deconvolution

corrdeconv corrects deconvolution's misreading of disease cell states as abundance, overturning benchmarks and outperforming every published deconvolution method

corrdeconv corrects deconvolution errors by addressing misread disease cell states as abundance, outperforming existing methods and reducing refuted effects.

TRUEDA overturns replicated, decontaminated low-biomass disease-microbe associations and clinical decisions, recovers true pathogens and outperforms every decontamination method

TRUEDA identifies true pathogens in low-biomass disease-microbe associations by accounting for contamination, outperforming existing decontamination methods and correcting false associations.

Differential sensitivity of k-mer based identification tools using closely related database sequences affects relative abundance estimation

This study evaluates how k-mer based tools affect abundance estimation in metagenomic data with closely related references, showing differential performance based on sample-reference distance and reference similarity.

DecoDiPhy: Deconvolving Phylogenetic Distance Mixtures

DecoDiPhy deconvolves phylogenetic distance mixtures to improve interpretability and downstream analysis in metagenomics.

Decoding natural scenes from patterned optogenetic responses in mouse visual cortex

This paper decodes natural scenes from optogenetic responses in mouse visual cortex and shows that patterned stimulation can reproduce visual representations with high fidelity.

Serval: A modular framework for decoding imaging based spatial transcriptomics data

Serval is a modular framework for decoding spatial transcriptomics data that introduces Cosine, a method improving transcript recovery and correlation with expression references.

Predictors Recover Most of the Metagenomic Signal for Antibiotic Resistance Gene Occurrence: A Cross-City Test of Geographic Transferability in Urban Wastewater

This study uses environmental predictors to predict antibiotic resistance gene occurrence in urban wastewater, achieving 78% of the predictive performance of metagenomic sequencing.

Benchmarking 16S rRNA gene amplicon analysis in high-diversity microbial communities reveals fundamental trade-offs in clustering and denoising

This study benchmarks 16S rRNA gene analysis pipelines in high-diversity datasets, revealing trade-offs between clustering accuracy, species recovery, and abundance reconstruction.

π-MNovo improves de novo peptide sequencing through microbial-domain adaptation and evidence-guided candidate selection

π-MNovo improves de novo peptide sequencing for metaproteomics by adapting to microbial spectra and selecting candidates based on evidence, achieving higher recall than existing models.

Incomplete references leave bulk deconvolution targets non-identifiable, but identification has a computable precision price

With an incomplete deconvolution reference, operator history changes estimates and associations, and the target is not point-identified; fitdrop scores remedies on shrinkage, coverage, certification and false certification, and prices measurements.

Effects of UniProtKB restructuring and taxonomic database restrictions on downstream Unipept peptide-centric profiling

The paper examines how changes to UniProtKB and taxonomic restrictions affect metaproteomics analysis, finding reduced peptide mapping coverage but stable dominant taxonomic profiles.

Using Mapping-Profiles to Refine Strain-Level Metagenomic Classification

StrainRefine improves strain-level metagenomic classification by refining read mappings using profile similarity, reducing false positives while maintaining accuracy.

FlashDeconv reveals resolution horizons in atlas-scale spatial transcriptomics

Leverage-weighted sketching plus sparse spatial regularization deconvolves 1.6 million Visium HD bins in 153 seconds on commodity hardware, revealing tissue-specific resolution limits where cell-type co-localization can reverse sign.

Benchmarking long-read RNA sequencing for de novo transcriptome assembly in non-model plant species: insights from Moricandia arvensis

The paper benchmarks long-read RNA sequencing pipelines for de novo transcriptome assembly in non-model plants, finding organ-specific optimal strategies for flower and leaf tissues.

Impact of single-cell RNA reference selection for the deconvolution of breast cancer spatial transcriptomics datasets

This study evaluates how reference selection affects deconvolution accuracy in breast cancer spatial transcriptomics datasets, finding that small references can be effective and that perfect reference-ST matching improves results.

Systematic evaluation of robustness to cell type mismatch of deconvolution methods for spatial transcriptomics data

This study evaluates how deconvolution methods for spatial transcriptomics data perform when cell types are missing from reference datasets, finding that performance decreases with the number of missing cell types.

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.

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.

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.

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.

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.

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.

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.

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.

When Less Is Not More: DICEPro Mitigates the Impact of Incomplete Reference Matrices on Cellular Frequency Deconvolution.

The paper evaluates deconvolution methods' robustness to incomplete reference matrices and introduces DICEPro to improve performance by adjusting reference signatures.

3dcon: tomogram denoising by deconvolution

This paper introduces 3dcon, a deconvolution algorithm for cryo-electron tomography to reduce structural artifacts in reconstructed 3D volumes.

Functional Templates in fMRI: Building Accurate and Interpretable Group-Level Decoders

This paper compares functional alignment methods and template construction strategies in fMRI to improve group-level decoding accuracy and interpretability.