deconvolution

Comprehensive evaluation of human brain gene expression deconvolution methods

This study evaluates deconvolution methods for human brain transcriptome data, finding that cell-type signatures impact accuracy more than methods, and partial methods outperform complete ones.

THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data

THUNDER is a deconvolution method that infers cell type proportions from bulk Hi-C data, outperforming existing methods in accuracy.

StrainGE: A toolkit to track and characterize low-abundance strains in complex microbial communities

StrainGE identifies low-abundance microbial strains from metagenomic data with higher sensitivity and resolution than existing tools.

Deconvolution of the epigenetic age discloses distinct inter-personal variability in epigenetic aging patterns

The paper deconvolves epigenetic age to identify CpG sites responsible for accelerated and decelerated aging, revealing inter-personal variability in aging patterns.

Incorporating the image formation process into deep learning improves network performance in deconvolution applications

This paper introduces RLN, a deep learning method for 3D fluorescence microscopy deconvolution that improves speed, performance, and generalizability compared to existing methods.

Pan-cancer analyses reveal genomics and clinical characteristics of the down regulated genes for recurrent myocardial infarction in cancer

This paper identifies key down-regulated genes in recurrent myocardial infarction and links them to cancer progression, prognosis, and immune infiltration across multiple tumor types.

STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing

STRIDE deconvolves spatial transcriptomics data using single-cell RNA sequencing to estimate cell-type proportions and improve spatial gene and domain identification.

Single-cell Bayesian deconvolution

This paper introduces a Bayesian deconvolution method to separate signal from noise in single-cell measurements, applied to study Brachyury expression in differentiating mouse embryonic stem cells.

Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling

The paper enhances cell deconvolution of peripheral blood using DNA methylation to provide high-resolution immune profiling by including 12 leukocyte subtypes and validated libraries.

LRcell: detecting the source of differential expression at the sub-cell type level from bulk RNA-seq data

LRcell identifies sub-cell types responsible for differential expression in bulk RNA-seq by using precomputed marker genes from scRNA-seq data, outperforming existing deconvolution methods.

Real-time Image Denoising of Mixed Poisson-Gaussian Noise in Fluorescence Microscopy Images using ImageJ

The paper develops a CNN-based denoising method for mixed Poisson-Gaussian noise in fluorescence microscopy images, achieving real-time performance and superior SNR improvement.

SEQUENCE SLIDER: integration of structural and genetic data to characterize isoforms from natural source

SEQUENCE SLIDER integrates structural, genetic, and biophysical data to characterize isoforms in natural products by analyzing mass spectrometry and genomic data.

Squidpy: a scalable framework for spatial omics analysis

Squidpy extends Scanpy/AnnData with spatial neighbor graphs and a lazy-loading image container, enabling spatial statistics, ligand-receptor tests, image features, and ~10-fold faster neighborhood enrichment than Giotto.

Single-Cell Sequencing Reveals Lineage-Specific Dynamic Genetic Regulation of Gene Expression During Human Cardiomyocyte Differentiation

The paper identifies lineage-specific dynamic eQTLs during human cardiomyocyte differentiation using single-cell RNA-seq and deconvolves bulk RNA-seq data to attribute dynamic effects to specific lineages.

DnoisE: Distance denoising by Entropy. An open-source parallelizable alternative for denoising sequence datasets

DnoisE is a denoising tool for sequence datasets that uses codon entropy to merge erroneous reads with correct ones, improving accuracy for coding markers.

Framework for Denoising Monte Carlo Photon Transport Simulations Using Deep Learning

This paper develops a deep learning framework to denoise Monte Carlo photon transport simulations, significantly improving image quality and reducing computational costs.

Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data

This paper introduces PhenoPop, a method to deconvolve tumor heterogeneity using drug screening data, identifying subpopulations with differential drug responses and their frequencies.

Using strain-resolved analysis to identify contamination in metagenomics data

This study identifies contamination in metagenomics data using strain-resolved analysis, finding that contamination is more common in low-biomass samples and adjacent extraction plate positions.

Probabilistic modeling of ambient noise in single-cell omics data

The paper develops scAR, a machine learning model to remove ambient noise from single-cell omics data across multiple technologies, showing high denoising accuracy.

Label2label: Training a neural network to selectively restore cellular structures in fluorescence microscopy

This paper introduces a neural network method to restore fluorescence microscopy images by reducing background signals from non-specific labeling.

SPITFIR(e): A supermaneuverable algorithm for restoring 2D-3D fluorescence images and videos,and background subtraction

The paper introduces SPITFIR(e), an algorithm for restoring 2D-3D fluorescence images/videos and subtracting background, demonstrating its effectiveness across multiple microscopy modalities.

BrumiR: A toolkit for de novo discovery of microRNAs from sRNA-seq data.

BrumiR is a toolkit for de novo discovery of microRNAs from sRNA-seq data, achieving high recall and efficiency without a reference genome.

Impacts of the COVID-19 Pandemic on a Human Research Islet Program

This study examines how the COVID-19 pandemic affected islet processing and distribution at a human research islet facility, finding reduced organ processing but maintained islet quality, though recipient feedback was poorer due to longer transit times.

From mass spectral features to molecules in molecular networks: a novel workflow for untargeted metabolomics.

The paper introduces MolNotator, a method that improves molecular networking in untargeted metabolomics by predicting neutral molecules from ion data, enhancing dereplication and network integration.

Genetic Sex Validation for Sample Tracking in Clinical Testing

The paper validates genetic sex using a 96-SNP PCR panel to track samples in clinical testing and identify inconsistencies.

swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution

swCAM estimates subtype-specific expressions in individual samples using unsupervised deconvolution, revealing changes in cell proportions and gene expression in bipolar disorder.

Inference of tissue relative proportions of the breast epithelial cell types luminal progenitor, basal, and luminal mature

The paper quantifies luminal progenitor and other epithelial cell types in breast tissue using DNA and RNA measurements, finding elevated levels in hormone receptor negative cancers and BRCA1 mutation carriers.

Distributed Feedforward and Feedback Processing across Perisylvian Cortex Supports Human Speech

This study uses a deep learning model to decode speech from neural signals, revealing that frontal and temporal cortex process both feedforward and feedback information during speech production.

AImmune: a new blood-based machine learning approach to improving immune profiling analysis on COVID-19 patients

AImmune is a deep learning model that predicts immune cell proportions from bulk RNA-seq data, outperforming existing methods and offering a cost-effective alternative to scRNA-seq for COVID-19 immune profiling.

Evaluating de novo assembly and binning strategies for time-series drinking water metagenomes.

This study evaluates assembly and binning strategies for time-series drinking water metagenomes to maximize the recovery of high-quality MAGs.