deconvolution

Accurate estimation of cell-type resolution transcriptome in bulk tissue through matrix completion

ENIGMA deconvolutes bulk RNA-seq into single-cell-type resolution using matrix completion, validated on simulated and disease-related datasets.

A Comprehensive Overview of the Physical Health of the Adolescent Brain Cognitive Development Study (ABCD) Cohort at Baseline

This paper provides descriptive analyses of physical health measures in the ABCD cohort at baseline, highlighting variations across demographic groups and linking them to health disparities.

Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples

This study evaluates ten deconvolution tools on 4,000 clinical and synthetic samples, finding EPIC performs best on Framingham data and HPCA references are generally accurate.

BayesDeBulk: A Flexible Bayesian Algorithm for the Deconvolution of Bulk Tumor Data

BayesDeBulk estimates immune and stromal cell composition from bulk proteomic and transcriptomic data, outperforming existing tools in synthetic and real data examples.

AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution

AutoGeneS automatically selects genes for RNA-seq deconvolution by optimizing multiple criteria, improving accuracy in identifying cell-type proportions from bulk RNA samples.

Determining subpopulation methylation profiles from bisulfite sequencing data of heterogeneous samples using DXM

DXM deconvolves methylation profiles of subpopulations from heterogeneous bisulfite sequencing data, showing improved performance over existing methods and linking methylation to relapse in DLBCLs.

Noise Reconstruction & Removal Network: A New Architecture to Denoise FIB-SEM Images

The paper introduces an unsupervised Noise Reconstruction and Removal Network for denoising FIB-SEM images, achieving results comparable to or better than supervised methods.

Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams

This paper introduces a deep learning deconvolution method for light-sheet microscopy using propagation-invariant beams, trained with physics-based images and unpaired data, achieving improved image contrast without requiring extensive paired training data.

CDSeqR: fast complete deconvolution for gene expression data from bulk tissues

CDSeqR improves computational efficiency of CDSeq for bulk RNA-seq deconvolution and adds a function for cell type annotation using scRNA-seq data.

SpatialDWLS: accurate deconvolution of spatial transcriptomic data

SpatialDWLS improves accuracy and speed in deconvolving spatial transcriptomic data, revealing spatial-temporal cell-type changes in human heart development.

Mining endophytic microbiome information from plant and animal transcriptome data

The paper develops a method to extract endophytic microbiome information from plant and animal transcriptome data, providing microbial composition alongside host gene expression.

Squeegee: de-novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes

Squeegee identifies microbial contaminants in low biomass microbiomes using de novo analysis, showing high precision in simulated and real datasets.

ToRQuEMaDA: Tool for Retrieving Queried Eubacteria, Metadata and Dereplicating Assemblies

TQMD is a tool for dereplicating prokaryotic genomes using k-mer based methods, optimized for high taxonomic levels and complementary to existing tools.

Generation of lineage-resolved complete metagenome-assembled genomes by precision phasing

The paper generates lineage-resolved complete MAGs using HiFi reads from sheep fecal samples, achieving high-resolution deconvolution of microbial communities.

Accurate de novo identification of biosynthetic gene clusters with GECCO

A linear conditional random field over ordered protein-domain annotations finds biosynthetic gene clusters de novo more precisely than DeepBGC and runs over three times faster on one CPU.

Entropy Regularized Deconvolution of Cellular Cryo-Transmission Electron Tomograms

The paper applies entropy regularized deconvolution to cryo-ET data to improve resolution and reduce noise, assessing results via Fourier analysis and subtomogram analysis.

Long Reads Capture Simultaneous Enhancer-Promoter Methylation Status for Cell-type Deconvolution

The paper develops a method to deconvolve cell-type mixtures using enhancer-promoter methylation profiles from long-read data, achieving higher accuracy than promoter-based approaches.

Nonlinear decoding of natural images from large-scale primate retinal ganglion recordings

This paper develops a nonlinear decoding method to infer natural images from primate retinal ganglion cell activity, showing improved accuracy over linear methods for high-pass features.

A Paradigm Free Regularization Approach to Recover Brain Activation from Functional MRI Data

This paper introduces PF-fMRI, a paradigm-free method to recover brain activation from fMRI data without prior knowledge of the experimental paradigm, showing higher accuracy and faster computation than existing methods.

A comparative study of deconvolution methods for RNA-seq data under a dynamic testing landscape

This study benchmarks 11 deconvolution methods on RNA-seq data across 1,766 conditions to identify challenges and guide future tool development.

A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

CellR imputes cell type-specific expression profiles and cellular compositions from bulk RNA-Seq data in brain disorders, showing improved accuracy and stability compared to existing methods.

GEM-DeCan: Improved tumor immune microenvironment profiling through novel gene expression and DNA methylation signatures predicts immunotherapy response

The paper introduces new gene expression and DNA methylation signatures for tumor immune microenvironment profiling, showing improved performance in predicting immunotherapy response.

Peptide location fingerprinting reveals modification-associated biomarkers of ageing in human tissue proteomes

The paper identifies age-related biomarkers in human skin and tendon proteomes using peptide location fingerprinting and a new webtool.

High-resolution single-molecule long-fragment rRNA gene amplicon sequencing for uncultured bacterial and fungal communities

This study develops a method to sequence nearly full-length rRNA genes from environmental samples, achieving high accuracy and coverage in microbial community analysis.

Improving Deconvolution Methods in Biology through Open Innovation Competitions: An Application to the Connectivity Map

This study evaluates deconvolution methods for gene expression data using an open innovation competition and finds that random forest regression outperforms other approaches in accuracy and reproducibility.

Normalization and De-noising of Single-cell Hi-C Data with BandNorm and 3DVI

This paper introduces BandNorm and 3DVI to normalize and de-noise scHi-C data, improving performance in cell type separation and 3D genomic structure recovery.

Ribo-ODDR: Ribo-seq focused Oligo Design pipeline for experiment-specific Depletion of Ribosomal RNAs

Ribo-ODDR improves rRNA depletion in Ribo-seq by designing experiment-specific oligos, increasing sequencing depth and information yield.

Choice of assemblers has a critical impact on de novo assembly of SARS-CoV-2 genome and characterizing variants

This study evaluates the impact of different assemblers on SARS-CoV-2 genome assembly quality and variant detection, showing significant differences in assembly outcomes and variant characterization.

BARcode DEmixing through Non-negative Spatial Regression (BarDensr)

BarDensr demixes spatial transcriptomics data by modeling the imaging process and using convex optimization, achieving state-of-the-art signal recovery in dense or low-resolution data.

The Role of Scale in the Estimation of Cell-type Proportions

This paper introduces a hybrid scale approach for cell-type deconvolution that improves biological plausibility and statistical efficiency compared to log or linear scales.