deconvolution

Optimizing effect sizes and specificity trumps machine learning when building DNA methylation reference panels for cell-type deconvolution

A dual-dimensional deconvolution environment for ZT Scan DIA in metabolomics

The paper introduces ZT Scan DIA, a metabolomics method improving annotation rates and isomer quantification in complex mixtures, with results validated in human plasma and mouse liver tissues.

Neuronal Decoding of Decisions in Multidimensional Feature Space Using a Gated Recurrent Variational Autoencoder

This paper develops a gated recurrent variational autoencoder to decode decision-making from neuronal data in monkeys, showing comparable accuracy in multidimensional feature space.

Exercise induces time-dependent but not sex-specific transcriptomic changes in healthy human skeletal muscle

Nine vastus lateralis biopsies from before exercise to 48 h after one high-intensity interval session reveal 16,625 exercise-responsive genes; only four respond differently between males and females.

StrainFacts accurately quantifies both endogenous and live biotherapeutic product strain abundances in simulated and clinical vaginal microbiota samples

Temporal Deconvolution of Mesoscale Recordings

The paper develops and evaluates four deconvolution methods to recover neuronal spiking rates from wide-field calcium imaging data, showing they outperform existing methods and highlight the importance of accurate temporal deconvolution.

Mitigation of multi-scale biases in cell-type deconvolution for spatially resolved transcriptomics using HarmoDecon

HarmoDecon mitigates multi-scale biases in spatial cell-type deconvolution by using a semi-supervised deep learning model, improving accuracy in spatial domain clustering and marker gene correlations.

Multi-omic Profiling of Senescence Following Myocardial Infarction Reveals Dynamic Characteristics in Cardiac Remodeling

This study uses multi-omic profiling to show that senescent cells, particularly late myofibroblasts, transiently contribute to cardiac remodeling after myocardial infarction.

Highly multiplexed spectral FLIM via physics informed data analysis

This paper introduces a Bayesian S-FLIM framework that learns spectra and lifetimes photon-by-photon, enabling efficient deconvolution of up to 9 fluorophore species with overlapping spectra.

spOT-NMF: Optimal Transport-Based Matrix Factorization for Accurate Deconvolution of Spatial Transcriptomics

spOT-NMF improves spatial transcriptomics deconvolution by using optimal transport, outperforming existing methods in accuracy and scalability across various resolutions and species.

StrainR2 accurately deconvolutes strain-level abundances in synthetic microbial communities

StrainR2 accurately deconvolutes strain-level abundances in synthetic microbial communities using shotgun metagenomic sequencing and k-mer normalization.

Development and optimization of the host DNA depletion in blood cultures using a saponin and SAN nucleases-based method

This paper develops and optimizes a method using saponin and SAN nucleases to deplete host DNA from blood cultures, improving bacterial DNA extraction for sepsis diagnosis.

Accounting for differences between Infinium MethylationEPIC v2 and v1 in DNA methylation-based tools

This study examines differences between Infinium MethylationEPIC v1 and v2, showing high array-level concordance but probe-level variation, and recommends accounting for version differences in analyses.

Intravital Multimodal Imaging of Human Cortical Organoids for Chronic Stroke Treatment in Mice

This paper develops a multimodal imaging platform to track human cortical organoids in mice after stroke, showing their survival and maturation in specific brain regions.

Targeted decontamination of sequencing data with CLEAN

The paper introduces CLEAN, a pipeline to remove contaminants from sequencing data, improving reproducibility and analysis quality.

Negativeome in Early-Life Virome Studies: Characterization and Decontamination

The paper identifies viral contaminants in early-life gut viromes and shows the effectiveness of strain-aware decontamination to preserve biological signals.

HIDE: Hierarchical cell-type Deconvolution

HIDE improves cell-type deconvolution by incorporating a cell hierarchy, showing better performance in simulations and applied to breast cancer data.

Genetic deconvolution of embryonic and maternal cell-free DNA in spent culture medium of human preimplantation embryos through deep learning

This paper introduces DECENT, a deep learning method to reconstruct embryonic copy number variations and reduce maternal DNA contamination in cfDNA from SECM, improving the accuracy of noninvasive preimplantation genetic testing.

Integrating Nanopore MinION Sequencing into National Animal Health AMR Surveillance Programs: An Indonesian Pilot Study of Chicken Slaughterhouse Effluent and Rivers

This study evaluates the integration of Nanopore MinION sequencing into a national AMR surveillance program, comparing its cost-benefit and effectiveness with other methods for monitoring antibiotic-resistant E. coli in slaughterhouse effluent and rivers.

MassNet: billion-scale AI-friendly mass spectral corpus enables robust de novo peptide sequencing

MassNet provides a large-scale mass spectral corpus for proteomics, enabling AI-driven de novo peptide sequencing with improved performance over existing methods.

Sparse deconvolution of cell type medleys in spatial transcriptomics

This paper introduces WISpR, a sparse deconvolution method for spatial transcriptomics that improves accuracy in predicting cell-type distributions across tissues.

Deacon: fast sequence filtering and contaminant depletion

Deacon efficiently filters and depletes contaminant sequences from large genomic datasets using a human pangenome index, achieving high accuracy and speed with minimal computational resources.

Dynamic Control of Argonautes by a Rapidly Evolving Immunological Switch

The paper identifies PALS-22 as a regulator of AGO expression in C. elegans, showing its role in balancing immunity and germline health through modulation of RNAi and epigenetic inheritance.

DuReS: An R package for denoising experimental tandem mass spectrometry-based metabolomics data

DuReS improves metabolomics data by removing noise while preserving significant peaks, enhancing spectral matching and reducing false positives.

Phase-free local ancestry inference mitigates the impact of switch errors on phase-based methods

This study compares phase-free and phase-based local ancestry inference methods, finding phase-free methods provide more accurate results despite lower phase quality.

Assessing the impact of artifact correction and artifact rejection on the performance of SVM-based decoding of EEG signals

This study evaluates how artifact correction and rejection affect SVM-based EEG decoding performance, finding that their combination rarely improves results but correction may still be necessary to avoid confounds.

Single Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting (SNACS): A tool for demultiplexing single-cell DNA sequencing data

The paper introduces SNACS, a method for demultiplexing single-cell DNA sequencing data using SNPs and cell-surface antibodies, achieving higher accuracy than existing methods.

Joint probabilistic modeling of pseudobulk and single-cell transcriptomics enables accurate estimation of cell type composition

The paper introduces MixupVI, a deep generative model for reference-free deconvolution of bulk RNA-seq data using single-cell transcriptomics, and releases PyDeconv for benchmarking deconvolution algorithms.

Unified high-resolution immune cell fraction estimation in blood tissue from birth to old age

The paper develops a DNAm reference panel called UniLIFE to estimate immune cell fractions in blood tissue across the lifespan, identifying biomarkers for type-1 diabetes in infants and children.

Deconvolution of multiplexed peptidoform mass spectra enables high-resolution profiling of complex protein modification patterns

The paper introduces PTMdecoder, a tool for deconvoluting multiplexed mass spectra to identify and quantify isobaric modification peptidoforms with high sensitivity and accuracy.