deconvolution

MALDI Deamidation Score (MDS): A Fast and Flexible Method for Assessing Deamidation in ZooMS Data and Its Application to the Denisova Cave Bone Assemblage

The paper introduces MDS, a method for assessing deamidation in ZooMS data, demonstrating its application to Denisova Cave bone assemblage to reveal time-varying deamidation patterns across peptides.

DeepCor: Denoising fMRI Data with Contrastive Autoencoders

DeepCor improves fMRI denoising by using contrastive autoencoders, outperforming CompCor on simulated data and enhancing connectivity differences in real datasets.

Chrom-Sig: de-noising 1-dimensional genomic profiles by signal processing methods

Chrom-Sig de-noises 1-dimensional genomic coverage tracks using signal processing to separate signal from noise, validated across multiple datasets.

A benchmark of DNA methylation deconvolution methods for tumoral fraction estimation using DecoNFlow

This study benchmarks DNA methylation deconvolution methods for tumoral fraction estimation in cfDNA, finding CelFiE most accurate and introducing DecoNFlow as a comprehensive pipeline.

WAD: a wavelet-based linear programming method using L1-minimal reconstruction loss for accessible chromatin data deconvolution

WAD deconvolves bulk accessible chromatin data to estimate cell-type proportions using wavelet-based denoising and L1-minimal linear programming, achieving lower error and higher concordance than existing methods.

Exploiting correlations across trials and behavioral sessions to improve neural decoding

This paper improves neural decoding by exploiting correlations across trials and sessions, showing better accuracy for four behaviors in mice using interpretable models.

A General Framework for Branch Length Estimation in Ancestral Recombination Graphs

This paper introduces POLEGON, a method for estimating branch lengths in Ancestral Recombination Graphs using uninformative priors, showing improved coalescence time estimates and population size inferences.

BeadBuddy: user-friendly, nanometer-scale registration of single-molecule imaging data

BeadBuddy corrects chromatic errors in single-molecule imaging data using fluorescent bead images, achieving sub-voxel resolution in DNA FISH and applicable across SMLM modalities.

CLEAR-AF: Improved Autofluorescence Subtraction for Multiplexed Tissue Imaging via Polar Transformation and Gaussian Mixture Models

CLEAR-AF improves autofluorescence subtraction in multiplexed tissue imaging by using polar transformation and Gaussian Mixture Models, enhancing specificity, sensitivity, and reproducibility.

Deconvolution of Sparse-count RNA Sequencing Data for Tumor Cells Using Embedded Negative Binomial Distributions

The paper develops DeMixNB, a deconvolution model for sparse-count RNA sequencing data, showing its utility in analyzing tumor cell plasticity in breast and lung cancer.

RamanMAE: Masked Autoencoders Enable Efficient Molecular Imaging by Learning Biologically Meaningful Spectral Representations

RamanMAE uses masked autoencoders to learn spectral representations from Raman data, enabling efficient processing and noise reduction in biological imaging.

Cell-type specific inference from bulk RNA-sequencing data by integrating single cell reference profiles via EPIC-unmix

EPIC-unmix improves cell-type specific analysis from bulk RNA-seq by integrating single-cell reference profiles, identifying novel genes and eQTLs in Alzheimer's disease.

Cleanifier: Contamination removal from microbial sequences using spaced seeds of a human pangenome index

Cleanifier removes human contamination from microbial sequences using a pangenome index and spaced seeds, achieving speed and accuracy comparable to existing tools.

Benchmarking of Human ReadRemoval Strategies for Viral andMicrobial Metagenomics

The paper benchmarks human read removal methods in metagenomics, finding that high-sensitivity Bowtie2 with T2T-CHM13 reference improves removal while preserving microbial data.

Deconvolution of bulk endometrial tissue identifies cell type proportions and expression signatures associated with endometrial function and disease

The paper uses deconvolution on bulk RNA-seq data to identify cell type proportions and expression signatures linked to endometrial function and disease in women with and without endometriosis.

Towards Personalized Epigenomics: Learning Shared Chromatin Landscapes and Joint De-Noising of Histone Modification Assays

This paper introduces DecoDen, a method that learns shared chromatin landscapes and de-noises histone modification assays across multiple tissues.

Accurate sample deconvolution of pooled snRNA-seq using sex-dependent gene expression patterns

This study demonstrates using sex-dependent gene expression patterns to accurately deconvolve pooled snRNA-seq data, achieving 93-95% accuracy in cell sex classification.

Return of the GEDAI: Unsupervised EEG Denoising based on Leadfield Filtering

GEDAI improves EEG denoising by removing artifacts without clean references, outperforming existing methods in simulations and enhancing neurobehavioral predictions.

A unified model for cell-type resolution genomics from heterogeneous omics data

This paper introduces Unico, a method for deconvolving bulk genomic data into cell-type-resolved tensors, showing improved performance across gene expression and DNA methylation data.

xCell 2.0: Robust Algorithm for cell type Proportion Estimation Predicts Response to Immune Checkpoint Blockade

xCell 2.0 improves cell type proportion estimation from bulk RNA-seq data and predicts immune checkpoint blockade response more accurately than existing methods.

Protein Language Model-Aligned Spectra Embeddings for De Novo Peptide Sequencing

PLMNovo improves de novo peptide sequencing by aligning spectrum and peptide embeddings using pre-trained protein language models.

DeepSpot2Cell: Predicting Virtual Single-Cell Spatial Transcriptomics from H&E images using Spot-Level Supervision

DeepSpot2Cell predicts virtual single-cell gene expression from H&E images using spot-level supervision, outperforming previous methods on a new benchmark.

Recommendations for Quantitative Data-Independent Acquisition (DIA) Proteomics using Controlled Quantitative Experiments (CQEs)

The paper evaluates protein quantitation in DIA proteomics using controlled experiments on two mass spectrometry platforms, identifying over 10,000 proteins and highlighting the impact of post-processing on accuracy and false quantitative rates.

Deconvoluting fiber type proportions from human skeletal muscle transcriptomics and proteomics data using FibeRtypeR

This paper introduces FibeRtypeR, a tool to estimate muscle fiber type proportions from bulk transcriptomics and proteomics data using single-fiber reference profiles.

Integrated single-cell atlas of human atherosclerotic plaques

This study creates an integrated single-cell atlas of human atherosclerotic plaques using scRNA-seq data, enabling accurate cell type annotation and downstream analyses.

Treasure: A Sensitive Pipeline for Species-Level and Functional Microbiome Profiling

The paper introduces a pipeline for functional microbiome profiling using NGS data, showing improved performance over existing methods in analyzing gastric cancer-associated microbiomes.

Population genetic assessment of marine megafauna using seawater environmental DNA: a case study of whale sharks from the Ningaloo Coast World Heritage Area.

This paper develops a genetic toolkit using seawater environmental DNA to study whale shark populations, showing that microhaplotype markers can reliably genotype individuals and capture population-level genetic diversity.

gnSPADE: a reference-free deconvolution method incorporating gene network structures in spatial transcriptomics

gnSPADE is a reference-free spatial deconvolution method that improves accuracy and resolution by incorporating gene network structures, applied to both synthetic and real spatial transcriptomics data.

Harp: Platform Independent Deconvolution Tool

Harp improves deconvolution accuracy by harmonizing cell reference profiles and overcoming batch effects in bulk gene expression data.

Integrated ambient modeling and genetic demultiplexing of single-cell RNA+ATAC multiome experiments with Ambimux

The paper introduces ambimux, a method to estimate and demultiplex ambient molecule contamination in single-cell RNA+ATAC multiome data, improving the detection of biologically meaningful signals.