Inferring tumor absolute copy number and clonal substructure from single-cell chromatin accessibility

This dataset is part of our project focusing on inferring absolute copy numbers and quantifying subclonal structures using single-cell ATAC-seq data. We employed two datasets. These datasets originate from Qilu Hospital of Shandong University, with proper authorization obtained for data collection and usage.

• Dataset 1: Two ccRCC (clear cell renal cell carcinoma) samples analyzed using single-cell ATAC sequencing(ccRCC1 and ccRCC2).
• Dataset 2: Two ccRCC samples analyzed using single-cell multiomics sequencing (ccRCC3 and ccRCC4).

All four samples were derived from frozen tissues, dissociated, and sequenced following 10x Genomics protocols. The datasets provided here are pre-processed and filtered, containing:

• 6,281 cells for ccRCC dataset from scATAC-seq,
• 8,432 cells for ccRCC dataset from scMultiomic-seq

In addition, we also provide paired WGS data of ccRCC1, ccRCC3 and ccRCC4 to evaluate the consistency of our model with the copy number at the DNA-seq level.

This resource provides valuable insights for inferring absolute copy numbers and subclonal heterogeneity.