deconvolution

Benchmarking the Intratumoral Microbiome in Pancreatic Ductal Adenocarcinoma: A Longitudinal Assessment of Contamination Sources and Decontamination Strategies

This study benchmarks decontamination methods for intratumoral microbiome analysis in PDAC, finding the Nj pipeline outperforms others in reducing contamination while preserving tumor signals.

HIDE-Deconv: A hierarchical deconvolution framework for multiscale characterization of cellular remodeling

HIDE-Deconv improves deconvolution by analyzing cellular composition across multiple resolution levels, revealing hidden cellular remodeling in diseases.

CCIDeconv: Hierarchical model for deconvolution of subcellular cell-cell interactions in single-cell data

The paper introduces CCIDeconv, a method to deconvolve whole-cell cell-cell interactions into subcellular compartments using scRNA-seq data and spatial transcriptomics datasets.

Extracellular matrix regulates lineage plasticity in prostate cancer through YAP/TEAD

Loss of extracellular matrix-integrin signaling switches off YAP/TEAD and lets prostate adenocarcinoma organoids turn neuroendocrine; LATS inhibition reverses it, and combined YAP, NOTCH and AR inhibition fully reprograms them.

bulk2scDiff: A Pseudobulk-Conditioned Diffusion Model for Bulk-to-Single-Cell RNASeq Generation

bulk2scDiff generates single-cell RNA-seq data from bulk profiles using a diffusion model, showing good reconstruction of cell populations in cancer datasets.

From variability to consensus: rescoring harmonizes peptide identification across diverse search engines and datasets

This study benchmarks rescoring methods across proteomics search engines and datasets, finding that prediction-based approaches improve identification consensus and reduce variability.

Minimally invasive monitoring of clonal evolution through integrated single cell and ctDNA analysis

cfClone, a Bayesian model, deconvolves cfDNA whole-genome sequencing using scWGS-derived haplotype-specific clone copy-number profiles to estimate tumour fraction, clonal prevalence, and flag missed clones.

Benchmarking computational decontamination of ambient RNA

This study benchmarks ambient RNA decontamination methods using simulated and experimental datasets, finding that no single method is optimal across all scenarios, with CellBender, DecontX, and SoupX performing well.

Benchmarking cell-type deconvolution in cross-platform transcriptomic data

This study benchmarks cell-type deconvolution methods across cross-platform transcriptomic data, finding that log-normal regression methods like SpatialDecon perform most reliably.

Qombucha: Reconstructing unobserved progenitor methylation profiles reveals distinct developmental programs in glioblastoma

Qombucha infers unobserved progenitor methylation profiles in GBM, revealing subtype-specific developmental programs and improved subtype classification.

A robust approach for preserving and sectioning fragile 3D spheroids for high-quality histological analysis

This paper introduces a method to preserve and section 3D spheroids for high-quality histological analysis, reducing sample loss and maintaining structural integrity.

Verification of nanopore sequencing technology for clinical carbapenem-resistant Enterobacterales surveillance

This study verifies nanopore sequencing for CRE surveillance by comparing it to routine diagnostics and determining required sequencing depths for various clinical tasks.

Accurate de novo transcription unit annotation from run-on and sequencing data

The paper develops CGAP-HMM and a voting system to improve transcription unit annotation from run-on and sequencing data, showing significant performance gains over existing methods.

High Resolution Ancestry Deconvolution for Next Generation Genomic Data

This paper introduces Gnomix, a high-resolution ancestry deconvolution method for next-generation genomic data, achieving higher accuracy and faster performance than existing methods.

RETROFIT: REFERENCE-FREE DECONVOLUTION OF CELL-TYPE MIXTURES IN SPATIAL TRANSCRIPTOMICS

RETROFIT deconvolves cell-type mixtures in spatial transcriptomics without requiring references, outperforming existing methods and matching reference-based approaches when references are imperfect.

cellGeometry: ultra-fast single-cell deconvolution of bulk RNA-Seq using a geometric solution

cellGeometry deconvolutes bulk RNA-Seq using geometric methods, offering faster and more accurate results than existing approaches.

HiC2Self: self-supervised denoising for bulk and single-cell Hi-C contact maps

HiC2Self denoises Hi-C contact maps using self-supervised learning, reconstructing 3D chromatin structures from low-coverage data across bulk and single-cell samples.

IOBRpy enables agentic multi-omics decoding of anti-tumor immunity

Python toolkit chains RNA-seq quantification, six-method tumour microenvironment (TME) profiling, SpecHLA typing and TRUST4 repertoire analysis with AI agents; TME outputs match R implementations (Spearman 0.986 to ~1.0).

Contamination of widely used databases compromises rRNA based taxonomic assignment of metagenomes and metatranscriptomes

The paper shows that contamination of rRNA databases with LSU sequences leads to incorrect taxonomic assignments in metagenomes and metatranscriptomes, and provides a cleaned database and tool to address this issue.

Extended t-cores for the de novo identification of transposable elements and other inexact repeats from short read RNAseq data

This paper introduces a de novo method to identify transposable elements and inexact repeats from RNA-seq data without a reference genome, showing effectiveness in both model and non-model species.

High-resolution reconstruction of cell-type specific transcriptional regulatory processes from bulk sequencing samples

This paper introduces DeepDETAILS, a method to reconstruct cell-type-specific genomic signals from bulk data, outperforming traditional methods and providing insights into disease mechanisms.

InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics

InstaNovo-P improves phosphorylated peptide detection and site localization in phosphoproteomics by using a transformer-based model fine-tuned on phosphoproteomics data.

Rectangle: robust and scalable multiscale deconvolution informed by single-cell RNA sequencing data

Rectangle provides a scalable deconvolution framework for bulk RNA-seq using single-cell data, achieving high accuracy and robustness across diverse benchmarks.

BLASE: Bulk Linkage Analysis for Single Cell Experiments - Teasing Out the Secrets of Bulk Transcriptomics with Trajectory Analysis

BLASE uses scRNA-seq references to analyze bulk RNA-seq data, identifying trajectory progress and correcting developmental differences in differential expression analysis.

Deep Learning Reveals Cross-Modal Neural Representations of Auditory and Visual Mental Imagery in MEG

This study uses MEG and machine learning to decode auditory and visual mental imagery, showing deep learning models can capture cross-modal neural representations even from non-task-relevant regions.

Cross-architecture ensembling of DNA foundation models improves the precision and stability of chimera detection in long-read metagenomic bins

The paper evaluates DNA foundation models for chimera detection in long-read metagenomic bins, finding that ensembling improves precision over single models.

ProteoDUDes: Taxonomic profiling for metaproteomics with false positive reduction

ProteoDUDes improves taxonomic profiling in metaproteomics by reducing false positives, achieving higher accuracy in identifying active organisms in complex samples.

Epigenetic Liquid Biopsy Enables Universal Mutation-Agnostic Molecular Surveillance for High-Risk Neuroblastoma

This paper develops a mutation-independent liquid biopsy method using DNA methylation to monitor high-risk neuroblastoma through blood plasma-derived cfDNA.

Neural Mechanisms of Mixed Speech and Grasp Representation in Sensorimotor Cortices

This study decodes speech and grasp from neural activity in sensorimotor cortices of tetraplegic participants, showing overlapping neural populations and reconfigured connectivity between tasks.

CoLa-VAE: A Cell-Cell Communication-Aware Variational Autoencoder for Representation Learning and Expression Denoising

CoLa-VAE improves denoising and representation learning in single-cell data by integrating cell-cell communication information, enhancing downstream analyses like differential communication detection and spatial deconvolution.