sciCNV

sciCNV infers DNA copy number variations (CNVs) from single-cell RNA sequencing (scRNA-seq) data and pairs CNV calls with transcriptome profiles to analyze the effects of CNVs on cellular programs.

Key Features:

• RTAM Normalization Methods: RTAM1 and RTAM2 normalize scRNA-seq to improve gene expression alignment across cells and facilitate CNV detection.
• High-throughput paired profiling: Provides paired profiling of transcriptomes and DNA copy number variations at single-cell resolution.
• sciCNV Pipeline: Infers CNVs at single-cell resolution from RTAM-normalized scRNA-seq and supports joint analysis of transcriptomic and CNV landscapes.
• Application in cancer research: Applied to multiple myeloma (MM) to examine CNVs such as +8q22-24 and to confirm upregulation of MYC and related genes.

Scientific Applications:

• CNV–transcriptome integration: Enables deconstruction of interactions between CNVs and cellular programs within single samples.
• Multiple myeloma analysis: Used to analyze MM-specific CNVs (e.g., +8q22-24) and associated transcriptional changes such as MYC upregulation.

Methodology:

Normalizes scRNA-seq with RTAM1 and RTAM2, then infers single-cell CNVs from RTAM-normalized data using the sciCNV pipeline.