Tensor decomposition discriminates tissues using scATAC-seq

• Published in: Biochimica et biophysica acta. General subjects Vol. 1867; no. 6; p. 130360
• Main Authors: Taguchi, Y.-H., Turki, Turki
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2023
• Subjects: Chromatin; chromosomes; data collection; Gene Expression Regulation; Genome; genomics; landscapes; Large sparse matrices; scATAC-seq; Single-cell applications; Tensor decomposition; transcription factors; Transcription Factors - metabolism; UMAP; Tensor decomposition; UMAP; scATAC-seq; Large sparse matrices; Single-cell applications
• ISSN: 0304-4165; 1872-8006; 1872-8006
• DOI: 10.1016/j.bbagen.2023.130360

•scATAC-seq was processed to reveal tissue specificity.•Tensor decomposition is used.•Tensor decomposition provided the low dimensional embedding used for the input to UMAP. ATAC-seq is a powerful tool for measuring the landscape structure of a chromosome. scATAC-seq is a recently updated version of ATAC-seq performed in a single cell. The problem with scATAC-seq is data sparsity and most of the genomic sites are inaccessible. Here, tensor decomposition (TD) was used to fill in missing values. In this study, TD was applied to massive scATAC-seq datasets generated by approximately 200 bp intervals, and this number can reach 13,627,618. Currently, no other methods can deal with large sparse matrices. The proposed method could not only provide UMAP embedding that coincides with tissue specificity, but also select genes associated with various biological enrichment terms and transcription factor targeting. This suggests that TD is a useful tool to process a large sparse matrix generated from scATAC-seq.