Sampling artifacts in single-cell genomics cohort studies
Massoni-Badosa, R.; Iacono, G.; Moutinho, C.; Kulis, M.; Palau, N.; Marchese, D.; Rodriguez-Ubreva, J.; Ballestar, E.; Rodriguez-Esteban, G.; Marsal, S.; Aymerich, M.; Colomer, D.; Campo, E.; Jula, A.; Martin-Subero, J. I.; Heyn, H.
Show abstract
Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Functional Inference of Gene Regulation using Single-Cell Multi-Omics 94%
- Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data 94%
- Scalable Screening of Ternary-Code DNA methylation Dynamics Associated with Human Traits. 93%
Similar papers in this journal
- Uncovering the hidden structure of dynamic T cell composition in peripheral blood during cancer immunotherapy: a topic modeling approach 93%
- Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues 93%
- Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.