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Record W4414325889 · doi:10.1101/2025.09.15.676443

Library Size in Spatial ATAC-seq: Technical Confounder or Biology?

2025· preprint· en· W4414325889 on OpenAlexaff
Kelly X. Ji, Hongkai Ji

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsCentennial College
Fundersnot available
KeywordsConfoundingNormalization (sociology)Variation (astronomy)Spatial analysisBiological dataSpatial variability

Abstract

fetched live from OpenAlex

Summary 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.040
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.214

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.083
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.005
Scholarly communication0.0050.003
Open science0.0030.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.234
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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