bioRxivBioinformaticsPreprint
Library Size in Spatial ATAC-seq: Technical Confounder or Biology?
Abstract
Spatially resolved assay for transposase-accessible chromatin with sequencing (spATAC-seq) is an emerging technology for studying spatial variation in gene regulatory landscapes within tissues. Current analysis pipelines commonly apply library size normalization, assuming that variation in sequencing library size across cells represents a technical confounder rather than biological signal. While recent studies have shown that library size can confound biological interpretation in spatial transcriptomics, its impact in spATAC-seq remains poorly understood. Here, we show that library size in spATAC-seq data is biologically informative and that standard normalization methods can obscure important biological signals and hinder downstream analyses. These findings underscore the need for caution and for the development of improved approaches to address library size in spATAC-seq analysis.
- assay: spatial transcriptomics
- assay: ATAC-seq
- research focus: dimensionality reduction
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: trajectory inference
- approach: statistical model
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Library size confounds biology in spatial transcriptomics data
Dharmesh Dinesh Bhuva, Chin Wee Tan, … Melissa J. Davis·Genome biologyPeer-reviewed·Apr 18, 2024
- SpaNorm: spatially-aware normalisation for spatial transcriptomics data
Agus Salim, Dharmesh Dinesh Bhuva, … Jean Yee Hwa Yang·bioRxiv·May 31, 2024
- Biological and technical spatial variation in spatial transcriptomics
Kayla C. Jackson, Gennady Gorin, Lior S. Pachter·bioRxiv·Oct 15, 2026
- Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects
Koen Van den Berge, Hsin-Jung Chou, … Sandrine Dudoit·Cell Reports MethodsPeer-reviewed·Nov 1, 2022
- Enabling automated and reproducible spatially resolved transcriptomics at scale
Linnea Stenbeck, Fanny Taborsak-Lines, Stefania Giacomello·HeliyonPeer-reviewed·Jun 1, 2022
- Transcriptional output, cell types densities and normalization in spatial transcriptomics
Manuel Saiselet, Joel Rodrigues Vitória, … Vincent Detours·Journal of Molecular Cell BiologyPeer-reviewed·Jun 15, 2020