deconvolution

ARCADE disentangles cell abundance from continuous transcriptional state in spatial transcriptomics

ARCADE improves spatial transcriptomic analysis by separating cell abundance from transcriptional state, enhancing deconvolution accuracy and interpretability in complex tissues.

Multi-omics analysis reveals chromatin and transcriptomic remodeling in hippocampal CA1 following adolescent social isolation

Two weeks of post-weaning isolation in male mice altered expression of 106 CA1 genes, enriched for myelination, while chromatin accessibility shifted only at nominal significance and tracked expression weakly.

OTTER: Orchestrated Transcriptomic, Tumor-xenograft (PDX/CDX), and Epigenomic Reporting Workflow

OTTER improves bisulfite deconvolution in tumor xenograft sequencing by restoring recall to 52%-54% while preserving transcriptomic accuracy.

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.

Clean Breaks: Attention Force Field Deconvolution of Recombination Across Sequence Manifolds

RhizAeon deconvolves reticulate evolution by analyzing sequence mosaics and detecting recombination breakpoints with high accuracy and efficiency.

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.

GOLDEN: Gene expression and allele-informed decomposition for genetic heterogeneity in spatial transcriptomic data

GOLDEN deconvolves genetic heterogeneity in spatial transcriptomic data by integrating gene expression and allele information, achieving high accuracy in fraction estimation and classification.

ClonoDeconv: reference-based α - β paired recurrent clonotype proportion estimation from bulk-TCR sequencing

ClonoDeconv estimates paired TCR clonotype proportions from bulk-TCR sequencing using a single-cell reference, capturing longitudinal T-cell clonal dynamics.

DecoDiPhy: Deconvolving Phylogenetic Distance Mixtures

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

Deciphering Spatial Cellular Organization through RNA-Anchored Multi-Omics Deconvolution

SMODER deconvolves spatial multi-omics data by prioritizing RNA for cell-type identification and integrating other modalities, improving accuracy and spatial coherence.

DECONVersation: Single Cell Foundation Model-Derived Embeddings for Robust Cell Type Deconvolution of Bulk RNA-seq

DECONVersation uses single-cell foundation model embeddings to improve bulk RNA-seq deconvolution by reducing batch effects and marker gene sensitivity.

ST-DISTAL: Dual-Branch Graph Convolution with Distributional Alignment for Cell-Type Deconvolution

ST-DISTAL improves cell-type deconvolution in spatial transcriptomics by integrating spatial and molecular information through a dual-branch graph convolutional framework.

DECIPHER integrates disentangled representation learning and prototype-based cell-type deconvolution across molecular modalities

DECIPHER improves cell-type deconvolution across molecular modalities by combining disentangled representation learning with prototype-based methods, showing robust performance in simulated and real datasets.

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.

Layer-to-cell-class correspondence is gene-specific for pyroptosis-related transcription in human skin

Epidermal NLRP1, PYCARD and CASP4 versus dermal GSDMD polarity in systemic sclerosis skin is reproduced by same-donor cell-class pseudobulk for PYCARD and GSDMD, but rarely NLRP1.

decemedip: hierarchical Bayesian modeling for cell type deconvolution of immunoprecipitation-based DNA methylomes

The paper introduces decemedip, a Bayesian hierarchical model for cell type deconvolution of MeDIP-seq data, showing its accuracy and utility in identifying methylation signatures.

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.

Accurate and scalable decontamination of imaging-based spatial transcriptomics via optimal transport

CellDot improves spatial transcriptomics by accurately decontaminating transcript assignments using optimal transport, enhancing biological interpretation and spatial pattern recovery.

CellMAGE: cell-type deconvolution for multi-parent population analysis of gene expression

CellMAGE deconvolves gene expression in multi-parent populations by leveraging known parental genetic contributions, outperforming existing methods in accuracy and efficiency.

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.