vmrseq: Probabilistic Modeling of Single-cell Methylation Heterogeneity
Shen, N.; Korthauer, K.
Show abstract
Single-cell DNA methylation measurements reveal genome-scale inter-cellular epigenetic heterogeneity, but extreme sparsity and noise challenges rigorous analysis. Previous methods to detect variably methylated regions (VMRs) have relied on predefined regions or sliding windows, and report regions insensitive to heterogeneity level present in input. We present vmrseq, a statistical method that overcomes these challenges to detect VMRs with increased accuracy in synthetic benchmarks and improved feature selection in case studies. vmrseq also highlights context-dependent correlations between methylation and gene expression, supporting previous findings and facilitating novel hypotheses on epigenetic regulation. vmrseq is available at https://github.com/nshen7/vmrseq.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software 95%
- Improved Quality Metrics for Association and Reproducibility in Chromatin Accessibility Data Using Mutual Information 95%
- Identification and Utilization of Copy Number Information for Correcting Hi-C Contact Map of Cancer Cell Line 95%
Similar papers in this journal
Similar papers in this journal
- Epitome: Predicting epigenetic events in novel cell types with multi-cell deep ensemble learning 96%
- Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity 95%
- Determining subpopulation methylation profiles from bisulfite sequencing data of heterogeneous samples using DXM 94%
"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.