deconvolution

The usage of transcriptomics datasets as sources of Real-World Data for clinical trialling

This paper constructs a patient repository by integrating transcriptomics datasets to generate real-world data for clinical trialling, improving downstream biological conclusions by 45%.

Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis

TAPE deconvolves bulk RNA-seq data into cell-type fractions and gene expression using a deep autoencoder, showing better performance and robustness compared to existing methods.

De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution

Bulk2Space deconvolves bulk RNA-seq data to reveal spatial and cellular heterogeneity using single-cell and spatial transcriptomics references, identifying immune cell spatial variance and novel gene patterns in mouse brain regions.

A Robust Bayesian Approach to Bulk Gene Expression Deconvolution withNoisy Reference Signatures

BEDwARS improves bulk gene expression deconvolution by estimating cell type proportions and signatures simultaneously, showing robustness to noisy references and identifying mechanisms in a rare pediatric condition.

LotuS2: An ultrafast and highly accurate tool for amplicon sequencing analysis

LotuS2 is a fast and accurate tool for amplicon sequencing analysis that outperforms existing pipelines in speed and diversity metrics.

Multi-histone ChIP-Seq Analysis with DecoDen

DecoDen removes shared biases in multi-histone ChIP-Seq data by learning a joint background signal and correcting for it using non-negative matrix factorisation and half-sibling regression.

Deep learning based decoding of local field potential events

This paper uses an auto-encoder to decode single-trial local field potential events, revealing neural activity patterns and information flux direction in rodents and humans.

Whole-brain multivariate hemodynamic deconvolution for multi-echo fMRI with stability selection

This paper introduces a novel hemodynamic deconvolution algorithm for multi-echo fMRI that improves reliability and provides statistical certainty without requiring prior knowledge of experimental paradigms.

MuSiC2: cell type deconvolution for multi-condition bulk RNA-seq data

MuSiC2 improves cell type proportion estimation in bulk RNA-seq data from different clinical conditions using an iterative method, outperforming traditional approaches.

Deconvoluting complex correlates of COVID19 severity with local ancestry inference and viral phylodynamics: Results of a multiomic pandemic tracking strategy

The paper integrates multi-omic data to identify genetic and microbiome factors associated with severe COVID-19, independent of ancestry and social factors.

SimBu: Bias-aware simulation of bulk RNA-seq data with variable cell type composition

SimBu simulates bulk RNA-seq data with variable cell type composition to create in silico gold standards for evaluating deconvolution methods.

The relationship between frequency content and representational dynamics in the decoding of neurophysiological data

This paper identifies a frequency doubling artifact in decoding accuracy metrics from neurophysiological data and proposes a filtering approach and alternative decoding method to mitigate it.

Template-based assembly of proteomic short reads for de novo antibody sequencing and repertoire profiling

The paper introduces Stitch, a software tool for mapping proteomic short reads to templates to sequence monoclonal and profile polyclonal antibodies with high accuracy.

Hamiltonian Monte Carlo with strict convergence criteria reduces run-to-run variability in forensic DNA mixture deconvolution

This paper introduces a Hamiltonian Monte Carlo algorithm with strict convergence criteria to reduce run-to-run variability in forensic DNA mixture deconvolution without increasing runtime.

The Entwined African and Asian Genetic Roots of the Medieval Peoples of the Swahili Coast

This study analyzes ancient DNA from the Swahili coast to trace genetic admixture between African and Asian populations over time.

Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes

This study benchmarks six sequencing-based and two array-based cell-type deconvolution methods for bulk DNA methylomes, identifying the best-performing method and factors affecting performance.

Executed and imagined grasping movements can be decoded from lower dimensional representation of distributed non-motor brain areas.

This paper decodes executed and imagined grasping movements from non-motor brain areas using a lower dimensional representation, achieving high accuracy without primary motor cortex signals.

A comprehensive comparison on cell type composition inference for spatial transcriptomics data

This paper compares eight ST deconvolution methods using four real datasets to evaluate their performance in inferring cell type composition from spatial transcriptomics data.

Fine-grained deconvolution of cell-type effects from human bulk brain data using a large single-nucleus RNA sequencing based reference panel.

The paper develops a method to deconvolute cell-type effects from bulk brain data using a reference panel of single-nucleus RNA-seq data, showing it can accurately estimate cell-type proportions and perform TWAS for rare cell-types.

Uncertainty quantification of reference based cellular deconvolution algorithms

This paper introduces and validates a metric to assess the accuracy of cellular deconvolution in DNA methylation studies, showing it identifies biases in different demographic groups.

Cell type deconvolution of methylated cell-free DNA atthe resolution of individual reads

This paper improves methylation-based cell type deconvolution of cfDNA by introducing CelFEER, which achieves higher accuracy in estimating cell type proportions and detects significant differences in skeletal muscle cfDNA between ALS patients and controls.

SNV-FEAST: microbial source tracking with single nucleotide variants

SNV-FEAST improves microbial source tracking by using signature SNVs instead of species abundance, showing better accuracy in three case studies.

Considerations for Deconvolution: A Case Study with GTEx Coronary Artery Tissues

The paper performs deconvolution on GTEx coronary artery data using CIBERSORT to assess challenges and limitations in estimating tissue composition from gene expression data.

Scalable microbial strain inference in metagenomic data using StrainFacts

StrainFacts enables scalable strain inference in metagenomic data, showing comparable accuracy to intensive tools and revealing strain diversity patterns in human stool samples.

An ancestral genomic sequence that serves as a nucleation site for de novo gene birth

The paper identifies a conserved non-coding spacer sequence that acts as a nucleation site for de novo gene formation in primates, leading to the birth of genes like BCRP3.

Reference-free cell-type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data

This paper introduces STdeconvolve, a reference-free method for cell-type deconvolution of spatially resolved transcriptomics data at multi-cellular pixel resolution, showing effective recovery of cell-type profiles without external references.

Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation

The paper introduces DestVI, a method for multi-resolution spatial transcriptomics analysis that captures continuous cell state variation, and applies it to study inflammation in lymph nodes and tumors.

Fast, Efficient, and Accurate Neuro-Imaging Denoising via Deep Learning

This paper introduces a deep learning framework for neuro-imaging denoising that improves speed, memory efficiency, and accuracy while requiring minimal training data.

Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human

This study decodes grasp and speech signals from high-level cortical areas in a tetraplegic human, showing that SMG and PMv can decode grasp intentions, while SMG also encodes speech.

Indirect decoding of behavior from brain signals via reconstruction of experimental conditions

The paper tests decoding behavior from brain signals by first reconstructing experimental conditions, finding this indirect approach works well despite being outperformed by direct decoding.