deconvolution

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

The paper evaluates deconvolution methods on human brain data, finding dtangle and bMIND perform best, and identifies decon-eQTLs that explain more schizophrenia heritability than bulk or single-cell eQTLs.

Low-degree decompositions of flux vectors in faces of the flux cone

The paper introduces a method to decompose flux vectors into elementary flux modes using geometric insights, focusing on low-degree decompositions in genome-scale metabolic networks.

Generating Synthetic Single Cell Data from Bulk RNA-seq Using a Pretrained Variational Autoencoder

This study develops a variational autoencoder to generate synthetic scRNA-seq data from bulk RNA-seq, enabling single-cell insights from large-scale bulk datasets.

π-PrimeNovo: An Accurate and Efficient Non-Autoregressive Deep Learning Model for De Novo Peptide Sequencing

π-PrimeNovo is a non-autoregressive deep learning model for de novo peptide sequencing that achieves higher accuracy and 69x faster performance than existing methods.

Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data

This paper introduces CellSNAP, a self-supervised method that integrates spatial, transcriptomic, and proteomic data to improve cell population delineation in spatial-omics datasets.

MAST-Decon: Smooth Cell-type Deconvolution Method for Spatial Transcriptomics Data

MAST-Decon improves spatial transcriptomics deconvolution by incorporating spatial neighborhood and histological image features to enhance accuracy and reliability.

Assessing Immune Microenvironment in TCGA-LUAD via CIBERSORTx Using Single-Cell Derived Signature Matrix and ESTIMATE Algorithm

This study validates a single-cell derived immune signature matrix using CIBERSORTx and ESTIMATE to assess immune microenvironment in TCGA-LUAD samples.

Placental and Immune Cell DNA Methylation Reference Panel for Bulk Tissue Cell Composition

The paper develops a DNA methylation reference panel for placental tissues to estimate cell composition from bulk DNAm data, showing improved performance over existing methods.

MUSTANG: MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance

MUSTANG performs multi-sample spatial transcriptomics analysis by integrating cross-sample similarity and spatial correlation to improve cellular deconvolution.

Unsupervised deep learning enables blur-free super-resolution in two-photon microscopy

This paper introduces an unsupervised deep learning method for deblurring, super-resolution, and segmentation of two-photon microscopy images, achieving better performance than conventional methods.

imply: improving cell-type deconvolution accuracy using personalized reference profiles

imply improves cell-type deconvolution by using personalized reference panels, showing reduced bias and better alignment with biological facts in real data.

Multimodal joint deconvolution and integrative signature selection in proteomics

The paper introduces a method for deconvolving bulk proteomes using transcriptome-proteome integration and applies it to identify cell-type-specific protein quantitative trait loci.

Integration of taxonomic signals from MAGs and contigs improves read annotation and taxonomic profiling of metagenomes

RAT improves metagenomic read annotation and taxonomic profiling by integrating MAGs and contigs, achieving high precision and sensitivity in diverse samples.

SPADE: Spatial Deconvolution for Domain Specific Cell-type Estimation

SPADE is a computational method that estimates cell-type proportions in spatial transcriptomics data, showing improved performance over existing methods in synthetic and real-world datasets.

Adaptive Digital Tissue Deconvolution

ADTD improves estimation of cellular compositions from bulk transcriptomics data by adapting reference profiles to the molecular environment and identifying hidden contributions.

Accurate estimation of rare cell type fractions fromtissue omics data via hierarchical deconvolution

HiDecon estimates rare cell type fractions in bulk transcriptomics data using hierarchical deconvolution with single-cell references and cell type trees.

Dynamic land-plant carbon sources in marine sediments inferred from ancient DNA

This study uses ancient DNA from marine sediments to infer dynamic land-plant carbon sources during the Last Glacial-Holocene transition, revealing vegetation dynamics and translocation patterns.

A Benchmark of state-of-the-art Deconvolution Methods in Spatial Transcriptomics: Insights from Cardiovascular Disease and Chronic Kidney Disease

This study benchmarks three deconvolution methods in spatial transcriptomics for cardiovascular and kidney diseases, finding comparable performance with method-specific trade-offs.

Systematic Evaluation of Cell Type Deconvolution Methods for Plasma Cell-free DNA

This study benchmarks five methylation-based methods for cell type deconvolution of plasma cell-free DNA using deep whole-genome bisulfite sequencing data from 35 human cell types.

SpatialData: an open and universal data framework for spatial omics

SpatialData, a Zarr/OME-NGFF-based format plus Python library, unifies images, labels, points, shapes and tables across spatial omics platforms with coordinate alignment, lazy loading and cross-modal aggregation.

The immune map of human body

The paper constructs a high-resolution immune map using single-cell deconvolution of RNA-seq samples from 30 tissues, revealing immune cell distributions across different sexes and ages.

Decoding the Baltic Sea's Past and Present: A simple Molecular Index for Ecosystem Assessment

This paper develops a DNA-based index using ancient sediment DNA to assess past and present phytoplankton dominance in the Baltic Sea, revealing historical shifts in species composition.

Pathway-enhanced Transformer-based robust model for quantifying cell types of origin of cell-free transcriptome

Deconformer improves cell type deconvolution of cell-free RNA by integrating biological pathways, showing better performance and interpretability across multiple datasets.

Benchmarking transcriptional deconvolution methods for estimating tissue- and cell type-specific extracellular vesicle abundances

The paper benchmarks deconvolution methods to estimate tissue- and cell type-specific extracellular vesicle abundances in biological fluids and identifies accurate methods.

InSpectro-Gadget: A tool for estimating neurotransmitter and neuromodulator receptor distributions for MRS voxels

InSpectro-Gadget estimates neurotransmitter and neuromodulator receptor distributions in MRS voxels using receptor mRNA data from the Allen Human Brain Atlas.

Optimal Decoding of Neural Dynamics Occurs at Mesoscale Spatial and Temporal Resolutions

The paper develops a model to decode neural activity and finds optimal spatial and temporal resolutions for decoding image information from MUA data.

ProteoMixture: A Cell Type Deconvolution Tool for Bulk Tissue Proteomics Data

ProteoMixture estimates cell type proportions in bulk tissue proteomics data using optimized protein signatures validated on high-grade serous ovarian cancer samples.

A universal and effective variational method for destriping: application to light-sheet microscopy, FIB-SEM and remote sensing images

This paper introduces a variational method for removing stripe artifacts from images, showing effectiveness in light-sheet microscopy, FIB-SEM, and remote sensing data.

SpatialDDLS: An R package to deconvolute spatial transcriptomics data using neural networks

SpatialDDLS deconvolutes spatial transcriptomics data using neural networks, showing accuracy and speed compared to existing methods.

Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles

The paper develops and validates deconvolution models using DNA methylation data to estimate cell-type proportions in the human cortex, finding glial cells are twice as abundant as neurons and linking cell-type proportions to Alzheimer's disease neuropathology.