scDEcrypter: Uncertainty-aware differential expression analysis for viral infection in scRNA-seq
Zhong, L.; Ensberg, K.; Tibbetts, S.; Molstad, A. J.; Bacher, R.
Show abstract
Single-cell RNA-seq studies of viral infection are limited by sparse viral reads, under-labeled infected cells, and bystander responses that confound differential expression (DE) analysis. We introduce scDEcrypter, a penalized two-way mixture model that leverages partial labels for infection status and additional variables such as cell type. Our approach employs data-splitting to avoid double-dipping and enables fast, likelihood-based inference for DE analysis. Through simulations and applications on two different viral infection datasets, scDE-crypter demonstrated improved recovery of infected cell states and identified more biologically coherent infection-associated genes and enriched pathways.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data 96%
- scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data 96%
- GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership 96%
Similar papers in this journal
Similar papers in this journal
- Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2 97%
- FastCCC: A permutation-free framework for scalable, robust, and reference-based cell-cell communication analysis in single cell transcriptomics studies 97%
- Normalisr: normalization and association testing for single-cell CRISPR screen and co-expression 96%
Similar papers in this journal
- LETSmix: a spatially informed and learning-based domain adaptation method for cell-type deconvolution in spatial transcriptomics 93%
- Diagnostic Evidence GAuge of Single cells (DEGAS): A flexible deep-transfer learning framework for prioritizing cells in relation to disease 93%
- Influence network model uncovers relations between biological processes and mutational signatures 93%
"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.