Skip to main content

# Multi-omics Integration

2 related articles

Subscribe to updates

bioRxiv Nov 4, 2026
Younger breast cancer survival models borrow from older cohorts: LSAT-EN tunes borrowing per omics layer and reaches 0.819 four-year AUC
Younger breast cancer patients have too few samples to train reliable prognostic models, so LSAT-EN treats older patients as a source cohort and sets the strength of borrowing toward the source coefficients separately for each of six omics layers, while elastic-net regularization selects variables. In TCGA-BRCA, with patients aged 60 and older as source and those under 50 as target, four-year binary survival prediction reached an AUC of 0.819, above 0.774 for pooled fitting, 0.757 for target-only fitting, 0.728 for glmtrans and 0.780 for GAS-EN. In simulation the advantage grew as source-target differences and target-sample scarcity increased.
Younger breast cancer patients have too few samples to train reliable prognostic models, so LSAT-EN treats older patients as a source cohort and sets the strength of borrowing toward the source coefficients separately for each of six omics layers, while elastic-net regularization selects variables. In TCGA-BRCA, with patients aged 60 and older as source and those under 50 as target, four-year binary survival prediction reached an AUC of 0.819, above 0.774 for pooled fitting, 0.757 for target-only fitting, 0.728 for glmtrans and 0.780 for GAS-EN. In simulation the advantage grew as source-target differences and target-sample scarcity increased.
University of Maryland, Baltimore
Wanru Guo（First author, Corresponding author）
LSAT-EN Transfer Learning Multi-omics Integration Survival Prediction Biomarker Discovery
bioRxiv Nov 9, 2026
Paired transcriptome and 3D genome from one cell: scHiR fuses two sparse modalities into a single cell embedding
Technologies that simultaneously sequence single-cell transcriptome and 3D genome already yield paired measurements from the same cell, but the two modalities differ sharply in noise, scale and biological meaning, and were previously analyzed separately with latent spaces that need not align. scHiR maps gene expression and chromatin contact matrices into one hyperspherical latent space through modality-specific encoders and reconstructs both signals. On paired data from HiRES, GAGE-seq and LiMCA, the joint embedding matched or exceeded single-omics methods in clustering, and on mouse main olfactory epithelium it raised the Spearman correlation between pseudotime and developmental stage from 0.357 and 0.205 for single-omics embeddings to 0.517.
Technologies that simultaneously sequence single-cell transcriptome and 3D genome already yield paired measurements from the same cell, but the two modalities differ sharply in noise, scale and biological meaning, and were previously analyzed separately with latent spaces that need not align. scHiR maps gene expression and chromatin contact matrices into one hyperspherical latent space through modality-specific encoders and reconstructs both signals. On paired data from HiRES, GAGE-seq and LiMCA, the joint embedding matched or exceeded single-omics methods in clustering, and on mouse main olfactory epithelium it raised the Spearman correlation between pseudotime and developmental stage from 0.357 and 0.205 for single-omics embeddings to 0.517.
Central South University Yale University Central China Normal University Broad Institute
Yichao Zhao（First author）Min Li（Corresponding author）Ruiqing Zheng（Corresponding author）
scHiR Hyperspherical Variational Autoencoder Multi-omics Integration Single-cell 3D Genome Pseudotime Inference

You’re all caught up