deconvolution

Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes

This paper introduces a spike sorting-free decoding method using density-based modeling to improve neural decoding performance without explicitly clustering spikes.

Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues

SAHMI identifies microbial nucleic acids in mammalian tissues and filters contaminants, enabling co-detection of somatic cells and microorganisms in single-cell and spatial genomic data.

SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and clustering in spatial transcriptomics

SpatialPrompt improves spot deconvolution and domain identification in spatial transcriptomics by integrating spatial coordinates and scRNA-seq data, showing scalability and accuracy across multiple datasets.

Slave Trade and Colonial Expansion Resulted in Strong Sex-Biased Admixture in South Africa

The paper uses genetic data to show sex-biased admixture patterns in South Africa due to colonial migration, with European males and Khoe-San females contributing disproportionately to different regions.

"Dark taxonomy": a new protocol for overcoming the taxonomic impediments for dark taxa and broadening the taxon base for biodiversity assessment

This paper introduces 'dark taxonomy' to address taxonomic impediments by using small-scale revisions and integrative methods, increasing described species in the Oriental region by 25%.

Ariadne: Synthetic Long Read Deconvolution Using Assembly Graphs

Ariadne deconvolves synthetic long read data using assembly graphs to improve taxonomic classification and de novo assembly of complex populations.

A hybrid machine learning and regression method for cell type deconvolution of spatial barcoding-based transcriptomic data

SDePER improves cell type deconvolution in spatial transcriptomic data by addressing platform effects, sparsity, and spatial correlation, achieving more accurate results than existing methods.

Identifying cellular markers of focal cortical dysplasia type II with cell-type deconvolution and single-cell signatures

This study uses cell-type deconvolution and single-cell signatures to analyze bulk RNA-seq data from FCD patients, revealing subtype-specific cellular changes, including astrogliosis in FCD IIb.

scMD: cell type deconvolution using single-cell DNA methylation references

scMD estimates cell type fractions from bulk DNAm data using single-cell DNA methylation references, showing improved performance in identifying Alzheimer's disease-related methylation patterns.

Effective methods for bulk RNA-Seq deconvolution using scnRNA-Seq transcriptomes

The paper evaluates deconvolution methods for bulk RNA-Seq using scnRNA-Seq data, identifies biases, and introduces SQUID, a method that outperforms others in predicting cell mixtures and identifying cancer subclones.

napari-threedee: a toolkit for human-in-the-loop 3D image analysis in napari

The paper introduces napari-threedee, a toolkit for interactive 3D image analysis and annotation in napari, applicable to fluorescence microscopy and cryoelectron tomography data.

Assessing feasibility and risk to translate, de-identify and summarize medical reports using deep learning

This paper evaluates deep learning methods for translating, de-identifying, and summarizing non-English medical reports, finding non-inferior performance in de-identification and high accuracy in summarization.

Degradation and release of dissolved environmental RNAs from zebrafish cells

This study measures the release and degradation of dissolved environmental RNAs from zebrafish cells, showing that RNAs degrade faster in the presence of living or dying cells.

hadge: a comprehensive pipeline for donor deconvolution in single cell

hadge combines hashing and genotype-based deconvolution methods to recover previously discarded cells in single-cell data.

Introducing π-HelixNovo for practical large-scale de novo peptide sequencing

The paper introduces π-HelixNovo, a Transformer-based model for de novo peptide sequencing that outperforms existing methods and improves taxonomic resolution in gut metaproteomes.

RNA Strain-Match: A tool for matching single-nucleus, single-cell, or bulk RNA-sequencing alignment data to its corresponding genotype

RNA Strain-Match matches RNA sequencing alignment data to genotype without variant call files, correcting 13% of sample mismatches in mouse models.

CimpleG: Finding simple CpG methylation signatures

CimpleG identifies small CpG methylation signatures for cell type classification and deconvolution, performing efficiently with single CpG sites per cell type.

Assembly processes of bacterial and fungal community differ between desert and oasis habitats in an arid inland river basin, northwest China

The paper compares bacterial and fungal community assembly processes in desert and oasis habitats, finding deterministic processes dominate bacteria in oases while stochastic processes dominate both fungal communities, with environmental and biotic factors influencing bacterial assembly.

SMART: reference-free deconvolution for spatial transcriptomics using marker-gene-assisted topic models

SMART deconvolves spatial transcriptomics data without a reference, inferring cell type profiles and composition using marker genes, and outperforms existing methods when references are unavailable.

Bio-based decontamination and detoxification of total petroleum hydrocarbon contaminated dredged sediments: perspectives to produce constructed technosols in the frame of the circular economy.

This study uses bioaugmentation with Lambertella sp. MUT 5852 to decontaminate and detoxify dredged sediments, achieving complete petroleum hydrocarbon depletion in 28 days.

Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings using SVD-based Adaptive Filtering

This paper introduces an SVD-based adaptive filtering method to remove both fast and slow artifacts from local field potentials during DBS, outperforming existing techniques on synthetic and real data.

Deconvolution reveals compositional differences in high-grade serous ovarian cancer subtypes

The paper uses deconvolution on bulk transcriptomic data to show that HGSOC subtypes are driven by tumor microenvironment composition, particularly fibroblast and immune cell content.

Restoration of metabolic functional metrics from label-free, two-photon cervical tissue images using multiscale deep-learning-based denoising algorithms

The paper develops a multiscale deep-learning denoising algorithm to restore metabolic function metrics from low-SNR two-photon images of human cervical tissue.

Decoding Arbitrary and Informed Decisions from Intracranial Recordings in Humans

The paper decodes arbitrary and informed decisions from intracranial recordings in humans, showing differences in spatio-temporal patterns between the two decision types.

Optimization-Based Decoding of Imaging Spatial Transcriptomics Data

The paper introduces JSIT, an optimization-based method for decoding imaging spatial transcriptomics data with lower magnification, improving throughput and recovery performance over standard methods.

Integration of HLA-DR linkagedisequilibrium to MHC class II predictions

The paper introduces HLAAssoc-1.0 to infer HLA-DRB3/4/5 alleles from HLA-DRB1 data, improving allele and motif deconvolutions in immunopetidomics and training of binding prediction models.

Cell type deconvolution of bulk blood RNA-Seq to reveal biological insights of neuropsychiatric disorders

This study uses bulk RNA-Seq from blood samples to estimate cell type proportions and eQTLs, revealing cell-type specific genetic associations with psychiatric disorders and medication effects.

Variational inference accelerates accurate DNA mixture deconvolution

This paper shows that variational inference methods like SVGD and VI can speed up DNA mixture deconvolution with minimal loss of accuracy.

Spatio-relational inductive biases in spatial cell-type deconvolution

This paper introduces GNN-C2L, a spatial cell-type deconvolution method that improves performance by incorporating spatial inductive biases, outperforming spatial-agnostic variants on simulated datasets.

Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data

Celloscope deconvolves cell types in spatial transcriptomics data using marker genes, outperforming existing methods and accurately identifying brain structures and immune infiltrates.