI on the combined project to compute latent semantic indexing jointly on accessibility peaks and gene expression. Finally, call addCombinedDims to generate a single reduced-dimension embedding that integrates both scATAC-seq and scRNA-seq signal into a unified coordinate space for downstream integrated clustering, visualization, and interpretation.